F1pace by F1bythenumbers

2025 Abu Dhabi GP: Race pace

Sun, 07 Dec 2025 00:00:00 +0000

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Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing. Click on the “expand methodology” button if you want to know more about how the data was analyzed.

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

This chart shows the same information as the previous chart, but in a summarized way. I’m not a huge fan of the traditional plain bar charts, so I created this chart which shows the same information and a little bit more.

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Laps in traffic

The first chart, taps in traffic, shows the percentage of each lap that a driver spent in traffic. The second chart, % of time spent in traffic, shows a summary of the total time a driver spent in traffic during a race. Both of these charts include backmarkers, which is how it should be done since backmarkers can have a big impact on the final results of a race.

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Abu Dhabi GP: FP2 session (Model-corrected long run analysis)

Fri, 05 Dec 2025 00:00:00 +0000

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This is an experimental analysis.

To provide a clearer picture of performance, anomalous laps (due to traffic, driver errors, etc.) have been corrected using predictions from a statistical model which learns each driver’s underlying pace and trends to generate plausible lap times.

Model-corrected race simulation pace

Example of model predictions

This chart shows how the model predicts lap times to replace the ones flagged as anomalies. You can see how the predictions follow a smooth curve that matches the overall trend of the remaining laps.

The predictions are based on complex linear algebra, so they’re not “guesswork” as some of you may believe. You have a better analysis? Cool, share it on your website.

2025 Qatar GP: Race pace

Mon, 01 Dec 2025 00:00:00 +0000

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Use the Get in touch link if you’re interested in supporting this project.

Race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Qatar GP: Sprint

Sun, 30 Nov 2025 00:00:00 +0000

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Sprint race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized sprint race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Laps in traffic

These are two new additions to the site. The first chart, taps in traffic, shows the percentage of each lap that a driver spent in traffic. The second chart, % of time spent in traffic, shows a summary of the total time a driver spent in traffic during a race. Both of these charts include backmarkers, which is how it should be done since backmarkers can have a big impact on the final results of a race.

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Sprint race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Las Vegas GP: Race pace

Mon, 24 Nov 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 21)

Mon, 10 Nov 2025 00:00:00 +0000

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Qualifying teammate battles seem to be of great interest to the current F1 community. While I’m a firm believer that Sundays matter far more than Saturdays, I can’t deny the growing importance of qualifying—especially with how tough overtaking has become with the latest generation of F1 cars. Track position is king, and starting ahead often means staying ahead. So while race day is where the points are handed out, Saturdays are playing a bigger role than ever in shaping the final results.

I mean, I’m not sure what’s the point. People are making comparisons based on titanium dioxide or other random stuff so I don’t know, maybe I should just focus on meme stats too. It’s funny for sure, but it’s not great when you realize more people are upvoting that thanthe charts and analyses you’ve worked so hard on.

Analysis

Click to expand methodology

Methodology!

As with race pace, we can’t directly compare qualifying pace between races. Different tracks, lengths, and deltas make it tricky. To handle this, I standardized the data using a metric called symmetric percent difference. Without getting too technical, it’s a more robust way of calculating percent differences — hence why I chose it.

I calculated the symmetric percent difference for all qualifying sessions between teammates, keeping only the maximum session where both drivers participated. For example, if George Russell made it to Q3 but his teammate only reached Q2, I used the Q2 data for the comparison. If a driver couldn’t set a lap time in Q1 while their teammate did, I removed that session entirely. While this isn’t ideal, using equally comparable data points is crucial for a fair performance comparison. Negative symmetric percent difference values mean that a driver was faster than his teammate, while positive values mean that the driver was slower than his teammate. A difference of 0% means that both drivers were just as fast.

I calculated the values for each race for each team and plotted them as individual data points in the chart. I then calculated the median of these values for the season (so far) and displayed it the left side of the plot, next to the team logo. Smaller overall values represent that both teammates were more evenly matched during quali, while larger overall values show a greater gap between teammates.

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

One of the main issues when gathering data from multiple races is that the deltas will change depending on the length of each track. A delta of 0.1 seconds in a short track (say, 1:05 per lap) will be greater than a delta of 0.1 seconds in a long track such as Spa (~ 1:45).

One way we can standardize the data is by converting the deltas to percentages, but there is one big issue with this. The traditional way of calculating a percent difference is with the following formula:

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

$$ Symmetric\ percent\ difference = 100\times\frac{value1-value2}{(value1+value2)/2} $$ This formula is reversible, meaning that regardless of the order of the values, we will get the same result. Because of this, I decided to use the symmetric percent difference formula as the basis for the analysis.

Quali delta between teammates

We’re more than halfway through the season. With 21 races—and now 5 sprints after the São Paulo GP—we have more representative results. Just as a reminder, this season features the anomaly of Lawson switching places with Tsunoda after just three races, which makes our usual analysis a bit trickier. Additionally, Alpine decided to replace Doohan with Colapinto after just six races. Normally, I’d use the median as the key metric of interest since it’s more robust to outliers. However, in this case, the median might skew the average delta between Lawson and Verstappen.

To counter that, I’ve decided to include both the median and the mean qualifying deltas in this article. This approach might change in future posts, but for now, I think it gives us a more complete picture.

It is important to note that after 21 races, the results have now stabilized, which means that at this point in time the mean is the most representative metric for most pairings.

I’ve changed the overall structure of the charts. The x-axis now shows the absolute margin of victory of a driver over his teammate. The main reason for this change was to optimize space. After the Belgian GP, the massive delta of Russell over Antonelli stretched the x-axis too far, making it difficult to see every data point.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates remains at Red Bull Racing. Tsunoda’s gap to Verstappen has remained fairly stable, around 0.7 to 0.8%. Currently, Verstappen is beating Yuki Tsunoda by 0.781%, which is less than the median delta Lawson left at 0.881%, but still stands as the highest gap on the grid by a wide margin.

If we look at the mean symmetric percent difference instead, the largest active gap is still at Red Bull, with Max ahead of Tsunoda by an average of 0.789%, the largest delta on the grid by a significant margin.

At the other end of the spectrum, the smallest delta is seen at McLaren or Sauber, depending on the metric of interest. Looking at the median symmetric percent difference, Lando Norris is now leading Oscar Piastri by a median delta of 0.027%. If we focus instead on the mean symmetric percent difference, we have Bortoleto and Hulkenberg separated by 0.023%. The McLaren pairing, as well as the Williams battle, also have incredibly tight deltas, with Norris beating Piastri by just 0.024% and Albon beating Sainz by 0.029%.

Delta in seconds

I will continue to include the analysis using seconds instead of the symmetric percent difference. I’m still firmly in the “symmetric percent difference is more representative” camp, but the difference between the two metrics is smaller than most people think—and time in seconds is a lot easier to interpret for most people.

Looking at seconds as the metric of interest, we see some incredible results. Looking at the median qualifying delta, there are two drivers with a gap to their teammates under 1 tenth fo a second, with the smallest gap seen at McLaren, with Norris now beating Piastri by 0.025 seconds on average, up from the 0.003 s delta that we saw 1 race ago. At Williams, Sainz had taken the lead over Albon, but is now once again trailing Albon by just 0.041 seconds. Using the mean, we see 3 teams with a delta of around half a tenth or less. Currently, the smallest gap is seen at McLaren, with Norris and Piastri separated by 0.007 seconds on average, but the deltas at Sauber, Haas, and Williams are impressively tight as well.

On the other side of the spectrum, the largest gap will most likely remain at Red Bull for the remainder of the season. Tsunoda is now 0.594 seconds slower on average based on the median delta, and 0.671 seconds slower based on the mean delta.

Qualifying stage appearances

If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

I’ve often seen tables showing how many times drivers reached Q1, Q2, and Q3, but I’ve never been fully satisfied with them. Tables tend to have too much text while still missing key insights. To address this, I created a bubble chart.

Each bubble represents a driver’s qualifying or sprint qualifying appearance throughout the 2025 F1 season. The bubble’s size reflects the driver’s final qualifying position, with larger bubbles indicating better results. The actual position is displayed as a big number inside the bubble, and the colors indicate the qualifying session reached.

As of the 21th race of the 2025 season, the differences between teammates have fully stabilized. Most pairings are quite even, with a few exceptions. The first is Aston Martin, where Alonso has reached Q3 on 13 occasions, while his teammate, Lance Stroll, has managed it only four times. The second is Alpine, where Gasly has qualified for Q3 on nine occasions, while Doohan and Colapinto combined have yet to make a single Q3 appearance. The third exception is, unsurprisingly, Red Bull Racing. So far, Max Verstappen has reached Q3 in all 25 out of the 26 possible sessions. Meanwhile, Lawson was eliminated in Q1 three times while racing for Red Bull. In Tsunoda’s case, his Q3 record is not favourable: the Japanese driver now has six Q3 appearances out of 23 outings for the team, putting his Q3 rate at 26.08%, so just slightly higher than 1 in 4. As a reference, Sergio Pérez reached Q3 18 times during the 2024 season, and was eliminated 5 times in Q2 and 7 times in Q3, resulting in a Q3 rate of 60%.

xQD (Expected Quali Delta)

How much quicker one teammate should be in qualifying, based on underlying pace rather than luck.

We’ve already looked at the mean and median averages, which show an interesting summary of the deltas between teammates in qualifying during the season. We can, however, take it one step further. For this, I created a new metric, which I’ve called Expected Quali Delta, or xQD.

xQD is a value created by a statistical model that uses the raw data and tries to separate the signal from the noise. In our case, the signal represents a driver’s typical edge over a teammate after accounting for track and season effects, while the noise shows the residual session-to-session variability, including but not limited to effects such as gusts of wind, track evolution, and driver errors, among others. With our latest metric, we can not only quantify what the expected delta between teammates would be, but also generate uncertainty intervals around it.

xQD (expected Quali Delta): The model’s best estimate of the qualifying gap between teammates after filtering out session noise.

How to read the chart

The number in the colored box next to each team’s logo: This number represents the model’s single best estimate of the qualifying gap between teammates after filtering out session noise.
The x-axis: The horizontal axis shows the xQD in seconds. Higher times represent bigger deltas, in favour of the winning driver.
The slabs The slabs, or “domes”, provide a range of plausible values for each team’s skill. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty about the actual delta in each team.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our expected value. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents is the zero threshold. It provides a quick reference to see which in how many simulations the result changed from driver A winning to driver B. If the slabs cross the zero threshold, then it means that on some simulations the current winning driver would be losing the quali battle against his teammate.

Looking at our xQD chart, we can see that the values produced by our model align with the mean and median qualifying deltas between teammates. For most drivers, xQD will align more closely with the median delta than the mean; however, it is calculated as an “expected mean delta” rather than a median one.

Based on our model, the closest deltas are found at Williams, McLaren, Sauber, and Haas, which is something we knew from the previously shown summary statistics. However, we can also see that for these three teams, many of the expected predictions cross the zero line. This means that over thousands of simulations, in many of them, our model found the losing driver to be faster than his teammate over a season. For example, at McLaren, our model expected Norris to win the overall qualifying battle over Piastri by an average of 0.008 seconds, but in 44% of the simulations, Pastri actually beat Norris, showing how tight the quali battle is between them. Something similar is seen at Williams and Sauber, with around 24.6% of the simulations showing Sainz beating Albon, and 16.3% of the simulations showing Hulkenberg ahead of Bortoleto. In these comparisons, the uncertainty shows that with just a couple of slightly different results, perhaps in a different universe, the delta would swing from one driver to another.

Regarding the Haas situation, you can see that our expected delta (0.111 seconds) is bigger than the raw mean delta (0.086 seconds), but smaller than the median delta of 0.181 seconds. This is because the model is calculating the mean, so it uses all of the values and not just the middle one as the median does. At the same time, the model understands that the anomaly seen at the Miami GP sprint, in which Ocon beat Bearman by 1.5 seconds, is not representative of the pace of either driver, and instead calculates a plausible, expected value. After doing this for every race, the model reached the conclusion that currently Bearman is faster than Ocon in quali by an expected 0.111 seconds per session.

Understanding our xQD metric

Since our model learns from the data but then creates expectations, it self-corrects sessions in which a driver over or underperformed. Take, for example, Kimi Antonelli at the Belgian Grand Prix sprint qualifying. In this session, Kimi had a qualifying time of 1:45.394, almost three seconds slower than his teammate’s time (1:42.650). The model automatically understands, based on previous data but also considering the disastrous qualifying session that he had, that he is definitely not as slow as he was in that session, but also that he is most likely not as fast as George Russell. With this information, the model creates thousands of simulations which represent plausible values of times that Antonelli could have had in this qualifying session.

In this case, our model expected Antonelli to have a qualifying time of 1:42.978, which would have put him 0.4 seconds behind George Russell. At the same time, 50% of the simulations had Antonelli achieving a time between 1:42.892 and 1:43.062. These values are all very plausible based on the observed data. Perhaps Antonelli would have been slightly faster than 1:42.978, or a bit slower, but generally speaking, a delta of three to five tenths to Russell would not have been completely unexpected. A delta of 2.7 seconds? Not only is it not expected, but it is something that can be considered a complete anomaly.

The limitations

As with any model, ours has limitations. In our case, the two main ones are the lack of data and the scope of it. Our model uses only data from the current season. It doesn’t know anything about Hamilton winning the World Championship seven times, or anything about Bearman being a rookie. The model uses the data from this season, which means that its scope is quite limited. At the same time, we’re only using the data in which both drivers from the same team participated in the same session. This leaves us with limited data, which creates wider uncertainty intervals. Having said that, as long as the model is properly calibrated, it can still provide valuable insights into driver performance relative to their teammates.

Conclusions

As a final conclusion, we can see that using the raw data and the xQD model provides a clear picture of the performance of each driver against his teammate. We have three teams that have a delta very close to zero, with the battle at McLaren, Williams and Sauber being extremely tight. In another universe, these three drivers could have been losing the qualifying battle against their teammates, even with their current level of talent and performance. At this level, the delta of performance between teammates could be almost nothing, and in many cases, it is nothing.

We’re in the final quarter of the season, so it’ll be very interesting to see what the final delta between teammates becomes when the season is done. Will Leclerc maintain what is a healthy delta against his teammate? Will Albon be the surprise of the season by beating Sainz in qualifying? We’ll find out in the next couple of months.

Disclaimer

I continue to make modifications to improve the model. This is a trend that will continue until I build a model that I’m fully happy with. Additionally, I’ve made some modifications to the way the data is presented. For both xQD charts I’ve changed the uncertainty intervals. Previously, I showed the 66% and 95% intervals, but I’ve switched them to instead display the middle 50% and 80% intervals. This isn’t a scientific paper for publication, so I believe the narrower intervals are easier to visualize and interpret for the vast majority of people.

2025 São Paulo GP: Race pace

Mon, 10 Nov 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 São Paulo GP: Sprint

Sun, 09 Nov 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Sprint race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized sprint race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

I’ve removed this chart because it takes quite a bit of time to generate and I don’t think anybody was looking at it. If you liked this chart and want me to bring it back, hit me up using the Get in touch link.

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Sprint race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 20)

Tue, 04 Nov 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

I’ve added a new section called xQD (Expected Quali Delta). In this section, I present the results of my latest statistical model that aims to improve our understanding of the quali delta between teammates. Check it out.

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

We’re more than halfway through the season. With 20 races—and now 4 sprints after the Mexico City GP—we have more representative results. Just as a reminder, this season features the anomaly of Lawson switching places with Tsunoda after just three races, which makes our usual analysis a bit trickier. Additionally, Alpine decided to replace Doohan with Colapinto after just six races. Normally, I’d use the median as the key metric of interest since it’s more robust to outliers. However, in this case, the median might skew the average delta between Lawson and Verstappen.

It is important to note that after 20 races, the results have now stabilized, which means that at this point in time the mean is the most representative metric for most pairings.

Symmetric percent difference

If we look at the mean symmetric percent difference instead, the largest active gap is still at Red Bull, with Max ahead of Tsunoda by an average of 0.795%, the largest delta on the grid by a significant margin.

At the other end of the spectrum, the smallest delta is seen at McLaren. Looking at the median symmetric percent difference, Oscar Piastri and Lando Norris are dead even, with a delta of 0% between them. If we focus instead on the mean symmetric percent difference, we have Lando Norris and Oscar Piastri separated by just 0.007%. There are 3 other teams—Sauber, Haas and Williams—that currently have a delta of less than 0.1% between their two drivers, showing how competitive the intra-team quali battles have been this season.

Delta in seconds

Looking at seconds as the metric of interest, we see some incredible results. Looking at the median qualifying delta, there are two drivers with a gap to their teammates under 1 tenth fo a second, with the smallest gap seen at McLaren, with Norris now beating Piastri by just 0.003 seconds on average. At Williams, Sainz has taken the lead over Albon, and is now beating Alex by a median time of 0.055 seconds per round. Using the mean, we see 4 teams with a delta of around half a tenth or less. Currently, the smallest gap is seen at McLaren, with Norris and Piastri separated by 0.016 seconds on average, but the deltas at Sauber, Haas, and Williams are impressively tight as well.

Qualifying stage appearances

If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the 20th race of the 2025 season, the differences between teammates have fully stabilized. Most pairings are quite even, with a few exceptions. The first is Aston Martin, where Alonso has reached Q3 on 12 occasions, while his teammate, Lance Stroll, has managed it only three times. The second is Alpine, where Gasly has qualified for Q3 on eight occasions, while Doohan and Colapinto combined have yet to make a single Q3 appearance. The third exception is, unsurprisingly, Red Bull Racing. So far, Max Verstappen has reached Q3 in all 24 possible sessions and is one of just three drivers with a 100% Q3 appearance rate, along with Lando Norris and Oscar Piastri. Meanwhile, Lawson was eliminated in Q1 three times while racing for Red Bull. In Tsunoda’s case, his Q3 record is still not favourable: the Japanese driver now has six Q3 appearances out of 21 outings for the team, putting his Q3 rate at 28.5%.

xQD (Expected Quali Delta)

How much quicker one teammate should be in qualifying, based on underlying pace rather than luck.

xQD (expected Quali Delta): The model’s best estimate of the qualifying gap between teammates after filtering out session noise.

How to read the chart

The number in the colored box next to each team’s logo: This number represents the model’s single best estimate of the qualifying gap between teammates after filtering out session noise.
The x-axis: The horizontal axis shows the xQD in seconds. Higher times represent bigger deltas, in favour of the winning driver.
The slabs The slabs, or “domes”, provide a range of plausible values for each team’s skill. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty about the actual delta in each team.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our expected value. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents is the zero threshold. It provides a quick reference to see which in how many simulations the result changed from driver A winning to driver B. If the slabs cross the zero threshold, then it means that on some simulations the current winning driver would be losing the quali battle against his teammate.

Based on our model, the closest deltas are found at Williams, McLaren, Sauber, and Haas, which is something we knew from the previously shown summary statistics. However, we can also see that for these three teams, many of the expected predictions cross the zero line. This means that over thousands of simulations, in many of them, our model found the losing driver to be faster than his teammate over a season. For example, at Williams, our model expected Sainz to win the overall qualifying battle over Albon by an average of 0.002 seconds, but in 48% of the simulations, Albon actually beat Sainz, showing how tight the quali battle is between them. Something similar is seen at McLaren, with around 46% of the simulations showing Norris beating Piastri. In these two comparisons, the uncertainty shows that with just a couple of slightly different results, perhaps in a different universe, the delta would swing from one driver to another.

Regarding the Haas situation, you can see that our expected delta (0.09 seconds) is bigger than the raw mean delta (0.033 seconds), but smaller than the median delta of 0.137 seconds. This is because the model is calculating the mean, so it uses all of the values and not just the middle one as the median does. At the same time, the model understands that the anomaly seen at the Miami GP sprint, in which Ocon beat Bearman by 1.5 seconds, is not representative of the pace of either driver, and instead calculates a plausible, expected value. After doing this for every race, the model reached the conclusion that currently Bearman is faster than Ocon in quali by an expected 0.09 seconds per session.

Understanding our xQD metric

In this case, our model expected Antonelli to have a qualifying time of 1:42.977, which would have put him 0.4 seconds behind George Russell. At the same time, 50% of the simulations had Antonelli achieving a time between 1:42.892 and 1:43.058. These values are all very plausible based on the observed data. Perhaps Antonelli would have been slightly faster than 1:42.977, or a bit slower, but generally speaking, a delta of three to five tenths to Russell would not have been completely unexpected. A delta of 2.7 seconds? Not only is it not expected, but it is something that can be considered a complete anomaly.

The limitations

Conclusions

As a final conclusion, we can see that using the raw data and the xQD model provides a clear picture of the performance of each driver against his teammate. We have three teams that have a delta very close to zero, with Bearman, Albon, and Piastri leading against their teammates by almost nothing. In another universe, these three drivers could have been losing the qualifying battle against their teammates, even with their current level of talent and performance. At this level, the delta of performance between teammates could be almost nothing, and in many cases, it is nothing.

Disclaimer

2025 Mexico City GP: Race pace

Mon, 27 Oct 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Mexico City GP: FP2 session (Model-corrected long run analysis)

Sat, 25 Oct 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

This is an experimental analysis.

Model-corrected race simulation pace

Example of model predictions

This chart shows how the model predicts lap times to replace the ones flagged as anomalies. You can see how the predictions follow a smooth curve that matches the overall trend of the remaining laps.

The predictions are based on complex linear algebra, so they’re not “guesswork” as some of you may believe. You have a better analysis? Cool, share it on your website.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 19)

Tue, 21 Oct 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

We’re more than halfway through the season. With 19 races—and now 4 sprints after the United States GP—we have more representative results. Just as a reminder, this season features the anomaly of Lawson switching places with Tsunoda after just three races, which makes our usual analysis a bit trickier. Additionally, Alpine decided to replace Doohan with Colapinto after just six races. Normally, I’d use the median as the key metric of interest since it’s more robust to outliers. However, in this case, the median might skew the average delta between Lawson and Verstappen.

It is important to note that after 19 races, the results have now stabilized, which means that at this point in time the mean is the most representative metric for most pairings.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates remains at Red Bull Racing. Tsunoda did not have a good spring qualifying at the United States GP, and qualified in 18th place, behind Verstappen by a whooping symmetric percent of 2.01%. Currently, Verstappen is beating Yuki Tsunoda by 0.803%, which is less than the median delta Lawson left at 0.881%, but still stands as the highest gap on the grid by a wide margin. It’s important to note that this gap has continued to increase in the past few weekends, so it wouldn’t be surprising to see Tsunoda having the widest gap of all drivers by the end of the season.

If we look at the mean symmetric percent difference instead, the largest active gap is still at Red Bull, with Max ahead of Tsunoda by an average of 0.822%, the largest delta on the grid by a significant margin.

At the other end of the spectrum, the smallest delta is seen at McLaren and Williams, depending on the metric of interest. Looking at the median symmetric percent difference, Oscar Piastri is beating Lando Norris by just 0.017%. If we focus instead on the mean symmetric percent difference, Sainz is now winning the battle against Albon by just 0.03%.

Delta in seconds

Looking at seconds as the metric of interest, we see some incredible results. Looking at the median qualifying delta, there are two drivers with a gap to their teammates under 1 tenth fo a second, with the smallest gap seen at McLaren, with Piastri beating Norris by just 0.012 seconds on average. Using the mean, we see 4 teams with a delta of around half a tenth or less. Currently, the smallest gap is seen at Sayber, with Bortoleto beating Hulkenberg by 0.03s on average, but the deltas at Haas, McLaren and Williams are impressively tight as well.

On the other side of the spectrum, the largest gap will most likely remain at Red Bull for the remainder of the season. Tsunoda continues to fall further back behind Verstappen, and is now 0.601 seconds slower on average based on the median delta, and 0.712 seconds slower based on the mean delta.

Qualifying stage appearances

If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the 19th race of the 2025 season, the differences between teammates have fully stabilized. Most pairings are quite even, with a few exceptions. The first is Aston Martin, where Alonso has reached Q3 on 10 occasions, while his teammate, Lance Stroll, has managed it only three times. The second is Alpine, where Gasly has qualified for Q3 on eight occasions, while Doohan and Colapinto combined have yet to make a single Q3 appearance. The third exception is, unsurprisingly, Red Bull Racing. So far, Max Verstappen has reached Q3 in all 23 possible sessions and is one of just three drivers with a 100% Q3 appearance rate, along with Lando Norris and Oscar Piastri. Meanwhile, Lawson was eliminated in Q1 three times while racing for Red Bull. In Tsunoda’s case, his Q3 record is still not favourable: the Japanese driver now has six Q3 appearances out of 20 outings for the team, putting his Q3 rate at 30%.

xQD (Expected Quali Delta)

How much quicker one teammate should be in qualifying, based on underlying pace rather than luck.

xQD (expected Quali Delta): The model’s best estimate of the qualifying gap between teammates after filtering out session noise.

How to read the chart

The number in the colored box next to each team’s logo: This number represents the model’s single best estimate of the qualifying gap between teammates after filtering out session noise.
The x-axis: The horizontal axis shows the xQD in seconds. Higher times represent bigger deltas, in favour of the winning driver.
The slabs The slabs, or “domes”, provide a range of plausible values for each team’s skill. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty about the actual delta in each team.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our expected value. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents is the zero threshold. It provides a quick reference to see which in how many simulations the result changed from driver A winning to driver B. If the slabs cross the zero threshold, then it means that on some simulations the current winning driver would be losing the quali battle against his teammate.

Based on our model, the closest deltas are found at Williams, McLaren, Sauber, and Haas, which is something we knew from the previously shown summary statistics. However, we can also see that for these three teams, many of the expected predictions cross the zero line. This means that over thousands of simulations, in many of them, our model found the losing driver to be faster than his teammate over a season. For example, at Williams, our model expected Albon to win the overall qualifying battle by an average of 0.008 seconds over Sainz, but in 44% of the simulations, Sainz actually beat Albon, showing how tight the quali battle is between them. Something similar is seen at McLaren, with around 35% of the simulations showing Norris beating Piastri. In these two comparisons, the uncertainty shows that with just a couple of slightly different results, perhaps in a different universe, the delta would swing from one driver to another.

Regarding the Haas situation, you can see that our expected delta (0.095 seconds) is bigger than the raw mean delta (0.032 seconds), but smaller than the median delta of 0.146 seconds. This is because the model is calculating the mean, so it uses all of the values and not just the middle one as the median does. At the same time, the model understands that the anomaly seen at the Miami GP sprint, in which Ocon beat Bearman by 1.5 seconds, is not representative of the pace of either driver, and instead calculates a plausible, expected value. After doing this for every race, the model reached the conclusion that currently Bearman is faster than Ocon in quali by an expected 0.095 seconds per session.

Understanding our xQD metric

In this case, our model expected Antonelli to have a qualifying time of 1:42.990, which would have put him 0.4 seconds behind George Russell. At the same time, 50% of the simulations had Antonelli achieving a time between 1:42.907 and 1:43.075. These values are all very plausible based on the observed data. Perhaps Antonelli would have been slightly faster than 1:42.990, or a bit slower, but generally speaking, a delta of three to five tenths to Russell would not have been completely unexpected. A delta of 2.7 seconds? Not only is it not expected, but it is something that can be considered a complete anomaly.

The limitations

Conclusions

As a final conclusion, we can see that using the raw data and the xQD model provides a clear picture of the performance of each driver against his teammate. We have three teams that have a delta very close to zero, with Bearman, Albon, and Piastri leading against their teammates by almost nothing. In another universe, these three drivers could have been losing the qualifying battle against their teammates, even with their current level of talent and performance. At this level, the delta of performance between teammates could be almost nothing, and in many cases, it is nothing.

Disclaimer

2025 United States GP: Race pace

Mon, 20 Oct 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 United States GP: Sprint

Sun, 19 Oct 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Sprint race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized sprint race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Sprint race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 18)

Thu, 16 Oct 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

We’re more than halfway through the season. With 18 races—and now 3 sprints after the Dutch GP—we have more representative results. Just as a reminder, this season features the anomaly of Lawson switching places with Tsunoda after just three races, which makes our usual analysis a bit trickier. Additionally, Alpine decided to replace Doohan with Colapinto after just six races. Normally, I’d use the median as the key metric of interest since it’s more robust to outliers. However, in this case, the median might skew the average delta between Lawson and Verstappen.

It is important to note that after 18 races, the results have now stabilized, which means that at this point in time the mean is the most representative metric for most pairings.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates remains at Red Bull Racing. Tsunoda qualified in 15th place at the Singapore GP (13th after the disqualifications to Albon and Sainz), behind Verstappen by a symmetric percent of 0.86%. Currently, Verstappen is beating Yuki Tsunoda by 0.759%, which is less than the median delta Lawson left at 0.881%, but still stands as the highest gap on the grid by a wide margin.

If we look at the mean symmetric percent difference instead, the largest active gap is still at Red Bull, with Max ahead of Tsunoda by an average of 0.752%, the largest delta on the grid by a significant margin.

At the other end of the spectrum, the smallest delta is seen at McLaren and Haas, depending on the metric of interest. Looking at the median symmetric percent difference, Oscar Piastri is beating Lando Norris by just 0.018%. If we focus instead on the mean symmetric percent difference, Ocon is winning the battle by just 0.012% against Oliver Bearman.

Delta in seconds

Looking at seconds as the metric of interest, we see some incredible results. Looking at the mean qualifying delta, the smallest gap is found at McLaren, with Piastri beating Norris by just 0.015 seconds on average. Using the median, we have Albon ahead of Sainz by just 0.033 seconds.

The largest gap will most likely remain at Red Bull for the remainder of the season. Tsunoda is falling further back behind Verstappen, and is now 0.587 seconds slower on average based on the median delta, and 0.639 based on the mean delta.

Qualifying stage appearances

If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the 18th race of the 2025 season, the differences between teammates have fully stabilized. Most pairings are quite even, with a few exceptions. The first is Aston Martin, where Alonso has reached Q3 on 10 occasions, while his teammate, Lance Stroll, has managed it only three times. The second is Alpine, where Gasly continues to do a stellar job in a struggling car. After the Singapore GP, Pierre has qualified for Q3 on eight occasions, while Doohan and Colapinto combined have yet to make a single Q3 appearance. The third exception is, unsurprisingly, Red Bull Racing. So far, Max Verstappen has reached Q3 in all 21 possible sessions and is one of just three drivers with a 100% Q3 appearance rate, along with Lando Norris and Oscar Piastri. Meanwhile, Lawson was eliminated in Q1 three times while racing for Red Bull. In Tsunoda’s case, his Q3 record is still not favourable: the Japanese driver now has six Q3 appearances out of 18 outings for the team, putting his Q3 rate at 33.3%.

xQD (Expected Quali Delta)

How much quicker one teammate should be in qualifying, based on underlying pace rather than luck.

xQD (expected Quali Delta): The model’s best estimate of the qualifying gap between teammates after filtering out session noise.

How to read the chart

The number in the colored box next to each team’s logo: This number represents the model’s single best estimate of the qualifying gap between teammates after filtering out session noise.
The x-axis: The horizontal axis shows the xQD in seconds. Higher times represent bigger deltas, in favour of the winning driver.
The slabs The slabs, or “domes”, provide a range of plausible values for each team’s skill. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty about the actual delta in each team.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our expected value. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents is the zero threshold. It provides a quick reference to see which in how many simulations the result changed from driver A winning to driver B. If the slabs cross the zero threshold, then it means that on some simulations the current winning driver would be losing the quali battle against his teammate.

Based on our model, the closest deltas are found at Williams, McLaren, and Haas, which is something we knew from the previously shown summary statistics. However, we can also see that for these three teams, many of the expected predictions cross the zero line. This means that over thousands of simulations, in many of them, our model found the losing driver to be faster than his teammate over a season. For example, at Haas, our model expected Bearman to win the overall qualifying battle by an average of 0.086 seconds over Ocon, but in 16.3% of the simulations, Ocon actually beat Bearman. Something similar is seen at Williams and McLaren, with around 25% of the simulations showing Sainz beating Albon and Norris beating Piastri.

Regarding the Haas situation, you can see that our expected delta is bigger than the raw mean delta (0.021 seconds), but smaller than the median delta of 0.127 seconds. This is because the model is calculating the mean, so it uses all of the values and not just the middle one as the median does. At the same time, the model understands that the anomaly seen at the Miami GP sprint, in which Ocon beat Bearman by 1.5 seconds, is not representative of the pace of either driver, and instead calculates a plausible, expected value. After doing this for every race, the model reached the conclusion that currently Bearman is faster than Ocon in quali by an expected 0.082 seconds per session.

Understanding our xQD metric

In this case, our model expected Antonelli to have a qualifying time of 1:42.989, which would have put him 0.4 seconds behind George Russell. At the same time, 50% of the simulations had Antonelli achieving a time between 1:42.906 and 1:43.071. These values are all very plausible based on the observed data. Perhaps Antonelli would have been slightly faster than 1:42.989, or a bit slower, but generally speaking, a delta of three to five tenths to Russell would not have been completely unexpected. A delta of 2.7 seconds? Not only is it not expected, but it is something that can be considered a complete anomaly.

The limitations

Conclusions

As a final conclusion, we can see that using the raw data and the xQD model provides a clear picture of the performance of each driver against his teammate. We have three teams that have a delta very close to zero, with Bearman, Albon, and Piastri leading against their teammates by almost nothing. In another universe, these three drivers could have been losing the qualifying battle against their teammates, even with their current level of talent and performance. At this level, the delta of performance between teammates could be almost nothing, and in many cases, it is nothing.

Disclaimer

2025 F1 Season: McLaren's pit story (rounds 1 - 18)

Fri, 10 Oct 2025 00:00:00 +0000

This short post takes a closer look at McLaren’s pit stops and the trends we’ve seen from them during the 2025 season. It’s a follow-up to my previous piece, Pit Stop Power Rankings (Rounds 1–18), but this time we’ll dig deeper than just average results. Using our statistical model, we’ll explore how McLaren, and the other two benchmark teams from recent seasons, are evolving as the season unfolds.

Use the Get in touch link if you’re interested in supporting this project.

Speed isn’t the problem, delays are

Looking at our chart, the gap between McLaren and this season’s top two pit stop performers, Ferrari and Red Bull, is clear. Ferrari currently boasts an expected Pit Time (xPT) of 2.57 seconds per stop, while Red Bull sits at 2.62 seconds. In contrast, our model projects McLaren’s average stop at 3.12 seconds, roughly half a second slower than Ferrari and Red Bull.

So, where does this difference come from?
Our model breaks down xPT into two components. The first is xPCT (expected Pit Core Time), which reflects a team’s baseline speed by stripping away average time lost to fumbles, hesitations, and other minor mishaps. The second is xPDT (expected Pit Delay Time), which measures the average delay added on top of that core speed—essentially, the time penalties teams pay for those inevitable interruptions during the pit stop process.

xPCT

Focusing on xPCT gives us a clear view of the raw pit stop speed each team has shown over the season. Here, McLaren stand out as the outright fastest crew in Formula 1. They hold the three quickest stops of the year, and when they’re in the zone, they really deliver. Not only are they the benchmark in this category, but they’ve also gotten quicker as the season has progressed—most notably with a sharp improvement after the summer break. Red Bull and Ferrari follow, around one and two tenths slower respectively, and while both are exceptionally quick, neither has matched McLaren’s pace in this department.

xPDT

When we turn our attention to xPDT, the story shifts completely. Ferrari’s pit crew have been models of consistency, keeping their delays short and trending in the right direction throughout the season. They began the year with an xPDT of 0.58 seconds per stop and have trimmed that down to 0.47 seconds by the final race. Red Bull have followed a similar upward curve, improving from 0.69 to 0.59 seconds per stop by round 18.

McLaren, however, tell a different story. They not only trail Ferrari and Red Bull in this metric but are now several steps behind. Their season started with an average xPDT of 0.89 seconds per stop—already about 50% slower than Ferrari and nearly 30% slower than Red Bull at that stage. Unfortunately, things have only worsened, with their xPDT ballooning to 1.22 seconds per stop in the final quarter of the season. That’s roughly two and a half times slower than Ferrari and twice as slow as Red Bull. Unlike their rivals, who have refined their processes and boosted consistency, McLaren appear to be moving in the opposite direction, struggling more as the season goes on.

Summary

🔴 Ferrari: Consistently quick with minimal delays. Fast in every way.
🟠 McLaren: Fastest potential with an xPCT of 1.90s, but delays of 1.22s per stop.
🔵 Red Bull: Steady performance with xPT at 2.62s. Strong in xPCT and xPDT.

McLaren’s xPCT (Expected Pit Core Time) shows they have the talent to execute sub-2 second stops. But when delays hit due to equipment issues, positioning problems, or coordination errors, they lose over a second per stop.

Conclusion

What this all tells us is that McLaren have the fastest pit crew in Formula 1 when it comes to pure speed—but that speed isn’t translating into overall pit stop efficiency. Their xPCT shows they can reach incredible times when everything goes to plan, yet their rising xPDT reveals a growing problem with delays and execution.

Ferrari and Red Bull don’t quite match McLaren’s peak pace, but they’ve mastered consistency, steadily improving pit stop reliability as the season has gone on. That’s where the real advantage lies: cutting out errors and keeping delays to a minimum.

McLaren’s situation is puzzling considering Andrea Stella has openly acknowledged they’ve identified the root causes of these issues, both in the human element, and in the complexity of their pit stop hardware. Yet despite this awareness, the numbers paint a worrying picture: delays have increased dramatically in the final stretch of the season. In theory, knowing the problem should help stop the bleeding and allow them to improve, but in practice, McLaren’s execution has been slipping further away from their rivals.

Support this project

This piece builds on the Pit Stop Power Rankings (Rounds 1–18), which remains the most detailed analysis I’ve published here so far. Doing inference with statistical models isn’t easy—it takes a lot of time, testing, and refinement to create something that produces reliable, meaningful insights. Developing this model initially took weeks of focused work, and I continue to work on it to improve it as the season goes on.

If you’ve enjoyed reading this article, please consider becoming a supporter. Your support truly makes a difference and helps me keep creating in-depth, data-driven F1 content like this. Thank you for reading and for being part of this growing community.

2025 F1 Season: Pit Stop Power Rankings (rounds 1 - 18)

Mon, 06 Oct 2025 00:00:00 +0000

Pit stops are one of the most important elements in modern F1, and the pit stop crews deserve a lot of respect for the job they do week in and week out. Still, one of the most difficult aspects of judging a pit crew is the fact that pit stop times are highly variable, and a single pit stop can make the best team look like the worst. So, I’ve decided to create a robust model to address this issue and answer the question: Which team has the best pit stop crew in Formula 1?

Would you be interested in a follow up to this article? Maybe exploring trends for some teams (how xPT/xPCT/xPDT changes over a season), or focusing on specific teams and their different philosophies (what teams focus on to improve, perhaps by improving top speed or by reducing mistakes)? Let me know in the comments or by using the Get in touch form.

The raw pit stop data

What exactly happened during the season, considering the luck and variability of the data?

First of all, let’s take a look at the raw pit stop data. It shows exactly how the stops happened during the season—no adjustments for conditions, just all the stops compiled and summarized. As you keep reading, you’ll see that this approach comes with some subtle but noticeable downsides.

Raw performance per team

Looking at the data, grouped and summarized by team, the Ferrari pit crew comes out on top. Ferrari and their mechanics continue to do a fantastic job, performing at the top of their ability during the first 18 races of the 2025 season. Their average of 2.44 seconds per stop is 0.22 seconds faster than Racing Bulls’. Considering that each pit stop takes, on average, less than three seconds, this is a significant gap.

Speaking of Racing Bulls, it’s quite interesting to see them ahead of their big brother, Red Bull. Their data is intriguing: on average, they are beating Red Bull by just 0.05 seconds per stop, but it appears that their maximum, raw pace isn’t quite on Red Bull’s level. However, they have made fewer major mistakes than the Red Bull crew, which helps lower their average substantially. Even including their slow 6.7 stop at the Zandvoort GP, their crew can be considered as more consistent than Red Bulls'.

Looking down the rankings, Mercedes are competing directly with Red Bull, with Mercedes behind the Austrian team by just 0.05 seconds per stop. Sauber trails Mercedes, with an average stop time of 2.88 seconds per stop.

The bottom five teams in this ranking are separated by less than a tenth of a second. Aston Martin leads this group with an average time of 2.97s, followed closely by Alpine at 2.98s and McLaren at 2.99s, while both Haas and Williams share the slowest time at 3.05s per stop. McLaren’s drop in the rankings is particularly notable—their 2.99-second average reflects several costly mistakes that have hurt their pit stop performance.

Is is all bad for the slower teams? We’ll take a look at their detailed performance in the following sections of this article.

Raw performance per driver

I think it’s fair to say that, normally, you would expect both drivers from the same team to have similar pit stop times. Each team has a single pit crew that works on both cars when they stop for new tires. Still, reality is messier, and it’s rare to see two teammates with exactly the same average pit stop times.

Most teammates are, in fact, very close to each other. Hamilton and Leclerc are separated by just 0.14 seconds; Russell and Antonelli by 0.05 seconds; Bortoleto and Hulkenberg are dead even, and Lawson and Hadjar by just 0.03 seconds. You get the idea. Nonetheless, there are a couple of very interesting results.

First, the Red Bull pairing. While Tsunoda’s data is slightly contaminated by his two races with Racing Bulls, the delta to Verstappen is quite stark. Tsunoda has an average pit stop time of 2.52 seconds compared to Verstappen’s 2.86 seconds, a delta of 0.34 seconds. This is quite a big difference considering that both drivers are assisted by the same pit crew.

Second, the results at Haas: While neither Ocon nor Bearman boast strong pit stops—their fastest stop of the season so far is 2.49 seconds—it’s clear that Ocon has a faster average time, currently by a whopping 0.32 seconds, which is massive in the world of F1 pit stops.

Our new metric: xPT

How fast a pit crew should be, based on their talent and equipment?

This is where things get interesting. We’ve already looked at the raw pit stops, which show us exactly what happened during the season—including all the messiness and chaos that are part of pit stops. However, we can try to separate the true skill level of the pit crews from this chaos and understand the expected, real performance of each crew. To do this, I created my own performance metric, which I’ve titled expected Pit Time, or xPT.

xPT is the output of a statistical model that aims to identify the patterns within pit stops. The xPT metric is our model’s best estimate of how fast a pit crew should be, based on their talent and equipment. It’s calculated by analyzing hundreds of pit stops and statistically adjusting for all the “noise” and context. By removing the factors that obscure our data, xPT gives us a single, fair number to compare the real, repeatable skill of each team’s pit crew. A team with a low xPT is fundamentally fast, regardless of whether they got lucky or unlucky on a particular Sunday.

xPT (expected Pit Time): Our model’s best estimate of how fast a pit crew should be, based on their talent and equipment.

The xPT model is adaptable and gets updated as more data is gathered week after week. The more data the model has, the more accurate it becomes. Its predictions and inferences get stronger as the season progresses.

Using xPT as a metric, we can see which teams are performing better than expected, which are performing at their average, and which are truly struggling. One of the main advantages of xPT is it’s interpretability since it’s expressed in the original units of our data—seconds. With it, we can create a true power ranking of the pit crews, revealing who is consistently the best—not just who happened to get it right once. Let’s take a look at our first results.

How to read the charts

The overall ranking: The teams are ranked from 1 (fastest) to 10 (slowest) based on their xPT, or Expected Pit Time.
The number in the colored box next to each team’s logo: This number represents the model’s single best estimate for their “true” pit stop skill, measured in seconds. This number is the estimated “normal” pit stop time that you would expect fomr each time, based on the model’s beliefs.
The x-axis: The horizontal axis shows the xPT in seconds. Just like a lap time, a lower number is better. Teams further to the left are faster.
The slabs The slabs, or “domes”, provide a range of plausible values for each team’s skill. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty about the actuall skill level of each pit crew.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our expected value. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents the midfield average (the median xPT of all teams). It provides a quick reference to see which teams are in the top half of the grid (to the left of the line) and which are in the bottom half (to the right).

xPT by team

Looking at the “xPT by team” chart, we can see what the model “believes” to be the actual expected average pit stop time for each pit crew. Based on 2025 season data, our model indicates that Ferrari’s real, repeatable performance should result in an average time of 2.50 seconds per pit stop. This number was estimated to be 2.51 seconds at the latest pit stop analysis that I made on round 16. Red Bull sits in second place, 0.03 seconds ahead of their sister team, Racing Bulls, but well behind Ferrari. Behind them, Mercedes, Sauber and Mclaren, are quite competitive with one another, and are separated by an xPT of only 0.09 seconds per stop.

While our main chart shows the full range of plausible skill levels for each team, we can also use the model to answer a more direct question: what is the probability that one team is truly faster than another? By leveraging statistical analysis, we estimate that there is a 96.7% probability that Ferrari’s true underlying pit stop skill (xPT) is better than Red Bull’s, the second fastest team based on our model’s estimates. This means that, after removing noise from the data, it’s almost certain that Ferrari currently has the best pit crew in the business.

McLaren’s distribution is very flat. This is because the model isn’t sure what to expect from them. They have had some very fast stops, but some horrible blunders. They are unpredictable, not only for you and me, but also for our statistical model.

xPT by driver

This chart shows xPT, but now stratified by driver. Why is there a difference between two drivers from the same team? I can’t really say for sure. Our model is good at detecting these differences, but from a statistical point of view, finding the “why” would require data that I don’t have access to. Still, we can observe some very interesting trends.

At Ferrari, for example, our model puts Leclerc at an xPT of 2.47 seconds, compared to Hamilton’s 2.53 seconds. While this is a minor difference—too small to say for certain whether it’s real or just an artifact of our model—it’s interesting to see that there may be slight but noticeable differences between drivers, although calling a 0.06-second difference in a stop significant would be a stretch. Even with this small delta, our model calculates that the probability that Leclerc has faster pit stop potential than Hamilton is 81.7%.

In general, you’ll notice that most drivers have a very similar xPT to that of their respective team, which is what we would expect. The same pit crew works on both cars, so a team’s pit crew has a much greater impact than individual drivers.

Norris vs Piastri: The McLaren drivers are separated by just 0.05 seconds in terms of xPT. This means that considering the entire season, our model expects that both drivers should have pit stops that are very close to each other.

Reality vs expectations

How fast has a pit crew been compared to the model’s expectations?

Our model is designed to smooth the data, separating a team’s underlying skill from the influence of luck and random variability. This is why you’ll notice that some teams have a faster or slower raw average than what the model predicts. By comparing these two numbers—the raw average and the model’s prediction—we can get a robust estimate of which teams are truly over- or underperforming relative to their expected potential.

Delta to xPT (expected Pit Time): A measure of a team’s performance against expectations, showing how much their simple raw average deviated from the model’s more robust prediction of their true ability.

Delta to xPT by team

How to read the charts

The number in the colored box next to each team’s logo: This number represents the estimated delta between the raw pit stop times and the estimated pit stop times (xPT), measured in seconds. Negative numbers represent higher performance than expected from each pit crew, while positive numbers show pit crews that are underperforming.
The x-axis: The horizontal axis shows the delta to xPT in seconds. Just like a lap time, a lower number is better. Teams further to the left are are over performing the expectations of the model by a higher margin, while teams further to the right are under performing the expectations of the model.
The slabs The slabs, or “domes”, provide a range of plausible values for the delta to the xPT. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our delta to the xPT. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents the 0 threshold. Teams or drivers on the left side of this line are beating the expectations, while teams on the right of it are performing at a lower level than expected.

You may have noticed that McLaren has an xPT (expected Pit Time) of 2.89 seconds. This is lower than their raw average pit stop time of 2.99 seconds per stop. Why is this? Based on their raw pit times, our model believes that McLaren’s crew has the potential to perform an average pit stop in 2.89 seconds per stop. If you look at the raw data, McLaren has performed several fast stops—faster than those of many other teams. However, they also have many stops that are much slower than you would expect given their underlying quality. At the moment, our model suggests that McLaren is underperforming by 0.11 seconds per stop, the worse of all teams competing in the 2025 season.

On the other extreme, we have Ferrari. While our model expects Ferrari to average 2.50 seconds per pit stop, in reality, their average is 2.44 seconds. The delta to xPT shows that Ferrari is not just meeting expectations but far exceeding them, outperforming their xPT by an average of 0.06 seconds. Not only are they the fastest crew based on our model, but they also have the “it” factor, which puts them on another level compared to the remainder of the crews.

Regarding the rest of the teams, you can see that their values hover the 0 second mark, which means that they are performing according to the model’s expectations. Some teams are significantly faster, as we’ve seen with Ferrari, while some are significantly slower, including Alpine, Sauber, and Mclaren, who seem to be struggling compared to what the model believes they can do.

Delta to xPT by driver

Just as with the previous section, this chart shows how much a driver is over or under performing based on the expectations (xPT) of the model. Some results are expected, such asLeclerc overperforming the expectations of the model. While the model already ranks the Ferrari as the best pit crew in F1, the delta to xPT shows that they are performing even better than expected. Is it down to luck? Motivation? Just a hot streak? Our model can’t answer that, so it’s up to you. If you have any additional information you can send me up, knowing that everything that you tell me is confidential, including your identity.

There are two main interesting results, one found at Red Bull, and the other at Haas. Regarding Red Bull, While Tsunoda is exceeding the model’s expectations by 0.08 seconds, his teammate, Max Verstappen is underperforming by 0.14 seconds per stop. Looking at the raw data shows that Verstappen has had many more slow stops than Tsunoda, causing this big discrepancy between both teammates. Currently, our model estimates with 88.5% certainty that Tsunoda’s stops have been faster compared to Verstappen’s.

At Haas, we see Ocon leading this chart, exceeding the expectations of the model by 0.15 seconds per stop. On the other hand, his teammate, Oliver Bearman, is underperforming by an average of 0.07 seconds per stop. Our model estimates with 86.9% certainty that Ocon’s stops have been faster than Bearman’s.

A world with no delays

What is the team’s baseline pit stop speed after removing the time lost to fumbles and delays?

One of the key benefits of this model is its ability to break down a pit stop into two main components. It allows us to statistically separate a team’s core performance—their baseline speed with all delays removed—from the delays themselves.

To measure this core performance, I created xPCT (expected Pit Core Time). It’s crucial to understand that “delays” aren’t just major errors. This metric also filters out the time from tiny hesitations and minor fumbles, giving us a true picture of a team’s fundamental speed.

In short, xPCT reveals a team’s raw potential. A team with a low xPCT has the underlying talent for elite pit stops, while a higher xPCT suggests a lower ceiling on their best possible times.

xPCT (expected Pit Core Time): A team’s baseline speed, calculated by removing the average time lost to fumbles, hesitations, and other common delays.

xPCT by team

Looking at our newest xPCT metric, we have McLaren. Our model believes that the Papaya team have the top xPCT, meaning that if we could remove all of the delays that affect pit stops, they could perform an average pit stop of 2.02 seconds.

In second place we have Ferrari. Based on their xPCT of 2.04 seconds, our model believes that their baseline is incredibly fast. This is just a mere 0.02 seconds per stop slower than McLaren, the current holder of the fastest pit stop of the season.

Red Bull aren’t too far behind the top 2 in this category. They’ve dominated the world of pit stops for years, so it’s not unexpected to see them close to the top of this metric. With an xPCT of 2.07 seconds, they trail McLaren by just 0.05 second per stop, and Ferrari by 0.03 seconds per stop.

xPCT by driver

Just as with the teams, you see the Ferrari, McLaren, and Red Bull drivers dominating in this metric, while teams such as Aston Martin, Williams, and Haas, find themselves in the bottom of the leaderboard.

This metric measures “skill” based on the input of a driver. Perhaps some drivers are faster at reacting to the green light, which would mean that at the top end, you would expect fast stops from them compared to their teammate.

The cost of delays

What is a team’s average delay time per pit stop?

A team’s core speed (xPCT) is only half the story. A crew might have elite potential, but if their delays are consistently costly, their overall pit times will suffer.

This brings us to our second key metric: xPDT (expected Pit Delay Time). This new metric measures the average time penalty a team incurs from any delay, whether it’s a major error or a minor hesitation.

xPDT (expected Pit Delay Time): The average amount of time added to a team’s core speed, representing the typical time penalty for any delay during the pit stop process.

Ultimately, the goal for any team is to master both: a low xPCT (high core speed) and a low xPDT (minimal, low-cost delays). The combination of both is what creates consistently world-class pit stops.

xPDT by team

I find this metric extremely interesting. At the top we have Ferrari, which is expected since they’re fast and consistent, but we have a surprise inclusion in Haas. While Haas are dead last in both raw average pit stops, as well as in xPT and xPCT, they are in second place based on our xPDT metric. What are our xPCT and xPDT metrics telling us about Haas? They show us that while Haas have been slower than all of the other pit crews, their delays are usually not very costly. They do not have top potential (xPCT is the worst of the grid) and overall are struggling, but on the consistency side, they are very strong.

At the other extreme, we have McLaren. They are pretty much a mixed bag of characteristics. You have seen their two extremes in recent races. On one hand, they are the best in xPCT, meaning that they have top potential. On the other, they are the worst team in our xPDT metric. This number is not only the worst of the grid, but it has continued to get even worse in the past few weekends. This essentially means that while the crew has the ability to perform consistently fast times, they are also very prone to having very costly delays, more than every other team in Formula 1. The result is a team that ranks closer to the middle of the pack in xPT. A simple way to describe them would be “Bags of talent, potential for maximum speed, but with costly mistakes”.

xPDT by driver

Just as with our xPCT metric, the xPDT should follow the same pattern of having both teammates close to each other. At the top you will find the drivers that have had the less costly delays, including both Haas, Fererari and Aston Martin drivers. These drivers are not expected to lose a lot of time on an average pit stop. At the bottom, you will see drivers that have been affected by costly delays, with both McLaren drivers finding themselves in the bottom two spots, meaning that generally the errors would be more costly for them.

The search for the fastest stop

How fast a pit crew should be, based on their talent and equipment?

One of the most exciting aspects of pit stops is discovering the limit of performance. Formula 1 pit crews are already extremely fast, but they’re always searching for that extra tenth in every stop. Using our model, we can calculate a prediction interval to answer the question: “How fast can a pit stop be?”—based on the current season’s data.

On our previous pit stop analysis, done after the 16th race of the season, our model predicted a 50% chance of seeing a pit stop under the 1.87 second Mark. As of the 18th race of the season, the fastest stop has been of 1.91 seconds, just slightly over the “expected” fastest pit stop time of the season.

Currently, our model believes there’s a 61.6% chance of seeing a faster stop than our current fastest of 1.91 seconds.

For reference, last year saw only two stops completed at or under the 1.91 s mark, with the fastest two clocking in at 1.9 seconds, done by McLaren and Red Bull. So, what about this year? Can the teams break the 1.9-second barrier?

Our model estimates that the most likely fastest stop this season would be 1.89 seconds, increasing from our previous estimate of 1.87 seconds.

You may be wondering about the best pit stop ever—the 1.8-second record set by McLaren in 2023. Can we expect to see that record broken this year? Currently, our model puts the probability of witnessing a record-breaking pit stop at 9.86%, down from the previously predicted 20.4% after the 16th race of the season.

Scenario	Probability
Pit stop < 1.91 s (fastest in 2025)	61.6%
Pit stop < 1.89 s (model’s best estimate)	50%
Pit stop < 1.8 s (fastest ever)	9.8%

Conclusions

Our xPCT and xPDT metrics show that McLaren have incredible potential to perform fast stops, but they have struggled with delays, as shown by their high xPDT. On the other hand, teams like Ferrari have been consistently fast, showing they’re the best in all of our metrics.

The data also uncovers some fascinating differences between teammates and highlights just how much small details matter when chasing every last tenth. The model believed that breaking the all-time pit stop record was unlikely this year, but its predictions have been increasing due to the fast stops done by McLaren. There is an increasingly higher chance of seeing a team breaking the 1.8 second pit stop record, claiming the throne as the fastest pit crew in the world. As the season goes on, it will be exciting to see which crews can truly push the limits and rewrite what’s possible in the pit lane.

Disclaimer

For the rounds 1 - 12 pit stop analysis I was using the 95% of stops. As of the 14th race of the season, I’ve decided to include all stops done under 7 seconds. This should provide a more robust estimate for the calculated metrics, while allowing me to exclude atypical stops caused by wing adjustments or penalties. Including all stops under 7 seconds means that we’re now using the top 96% of stops in instead of the previous 95%. A minor change, but one that could have major implications on our final results.

Additionally, I keep improving the model. While the results shouldn’t vary too much, creating a better model can help us to better understand the data. Regarding the latest modification, I managed to increase the flexibility of our model compared to the previous version.

My plea

This is the most advanced article I’ve written for this blog. Doing inference with statistical models isn’t easy—it takes a lot of time to develop a useful and reliable model capable of producing meaningful insights. Creating this model and writing this article took me around two weeks of hard work. If you’ve enjoyed reading it, please consider becoming a supporter to help me continue writing in-depth articles like this one. Your support truly makes a difference and helps keep the blog going. Thank you!

2025 Singapore GP: Race pace

Sun, 05 Oct 2025 00:00:00 +0000

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Race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 17)

Thu, 02 Oct 2025 00:00:00 +0000

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Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

We’re already more than halfway through the season. With 17 races—and now 3 sprints after the Dutch GP—we have more representative results. Just as a reminder, this season features the anomaly of Lawson switching places with Tsunoda after just three races, which makes our usual analysis a bit trickier. Additionally, Alpine decided to replace Doohan with Colapinto after just six races. Normally, I’d use the median as the key metric of interest since it’s more robust to outliers. However, in this case, the median might skew the average delta between Lawson and Verstappen.

It is important to note that after 17 races, the results have now stabilized, which means that at this point in time the mean is the most representative metric for most pairings.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates remains at Red Bull Racing. Tsunoda qualified in 6th place at the Azerbaijan GP, making it to Q3, but fell behind Verstappen by a symmetric percent of 1%. Currently, Verstappen is beating Yuki Tsunoda by 0.74%, which is less than the median delta Lawson left at 0.881%, but still stands as the highest gap on the grid by a wide margin.

If we look at the mean symmetric percent difference instead, the largest active gap is still at Red Bull, with Max ahead of Tsunoda by an average of 0.745%, the largest delta on the grid by a significant margin.

At the other end of the spectrum, the smallest delta is seen at Williams. Looking at the median symmetric percent difference, Carlos Sainz is winning the battle against Alex Albon by just 0.004%. If we focus instead on the mean symmetric percent difference, Albon is winning the battle by just 0.028%. As of today, the battle at Williams and Haas is even in terms of round victories for each driver.

Delta in seconds

Looking at seconds as the metric of interest, we see some incredible results. Currently both the median and mean delta at Williams show a gap of only 0.001 seconds. Using the median, we have Sainz ahead by just 1 thousand of a second, while using the mean we have Albon ahead by just 1 thousand of a second.

The largest gap will most likely remain at Red Bull for the remainder of the season. At the Azerbaijan GP, he fell behind Verstappen by 1.02 seconds, increasing his delta to Max to a median of 0.563 seconds and a mean of 0.63 seconds.

Qualifying stage appearances

If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the 17th race of the 2025 season, the differences between teammates have fully stabilized. Most pairings are quite even, with a few exceptions. The first is Aston Martin, where Alonso has reached Q3 on 9 occasions, while his teammate, Lance Stroll, has managed it only three times. The second is Alpine, where Gasly continues to do a stellar job in a struggling car. After the Azerbaijan GP, Pierre has qualified for Q3 on eight occasions, while Doohan and Colapinto combined have yet to make a single Q3 appearance. The third exception is, unsurprisingly, Red Bull Racing. So far, Max Verstappen has reached Q3 in all 20 possible sessions and is one of just three drivers with a 100% Q3 appearance rate, along with Lando Norris and Oscar Piastri. Meanwhile, Lawson was eliminated in Q1 three times while racing for Red Bull. In Tsunoda’s case, his Q3 record improved but is still not favourable: the Japanese driver now has six Q3 appearances out of 17 outings for the team, putting his Q3 rate at 35.2%.

xQD (Expected Quali Delta)

How much quicker one teammate should be in qualifying, based on underlying pace rather than luck.

xQD (expected Quali Delta): The model’s best estimate of the qualifying gap between teammates after filtering out session noise.

How to read the chart

The number in the colored box next to each team’s logo: This number represents the model’s single best estimate of the qualifying gap between teammates after filtering out session noise.
The x-axis: The horizontal axis shows the xQD in seconds. Higher times represent bigger deltas, in favour of the winning driver.
The slabs The slabs, or “domes”, provide a range of plausible values for each team’s skill. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty about the actual delta in each team.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our expected value. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents is the zero threshold. It provides a quick reference to see which in how many simulations the result changed from driver A winning to driver B. If the slabs cross the zero threshold, then it means that on some simulations the current winning driver would be losing the quali battle against his teammate.

Based on our model, the closest deltas are found at Haas, Williams, and McLaren, which is something we knew from the previously shown summary statistics. However, we can also see that for these three teams, many of the expected predictions cross the zero line. This means that over thousands of simulations, in many of them, our model found the losing driver to be faster than his teammate over a season. For example, at Haas, our model expected Bearman to win the overall qualifying battle by an average of 0.057 seconds over Ocon, but in 24% of the simulations, Ocon actually beat Bearman. Something similar is seen at Williams and McLaren, with around 20% of the simulations showing Sainz beating Albon and Norris beating Piastri.

Understanding our xQD metric

In this case, our model expected Antonelli to have a qualifying time of 1:43.070, which would have put him 0.42 seconds behind George Russell. At the same time, 66% of the simulations had Antonelli achieving a time between 1:42.952 and 1:43.192. These values are all very plausible based on the observed data. Perhaps Antonelli would have been slightly faster than 1:43.070, or a bit slower, but generally speaking, a delta of three to five tenths to Russell would not have been completely unexpected. A delta of 2.7 seconds? Not only is it not expected, but it is something that can be considered a complete anomaly.

The limitations

Conclusions

As a final conclusion, we can see that using the raw data and the xQD model provides a clear picture of the performance of each driver against his teammate. We have three teams that have a delta very close to zero, with Bearman, Albon, and Piastri leading against their teammates by almost nothing. In another universe, these three drivers could have been losing the qualifying battle against their teammates, even with their current level of talent and performance. At this level, the delta of performance between teammates could be almost nothing, and in many cases, it is nothing.

2025 Azerbaijan GP: Race pace

Mon, 22 Sep 2025 00:00:00 +0000

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Race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Some of the laps here don’t look perfect to me. The overall results should be generally good, but the Baku circuit is quite narrow and tight, which may affect the results of our algorithm.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Azerbaijan GP: Quali session

Sat, 20 Sep 2025 00:00:00 +0000

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Quali session classification

This is a new plot that I came up with. It’s a very simple classification plot, similar to what the Formula 1 team used to do back in 2021, before they simplified their graphics and started removing features. As a new plot, it’s still in early stages, so most likely it will be refined and improved in future iterations. If you have any suggestions, please send them to me using the Get in touch link.

The plot shows the best lap time for each driver in every session, along with their delta to the leader. One feature that was removed from the official Formula 1 plots, but I’ve decided to bring back, is the tire compound used during each session. Since tires play such a critical role in performance, I felt it was worth including. New tires are represented by filled circles, while used tires are shown as empty circles with just the tire outline. I also added a small bar under each delta so you can quickly get a sense of whether the gap to the leader was large or small.

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

It’s surprising I didn’t think of this earlier. Often, we witness drivers either advancing or getting eliminated by a mere fraction of a second, sometimes less than a tenth. In such cases, the evolving track conditions play a significant role and can alter the session’s outcome drastically. Even a 30-second difference in sending a driver out for their run could potentially result in a gain of half a tenth or more, depending on the track conditions.

The charts are straightforward but insightful. Each chart displays the three fastest laps for every driver in each session. Now, it becomes evident when the driver was sent out to perform their run, allowing us to assess whether the team’s strategy of sending the driver early or waiting until the end of the session was the right call.

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is to show the order in which the drivers were sent out.

Quali best sector times

I’ve decided to add some additional information about the FP2 section to this article. The two charts that I’m showing have very similar information about the best sector times of the session. The difference between them is as follows:

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Usually, you will see that the best sector times were done during their best lap of the session. Having said that, this won’t always be the case. For example, if a driver had 2 great sector times during a flying lap and then made a mistake on the final sector then chances are his best third sector time was done on an entirely different lap.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Azerbaijan GP: FP2 session (Model-corrected long run analysis)

Fri, 19 Sep 2025 00:00:00 +0000

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This is an experimental analysis.

Model-corrected race simulation pace

Example of model predictions

This chart shows how the model predicts lap times to replace the ones flagged as anomalies. You can see how the predictions follow a smooth curve that matches the overall trend of the remaining laps.

The predictions are based on complex linear algebra, so they’re not “guesswork” as some of you may believe. You have a better analysis? Cool, share it on your website.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 16)

Mon, 15 Sep 2025 00:00:00 +0000

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I will be taking this analysis to the next level very soon. I have been working on some more advanced charts for the qualifying delta between teammates, so the next article (rounds 1 - 17) will be much more comprehensive. Stay tuned for it!

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

We’re already more than halfway through the season. With 15 races—and now 3 sprints after the Dutch GP—we have more representative results. Just as a reminder, this season features the anomaly of Lawson switching places with Tsunoda after just three races, which makes our usual analysis a bit trickier. Additionally, Alpine decided to replace Doohan with Colapinto after just six races. Normally, I’d use the median as the key metric of interest since it’s more robust to outliers. However, in this case, the median might skew the average delta between Lawson and Verstappen.

It is important to note that after 16 sessions, the results have now stabilized, which means that at this point in time the mean is the most representative metric for most pairings.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates remains at Red Bull Racing. Tsunoda looked better for the first two quali sessions at the Italian GP, only to fall behind in Q3 and finish behind Verstappen by a symmetric percent of 0.91%. Currently, Verstappen is beating Yuki Tsunoda by 0.721%, which is less than the median delta Lawson left at 0.881%, but still stands as the highest gap on the grid by a wide margin.

If we look at the mean symmetric percent difference instead, the largest active gap is still at Red Bull, with Max ahead of Tsunoda by an average of 0.728%, the largest delta on the grid by a significant margin.

At the other end of the spectrum, the smallest delta depends on our metric of interest. Looking at the median symmetric percent difference, the closest battle is at McLaren, with Oscar Piastri leading Lando Norris by just 0.018%. If we focus instead on the mean symmetric percent difference, the smallest gap is at Haas, where Ocon still leads Bearman in qualifying—now by just 0.047%. As of today, the Ocon-Bearman battle is the only one that is tied in terms of round victories.

Delta in seconds

Just as with the symmetric percent difference, the smallest gap depends on our metric of interest. Looking at the median delta, the gap is just 0.015 seconds in favor of Piastri over Norris. The mean qualifying delta tells a different story, with the smallest mean at Sauber: Gabriel leads Nico by just 0.049 seconds.

The largest gap will most likely remain at Red Bull for the remainder of the season. At the Italian GP, he fell behind Verstappen by 0.72 seconds, increasing his delta to Max to a median of 0.52 seconds and a mean of 0.604 seconds.

Qualifying stage appearances

If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the 16th race of the 2025 season, the differences between teammates have fully stabilized. Most pairings are quite even, with a few exceptions. The first is Aston Martin, where Alonso has reached Q3 on 9 occasions, while his teammate, Lance Stroll, has managed it only three times. The second is Alpine, where Gasly continues to do a stellar job in a struggling car. After the Italian GP, Pierre has now qualified for Q3 on eight occasions, while Doohan and Colapinto combined have yet to make a single Q3 appearance. The third exception is, unsurprisingly, Red Bull Racing. So far, Max Verstappen has reached Q3 in all 19 possible sessions and is one of just three drivers with a 100% Q3 appearance rate, along with Lando Norris and Oscar Piastri. Meanwhile, Lawson was eliminated in Q1 three times while racing for Red Bull. In Tsunoda’s case, his Q3 record improved a little bit, but is still not favourable: the Japanese driver now has five Q3 appearances out of 16 outings for the team, putting his Q3 rate at 31.2%.

2025 F1 Season: Pit Stop Power Rankings (rounds 1 - 16)

Fri, 12 Sep 2025 00:00:00 +0000

The raw pit stop data

What exactly happened during the season, considering the luck and variability of the data?

Raw performance per team

Looking at the data, grouped and summarized by team, the Ferrari pit crew comes out on top. Ferrari and their mechanics continue to do a fantastic job, performing at the top of their ability during the first 16 races of the 2025 season. Their average of 2.42 seconds per stop is now an incredible 0.26 seconds faster than Racing Bulls’. Considering that each pit stop takes, on average, less than three seconds, this is a significant gap.

Speaking of Racing Bulls, it’s quite interesting to see them ahead of their big brother, Red Bull. Their data is intriguing: on average, they are beating Red Bull by just 0.06 seconds per stop, down from 0.15 seconds per stop at round 14, but it appears that their maximum, raw pace isn’t quite on Red Bull’s level. However, they have made fewer major mistakes than the Red Bull crew, which helps lower their average substantially. Even including their slow 6.7 stop at the Zandvoort GP, their crew can be considered as more consistent than Red Bulls'.

Looking down the rankings, Mercedes are competing directly with Red Bull, with Mercedes behind the Austrian team by just 0.04 seconds per stop. Sauber trail Mercedes, with an average stoptime of 2.9 seconds per stop. Behind them we have McLaren, who have increased their top speed and have posted the fastest stop of the season, but have also made several costly mistakes.

The bottom five teams in this ranking are separated by around 1 tenth of a second, with Alpine and Aston Martin matching McLaren with an average pit stop time of 2.95 seconds per stop. At the bottom, we have Williams and Aston Martin, separated by just 1 hundreth of a second with respective average times of 3.06 and 3.07 seconds per stop. Is is all bad for the slower teams? We’ll take a look at their detailed performance in the following sections of this article.

Raw performance per driver

Most teammates are, in fact, very close to each other. Hamilton and Leclerc are separated by just 0.13 seconds; Russell and Antonelli by 0.04 seconds; Bortoleto and Hulkenberg by 0.01 seconds, and Lawson and Hadjar by just 0.03 seconds. You get the idea. Nonetheless, there are a couple of very interesting results.

First, the Red Bull pairing. While Tsunoda’s data is slightly contaminated by his two races with Racing Bulls, the delta to Verstappen is quite stark. Tsunoda has an average pit stop time of 2.55 seconds compared to Verstappen’s 2.87 seconds, a delta of 0.32 seconds. This is quite a big difference considering that both drivers are assisted by the same pit crew.

Second, the results at Haas: While neither Ocon nor Bearman boast strong pit stops—their fastest stop of the season so far is 2.49 seconds—it’s clear that Ocon has a faster average time, currently by a whopping 0.31 seconds, which is massive in the world of F1 pit stops.

Our new metric: xPT

How fast a pit crew should be, based on their talent and equipment?

xPT (expected Pit Time): Our model’s best estimate of how fast a pit crew should be, based on their talent and equipment.

How to read the charts

The overall ranking: The teams are ranked from 1 (fastest) to 10 (slowest) based on their xPT, or Expected Pit Time.
The number in the colored box next to each team’s logo: This number represents the model’s single best estimate for their “true” pit stop skill, measured in seconds. This number is the estimated “normal” pit stop time that you would expect fomr each time, based on the model’s beliefs.
The x-axis: The horizontal axis shows the xPT in seconds. Just like a lap time, a lower number is better. Teams further to the left are faster.
The slabs The slabs, or “domes”, provide a range of plausible values for each team’s skill. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty about the actuall skill level of each pit crew.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our expected value. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents the midfield average (the median xPT of all teams). It provides a quick reference to see which teams are in the top half of the grid (to the left of the line) and which are in the bottom half (to the right).

xPT by team

Looking at the “xPT by team” chart, we can see what the model “believes” to be the actual expected average pit stop time for each pit crew. Based on 2025 season data, our model indicates that Ferrari’s real, repeatable performance should result in an average time of 2.51 seconds per pit stop. This number was estimated to be 2.53 seconds at the latest pit stop analysis that I made on round 14. Red Bull have reclaimed the second spot, now sitting 0.02 second ahead of their sister team, Racing Bulls. Behind them, Mercedes, Sauber, Mclaren, and Alpine are quite competitive with one another, and are separated by an xPT of only 0.08 seconds per stop.

While our main chart shows the full range of plausible skill levels for each team, we can also use the model to answer a more direct question: what is the probability that one team is truly faster than another? By leveraging statistical analysis, we estimate that there is a 99.2% probability that Ferrari’s true underlying pit stop skill (xPT) is better than Red Bull’s, the second fastest team based on our model’s estimates. This means that, after removing noise from the data, it’s almost certain that Ferrari currently has the best pit crew in the business.

xPT by driver

At Ferrari, for example, our model puts Leclerc at an xPT of 2.48 seconds, down from 2.51 seconds at round 14, compared to Hamilton’s 2.53 seconds, also down from 2.55 seconds at round 14. While this is a minor difference—too small to say for certain whether it’s real or just an artifact of our model—it’s interesting to see that there may be slight but noticeable differences between drivers, although calling a 0.05-second difference in a stop significant would be a stretch.

Norris vs Piastri: The McLaren drivers are separated by just 0.02 seconds in terms of xPT. This means that considering the entire season, our model expects that both drivers should have pit stops that are very close to each other.

Reality vs expectations

How fast has a pit crew been compared to the model’s expectations?

Delta to xPT by team

How to read the charts

The number in the colored box next to each team’s logo: This number represents the estimated delta between the raw pit stop times and the estimated pit stop times (xPT), measured in seconds. Negative numbers represent higher performance than expected from each pit crew, while positive numbers show pit crews that are underperforming.
The x-axis: The horizontal axis shows the delta to xPT in seconds. Just like a lap time, a lower number is better. Teams further to the left are are over performing the expectations of the model by a higher margin, while teams further to the right are under performing the expectations of the model.
The slabs The slabs, or “domes”, provide a range of plausible values for the delta to the xPT. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our delta to the xPT. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents the 0 threshold. Teams or drivers on the left side of this line are beating the expectations, while teams on the right of it are performing at a lower level than expected.

You may have noticed that McLaren has an xPT (expected Pit Time) of 2.85 seconds. This is lower than their raw average pit stop time of 2.95 seconds per stop. Why is this? Based on their raw pit times, our model believes that McLaren’s crew has the potential to perform an average pit stop in 2.85 seconds per stop. If you look at the raw data, McLaren has performed several fast stops—faster than those of many other teams. However, they also have many stops that are much slower than you would expect given their underlying quality. At the moment, our model suggests that McLaren is underperforming by 0.1 seconds per stop.

On the other extreme, we have Ferrari. While our model expects Ferrari to average 2.51 seconds per pit stop, in reality, their average is 2.42 seconds. The delta to xPT shows that Ferrari is not just meeting expectations but far exceeding them, outperforming their xPT by an average of 0.09 seconds. Not only are they the fastest crew based on our model, but they also have the “it” factor, which puts them on another level compared to the remainder of the crews.

Currently, Racing Bulls are overperforming by an average of 0.06 seconds per stop, slightly less than Ferrari, while Alpine and Sauber are underperforming by 0.06 and 0.07 seconds respectively.

Delta to xPT by driver

The most interesting result is found at Red Bull. While Tsunoda leads in this metric, exceeding the model’s expectations by 0.14 seconds, his teammate, Max Verstappen is underperforming by 0.13 seconds per stop compared to the model’s expectations. Looking at the raw data shows that Verstappen has had many more slow stops than Tsunoda, causing this big discrepancy between both teammates. Currently, our model estimates with 77.2% certainty that Tsunoda’s stops have been faster compared to Verstappen’s.

A world with no delays

What is the team’s baseline pit stop speed after removing the time lost to fumbles and delays?

In short, xPCT reveals a team’s raw potential. A team with a low xPCT has the underlying talent for elite pit stops, while a higher xPCT suggests a lower ceiling on their best possible times.

xPCT (expected Pit Core Time): A team’s baseline speed, calculated by removing the average time lost to fumbles, hesitations, and other common delays.

xPCT by team

Looking at our newest xPCT metric, we have the usual winner, Ferrari. Our model believes that Ferrari have the top xPCT, meaning that if we could remove all of the delays that affect pit stops, they could perform an average pit stop of 2.03 seconds.

In second place we have McLaren. We’ve already talked about the duality of them. Based on their xPCT of 2.05 seconds, our model believes that their baseline is incredibly fast, and that they have the potential to perform pit stops with an average time of 2.05 seconds. This is just a mere 0.02 seconds slower than Ferrari, the gold standard in this 2025 season. Our model believes taht McLaren have shown that on the fast end of stops, they are elite.

Red Bull aren’t too far behind the top 2 in this category. They’ve dominated the world of pit stops for years, so it’s not unexpected to see them close to the top of this metric. With an xPCT of 2.09 seconds, they trail Ferrari by just 0.06 second per stop, and McLaren by 0.04 seconds per stop.

xPCT by driver

On a driver-by-driver stratification, we do not expect to see big differences between teammates. While drivers have an input, and the results won’t be exactly the same for them, a team and their crew have much more influence over pit stop times than drivers. Some notable exceptions are seen at Racing Bulls, with Lawson having an xPCT of 2.14 compared to Hadjar’s 2.22, at Williams, with Albon having an xPCT of 2.43 vs Sainz’s 2.53, at Haas with Ocon xPCT’s of 2.56 vs Bearman’s 2.63, and at Sauber with Bortoleto leading Hulkenberg with an xPCT of 2.18 vs 2.25 for Nico.

The cost of delays

What is a team’s average delay time per pit stop?

A team’s core speed (xPCT) is only half the story. A crew might have elite potential, but if their delays are consistently costly, their overall pit times will suffer.

xPDT (expected Pit Delay Time): The average amount of time added to a team’s core speed, representing the typical time penalty for any delay during the pit stop process.

xPDT by team

I find this metric extremely interesting. At the top we have Haas, Ferrari, which is expected since they’re fast and consistent, but we have a suprise inclusion in Haas. While Haas are dead last in both raw average pit stops, as well as in xPT and xPCT, they are at the top in our xPDT metric. What are our xPCT and xPDT metrics telling us about Haas? They show us that while Haas have been slower than all of the other pit crews, their delays are usually not very costly. They do not have top potential, and overall are struggling, but on the consistency side, they are very strong.

At the other extreme, we have McLaren. They are pretty much a mixed bag of characteristics. You have seen their two extremes in recent races. On one hand, thy are close to the top in xPCT, almost on par with Ferrari, meaning that they have top potential. On the other, they are the worst team in our xPDT metric. This essentially means that while the crew has the ability to perform consistently fast times, they are also very prone to having very costly delays, more than every other team in Formula 1. The result is a team that ranks closer to the middle of the pack in xPT. A simple way to describe them would be “Bags of talent, potential for maximum speed, but with costly mistakes”.

xPDT by driver

The search for the fastest stop

How fast a pit crew should be, based on their talent and equipment?

On our previous pit stop analysis, done after the 14th race of the season, our model predicted a 50% chance of seeing a pit stop under the 1.9 second Mark. As of the 16th race of the season, the fastest stop has been of 1.91 seconds, just slightly over the “expected” fastest pit stop time of the season.

Currently, our model believes there’s a 72.3% chance of seeing a faster stop than our current fastest of 1.91 seconds.

Our model estimates that the most likely fastest stop this season would be 1.87 seconds, down from our previous estimate of 1.9 seconds. A 1.87 seconds stop would rank as the fastest in the previous 2024 season.

You may be wondering about the best pit stop ever—the 1.8-second record set by McLaren in 2023. Can we expect to see that record broken this year? Our model didn’t have a lot of confidence, and estimated the chance as only 8.38% as of the 14th race of the season. However, we have recently seen some incredibly fast times posted by the McLaren pit crew, which means that the beliefs of the model have been updated. Now, as the 16th race of the season, our model puts the probability of witnessing a record-breaking pit stop at an increased 20.4%. This is basically a 1 in 5 chance of seeing a world-record pit stop this year. We may see something special in the upcoming weeks.

Scenario	Probability
Pit stop < 1.91 s (fastest in 2025)	72.3%
Pit stop < 1.87 s (model’s best estimate)	50%
Pit stop < 1.8 s (fastest ever)	20.4%

Conclusions

Disclaimer

My plea

2025 Italian GP: The McLaren pit stop disaster

Sun, 07 Sep 2025 00:00:01 +0000

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Lando Norris was leading his teammate Oscar Piastri until he pitted on Lap 47. Unfortunately, a mistake by the pit crew resulted in a very slow stop, allowing Oscar to undercut him. While much can be said about the team ordering Piastri to let Lando regain the position, I decided to analyze the pit stops themselves to see just how likely this scenario was at the 2025 Italian Grand Prix. Let’s take a look at the numbers.

The model’s predictions

If you’ve been following this blog for a while, you’ll know I’ve created a model to analyze pit stops across the 2025 F1 season. While its main purpose is to understand season-long trends, it can also make specific predictions for drivers and teams. For this race, I used it to calculate the probability of two key events: Piastri’s season-record-breaking 1.91-second stop and Norris’s very slow 5.87-second stop.

I usually update the model weekly, and as of today, it includes data from Rounds 1 to 15 (up to the Dutch GP). Crucially, the model hasn’t seen any data from the 2025 Italian GP, so it’s not “cheating” with its predictions.

The model has only learned from the first 15 races of the season. Our model hasn’t seen any data from the Italian GP, so it isn’t “cheating” by knowing the outcome in advance.

Our model predicted that McLaren had a 7.13% probability (a 1 in 14 chance) of performing a pit stop of 1.91 seconds or faster for Piastri. Regarding Norris, the model gave the team only a 1.12% probability (a 1 in 89 chance) of having a stop of 5.87 seconds or slower.

This result shows that the sequence of events that allowed Piastri to overtake Norris was an extreme anomaly—but one that can still happen. Clearly, McLaren had a much higher chance of achieving an extremely fast stop like Piastri’s than suffering a catastrophic one like Norris’s. The odds of both happening in the same race were almost zero, and yet, here we are.

How to read the chart

If you are struggling to understand the chart, this section was made for you.

The y-axis: Shows the two McLaren drivers being analyzed, Norris and Piastri.
The x-axis: Shows the predicted pit stop duration in seconds, as generated by our model.
The slabs: The slabs, or “domes”, provide a range of predicted values for a driver’s pit stop. These shapes visualize the full range of likely pit stop times for each driver. The higher the dome, the more probable the time. The very peak represents the single most likely outcome.
The shaded area: The colored sections highlight the specific probabilities we’re interested in. The blue area shows the chance of Piastri having a stop of 1.91 seconds or faster, while the red area shows the chance of Norris having a stop of 5.87 seconds or slower.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our predicted value.

Conclusion

So after looking at the numbers, what’s the takeaway?

Well, the model showed that a super-fast stop for Piastri wasn’t actually that surprising. While it was a season-best pit stop, our model shows that it had about a 1 in 14 chance of happening, which is rare, but definitely possible. The real shocker was Norris’s stop. The probability of a stop being that slow was just over 1%, a true 1 in 89 long shot.

What this tells me is that the whole situation that led to the controversial team orders was caused by an extremely unlucky moment for Norris. It just goes to show that while we can talk about strategy and performance all day, sometimes a race is decided by a single moment of incredibly bad luck.

Final remarks

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Stay tuned for future updates to the F1pace Pit Stop Power Rankings and many more data-driven stories to come. Thank you for reading!

2025 Italian GP: Race pace

Sun, 07 Sep 2025 00:00:00 +0000

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Race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Italian GP: Quali session

Sat, 06 Sep 2025 00:00:01 +0000

Use the Get in touch link if you’re interested in supporting this project.

Quali session classification

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Italian GP: FP2 session (Model-corrected long run analysis)

Sat, 06 Sep 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

This is an experimental analysis.

Model-corrected race simulation pace

Example of model predictions

This chart shows how the model predicts lap times to replace the ones flagged as anomalies. You can see how the predictions follow a smooth curve that matches the overall trend of the remaining laps.

The predictions are based on complex linear algebra, so they’re not “guesswork” as some of you may believe. You have a better analysis? Cool, share it on your website.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 15)

Thu, 04 Sep 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

No, ChatGPT didn’t write this article. Yes, there are some of us out there who use the em dash.

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

It is important to note that after 16 sessions, the results have now stabilized, which means that at this point in time the mean is the most representative metric for most pairings.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates remains at Red Bull Racing. Tsunoda struggled once again in the 2025 Dutch GP, finishing behind Verstappen by a symmetric percent of 0.72%. Currently, Verstappen is beating Yuki Tsunoda by 0.683%, which is less than the median delta Lawson left at 0.881%, but still stands as the highest gap on the grid by a wide margin.

If we look at the mean symmetric percent difference instead, the largest active gap is still at Red Bull, with Max ahead of Tsunoda by an average of 0.714%. Yuki has managed to reduce the average gap from 0.809% to 0.714% after the latest Grand Prix, but this remains the largest delta on the grid by a significant margin. While this number is large, the delta between Russell and Antonelli currently stands at 0.581% and could challenge for the top (worst?) spot on this list.

At the other end of the spectrum, the smallest delta depends on our metric of interest. Looking at the median symmetric percent difference, the closest battle is at McLaren, with Oscar Piastri leading Lando Norris by just 0.019%. If we focus instead on the mean symmetric percent difference, the smallest gap is at Sauber, where Bortoleto still leads Hulkenberg in qualifying—now by 0.064%.

Delta in seconds

I think the biggest gap will remain at Red Bull for the rest of the season. I just don’t see how Tsunoda can close the gap enough to approach any other pairing. As I mentioned, the delta between Max and Yuki has decreased, but that’s mostly due to the extreme gaps earlier in the season—Bahrain (0.879s), Saudi Arabia (0.91s), and Miami (0.739s). Yuki had a stronger showing at the Belgian GP qualifying session, but he was still 0.38 seconds slower than his teammate. At the Dutch GP, his delta was once again of half a second, which is large but continues to reduce the massive gap that was mostly created at the beginning of the season. At the moment, Max Verstappen is ahead of Yuki Tsunoda by a median of 0.525 seconds and a mean of 0.595 seconds.

Qualifying stage appearances

If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the 15th race of the 2025 season, most teammate pairings have been fairly evenly matched, with just a few notable exceptions. The first is Aston Martin, where Alonso has reached Q3 on 8 occassions, while his teammate, Lance Stroll, has managed it only three times. The second is Alpine, where Gasly continues to do a stellar job in a struggling car. After the Dutch GP, Pierre has now qualified for Q3 on eight occasions, while Doohan and Colapinto combined have yet to make a single Q3 appearance. The third exception is, unsurprisingly, Red Bull Racing. So far, Max Verstappen has reached Q3 in all 18 possible sessions and is one of just three drivers with a 100% Q3 appearance rate, along with Lando Norris and Oscar Piastri. Meanwhile, Lawson was eliminated in Q1 three times while racing for Red Bull. In Tsunoda’s case, his Q3 record is not favourable in any way: the Japanese driver now has four Q3 appearances out of 15 outings for the team, putting his Q3 rate at 26.6%.

2025 Dutch GP: Race pace

Mon, 01 Sep 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Dutch GP: Quali session

Sun, 31 Aug 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Quali session classification

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Dutch GP: FP2 session (Model-corrected long run analysis)

Sat, 30 Aug 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

This is an experimental analysis.

Model-corrected race simulation pace

Example of model predictions

This chart shows how the model predicts lap times to replace the ones flagged as anomalies. You can see how the predictions follow a smooth curve that matches the overall trend of the remaining laps.

The predictions are based on complex linear algebra, so they’re not “guesswork” as some of you may believe. You have a better analysis? Cool, share it on your website.

2025 F1 Season: Pit Stop Power Rankings (rounds 1 - 14)

Sun, 17 Aug 2025 00:00:00 +0000

For my analysis, I’m only using stops under 7 seconds to avoid including slow stops caused by things like front wing changes or penalties. I would like to use all of the data, but I don’t have the time to verify each stop individually.

If you’re up for helping me check the problematic stops, hit me up and I’ll send you the list of which ones need to be verified. If you help me out, I’ll credit you in this post =).

The raw pit stop data

What exactly happened during the season, considering the luck and variability of the data?

Raw performance per team

Looking at the data, grouped and summarized by team, the Ferrari pit crew comes out on top. Unlike the past few years, when Red Bull were dominant in the pit stop department, the battle is much more competitive in 2025. Ferrari and their mechanics are doing a fantastic job, performing at the top of their ability during the first 14 races of the 2025 season. Their average of 2.44 seconds per stop is 0.14 seconds faster than Racing Bulls’. It may not sound like much, but considering that each pit stop takes, on average, less than three seconds, this is actually a significant gap.

Speaking of Racing Bulls, it’s quite interesting to see them ahead of their big brother, Red Bull. Their data is intriguing: on average, they are beating Red Bull by 0.15 seconds per stop, but it appears that their maximum, raw pace isn’t quite on Red Bull’s level. However, they have made fewer major mistakes than the Red Bull crew, which helps lower their average substantially.

Looking down the rankings, Mercedes are competing directly with Red Bull, with Mercedes behind the Austrian team by just 0.01 seconds per stop. McLaren trail Mercedes by just over a tenth, albeit with a very interesting pattern. The Papaya team have several fast stops, including the two fastest stops of the season, but they have made more mistakes than all of the other teams.

The bottom five teams in this ranking are separated by around a 1.5 tenths, with sixth-placed Sauber averaging 2.89 seconds per stop, compared to 3.05 seconds per stop for Haas. The raw data, however, tells an interesting story. While Sauber and Alpine show similar pit stop patterns, the bottom three teams—Aston Martin, Williams and Haas—do not seem to have the raw top speed as the remaining teams. Is is all bad for them? We’ll take a look at their detailed performance in the following sections of this article.

Raw performance per driver

Most teammates are, in fact, very close to each other. Hamilton and Leclerc are separated by just 0.11 seconds; Russell and Antonelli by 0.01 seconds; Bortoleto and Hulkenberg by 0.04 seconds. You get the idea. Nonetheless, there are a couple of very interesting results.

Second, the Racing Bulls pairing: Again, Lawson’s times are a bit contaminated by his two races with Red Bull, but the results should be quite stable after 14 races. So far, Lawson has an average pit stop time of 2.52 seconds, placing him just behind both Ferrari drivers. Isak Hadjar, however, has an average pit stop time of 2.68 seconds—0.16 seconds slower than his teammate. While his average pit stop times are still close to the best, the delta to his teammate is quite significant.

Finally, the results at Haas: While neither Ocon nor Bearman boast strong pit stops—their fastest stop of the season so far is 2.49 seconds—it’s clear that Ocon has a faster average time, currently by a whopping 0.27 seconds, which is massive in the world of F1 pit stops.

Our new metric: xPT

How fast a pit crew should be, based on their talent and equipment?

xPT (expected Pit Time): Our model’s best estimate of how fast a pit crew should be, based on their talent and equipment.

How to read the charts

The overall ranking: The teams are ranked from 1 (fastest) to 10 (slowest) based on their xPT, or Expected Pit Time.
The number in the colored box next to each team’s logo: This number represents the model’s single best estimate for their “true” pit stop skill, measured in seconds. This number is the estimated “normal” pit stop time that you would expect fomr each time, based on the model’s beliefs.
The x-axis: The horizontal axis shows the xPT in seconds. Just like a lap time, a lower number is better. Teams further to the left are faster.
The slabs The slabs, or “domes”, provide a range of plausible values for each team’s skill. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty about the actuall skill level of each pit crew.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our expected value. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents the midfield average (the median xPT of all teams). It provides a quick reference to see which teams are in the top half of the grid (to the left of the line) and which are in the bottom half (to the right).

xPT by team

Looking at the “xPT by team” chart, we can see what the model “believes” to be the actual expected average pit stop time for each pit crew. Based on 2025 season data, our model indicates that Ferrari’s real, repeatable performance should result in an average time of 2.53 seconds per pit stop. Racing Bulls, Red Bull, McLaren (yes, McLaren), Mercedes, Sauber and Alpine trail Ferrari, but are highly competitive with one another, while Aston Martin, Williams, and Haas have slower estimated pit stop times of close to three seconds per stop.

While our main chart shows the full range of plausible skill levels for each team, we can also use the model to answer a more direct question: what is the probability that one team is truly faster than another? By leveraging statistical analysis, we estimate that there is a 95.6% probability that Ferrari’s true underlying pit stop skill (xPT) is better than Racing Bulls’s, the second fastest team based on our model’s estimates. This means that, after removing noise from the data, it’s incredibly likely that Ferrari currently has the best pit crew in the business.

xPT by driver

At Ferrari, for example, our model puts Leclerc at an xPT of 2.51 seconds, compared to Hamilton’s 2.55 seconds. While this is a minor difference—too small to say for certain whether it’s real or just an artifact of our model—it’s interesting to see that there may be slight but noticeable differences between drivers, although calling a 0.04-second difference in a stop significant would be a stretch.

Reality vs expectations

How fast has a pit crew been compared to the model’s expectations?

Delta to xPT by team

How to read the charts

The number in the colored box next to each team’s logo: This number represents the estimated delta between the raw pit stop times and the estimated pit stop times (xPT), measured in seconds. Negative numbers represent higher performance than expected from each pit crew, while positive numbers show pit crews that are underperforming.
The x-axis: The horizontal axis shows the delta to xPT in seconds. Just like a lap time, a lower number is better. Teams further to the left are are over performing the expectations of the model by a higher margin, while teams further to the right are under performing the expectations of the model.
The slabs The slabs, or “domes”, provide a range of plausible values for the delta to the xPT. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our delta to the xPT. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents the 0 threshold. Teams or drivers on the left side of this line are beating the expectations, while teams on the right of it are performing at a lower level than expected.

You may have noticed that McLaren has an xPT (expected Pit Time) of 2.76 seconds. This is much lower than their raw average pit stop time of 2.85 seconds per stop. Why is this? Based on their raw pit times, our model believes that McLaren’s crew has the potential to perform an average pit stop in 2.76 seconds per stop. If you look at the raw data, McLaren has performed several fast stops—faster than those of many other teams, such as Sauber. However, they also have many stops that are much slower than you would expect given their underlying quality. At the moment, our model suggests that McLaren is underperforming by 0.09 seconds per stop, down from the 0.22 seconds calculated during our last analysis done after the 12th race of the season.

On the other extreme, we have Ferrari. While our model expects Ferrari to average 2.53 seconds per pit stop, in reality, their average is 2.44 seconds. The delta to xPT shows that Ferrari is not just meeting expectations but far exceeding them, outperforming their xPT by an average of 0.09 seconds.

Currently, Racing Bulls are overperforming by an average of 0.09 seconds per stop, the same as Ferrari. This is why, even though their xPT is just 0.04 seconds faster than Red Bull’s (as seen in our xPT by team chart), they are still outperforming their sister team in raw performance by 0.15 seconds.

For most teams, you see values around the 0 second mark, which means that they are performing according to the model’s expectations. Some teams are significantly faster, as we’ve seen with Racing Bulls and Ferrari, while some are significantly slower, including Sauber, Alpine, and Mclaren, who seem to be struggling compared to what the model believes they can do.

Delta to xPT by driver

Just as with the previous section, this chart shows how much a driver is over or under performing based on the expectations (xPT) of the model. Some results are expected, such as Hamilton or Leclerc over performing the expectations of the model. While the model already ranks the Ferrari as the best pit crew in F1, the delta to xPT shows that they are performing even better than expected. Is it down to luck? Motivation? Just a hot streak? Our model can’t answer that, so it’s up to you. If you have any additional information you can send me up, knowing that everything that you tell me is confidential, including your identity.

The most interesting result is found at Red Bull. While Tsunoda leads in this metric, exceeding the model’s expectations by 0.15 seconds, his teammate, Max Verstappen, ranks dead last, underperforming by 0.15 seconds per stop compared to the model’s expectations. Looking at the raw data shows that Verstappen has had many more slow stops than Tsunoda, causing this big discrepancy between both teammates. Currently, our model estimates with 100% certainty that Tsunoda’s stops have been faster compared to Verstappen’s.

As I mentioned, another discrepancy is found at Racing Bulls. Liam Lawson’s stops are expecting the expectations by an average of 0.15 seconds per stop, while Hadjar is doing it by 0.02 seconds per stop. While both drivers are technically doing good based on our model’s expectations, the delta between both of them shows a problemating trend. Our model estimates with 95.2% certainty that Lawson’s pit stops have been, on average, faster than Hadjar’s, which may be indicative of a systematic issue with the team, the drivers, or both.

A world with no delays

What is the team’s baseline pit stop speed after removing the time lost to fumbles and delays?

In short, xPCT reveals a team’s raw potential. A team with a low xPCT has the underlying talent for elite pit stops, while a higher xPCT suggests a lower ceiling on their best possible times.

xPCT (expected Pit Core Time): A team’s baseline speed, calculated by removing the average time lost to fumbles, hesitations, and other common delays.

xPCT by team

In second place we have McLaren. We’ve already talked about the duality of them. Based on their xPCT of 2.07 seconds, our model believes that their baseline is incredibly fast, and that they have the potential to perform pit stops with an average time of 2.07 seconds. While not exacly the same as Ferrari, they have shown that on the fast end of stops, they are elite.

Red Bull aren’t too far behind the top 2 in this category. They’ve dominated the world of pit stops for years, so it’s not unexpected to see them close to the top of this metric. With an xPCT of 2.09 seconds, they trail Ferrari by just 0.06 second per stop, and McLaren by a tiny 0.02 seconds per stop.

xPCT by driver

On a driver-by-driver stratification, we do not expect to see big differences between teammates. While drivers have an input, and the results won’t be exactly the same for them, a team and their crew have much more influence over pit stop times than drivers. Some notable exceptions are seen at Racing Bulls, with Lawson having an xPCT of 2.17 compared to Hadjar’s 2.26, and at Williams, with Albon having an xPCT of 2.44 vs Sainz’s 2.54.

Just as with the teams, you see the Ferrari, McLaren, and Red Bull drivers dominating in this metric, while teams such as Astin Martin, Williams, and Haas, find themselves in the bottom of the leaderboard.

The cost of delays

What is a team’s average delay time per pit stop?

A team’s core speed (xPCT) is only half the story. A crew might have elite potential, but if their delays are consistently costly, their overall pit times will suffer.

xPDT (expected Pit Delay Time): The average amount of time added to a team’s core speed, representing the typical time penalty for any delay during the pit stop process.

xPDT by team

I find this metric extremely interesting. At the top we have Racing Bulls, which is expected since they’re fast and consistent, but we have a suprise inclusion in Haas. While Haas are dead last in both raw average pit stops, as well as in xPT and xPCT, they are close to the top in our xPDT metric. What are our xPCT and xPDT metrics telling us about Haas? They show us that while Haas have been slower than all of the other pit crews, their delays are usually not very costly. They do not have top potential, and overall are struggling, but on the consistency side, they are very strong.

At the other extreme, we have McLaren. They are pretty much a mixed bag of characteristics. On one hand, thy are close to the top in xPCT, meaning that they have top potential. On the other, they are the worst team in our xPDT metric. This essentially means that while the crew has the ability to perform consistently fast times, they are also very prone to having very costly delays, more than every other team in Formula 1. The result is a team that ranks closer to the middle of the pack in xPT. A simple way to describe them would be “Bags of talent, potential for maximum speed, but with costly mistakes”.

xPDT by driver

Just as with our xPCT metric, the xPDT should follow the same pattern of having both teammates close to each other. At the top you will find the drivers that have had the less costly delays, including Hadjar, Ocon, Hamilton and Alonso. At the bottom, you will see drivers that have been affected by costly delays, with both McLaren drivers finding themselves in the bottom two spots.

The search for the fastest stop

How fast a pit crew should be, based on their talent and equipment?

On our previous pit stop analysis, done after the 12th race of the season, our model predicted a 50% chance of seeing a pit stop under the 1.95 second Mark. As of the 14th race of the season, the fastest stops have been exactly 1.94 seconds, both performed by the McLaren pit crew at the Hungarian GP, which helps to validate the results provided by our model.

Currently, our model believes there’s a 75.4% chance of seeing a faster stop than our current fastest of 1.94 seconds.

For reference, last year saw four stops completed at or under the 1.94 s mark, with the fastest two clocking in at 1.9 seconds, done by McLaren and Red Bull. So, what about this year? Can the teams break the 1.9-second barrier?

Our model estimates that the most likely fastest stop this season would be 1.90 seconds, down from our previous estimate of 1.95 seconds. A 1.90 seconds stop would tie for fastest place in the 2024 season. The model predicts that there is an exactly 50% probability of breaking the 1.9 second mark, which would beat the fastest done in 2024.

You may be wondering about the best pit stop ever—the 1.8-second record set by McLaren in 2023. Can we expect to see that record broken this year? I think it’s unlikely, but not impossible. After the 12th race, our model estimated just a 1.72% chance of seeing a record-breaking stop this year. After the 14th race of the season, our model puts the probability of witnessing a record-breaking pit stop at an increased 8.38%. While this number is quite low, it has increased, meaning that our model now believes that there is roughly a 1 in 12 chance of seeing a team breaking the world-record pit stop this year.

Scenario	Probability
Pit stop < 1.94 s (fastest in 2025)	75.4%
Pit stop < 1.9 s (model’s best estimate & fastest in 2024)	50%
Pit stop < 1.8 s (fastest ever)	8.38%

Conclusions

To sum it up, this deep dive into pit stop performance using both raw data and the xPT model has offered a much clearer picture of where each team really stands in 2025. Ferrari continues to set the standard, leading in both raw pace and underlying consistency, while teams like McLaren show there’s a lot of untapped potential still on the table. The data also uncovers some fascinating differences between teammates and highlights just how much small details matter when chasing every last tenth. While breaking the all-time pit stop record seems unlikely this year, the model keeps evolving with every race, so the story is far from over. As the season goes on, it will be exciting to see which crews can truly push the limits and rewrite what’s possible in the pit lane.

Disclaimer

For the previous pit stop analysis I was using the 95% of stops. As of today, I’ve decided to include all stops done under 7 seconds. This should provide a more robust estimate for the calculated metrics, while allowing me to exclude atypical stops caused by wing adjustments or penalties. Including all stops under 7 seconds means that we’re now using the top 96% of stops in instead of the previous 95%. A minor change, but one that could have major implications on our final results.

My plea

2025 F1 Season: The mid-season tri-factor F1 power rankings

Tue, 12 Aug 2025 00:00:00 +0000

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Introduction

Welcome to our 2025 mid-season F1 power rankings! Instead of just relying on gut feeling or raw stats, I’ve created a unique tri-factor system to get a more complete picture of who is truly performing on track.

Here’s how it works. I combined three distinct perspectives, giving each an equal 33% weight in the final score:

The statistical model: An objective, data-driven engine that evaluates performance against expectations. It doesn’t just look at results; it considers the car’s strength and a driver’s rating coming into the season to see who is over or under-performing. It is unbiased, but can miss details that only a human can see.
The public & experts: The collective wisdom of the F1 community. We combine ratings from fans and pundits to get a sense of the general consensus around the paddock. More biased than a statistical model, but more robust, with varied opinions from all over the world.
My personal ranking: Finally, the human element. This is my own “eye test” rating, where I factor in the moments, mistakes, and flashes of brilliance that numbers alone can’t always capture. I’m perfect in every single way. Just kidding. Just as with the public & experts ratings, I am biased, and while I do my best to be as objective as possible, I will never be 100% neutral.

By combining the machine, the crowd, and a personal touch, I aimed for a balanced and robust ranking that irons out the biases of any single source. So, who comes out on top? Let’s take a look at the numbers.

The rankings

Oscar Piastri is the #1 driver in our rankings. He was ranked #4 by our model, #1 by journalists and the public, and tied for 4th in my personal ranking.

While his season in a dominant car has been far from stellar, he’s currently leading the Championship over his teammate, Lando Norris. Oscar looks like a stronger driver than last year, having improved on weaknesses like his quali pace, all while retaining the consistency required to lead the Championship.

Lando Norris is ranked highly by our model for his consistency, especially next to such a strong teammate. Much has been said about Piastri’s consistency, but Norris has also been quite strong in this department. Aside from his major blunder in Canada, Norris has finished in the top 4 in every other race this season.

In the public and expert rankings, Norris also ranks highly, albeit a few spots below his teammate (5th vs. Piastri’s 1st), and he’s 7th in my personal ranking. Perhaps viewers, myself included, believe Norris should be leading the Championship, and that some of his mistakes in quali have cost him valuable points 14 races into the season.

George Russell has been my #1 driver this season. With one race victory, he’s sitting just 15 points behind Max Verstappen in the World Drivers’ Championship. I believe his performances continue to be elite, and while he hasn’t yet faced the pressure of a title challenge, he has shown he has the skills to do so.

Russell loses a few points in our model because he’s paired with a weaker teammate. Kimi Antonelli may have a bright future ahead of him, but he’s still not the finished product. Our model interprets this to mean the Mercedes car is quite strong, so while George has been performing up to expectations, he isn’t necessarily exceeding them.

Finally, public and expert opinion places Russell highly—at 3rd in my data—but noticeably below Piastri and Verstappen. Is Russell underrated? Let me know what you think.

Alex Albon has been a welcome surprise this year. The Williams FW47 has had its ups and downs, but Albon has shown a knack for being in the right place at the right time to secure strong points for the team. He’s the #1 pick in our model, just ahead of Lando Norris and well above the rest of the field.

Expert and public rankings place Alex as the 6th best driver of the season, but well below the top five (Piastri, Verstappen, Russell, Leclerc, and Norris). Public opinion tends to be a bit skewed towards drivers in stronger teams, so it’s nice that our model provides a counterweight to this implicit bias.

Personally, I have Alex Albon as the 5th best driver of the season, tied with Pierre Gasly. His performances have been excellent, especially at the beginning of the year. He is well ahead of his teammate, Carlos Sainz—a driver who was highly rated last year and who many believed would easily outperform him. All credit goes to Albon for his strong performances this season.

Max Verstappen, considered by many (myself included) to be the best driver in the world, finds himself in a strange position in these mid-season rankings. His 5th-place spot may seem wrong, but there are multiple reasons he’s lost points.

Our model ranks Max all the way down in 10th, but it’s important to remember that it heavily considers the expectations placed on each driver. Last season, Max was incredible, winning his fourth consecutive World Championship and beating Lando Norris in emphatic fashion. It’s precisely because of this that our model is so tough on him; while he has been extremely good this season, the expectations were sky-high. Against that backdrop, his 10th place in Spain, 9th in Hungary, and 6th in Bahrain are viewed as significant underperformances.

Public and expert opinion, on the other hand, has Max as the second-best driver this season—most likely because, well, he’s Max Verstappen. His raw speed is undeniable. In my personal ranking, I placed him 4th, tied with Oscar Piastri. While I believe Max would improve almost any team he drove for, he has made some uncharacteristic mistakes this season. His crash with Russell at the Spanish GP was egregious, and his results in Bahrain and Hungary were below expectations.

Remember, this is all based on relative performance. As I’ve said, expectations for Max are stratospheric, so these point deductions aren’t due to a lack of quality, but a reflection of the minor issues he’s had this season.

Charles is 6th in our overall rankings, a position largely pulled down by our model. The model ranks Leclerc fairly low for three main reasons: First, expectations were incredibly high for him after the 2024 season. Second, it believes the Ferrari is a very strong car, making top results the standard expectation. And third, it isn’t awarding him many points for beating Hamilton, mostly due to Hamilton’s perceived dip in form at Mercedes last year.

The public and experts, however, have rated Charles as the 4th best driver of the season, just ahead of Lando Norris but behind Russell, Verstappen, and Piastri. Personally, I rated Leclerc as the best driver of the season, tied for 1st place with George Russell. His season hasn’t been perfect, his performance at the British GP is a blemish on an otherwise strong record, but Charles continues to prove he’s an elite driver.

This might be a surprise inclusion in the top 10 for some of you. Our model rates Hamilton highly, largely because his ratings were so low at the end of 2024. Lewis didn’t have a great season last year; he was beaten by his teammate at Mercedes, and many of you—as well as the experts—ranked him at or below 10th place. Our model takes that data, compares it to Hamilton’s 2025 results, and concludes that he is actually outperforming those low expectations. Add to that the fact he’s paired with a very strong driver, and the model has additional certainty that he’s not doing as badly as he’s let on.

Remember, our model doesn’t know Hamilton is a seven-time World Champion. It only knows what happened last season and how F1 fans and experts rated him.

Looking at the ratings from fans and experts, Lewis is ranked 8th, which isn’t far off from our own result. Personally, I have him in 9th place, below Hülkenberg and tied with Esteban Ocon and Fernando Alonso.

Nico Hülkenberg is consistently rated in the 7th to 9th range across the board. Our model places him 9th, the public and experts have him in 7th, and I rank him 8th.

There isn’t much controversy when it comes to Hülkenberg; he’s a Formula 1 veteran who consistently delivers strong results. While his podium at Silverstone was the highlight of a strong season, his 5th place in Spain and 7th in Australia prove it was no coincidence. Rather, it was the result of the hard work he and Sauber have put in all year.

Oliver Bearman might be another controversial name in these mid-season rankings. I’m sure many of you will think he’s ranked too high, and while you may be right, let me explain the reasoning. Our model places Bearman 9th, largely because even though he’s finished 11th and out of the points on several occasions, our simulations show him frequently finishing 10th or higher.

It’s the model’s way of saying, “Perhaps he’s been a bit unlucky, and in another universe, he would have scored more points.” Additionally, the model gives rookies some leeway; since expectations are lower, it provides a slight boost to their rating compared to more experienced drivers.

In contrast, the public, experts, and I have based our rankings more on his actual results, perhaps overlooking how many times he was on the cusp of scoring. The combined public and expert ranking has Bearman in 14th place (though almost tied with Pierre Gasly), while my personal ranking puts him at 13th, tied with Lance Stroll.

Pierre Gasly is tied with Oliver Bearman in 9th place. Our model ranked Gasly surprisingly low, which I believe is a similar case to Leclerc’s: his strong 2024 ratings set expectations for the 2025 season that were perhaps too high to meet.

Still, this is a perfect example of why we use an ensemble rating system rather than relying on a single source. Gasly’s low score from the model is balanced by an average rating from the public and experts, as well as a high mark in my personal opinion. In this case, no single factor overpowers the others, leading to a stable and well-rounded final result.

Just behind Gasly, we find his ex-teammate, Esteban Ocon. The Frenchman is a tiny 0.1 points behind both Gasly and his own current teammate, Oliver Bearman.

Our model ranks Ocon behind Bearman, but as we’ve noted, it gives extra leeway to rookies still getting the hang of Formula 1. The public and experts rated Ocon as the 9th best driver of the season, placing him just below Hamilton but ahead of Hadjar and Alonso. I tend to agree with them, ranking Ocon 9th myself, tied with Hamilton and Fernando Alonso.

Isack Hadjar started the season very strong but has struggled more in the second half. While his results are still quite good, his last few races have definitely cost him places in these mid-season rankings.

Hadjar is an interesting case because there’s a strong consensus around his position. Our model ranks him near the middle of the pack, which is very similar to where the public and experts have him. I rated him slightly lower, but not by much, so his final 12th-place spot—surrounded by other competitive drivers—is quite stable.

Tied with Isack Hadjar, we have the old fox himself, Fernando Alonso. The veteran Spaniard has had an interesting season so far; the first eight races were largely a write-off, but recent outings have been much stronger, with the AMR25 looking far more competitive.

When it comes to the score, our model ranks Alonso near the bottom of the list. “Outrageous!” I hear you say, but let’s look at the logic. The model sees the 2024 ratings, where Alonso was considered a dependable driver with a strong reputation. At the same time, it sees Lance Stroll ranked as one of the worst drivers on the grid. Fast forward to 2025, the model observes that Alonso and Stroll have almost identical results. So, it adjusts accordingly: Alonso’s score is lowered while Stroll’s is increased.

The model is essentially asking, “If he’s that good, why isn’t he beating his teammate?”

The public and experts, however, have ranked Alonso 11th—a spot I agree with. The final weighted average puts him in 12th place overall, which feels quite reasonable.

Lance Stroll lands in 14th place in our rankings, just behind his teammate, Fernando Alonso. Interestingly, they are tied on points so far this season with 26 each. Because he’s keeping pace with a highly reputable teammate, our model concludes that Lance Stroll is actually doing a good job.

The public and experts were much harsher in their assessment, ranking Stroll as the 17th best driver, near the bottom of the list. I rated him as the 14th best, which means we have three very different opinions from our three sources. Once again, this shows the strength of our ranking method; by combining these perspectives, we get a more stable result than you could from just “eye-balling it.”

Kimi Antonelli started the season strong, showing good pace and impressive composure for a rookie. As the season has progressed, however, his year has been defined by extreme highs and lows, lacking the consistency you’d expect from a veteran. Granted, Kimi is a young driver, so an adaptation period is expected. Still, his results have been less than stellar, especially considering the strength of the Mercedes W16.

Our model ranks Kimi Antonelli in 11th, giving him the benefit of the doubt as a rookie. The public and experts place him just one spot behind in 12th, below Alonso but above Gasly. I was his harshest critic in this scenario, ranking him 17th. While I fully understand he’s a rookie who needs time, I feel his results are the weakest among the newcomers, particularly when you factor in the strength of his car.

Tied with Kimi Antonelli is another rookie, Gabriel Bortoleto. The Brazilian driver has started to make an impact in recent weeks, clearly aided by a Sauber car that is getting stronger as the season progresses. Gabriel has been particularly impressive in qualifying against a fast teammate like Nico Hülkenberg, and while his race results haven’t always matched that pace, he has shown clear glimpses of his talent.

The model and the public/expert opinion are closely aligned, ranking Gabriel 16th and 15th, respectively. I was a bit more generous, placing him in 12th. Recency bias likely plays a role in my higher rating, which is another case where the model helps balance the natural enthusiasm of being a fan with its own impartial, data-driven perspective.

Perhaps one of the most controversial names this season, Liam Lawson finds himself in 17th place in our tri-factor power rankings. Liam had a nightmare start to the season, struggling badly as Max Verstappen’s teammate before being demoted to Racing Bulls after just two races. While he hasn’t fully recovered from that rough start, his results have improved recently. He’s looking much more competitive and now sits just two points behind his teammate.

The public, experts, and I all rate Lawson quite low. And while the model rates him higher, it’s not enough to overcome what has been a difficult season for the New Zealander.

Let me start by saying that I don’t think Doohan was given a fair shot. Six races is simply not enough for a rookie to show what they have in the tank. We’ve seen how volatile rookie seasons can be: Antonelli and Hadjar started strong but are now struggling, while Bortoleto started slow and is now finding his speed. That said, we have to rate Jack based on those six races, which is a difficult task. I think he could have shown more and looked quite clumsy at times, but it’s hard to pile on a driver who was dropped just a quarter of the way through the season.

Our model doesn’t have enough data to get a clear read on his performance, so it’s essentially making predictions with high uncertainty. In this case, the public and expert rankings, along with my own opinion, are doing the heavy lifting. The public has Doohan almost at the bottom of the list in 20th place, just ahead of Franco Colapinto, the driver who replaced him. I rated Doohan 18th, which is still low but also ahead of Franco. The model places him up in 13th, but it’s crucial to remember this comes from very limited data, so that ranking should be taken with a huge grain of salt.

What could have been a dream season for Yuki Tsunoda has turned into a nightmare. Driving for Red Bull is a daunting task, and one he has clearly struggled with. The Japanese driver has looked lost at times next to Verstappen, amassing just 10 points across 14 races and three sprints, leaving him near the bottom of the Championship standings.

While it’s true the RB21 isn’t the dominant car it was in 2023, the results have still been subpar. Yuki has only shown fleeting glimpses of speed, and though his qualifying performances have improved recently, the overall results are not good enough for a driver who wants to stay at one of Formula 1’s top teams.

Our statistical model ranks Tsunoda 20th out of 21 drivers. The public and expert ratings place him 19th, while I personally rated him 20th.

Franco Colapinto was the subject of much praise last year. As a late-year replacement for Sargeant, he had strong races with Williams before repeatedly crashing and costing the team millions of dollars. This season, while less chaotic, has also shown less of the raw speed we saw from him in 2024.

The Argentinian has struggled badly in 2025. He has been completely eclipsed by his teammate, Pierre Gasly, and hasn’t established himself as a massive upgrade over his predecessor, Jack Doohan. His best results are a pair of 13th-place finishes in Monaco and Canada, and he remains the only active driver with no points to his name.

Regarding his ranking, our model has Colapinto in 19th place, the public and experts have him in 21st, and I also ranked him 19th. Pretty much the expected result for a struggling driver.

We’ve saved the most surprising ranking of the season for last. Carlos Sainz lands in 21st place—dead last—in our tri-factor power rankings. After leaving Ferrari for Williams last year, many were left wondering why he wasn’t hired by a top team like Red Bull. The vast majority of pundits and fans praised Carlos in 2024, rating him as one of the best drivers in Formula 1. Many, myself included, believed Sainz would start the season strong and eventually get the upper hand on his teammate, Alex Albon. The reality could not be more different.

Carlos has just 16 points compared to Albon’s 54 and has shown none of the speed that made him such a strong competitor to the notoriously fast Charles Leclerc. In my opinion, Carlos has been the most disappointing driver of the season so far. I ranked him in last place, tied with Yuki Tsunoda. While the model also ranked him dead last, it gave him an even worse score than I did. The public and expert ratings were a bit more lenient, placing Sainz in 16th.

“Why the harsh rating?” you might be asking. I think the answer is quite simple: Carlos is a veteran who has competed at the top of the sport for years. He had a brilliant 2024 season and seemed to have the world at his feet, and yet, he’s looked completely out of place at Williams. I believe he will show much more of his talent in the second half of the season, but for these first 14 races, the results have been deeply disappointing.

Summarized power rankings

Conclusion

And so that wraps up our 2025 mid-season power rankings. The whole idea behind this tri-factor system was to look beyond the raw championship standings and create a more nuanced, holistic view of driver performance—and I think the final list certainly delivered on that.

By focusing purely on performance versus expectation, it gave us some of our most controversial results, like ranking Verstappen and Alonso surprisingly low while seeing hidden value in drivers like Hamilton. It’s a cold, hard look at the data, completely free of the human biases that we fans (myself included) bring to the table.

On the other hand, the human element—both from the public and my own ratings—brought in the context that numbers can miss. Our model is not perfect, and the human element cannot be replaced. Still, in some cases, all of the elements will converge to reach a single conclusion. We saw that with drivers like Hülkenberg, where all three sources came to a consensus, and with rookies like Bearman and Doohan, where the uncertainty of a short career makes the “eye test” so important.

Ultimately, this process gives us a list with Oscar Piastri at the top and Carlos Sainz at the very bottom—a result I’m sure almost no one would have predicted back in March. It shows just how much expectations, car performance, and consistency shape our perception of a driver’s season.

Now I want to hear from you. What was the most surprising ranking on this list for you? Who is too high, who is too low, and who did we get just right? Let me know in the comments below.

My plea

Just as I’ve started doing in the recent weeks, this is another advanced article in which I embraced statistical modelling to take my analyses to the next level. Doing inference with statistical models isn’t easy—it takes a lot of time to develop a useful and reliable model capable of producing meaningful insights. Creating this model and writing this article took me dozens of hours. If you’ve enjoyed reading it, please consider becoming a supporter to help me continue writing in-depth articles like this one. Your support truly makes a difference and helps keep the blog going. Thank you!

2025 Hungarian GP: What really happened to Charles Leclerc? The story the raw lap times don't tell

Tue, 05 Aug 2025 00:00:00 +0000

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The 2025 Hungarian Grand Prix featured several notable incidents, but none more significant than Charles Leclerc’s sudden loss of performance midway through the race. Leclerc started on pole and appeared to be on course for victory, but something changed. What really happened? There has been much speculation, and while I can’t provide a definitive answer, I can use the power of statistics to uncover more of the truth. Let’s take a closer look at the numbers.

The limitations of raw data

Let’s start by examining the raw lap times—the exact times recorded by the drivers during the race. While these numbers are insightful, they don’t tell the whole story. In reality, lap times are influenced by much more than a driver’s pure speed. A multitude of factors come into play, including but not limited to:

Fuel: As cars burn fuel, they become lighter and therefore faster as the race progresses. This is why you often see slower lap times at the beginning of the race and much quicker times toward the end.
Track evolution: The track surface is constantly changing. As cars repeatedly circle the track, they deposit rubber onto the racing line, increasing grip. That’s why you’ll notice lap times dropping dramatically early in the race as the track “rubbers in.”
Weather: Conditions such as humidity, ambient temperature, track temperature, and wind significantly affect lap times. A track that’s too cold or too hot can slow drivers down and accelerate tire wear. These variables are always shifting throughout the race.
Tire degradation: Perhaps one of the most interesting topics in current Formula 1. Drivers must carefully balance between preserving their tires to reduce degradation and pushing for faster lap times. Overheating the tires can lead to a sudden loss of performance. On the other hand, being too conservative might allow a rival to overtake. It’s a delicate balance between endurance and speed.
Traffic: This one’s almost self-explanatory. Running behind another car—known as being in “dirty air”—reduces aerodynamic efficiency and hampers cornering speed. Conversely, running in clean air allows a driver to extract the car’s full potential.

When you look at a graph of lap times, all these effects are mixed into each number. So if a driver suddenly sets a faster lap, was it because of fuel, track conditions, or something else? The raw times alone can’t give you a clear answer. Maybe a driver was excellent at looking after his tires, but with all this noise—from fuel, track evolution, weather, and traffic—it’s impossible to tell just from the lap chart.

Traffic makes things even messier. If someone gets stuck behind another car without the chance to overtake, their real speed might never show up in the results. They could have been the fastest on track, but the numbers don’t always reveal that.

So, who was really the quickest this weekend? We know Leclerc’s pace fell off sharply in the final stint, but did something go wrong during his last pit stop, or was there a problem creeping in from earlier on? That’s the question we really want to answer.

Wouldn’t it be great if we could filter out all the distractions and compare every driver on a level playing field? Then we’d really see when someone’s performance picked up or dropped off. Believe it or not, that’s something we can do using statistical modeling.

Understanding our analysis

Using a statistical model, we can isolate the effects we actually want to analyze from everything else that gets mixed into the lap times. What I’m interested in here is the relative pace of Leclerc, Piastri, and Russell, if we assume they were all racing under the same circumstances.

Some effects are similar for everyone. Fuel, for example, drops at a similar rate for all drivers, so the effect of a lighter car as the race goes on is easy to correct for. The same goes for track evolution since the grip on the track improves for everyone, regardless of who’s behind the wheel. But traffic is a different story. If a driver gets stuck behind slower cars, it’s going to hurt their lap times, badly, even if they have plenty of pace in reserve. On the other hand, a race leader in clean air might post deceptively strong times, partly thanks to clear track rather than sheer speed.

To work through this, I built a statistical model that decouples these effects and lets us see each driver’s true pace. The details of the model aren’t the main focus here since my goal is mainly to use its output to understand what actually happened, especially with Leclerc’s race.

Using the model, I predicted the lap times for Leclerc, Russell, and Piastri, but these times are “clean.” In other words, I adjusted the laps so that fuel levels don’t factor in, and track evolution is held constant. For track evolution, I chose to freeze its effect as it was on lap 35, whic is the halfway point of the race. That way, we’re imagining no further changes in grip, lap after lap. Most importantly, I removed the impact of traffic. For example, Piastri spent much of his second stint stuck behind Leclerc, which clearly cost him time. So I had the model estimate what his laps would have looked like if he’d been able to run in free air for the entire stint.

For this analysis, here’s a quick summary of the effects I corrected for:

Fuel: Corrected. I added back a 0.03-second time penalty per lap, which is a widely used estimate of how much lap times improve as cars burn fuel. This was a straight correction based on industry knowledge. It’s not perfect, but it’s accurate enough for our needs.
Track evolution: Controlled for. Track evolution was modelled, which means that this effect is not constant, and instead is allowed to vary throughout a race. For this comparison I fixed track evolution at the value from lap 35, so we’re comparing everyone on an even surface.
Traffic: Controlled for. I asked the model to predict lap times as if each driver spent the whole race in clean air, with no time lost following slower cars.

With these corrections, the lap times we’re looking at show how fast each driver could have gone if all the outside factors were neutralized. In other words we combined all of these adjustments, and we create a fuel, track evolution, and traffic-corrected, view of the race.

A corrected view of the race

Looking at the corrected version of the race, a very interesting story starts to emerge. When we break down each stint, you’ll notice that lap times get steadily slower, rather than jumping up and down. That’s because, with our corrections in place, what we’re seeing now is mainly the impact of tire degradation and the driver’s own input.

Of course, we can’t directly model whether a driver was pushing hard or taking it easy to save their tires, but we can observe the overall effect. What these smoothed lap times show us is the combined influence of tire wear and the way each driver chose to manage their race. We’re effectively isolating the signal (tire deg and driver performance) from the noise (fuel effect, track evolution & traffic.)

Raw stint averages: Leclerc vs. Piastri
Stint	Mean lap time (Leclerc)	Mean lap time (Piastri)	Delta
1	1:22.157	1:22.264	-0.11 secs
2	1:21.323	1:21.339	-0.02 secs
3	1:21.516	1:19.947	1.57 secs

The first stint

If we focus on the first stint, it’s clear that Piastri and Leclerc were almost identical on pace. Their corrected average lap times were separated by just 0.017 seconds—less than two hundredths of a second. Meanwhile, George Russell was a bit off compared to the Ferrari and McLaren, trailing by just over two tenths.

Now, even though the model shows only a tiny gap between Piastri and Leclerc, the raw lap times tell a different story: about a tenth in Leclerc’s favor. But this makes sense when you remember Piastri spent a few laps right on Leclerc’s tail, and even more laps still dealing with the dirty air from the Ferrari. By correcting for those effects, the model gives us a much clearer picture of how closely matched they actually were.

The second stint

In the second stint, the picture changes quite a bit. On raw lap times, Leclerc and Piastri look pretty much the same, with just a 0.02 second gap per lap in Leclerc’s favor. But when we look at the corrected data, we see a diffent picture.

According to the model, Piastri was actually the fastest driver in the second stint, beating Leclerc by an estimated two tenths per lap. So, why does the corrected data show such a bigger gap? If you check the traffic chart, you’ll notice Piastri spent almost the whole stint stuck behind Leclerc. That meant he was running in dirty, turbulent air, which slows you down. By removing the effect of traffic, we get a better read on Piastri’s true pace for this part of the race.

What’s even more interesting is that you can track the moment Leclerc’s performance started to drop off. Early in the stint, he was already a bit slower than Piastri, but both were lapping pretty consistently. Then there were those two radio messages from Leclerc—the first one on lap 26, where he mentioned he could “feel” something that was already discussed with the team, and another a lap later, this time sounding more concerned. On lap 27, he said they were “losing so much time” and might lose the race if things stayed the same. Right around these laps, you can see Leclerc’s lap times getting worse. From lap 26 to lap 39, his times start to drift away from Piastri’s and get closer to Russell’s.

By the end of the stint, Leclerc wasn’t just trailing Piastri by almost four tenths per lap (0.385 seconds), but he was basically just as fast as Russell, just a hundredth slower per lap. For me, that pretty convincingly shows that something started going wrong with Leclerc’s car well before his final pit stop on lap 40.

The third stint

The last stint really tells its own story. As soon as Leclerc came out of the pits for the second time, his performance just disappeared. By lap 50, he was already about seven tenths slower per lap than Russell and more than 1.3 seconds off Piastri’s pace. It just kept getting worse and by lap 70, he was over two seconds a lap slower than Oscar.

Whatever the problem was, it totally took away his pace. In those final laps, Leclerc just didn’t have anything to fight with and there wasn’t much he could do to hold off Piastri and Russell.

Findings overview

What can we take away from the output of our model?

Piastri and Leclerc were very evenly matched during the first stint. There was nothing to separate them; they were virtually just as fast.
After the first pit stop, in the second stint, Piastri was already faster than Leclerc. On lap 21, Piastri was estimated to be just over two tenths (0.225 s) quicker per lap than Charles. By the end of Charles’s stint on lap 39, Oscar was almost four tenths (0.385 s) faster per lap.
Leclerc’s second stint was already worse than Piastri’s right from the start, but it got progressively worse after laps 26 to 28. This, coincidentally (or not), matches the laps when Charles complained on the radio about issues with the car’s performance.
After the final pit stop, Charles lost all of the pace he had at the start of the race. His lap times completely fell off a cliff, and he was much slower than both Russell and Piastri.

In my opinion, all of these findings—combined with the cryptic radio messages from Charles during the second stint—point to a problem that started to show up around lap 25. Charles complained about losing time, and the corrected lap times immediately show a drop in performance. While his lap times were still competitive for a while, there was a clear negative trend in car performance. I don’t think the problem started right after the second pit stop; it seems like it began well before he came in for new tires on lap 40.

The third and decisive stint just makes it obvious how bad things got for Charles. After that last stop, his lap times didn’t just get worse, but they got dramatically slower in a way that was totally unexpected.

Speculation on what happened

The data helps us understand what happened more effectively, but it doesn’t answer why Leclerc lost so much performance. Still, we can speculate. Please note that the following is just my personal opinion, based on my knowledge of Formula 1 and what we’ve seen in the model’s output.

I think the plank wear issue mentioned by George Russell after the race is probably the most likely cause. Ferrari has already been disqualified this season for excessive plank wear, and they’ve been telling Lewis and Charles to lift and coast aggressively at several races. To me, it seems quite possible that the engine mapping, which controls how the engine responds to both driver commands and sensor inputs, began dialing back the power to preserve fuel and protect the plank. By reducing ERS (Energy Recovery System) deployment and cutting fuel flow, the car would have less top speed in certain sections. That reduced top speed also lowers compression under load and braking. I suspect this is about the time Leclerc started complaining, saying things like “I can feel what we discussed before the race” and “losing so much time.” Charles probably knew this behavior from the ECU was on the cards, and that it would seriously hurt his performance.

Now, regarding the final stint, I think that although both Ferrari and Charles expected the ECU to step in and limit the car to preserve the plank, the team may have realized that the plank wear was worse than they thought. Teams can’t directly measure plank wear, but all F1 teams have ride height and temperature sensors, along with accelerometers and onboard cameras. These tools together can give a pretty good idea of how quickly the plank is wearing away. It’s quite likely that, once they saw the wear was above expectations, Ferrari felt they had to act fast to avoid disqualification. This is probably where tire pressures became a factor.

If the SF-25’s ECU was already cutting performance to minimize plank wear, but it still wasn’t enough, then the team had to try something else. Increasing tire pressure was probably the only remaining option. Normally, teams want to run the lowest tire pressure possible, since this increases grip and usually helps tire performance as a side effect. With lower pressures, the tire is a bit shorter, which brings the car closer to the ground. In turn, this has a negative effect on plank wear. Fitting new tires with higher pressure, and therefore a taller profile, would raise the ride height and help protect the plank. Teams would never do this unless it was absolutely necessary to avoid getting disqualified, since there is a big performance trade-off. My guess is that Ferrari had no choice on Leclerc’s final stop; they put on fresh tires with increased pressure, which severely hurt his cornering grip. Combine that with the ECU already holding back the power, and you get a massive drop in pace, which became really obvious in that last stint but actually started showing up at the beginning of the second stint.

My conclusion

I think the easiest way to sum things up is with these bullet points:

The team was aware that plank wear could be a problem during the race.
To manage this, they had pre-planned power reduction modes built into the ECU, which were activated mid-race and caused the first big drop in performance. This is around the time Leclerc started to complain about “losing so much time.”
Real-time data from the sensors and feedback from Leclerc showed that even with reduced power, the car was still wearing the plank too quickly. This likely caused panic within the team about a potential disqualification.
The team decided to put over-inflated tires on Leclerc’s car as a last resort to physically raise the car and save the plank.
The combination of power reduction in the ECU + over-inflated tires caused a “double penalty”: Leclerc was driving with a car that was down on power (due to the engine mode) and had terrible grip (due to the high tire pressures).
This combination of issues culminated in catastrophic performance that started gradually (first due to ECU programming and power reduction), and then acceleratedly (due to overly-inflated tires) by the end of the race.

My plea

This is the one of the most advanced articles I’ve written for this blog. I’ve decided to embrace modelling to take my analyses to the next level. Doing inference with statistical models isn’t easy—it takes a lot of time to develop a useful and reliable model capable of producing meaningful insights. Creating this model and writing this article took me dozens of hours. I know the race was just a couple of days ago, but since it ended, I’ve been working on getting this article done. If you’ve enjoyed reading it, please consider becoming a supporter to help me continue writing in-depth articles like this one. Your support truly makes a difference and helps keep the blog going. Thank you!

2025 Hungarian GP: Race pace

Sun, 03 Aug 2025 00:00:00 +0000

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Race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Hungarian GP: Quali session

Sat, 02 Aug 2025 00:00:00 +0000

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Quali session classification

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Hungarian GP: FP2 session (Model-corrected long run analysis)

Fri, 01 Aug 2025 00:00:01 +0000

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This is an experimental analysis.

To provide a clearer picture of performance, anomalous laps (due to traffic, driver errors, etc.) have been corrected using predictions from a statistical model. The model learns each driver’s underlying pace and trends to generate these plausible lap times.

While I’m preparing a more detailed post for the next Grand Prix, I was excited to share these first results now. To see the difference this makes, you can compare this chart with the uncorrected version in the the 2025 Hungarian GP: FP2 session post.

2025 Hungarian GP: FP2 session

Fri, 01 Aug 2025 00:00:00 +0000

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Click to expand methodology

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:25.000. Some drivers had laps above that threshold but I am not displaying them here.

I’ve updated the FP2 race simulation plots to only show the laps considered valid, so no anomalies. In the new chart, the quantiles now reflect the spread of these filtered laps. However, the crossbar and median time displayed at the top still comes from the full, unfiltered data. This way, you get a clean visual of the underlying pace while still keeping the more representative, overall median, from the original data.

It’s pretty clear that Hulkenberg was on a different program, most likely running with less fuel than everyone else.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

As an extra addition to the blog, I’m adding a simple chart that shows the best lap time done by each driver. The sum of the “Sector times from the best lap done by each driver” chart should add up to the total time seen in this chart.

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 13)

Tue, 29 Jul 2025 00:00:00 +0000

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No, ChatGPT didn’t write this article. Yes, there are some of us out there who use the em dash.

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

We’re already more than halfway through the season. With 13 races—and now 3 sprints after the Belgian GP—we have more representative results. Just as a reminder, this season features the anomaly of Lawson switching places with Tsunoda after just three races, which makes our usual analysis a bit trickier. Additionally, Alpine decided to replace Doohan with Colapinto after just six races. Normally, I’d use the median as the key metric of interest since it’s more robust to outliers. However, in this case, the median might skew the average delta between Lawson and Verstappen.

It is important to note that after 16 sessions, the results have now stabilized, which means that at this point in time the mean is the most representative metric for most pairings.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates remains at Red Bull Racing. Tsunoda had a slightly better session at the Belgian GP, managing to qualify for Q3—his first appearance in the top qualifying session since the Miami GP. In the latest Belgian GP, he trailed Verstappen by 0.589% in the sprint qualifying session (SQ) and by 0.376% in the main qualifying session (Q). His performance in qualifying is his best yet at Red Bull, just edging out his previous result at the Austrian GP (0.403%). Currently, Verstappen is beating Yuki Tsunoda by 0.702%, which is less than the median delta Lawson left at 0.881%, but still stands as the highest gap on the grid by a wide margin.

If we look at the mean symmetric percent difference instead, the largest active gap is still at Red Bull, with Max ahead of Tsunoda by an average of 0.755%. Yuki has managed to reduce the average gap from 0.809% to 0.755% after the latest Grand Prix, but this remains the largest delta on the grid by a significant margin.

At the other end of the spectrum, the smallest delta depends on our metric of interest. Looking at the median symmetric percent difference, the closest battle is at McLaren, with Oscar Piastri leading Lando Norris by just 0.041%. If we focus instead on the mean symmetric percent difference, the smallest gap is at Sauber, where Bortoleto still leads Hulkenberg in qualifying—now by a tiny 0.017%.

Delta in seconds

Just as with the symmetric percent difference, the smallest gap depends on our metric of interest. Looking at the median delta, the gap is just 0.035 seconds in favor of Piastri over Norris. The mean qualifying delta tells a different story, with the smallest mean at Sauber: Gabriel leads Nico by just 0.007 seconds, or a minuscule 7 milliseconds over the entire season. Quite incredible.

I think the biggest gap will remain at Red Bull for the rest of the season. I just don’t see how Tsunoda can close the gap enough to approach any other pairing. As I mentioned, the delta between Max and Yuki has decreased, but that’s mostly due to the extreme gaps earlier in the season—Bahrain (0.879s), Saudi Arabia (0.91s), and Miami (0.739s). Yuki had a stronger showing at the Belgian GP qualifying session, but he was still 0.38 seconds slower than his teammate. These performances are not enough to make the battle much closer. At the moment, Max Verstappen is ahead of Yuki Tsunoda by a median of 0.563 seconds and a mean of 0.639 seconds.

Qualifying stage appearances

If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the 13th race of the 2025 season, most teammate pairings have been fairly evenly matched, with just a few notable exceptions. The first is Aston Martin, where Alonso has reached Q3 six times, while his teammate, Lance Stroll, has managed it only twice. The second is Alpine, where Gasly continues to do a stellar job in a struggling car. After the Belgian GP, Pierre has now qualified for Q3 on eight occasions, while Doohan and Colapinto combined have yet to make a single Q3 appearance. The third exception is, unsurprisingly, Red Bull Racing. So far, Max Verstappen has reached Q3 in all 16 possible sessions and is one of just three drivers with a 100% Q3 appearance rate, along with Lando Norris and Oscar Piastri. Meanwhile, Lawson was eliminated in Q1 three times while racing for Red Bull. In Tsunoda’s case, his Q3 record has improved slightly: the Japanese driver now has four Q3 appearances out of 13 outings for the team, putting his rate at 30.7%.

2025 Belgian GP: Quantifying the cost of Norris's Mistakes

Mon, 28 Jul 2025 00:00:00 +0000

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There has been considerable controversy following the outcome of the recent 2025 Belgian GP. While Piastri rightfully claimed victory, many have questioned whether Norris could have done more to challenge his teammate. Norris made several mistakes during the race that cost him valuable time. If he had avoided those errors, could he have caught up to Piastri? Let’s take a look at the numbers

Our simulated world

Lando Norris had a strong race, but there’s a chance he left some gains on the table. He made three costly mistakes—on laps 26, 34, and 43—and ultimately finished just over three seconds behind his teammate. While we can’t say for certain what would have happened if Norris had avoided these errors, we can model a simulated scenario in which his laps were clean. For this scenario, I’ve also asked the model to predict lap 27. Although Norris didn’t make a mistake in that lap, it seems likely his lap time was affected by what happened on the previous lap. As you’ll see, the model estimates a gain of just one tenth there, which isn’t particularly significant in the grand scheme of things.

According to our simulation, if Norris had not made those mistakes, he likely would have caught up to Piastri and entered DRS range by lap 41. The model suggests that Lando lost around 4.75 seconds due to his three major errors. However, our analysis can’t predict how Norris’s presence might have influenced Piastri’s performance, or whether Piastri had any extra pace in reserve. Assuming Piastri was already driving at his limit, there’s a strong chance Norris could have been close enough to challenge for the lead in the final 2–3 laps of the race.

The verdict: Lando appeared to have enough pace to catch Piastri. He lost approximately 4.75 seconds due to three costly mistakes and, as a result, wasn’t able to put pressure on his teammate in the final laps of the race.

Detailed data

You can check the detailed tables to review the model’s predictions. The first table compares Norris’s actual and predicted lap times for the key laps—26, 27, 34, and 43. The second table presents the same data found in the “Delta to Piastri” section of the plot, but in a clear, tabular format.

Real and model-predicted laps

Lap	Real lap time	Predicted lap time	Time lost
26	1:47.660	1:46.284	1.37 s
27	1:46.389	1:46.269	0.11 s
34	1:47.120	1:46.046	1.07 s
43	1:47.721	1:45.536	2.18 s
Total			4.75 s

Real and model-predicted delta to Piastri

Lap	Real delta	Predicted delta
15	8.98 secs	8.98 secs
16	9.13 secs	9.13 secs
17	8.80 secs	8.80 secs
18	8.63 secs	8.63 secs
19	8.21 secs	8.21 secs
20	8.09 secs	8.09 secs
21	8.15 secs	8.15 secs
22	8.29 secs	8.29 secs
23	8.39 secs	8.39 secs
24	8.11 secs	8.11 secs
25	7.89 secs	7.89 secs
26	9.14 secs	7.77 secs
27	9.29 secs	7.79 secs
28	8.79 secs	7.30 secs
29	8.62 secs	7.13 secs
30	8.30 secs	6.81 secs
31	8.19 secs	6.70 secs
32	7.80 secs	6.30 secs
33	7.23 secs	5.74 secs
34	8.00 secs	5.43 secs
35	7.53 secs	4.96 secs
36	6.92 secs	4.35 secs
37	6.23 secs	3.66 secs
38	5.57 secs	3.00 secs
39	4.74 secs	2.18 secs
40	4.41 secs	1.84 secs
41	3.81 secs	1.24 secs
42	3.13 secs	0.56 secs
43	5.15 secs	0.39 secs
44	3.41 secs	-1.34 secs

2025 Belgian GP: Race pace

Mon, 28 Jul 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

I try to make the charts look good but I can’t make miracles. A 20-second range between the fastest and slowest laps is just massive.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Belgian GP: Quali session

Sun, 27 Jul 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Quali session classification

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Belgian GP: Sprint

Sun, 27 Jul 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Sprint race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized sprint race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Sprint race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Belgian GP: Sprint qualifying

Sat, 26 Jul 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

Quali session classification

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 F1 Season: Pit Stop Power Rankings (rounds 1 - 12)

Thu, 17 Jul 2025 00:00:00 +0000

I’m back with the pit stop data! This time, I decided to take it to a whole new level. Pit stops are one of the most important elements in modern F1, and the pit stop crews deserve a lot of respect for the job they do week in and week out. Still, one of the most difficult aspects of judging a pit crew is the fact that pit stop times are highly variable, and a single pit stop can make the best team look like the worst. So, I’ve decided to create a robust model to address this issue and answer the question: Which team has the best pit stop crew in Formula 1?

The raw pit stop data

Raw performance per team

Looking at the data, grouped and summarized by team, the Ferrari pit crew comes out on top. Unlike the past few years, when Red Bull were dominant in the pit stop department, the battle is much more competitive in 2025. Ferrari and their mechanics are doing a fantastic job, performing at the top of their ability during the first 12 races of the 2025 season. Their average of 2.41 seconds per stop is 0.17 seconds faster than Racing Bulls’. It may not sound like much, but considering that each pit stop takes, on average, less than three seconds, this is actually a significant gap.

Speaking of Racing Bulls, it’s quite interesting to see them ahead of their big brother, Red Bull. Their data is intriguing: on average, they are beating Red Bull by 0.07 seconds per stop, but it appears that their maximum, raw pace isn’t quite on Red Bull’s level. However, they have made fewer major mistakes than the Red Bull crew, which helps lower their average substantially.

Looking down the rankings, Mercedes and Sauber are competing neck and neck, with Mercedes ahead by just 0.02 seconds per stop. The bottom five teams in this ranking are separated by around a single tenth, with sixth-placed McLaren averaging 2.89 seconds per stop, compared to 3.01 seconds per stop for Haas. The raw data, however, tells an interesting story. While McLaren are technically just a little faster than the worst-performing teams, it seems like they have a lot of pace in reserve. They have posted several fast stops—even more than better-ranked teams such as Mercedes and Sauber. Their problem? They have been very inconsistent and have had many slow pit stops, which heavily pulls their average up.

Raw performance per driver

Most teammates are, in fact, very close to each other. Hamilton and Leclerc are separated by just 0.01 seconds; Tsunoda and Verstappen by 0.08 seconds; Russell and Antonelli by 0.04 seconds. You get the idea. Nonetheless, there are a couple of very interesting results.

First, the McLaren pairing: Lando Norris has an average pit stop time of 2.82 seconds, placing him right in the middle of the pack. Oscar Piastri, however, has an average pit stop time of 2.97 seconds—0.15 seconds slower than his teammate—putting him close to the bottom of the grid, just ahead of Alonso and Bearman.

Second, the results at Haas: While neither Ocon nor Bearman boast strong pit stops—their fastest stop of the season so far is 2.49 seconds—it’s clear that Ocon has a faster average time, currently by a whopping 0.2 seconds, which is massive in the world of F1 pit stops.

Our new metric: xPT

How to read the charts

The overall ranking: The teams are ranked from 1 (fastest) to 10 (slowest) based on their xPT, or Expected Pit Time.
The number in the colored box next to each team’s logo: This number represents the model’s single best estimate for their “true” pit stop skill, measured in seconds. This number is the estimated “normal” pit stop time that you would expect fomr each time, based on the model’s beliefs.
The x-axis: The horizontal axis shows the xPT in seconds. Just like a lap time, a lower number is better. Teams further to the left are faster.
The slabs The slabs, or “domes”, provide a range of plausible values for each team’s skill. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty about the actuall skill level of each pit crew.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our expected value. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents the midfield average (the median xPT of all teams). It provides a quick reference to see which teams are in the top half of the grid (to the left of the line) and which are in the bottom half (to the right).

xPT by team

Looking at the “xPT by team” chart, we can see what the model “believes” to be the actual expected average pit stop time for each pit crew. Based on 2025 season data, our model indicates that Ferrari’s real, repeatable performance should result in an average time of 2.55 seconds per pit stop. Red Bull, McLaren (yes, McLaren), Racing Bulls, Sauber, Alpine, and Mercedes trail Ferrari, but are highly competitive with one another, while Williams, Aston Martin, and Haas have slower estimated pit stop times of three seconds or more per stop.

While our main chart shows the full range of plausible skill levels for each team, we can also use the model to answer a more direct question: what is the probability that one team is truly faster than another? By leveraging statistical analysis, we estimate that there is a 97.8% probability that Ferrari’s true underlying pit stop skill (xPT) is better than Red Bull’s. This means that, after removing noise from the data, it’s incredibly likely that Ferrari currently has the best pit crew in the business—at least for the time being.

xPT by driver

At Ferrari, for example, our model puts Leclerc at an xPT of 2.53 seconds, compared to Hamilton’s 2.57 seconds. While this is a minor difference—too small to say for certain whether it’s real or just an artifact of our model—it’s interesting to see that there may be slight but noticeable differences between drivers, although calling a 0.01-second difference in a stop significant would be a stretch.

Delta to xPT by team

How to read the charts

The number in the colored box next to each team’s logo: This number represents the estimated delta between the raw pit stop times and the estimated pit stop times (xPT), measured in seconds. Negative numbers represent higher performance than expected from each pit crew, while positive numbers show pit crews that are underperforming.
The x-axis: The horizontal axis shows the delta to xPT in seconds. Just like a lap time, a lower number is better. Teams further to the left are are over performing the expectations of the model by a higher margin, while teams further to the right are under performing the expectations of the model.
The slabs The slabs, or “domes”, provide a range of plausible values for the delta to the xPT. The peak of the hill is the single most likely value (the number in the box). As you move down the slopes of the hill, those values are less likely but still plausible. Narrower slabs shows that the model has high confidence in the results, while wider slabs show higher uncertainty.
The black dot and horizontal bars: The dot and bars below each slab represent the represents the median value and the the “most likely” ranges for our delta to the xPT. The dot is the same as the number displayed on the left side of the plot.
The dashed, vertical line: This line represents the 0 threshold. Teams or drivers on the left side of this line are beating the expectations, while teams on the right of it are performing at a lower level than expected.

You may have noticed that McLaren has an xPT (expected Pit Time) of 2.68 seconds. This is much lower than their raw average pit stop time of 2.89 seconds per stop. Why is this? Based on their raw pit times, our model believes that McLaren’s crew has the potential to perform an average pit stop in 2.68 seconds per stop. If you look at the raw data, McLaren has performed several fast stops—faster than those of many other teams, such as Mercedes and Sauber. However, they also have many stops that are much slower than you would expect given their underlying quality. At the moment, our model suggests that McLaren is underperforming by 0.22 seconds per stop, meaning that based on their ability, they would normally average 2.68 seconds per stop instead of the current 2.89 seconds.

On the other extreme, we have Ferrari. While our model expects Ferrari to average 2.55 seconds per pit stop, in reality, their average is 2.41 seconds. The delta to xPT shows that Ferrari is not just meeting expectations but far exceeding them, outperforming their xPT by an average of 0.15 seconds.

Currently, Racing Bulls are overperforming by an average of 0.14 seconds per stop. This is why, even though they have a slower xPT than Red Bull (as seen in our xPT by team chart), they are still outperforming their sister team in raw performance.

As for Red Bull, their delta to xPT is 0.00 seconds. This means they are performing exactly at the level the model expects from them based on the 2025 season so far. This is quite interesting, especially since you might expect to see them topping the leaderboard, but our model suggests that the level they have shown in the first 12 races is exactly what we should expect. Remember, however, that the model gets updated as more data is added, so these results can—and likely will—change as the season goes on.

Delta to xPT by driver

One of the most interesting findings of this chart, in my opinion, is the discrepancy between the results of Esteban Ocon and Oliver Bearman. While both Haas drivers operate with the same crew, equipment, and have similar baseline potential (xPT of 3.05 and 3.06), our model reveals a significant difference in their race-day execution. Currently, our model estimates with 99.6% certainty that Ocon’s stops have been faster compared to Bearman’s.

Another big discrepancy is found at McLaren. Similar to the the Haas results, our model estimates with a 100% certainty that the pit stops of Piastri have been slower than those of Lando Norris. While the stops of both drivers is worse than expected (+0.12 s for Norris and +0.31 s for Piastri), Piastri’s stops have been consistently worse. This points to a persistent issue tied specifically to his car or his side of the garage. Maybe Piastri is missing his marks in the pit box by a little, or maybe there are subtle issues with Piastri’s car that makes pit stops more difficult. There are many possibilities, but what we can say for sure is that the team’s overall underperformance is not being felt equally by its two drivers.

The search for the fastest stop

As of the 12th race of the season, the fastest stops have been exactly 2.00 seconds, both performed by the Ferrari pit crew at the Monaco GP and Saudi Arabian GP. For reference, last year saw seven stops completed in under two seconds, with the fastest two clocking in at 1.9 seconds, done by McLaren and Red Bull. So, what about this year? Can the teams break the 1.9-second barrier?

Our model estimates that the most likely fastest stop this season would be 1.95 seconds, which would tie for fifth place in 2024. However, the model also predicts several stops under the 1.95-second mark. To start, 50% of the curve values are under 1.95, which translates to a 50% chance of seeing a pit stop of 1.95 seconds or less.

What about the probability of seeing a pit stop under 1.9 seconds this year? The answer, according to our model’s predictive power, is 22.6%. This means there’s roughly a one-in-five chance of witnessing a 1.9-second stop, which would surpass the fastest from 2024.

You may be wondering about the best pit stop ever—the 1.8-second record set by McLaren in 2023. Can we expect to see that record broken this year? I think it’s unlikely, but not impossible. Our model puts the probability of witnessing a record-breaking pit stop at just 1.72%. While this number is quite low, keep in mind that the model’s predictions become more accurate as it incorporates additional data, so this probability could increase if pit stop times improve in the upcoming races.

Scenario	Probability
Pit stop < 2 s (fastest in 2025)	85%
Pit stop < 1.95s (model’s best estimate)	50%
Pit stop < 1.9 s (fastest in 2024)	22.6%
Pit stop < 1.8 s (fastest ever)	1.72%

Conclusions

My plea

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 12)

Tue, 08 Jul 2025 00:00:00 +0000

Use the Get in touch link if you’re interested in supporting this project.

No, ChatGPT didn’t write this article. Yes, there are some of us out there who use the em dash.

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

We’re already halfway into the season. With 12 races, and 2 sprints, we now have more representative results. Just as a reminder, this season, we have the anomaly of Lawson switching places with Tsunoda after just three races, which makes our usual analysis a little trickier. Additionally, Alpine have decided to replace Doohan with Colapinto after just 6 races. Normally, I’d just use the median as the key metric of interest since it’s more robust to outliers. However, in this case, the median might skew the average delta between Lawson and Verstappen.

The results have now stabilized after 14 sessions, which means that at this point in time the mean is the most representative metric for most pairings.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates remains at Red Bull Racing. Tsunoda continues to struggle and hasn’t made a Q3 appearance since the Miami GP, which was two months ago. At the latest British GP, he trailed Verstappen by 0.595%. Currently, Verstappen is beating Yuki Tsunoda by 0.781%, which is still slightly less than the median delta left by Lawson at 0.881%.

If we look at the mean symmetric percent difference instead, the largest gap shifts to Verstappen vs. Lawson, with Max ahead by an average of 1.034%. Yuki continues to reduce the gap to Max, but I doubt he’ll be satisfied with his recent performances. His current delta to Max stands at 0.809%, which is 80% more than the next largest gap—Alonso vs. Stroll.

At the other end of the spectrum, the smallest delta has fully shifted to Sauber. Bortoleto has taken the lead against Hulkenberg in quali, which is a great achievement for a rookie. Using the median, the gap is just 0.02%, while using the mean, it stands at 0.035%, both in favour of the Brazilian driver.

Delta in seconds

Just as with the symmetric percent difference, the smallest gap is found at Sauber. Looking at the median delta, the gap is just 0.016 seconds in Bortoleto’s favo, while the smallest mean gap has Gabriel leading Nico by 0.048 seconds.

I think the biggest gap will remain at Red Bull for the rest of the season. I just don’t see how Tsunoda can close the gap enough to come close to any other pairing. As I mentioned, the delta between Max and Yuki has decreased, but that’s mostly due to the extreme gaps he had earlier in the season—Bahrain (0.879s), Saudi Arabia (0.91s), and Miami (0.739s). Tsunoda could still be getting deltas to Max of over half a second, and it would improve his overall relative performance. At the moment, Max Verstappen is ahead of Yuki Tsunoda by a median of 0.563 seconds and a mean of 0.668 seconds.

Qualifying stage appearances

If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the 12th race of the 2025 season, most teammate pairings have been fairly evenly matched, with just a few notable exceptions. The first remains Aston Martin, where Alonso has reached Q3 six times, while his teammate, Lance Stroll, has managed it only twice. The second is at Alpine, with Gasly doing a great job in a struggling car. Pierre has qualified for Q3 on seven occasions, while Doohan and Colapinto combined have yet to make a single Q3 appearance. The third exception is, unsurprisingly, Red Bull Racing. So far, Max Verstappen has reached Q3 in all 14 possible sessions and is one of just three drivers to do so, along with Lando Norris and Oscar Piastri. Meanwhile, Lawson was eliminated in Q1 three times while racing for Red Bull. In Tsunoda’s case, his Q3 record continues to suffer, with just three appearances out of 11 outings for the team—putting his rate at 27.2%.

2025 British GP: Race pace

Sun, 06 Jul 2025 00:00:01 +0000

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Race pace

Click to expand methodology

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps”, so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

This was a very difficult race to analyze. With so many virtual and safety cars, race control went wild sending track status information.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 British GP: Quali session

Sun, 06 Jul 2025 00:00:00 +0000

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Quali session classification

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 British GP: FP2 session

Fri, 04 Jul 2025 00:00:00 +0000

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The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:35.000. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 11)

Tue, 01 Jul 2025 00:00:00 +0000

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Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

We’re already one quarter into the season. With 7 races, and 2 sprints, we now have more representative results. Just as a reminder, this season, we have the anomaly of Lawson switching places with Tsunoda after just three races, which makes our usual analysis a little trickier. Additionally, Alpine have decided to replace Doohan with Colapinto after just 6 races. Normally, I’d just use the median as the key metric of interest since it’s more robust to outliers. However, in this case, the median might skew the average delta between Lawson and Verstappen.

The results can change dramatically by switching between the mean and median as the key metric of interest. These numbers will eventually stabilize and converge as the season goes on.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates remains at Red Bull Racing. Tsunoda had a tough weekend in Austria, finishing 18th and being eliminated in Q1. While this time he trailed Max Verstappen by only 0.263 seconds, it’s important to remember that Austria is one of the shortest and fastest tracks on the calendar. If we consider the more representative symmetrical percent delta, the number stands at 0.403%, indicating that his gap would be closer to four-tenths on a more “traditional” track. Currently, Verstappen is beating Yuki Tsunoda by 0.803%, which is still slightly less than the median delta left by Lawson at 0.881%.

If we look at the mean symmetric percent difference instead, the largest gap shifts to Verstappen vs. Lawson, with Max ahead by an average of 1.034%. Yuki has continued to close this number in recent weeks and, based on the symmetrical percent difference, just had his best qualifying session at Red Bull. His current delta to Max stands at 0.833%.

At the other end of the spectrum, the smallest delta depends on whether we use the median or mean symmetrical percent difference. Using the median, the smallest gap is now at Haas, with Bearman leading Ocon by just 0.01%. Looking at the mean, the smallest gap is at Sauber, where Hulkenberg leads Bortoleto by only 0.049%.

Delta in seconds

The race at the Austrian GP has widened the gaps in our results when looking at both metrics. A short and fast race like the one in Spielberg tends to produce smaller differences between teammates, often underestimating the qualifying session deltas. In this case, it’s important to mention that the results shown below—using seconds as the metric of interest—won’t be as representative for the upcoming races until we reach the 2025 Belgian GP. Still, they are quite similar to the percent-based version, with just a few small differences.

Looking at the median delta, the smallest gap is at Haas—a tiny 0.007 seconds in Bearman’s favor—while the smallest mean gap is still at Sauber, with Hulkenberg leading Bortoleto by an average of 0.061 seconds.

The biggest gap remains at Red Bull. As I mentioned, the delta between Max and Yuki has decreased considerably, but this is partly due to the slightly unrepresentative result in Austria, given the short track length. Nevertheless, the result is still valid when viewed in this context. At the moment, Max Verstappen is ahead of Yuki Tsunoda by a median of 0.587 seconds and a mean of 0.686 seconds.

Qualifying stage appearances

This chart was exported in very high resolution. If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the 11th race of the 2025 season, most teammate pairings have been fairly evenly matched, with just a few notable exceptions. The first remains Aston Martin, where Alonso has reached Q3 five times, while his teammate, Lance Stroll, has managed it only twice. The second is, unsurprisingly, Red Bull Racing. So far, Max Verstappen has reached Q3 in all 13 possible sessions, while Lawson has been eliminated in Q1 three times. Meanwhile, Tsunoda’s rate of Q3 appearances is now at its lowest point this season, with just three Q3 appearances out of 10 outings for the team—putting his rate at 30%.

2025 Austrian GP: Race pace

Sun, 29 Jun 2025 00:00:00 +0000

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Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Austrian GP: Quali session

Sat, 28 Jun 2025 00:00:00 +0000

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Quali session classification

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Austrian GP: FP2 session

Fri, 27 Jun 2025 00:00:00 +0000

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Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:14.500. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

This is a fun little chart that I came up with after just a bit of thinking. It shows all the laps shown in the previous 3 charts, but in a single chart. I think of this as a lap cloud—if that’s even a thing. In any case, I like the way it looks. This chart takes a little bit of time to generate, so I may consider removing it in the near future unless I get any feedback saying that you like it or wnat me to keep it.

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 10)

Tue, 17 Jun 2025 00:00:00 +0000

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Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

The results can change dramatically by switching between the mean and median as the key metric of interest. These numbers will eventually stabilize and converge as the season goes on.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates remains at Red Bull Racing. Tsunoda finished in 11th place in the Canadian GP qualifying session, trailing Max Verstappen by close to half a second (0.464 seconds). Verstappen is beating Yuki Tsunoda by 0.828%, which is still slightly smaller than the median delta left by Lawson at 0.881%.

If we consider the mean symmetric percent difference instead, the largest gap shifts to Verstappen vs. Lawson, with Max ahead by an average of 1.034%. Yuki continues to reduce this number in recent weeks, but not at a significant rate. The delta to Max currently stands at 0.887%.

At the other end of the spectrum, the smallest delta has now fully shifted from McLaren to Sauber. Hulkenberg leads Bortoleto by just 0.051% based on the median, or 0.115% based on the mean.

Meanwhile, Franco Colapinto managed to beat his teammate, Pierre Gasly. Due to the small sample size, he has managed to reduced the delta from 0.419% to 0.382% based on the median, and from 0.533% to 0.25% based on the mean.

I don’t know if anyone is even reading this, so if you are, please send me a message. If nobody is reading this then why am I wasting my time writing it?

Delta in seconds

As a new addition, I’ve included an analysis using seconds instead of the symmetric percent difference. I’m still firmly in the “symmetric percent difference is more representative” camp, but the difference between the two metrics is smaller than most people think—and time in seconds is a lot easier to interpret for most people.

The results are very similar to the percent-based version, with just a few minor changes. Looking at the median delta, the smallest gap is found at Sauber—just 0.041 seconds in Hulkenberg’s favour—while the smallest mean gap has now shifted to Sauber as well, with Hulkenberg ahead of Bortoleto by an average of 0.107 seconds.

The biggest gap remains at Red Bull. Currently, Max Verstappen is ahead of Yuki Tsunoda by a median of 0.663 seconds—down from 0.739 seconds—and a mean of 0.739 seconds (down from the previous gap of 0.778 s). This gap has reduced in recent weeks, but at this rate, it is becoming increasingly unlikely that he’ll meet the three-tenths target set by Helmut Marko.

Qualifying stage appearances

This chart was exported in very high resolution. If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the 10th race of the 2025 season, most of the pairings have been fairly evenly matched, with just a few exceptions. The first is at Aston Martin, where Alonso has reached Q3 five times, while his teammate, Lance Stroll, has only managed it twice. The second is, you guessed it, Red Bull Racing. So far, Max Verstappen has reached Q3 in all 12 possible sessions, while Lawson has been eliminated in Q1 three times. Meanwhile, Tsunoda’s rate of Q3 appearances continues to drop — he’s only made it to the final session in three of his nine outings with the team, putting his rate at 33.3%.

2025 Canadian GP: Race pace

Mon, 16 Jun 2025 00:00:00 +0000

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Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops. For this race, the safety car forced drivers to go through the pit lane at the end of the race, which means that the number of stops isn’t properly calculated.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Canadian GP: Quali session

Sun, 15 Jun 2025 00:00:00 +0000

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Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

Quali session classification

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Canadian GP: FP2 session

Sat, 14 Jun 2025 00:00:00 +0000

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Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:20.500. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 9)

Tue, 03 Jun 2025 00:00:00 +0000

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I’m launching the F1pace supporters program and giving away 5 free one-month memberships. If you want your name or handle featured in my F1 charts, this is your chance. These charts go out to thousands of fans around the world every race weekend.

Use the Get in touch link to send your email and the name or handle you’d like shown. Thanks for the support.

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

The results can change dramatically by switching between the mean and median as the key metric of interest. These numbers will eventually stabilize and converge as the season goes on.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates remains—and likely will continue to remain for the rest of the season—at Red Bull Racing. Tsunoda finished 20th in the Spanish GP qualifying session, trailing Max Verstappen by over half a second despite Max only doing a single qualifying run. Verstappen is beating Yuki Tsunoda by 0.854%, which is still slightly smaller than the median delta left by Lawson at 0.881%.

If we consider the mean symmetric percent difference instead, the largest gap shifts to Verstappen vs. Lawson, with Max ahead by an average of 1.034%. Yuki has been gradually reducing this number in recent weeks, but only marginally, as it currently stands at a sizable 0.921%.

At the other end of the spectrum, the smallest delta has now shifted from McLaren to Sauber. Hulkenberg leads Bortoleto by just 0.043% based on the median, or 0.117% based on the mean.

Meanwhile, Franco Colapinto has continued to struggle after three sessions with the team so far and has yet to make a significant impression. He did, however, suffer a mechanical failure at the Spanish GP, which was completely out of his control. His current gap to Gasly stands at 0.419% based on the median, or 0.533% based on the mean.

Delta in seconds

The results are very similar to the percent-based version, with just a few minor changes. Looking at the median delta, the smallest gap is found at Sauber—just 0.031 seconds in Hulkenberg’s favour—while the smallest mean gap is at McLaren, with Piastri ahead of Norris by an average of 0.103 seconds.

The biggest gap remains at Red Bull. Currently, Max Verstappen is ahead of Yuki Tsunoda by a median of 0.739 seconds and a mean of 0.778 seconds. Although this gap has reduced in recent weeks, at this rate, it likely won’t be enough to meet the three-tenths target set by Helmut Marko.

Qualifying stage appearances

This chart was exported in very high resolution. If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the 9th race of the 2025 season, one of the biggest gaps in qualifying performance can be seen at Alpine. Pierre Gasly has reached Q3 five times, while his first teammate, Jack Doohan, never managed to make it past Q2. His second teammate, Franco Colapinto, was eliminated in Q2 in his first qualifying session, finished 15th at the Monaco GP, and 19th at the Spanish GP (albeit with an unfortunate mechanical failure).

At Red Bull Racing, we’ve seen Max Verstappen reach Q3 in all 11 possible sessions, while Lawson was eliminated in Q1 three times. Meanwhile, Tsunoda now has a Q3 rate of only 37.5%, managing to qualify for the final session in three of his eight appearances with the team.

2025 Spanish GP: Race pace

Sun, 01 Jun 2025 00:00:00 +0000

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To launch the F1pace supporters program, I’m giving away 5 free one-month memberships. If you want your name or handle featured in my F1 charts, this is your chance. These charts go out to thousands of fans around the world every race weekend.

Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Spanish GP: Quali session

Sat, 31 May 2025 00:00:00 +0000

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Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Spanish GP: FP2 session

Fri, 30 May 2025 00:00:00 +0000

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The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:24.500. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

This is a fun little chart that I came up with just a few hours ago. It shows all the laps shown in the previous 3 charts, but in a single chart. I think of this as a lap cloud—if that’s even a thing. In any case, I like the way it looks.

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 8)

Mon, 26 May 2025 00:00:00 +0000

Is this site worth at least $1 to you?

Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

The results can change dramatically by switching between the mean and median as the key metric of interest. These numbers will eventually stabilize and converge as the season goes on.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates continues to be at Red Bull Racing. Tsunoda had another difficult session at the Monaco GP, failing to reach Q3 and trailing Max Verstappen by over half a second. Verstappen is beating Yuki Tsunoda by 0.911%, which remains larger than the median delta left by Lawson, at 0.881%.

If we consider the mean symmetric percent difference instead, the largest gap shifts to Verstappen vs Lawson, with Max ahead by an average of 1.034%. Yuki has managed to decrease this number in recent weeks, but it remains a sizable 0.941%.

At the other end of the spectrum, the smallest delta continues to be at McLaren, now at a tiny 0.013% based on the median, or 0.101% based on the mean. In surprising fashion, the battle between Nico Hulkenberg and Gabriel Bortoleto has started to heat up. Nico is still ahead, but just by 0.051% based on the median, and by 0.149% based on the mean.

Franco Colapinto has only had two sessions with the team so far and has yet to make a significant impression. His gap to Gasly stands at 0.627%, but he may need a bit more time to settle in.

Delta in seconds

The results are very similar to the percent-based version, with just a few minor changes. Looking at the median delta, the smallest gap is still found at McLaren—just 0.012 seconds based on the median, and 0.093 seconds based on the mean.

The biggest gap remains at Red Bull. Currently, Max Verstappen is ahead of Yuki Tsunoda by a median of 0.809 seconds, and a mean of 0.81 seconds. It’s becoming increasingly hard to see how Tsunoda could close the gap to meet the three-tenths target set by Helmut Marko.

Qualifying stage appearances

This chart was exported in very high resolution. If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the eighth race of the 2025 season, one of the biggest gaps in qualifying performance can be seen at Alpine. Pierre Gasly has reached Q3 four times, while his first teammate, Jack Doohan, never managed to make it past Q2. His second teammate, Franco Colapinto, was eliminated in Q2 in his first qualifying session and finished 15th at the Monaco GP.

At Red Bull Racing, we’ve seen Max Verstappen reach Q3 in all ten possible sessions, while Lawson was eliminated in Q1 three times. Meanwhile, Tsunoda now has a Q3 rate of less than 50%, only managing to qualify for the final session in three of his seven appearances with the team.

2025 Monaco GP: Race pace

Mon, 26 May 2025 00:00:00 +0000

F1pace giveaway

Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Monaco GP: Quali session

Sat, 24 May 2025 00:00:00 +0000

F1pace giveaway

Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Monaco GP: FP2 session

Fri, 23 May 2025 00:00:00 +0000

F1pace giveaway

Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:20.500. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 7)

Tue, 20 May 2025 00:00:00 +0000

F1pace giveaway

Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

The results can change dramatically by switching between the mean and median as the key metric of interest. These numbers will eventually stabilize and converge as the season goes on.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates continues to be at Red Bull Racing. Tsunoda crashed his RB21 before setting a lap at the Emilia Romagna GP, so the delta from the previous week remains unchanged. Currently, Max Verstappen is beating Yuki Tsunoda by 0.968%. That’s now a larger difference than the one between Max and his previous teammate, Liam Lawson.

If we consider the mean symmetric percent difference instead, the largest gap shifts to Verstappen vs Lawson, with Max ahead by an average of 1.034%. I originally assumed that comparison would remain the largest all season, but now I’m not so sure. Verstappen is currently outpacing Tsunoda by an average of 0.977%, which isn’t far off the gap to Lawson.

At the other end of the spectrum, the smallest delta continues to be at McLaren—just 0.037% based on the median, or 0.059% based on the mean. The battle at Williams is now becoming closer as well, with Albon outpacing Sainz by only 0.046% based on the median, and just 0.098% based on the mean delta.

Overall, most teammate pairings have been fairly competitive so far, with a few exceptions. Russell continues to maintain a strong gap over Antonelli, Hadjar is still outpacing Lawson, and Gasly finished his run against Doohan with a median delta of 0.51%. Finally, Verstappen vs Lawson and Verstappen vs Tsunoda remain the two largest gaps in qualifying so far this season.

Delta in seconds

The results are pretty similar to the percent-based version, with just a few small changes. Looking at the median delta, then the results are very similar as the percent-based ones, with the smallest gap found at McLaren, at just 0.032 seconds based on the median delta, and 0.062 seconds based on the mean delta.

The biggest remain at Red Bull. Right now, Max Verstappen is ahead of Yuki Tsunoda by a median of 0.88 seconds—just over half a tenth more than the gap between Max and Lawson, and by a mean of 0.864 seconds.

Qualifying stage appearances

This chart was exported in very high resolution. If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the seventh race of the 2025 season, one of the biggest gaps in qualifying performance can be seen at Alpine. Pierre Gasly has reached Q3 four times, while his first teammate, Jack Doohan, never managed to make it past Q2. His second teammate, Franco Colapinto, was eliminated in Q2 in his first qualifying session. A similar pattern is emerging at Red Bull Racing: Max Verstappen has reached Q3 in all nine sessions, while Lawson has been eliminated in Q1 three times, and Tsunoda has only managed to qualify for Q3 in three of his six appearances.

2025 Emilia Romagna GP: Race pace

Sun, 18 May 2025 00:00:00 +0000

F1pace giveaway

Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing so inaccuracies are possible.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Laps in traffic

It’s important to note that the % of Time Spent in Traffic chart doesn’t include laps completed under safety car (SC) or virtual safety car (VSC) conditions.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Emilia Romagna GP: Quali session

Sat, 17 May 2025 00:00:00 +0000

F1pace giveaway

Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Emilia Romagna GP: FP2 session

Fri, 16 May 2025 00:00:00 +0000

F1pace giveaway

Use the Get in touch link to send your email and the name or handle you’d like shown. I’ll randomly pick 5 people. Thanks for the support.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:44.000. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

The F1pace supporters program

Mon, 12 May 2025 00:00:00 +0000

I rarely do personal posts in this blog. The philosophy of this site has always been going straight to the point, showing the analyses that you want to see. Still, I think it’s important to remind my viewers that this blog is in fact ran by a person (me!).

If you just want to support the site right now, you can join the F1pace supporters program here.
If you’d like to learn more first, just keep reading for all the details

Beginnings

I started this project in 2019, not knowing much about coding or statistics. I have the fundamentals, I am an engineer, and I have a master’s degree, but I was working in the biological sciences field and starting a project from zero is never easy. Things were difficult, and for a time, I had to work as a data entry operator, working during the COVID-19 era, processing requisitions of people who were getting tested for the disease. Still, I’ve continued to maintain and increase the scope of this blog. During these 6+ years, this project has gone from getting a few views every day to dozens of thousands coming to check my charts and articles every week. Just this past month, I’ve had visitors coming from over 100 different countries. I never thought this would be possible, but it is now a reality. Having said that, I need your support to keep this project going.

My support

I’ve been able to keep this project going mostly due to my girlfriend’s support. I struggled for a long time to get a proper job. After the data entry job, I was unemployed for a full year. I applied to hundreds of positions, but I rarely got a call. During this time, my girlfriend never stopped supporting me, always encouraging me and allowing me to do my own thing. Right now, she’s still the reason this project continues. While I’m making a bit more money now, working as a freelancer statistical analyst in the medical field, my work hours and therefore my pay are quite unstable. Believe it or not, hosting this site requires a substantial amount of money and time.

The issues

I would like to say that this website is profitable, or sustainable in any way, but generally speaking, it isn’t. Last year, I was extremely lucky to get an important project related to F1, which paid enough to cover the server costs for the entire year and more. I’m extremely grateful to this client for giving me the opportunity. Unfortunately, while this project was amazing, I do not know if another project will come up in the future. So is the life of the independent content creator.

As you may have noticed, I do not run ads on this site. I’m against the current trend of bombarding users with advertising left and right while destroying their experience when they’re visiting a site. Many sites have more ads than actual content and I don’t like it. In the past few weeks, however, I’ve considered joining an ad program to try to get some revenue from this blog. If I do it, it would be as minimal as possible, trying not to impact the experience that you have while browsing this site. However, the reality is that I would like to avoid this. I’m just not a fan of having ads on this site. I’m proud of how it looks and the quality of the content that I produce, and I would love to keep it this way.

My proposal

For a while, I’ve had a Buy Me a Coffee account where users can become members and donate a bit of money to support me. My main problem has always been that I haven’t been able to give my supporters something back. Recently, I came up with the idea of acknowledging them directly in my charts. I realized that it is important to show the world that this project has wonderful supporters and that they deserve recognition for their support. It is because of this that I am creating the F1pace supporters program.

The F1pace supporters program is very simple. If you become a supporter of this site on my Buy Me a Coffee, your name or handle will be featured in my charts. As I mentioned, this site is seen by thousands of people from around the world. This would be an opportunity to give you global recognition for supporting me and the F1pace project.

How will the program work

Wait a minute, how are you putting all the names on the charts? That is an excellent question. As you may have noticed, there isn’t a lot of additional space on my charts. Because of this, I’ve decided to make it fair and randomly draw from my list of supporters to get a name/handle on each chart. The process would work like this:

You sign up for a membership on my Buy Me a Coffee page.
You will be added to a database with a list of my supporters. Initially, this will be done manually, but if the program is successful, then I will automate this process.
When creating an article, I will randomly draw a name/handle from this database for each chart that I create. This means that you will have a chance of being featured on every chart that I post.
- As a reference, I publish between 20 and 30 charts every weekend. At times, I’ve published more, and if this program is successful, then I would like to publish more of them to give you even more content.
- If you become a supporter, you will have ample opportunities to be featured in my charts.

It is that simple. To make things a little bit more fun, I came up with fun, F1-themed phrases related to Formula 1, which will be a part of my charts for the future. Your name/handle will be featured as a part of these phrases. Currently, I have a list of over 200+ phrases, which will also be randomly selected for each chart. You can look forward to seeing your user/handle and a random phase related to F1 each weekend. Take a look at the following examples to give yourselves an idea of what things will look like.

Examples

Frequently asked questions (FAQ)

How much will it cost to become a member and what are the benefits?

The program will consist of 3 tiers, with each tier having specific benefits. I am based in Canada and take donations in Canadian Dollars. 1 CAD equals around 0.7 USD.

Tier 1: Performance Analyst.
- Cost: $1 CAD per month.
- Benefits:
  - 1× chance of being featured in any chart.
Tier 2: Data Engineer.
- Cost: $2 CAD per month.
- Benefits:
  - 1.5× chance of being featured in any chart.
  - +0.5× extra chance to appear in the main FP2 race simulation charts.
Tier 3: Race Strategist.
- Cost: $3 CAD per month.
- Benefits:
  - 2× chance of being featured in any chart.
  - +0.5× extra chance to appear in the main race pace charts.

Tier	Cost (CAD/month)	Benefits
Performance Analyst	$1	1× chance of being featured in any chart
Data Engineer	$2	1.5× chance in any chart +0.5× extra in FP2 race simulation charts
Race Strategist	$3	2× chance in any chart +0.5× extra in race pace charts

How do I join?

You can go to my Buy Me a Coffee page and follow the steps that you see on the screen. Additionally, you can click on the “Support my work” buttons at the top and bottom of my articles, which will take you to the same page.

What names are used in the charts and how can I choose the name to be displayed?

You can comment on my Buy Me a Coffee page, or you can use the Get in touch submission form and tell me which name you used to sign up for your membership, and which name do you want for it to be displayed. It can be a name, a handle from Twitch or Reddit, or maybe even the name of your pet. The only requirements are as follows:

Your name or handle must have 18 characters or less, mostly due to space constraints. My recommendation would be to submit a name of maximum 15 characters, but I will allow 18 characters in case you already have a long handle or username that you want to keep.
Your name or handle should consist only of alphanumerical characters. The font that I use doesn’t allow for emojis or similar, so only text and numbers will be allowed.
The name should not have any content that can be considered offensive or discriminatory. Keep it clean.
No spammy formatting. Basically nothing like “FREE-MONEY-NOW” or similar.
Self promotion will be judged on a case by case basis. You can use your Twitch, TikTok, Instagram, etc, handle, but not in a way that promotes clicking on a link or URL.

I reserve the right to not display a name if it doesn’t fit the mentioned policies. If I do not agree with your name submission, I will contact you and ask you for an alternative name.

Can I use my business or brand name to be featured in the charts?

If you’re interested in supporting my work through a sponsorship or similar collaboration, feel free to reach out via the Get in touch form so we can discuss options. The F1pace supporters program is primarily designed to acknowledge individual contributors, but I’m open to business or brand name submissions, as long as they fit the tone and style of the project.

Keep in mind:

Names must follow the same formatting and character limits (18 characters or less, no emojis or special characters).
Obvious ad-style formats or clickbait (e.g. “BUY-STUFF-NOW”) won’t be accepted.
User handles (e.g. Twitch, TikTok, Instagram usernames) are fine, as long as they don’t look like clickable URLs or contain aggressive promotion.

If you’re interested in a more visible or formal sponsorship (e.g. displaying your logo), feel free to reach out directly using the Get in touch submission form. I’m open to creative collaborations that still respect the spirit of the project, but they will be evaluated on a case-by-case basis.

Where will my name appear?

Your name will appear as a part of a phrase in the bottom left corner of a chart. Wider charts, such as the FP2 and race pace charts, will contain longer phrases that won’t show up on narrower charts.

How are supporters selected to be featured?

The selection process will be random, based on the chances described in each supporter tier. I will not have any input on which member is selected for each chart. You name can show up on 3 or 4 charts in a single post, or perhaps not show up in a specific article. Last year I posted over 100 articles, each with multiple charts, so you will have ample chances of getting featured multiple times during a season.

Can I choose which chart I appear in?

The selection process will be fully random. I have created a function which randomly selects a name from a database, so it will be up to the probability gods to decide in which chart you are featured. Having said that, signing up for a higher tier—tier 2 or tier 3—will give you more chances of being featured on either the main FP2 race simulation pace (tier 2) or main race pace (tier 3) charts. These charts are the most viewed based on my analytics.

For how long will I be a part of the draw to be featured?

Your name or handle will have a chance of being featured for as long as you are an active member. If you cancel your membership, your name will continue to be featured on the charts that have already been posted, but you won’t get a chance to be featured in the latest charts.

When are the charts published?

I usually publish charts around race weekends. You can expect FP2 charts on Fridays, qualifying charts on Saturdays, and race charts on Sundays or Mondays. I additionally produce some midweek charts throughout the season. If I get more support, I will be able to dedicate more time to this project and create even more articles during the week.

Will I be notified if my name is featured?

I currently don’t have automated notifications, but you check the latest articles around race weekends to spot your name.

I’ve donated in the past—will I be featured?

Yes! I really appreciate the support I’ve received over the years. I’ll be reaching out to past donors and adding names to the supporter list based on the total amount contributed. If you’ve supported the project before, you’ll still have a chance to be featured in the charts.

Is my payment secure?

All payments are processed securely through Buy Me a Coffee. I never see your card or payment info.

Can I change or cancel my tier?

Sure. Memberships are renewed on a monthly basis. If you want to upgrade your membership, or cancel it, you can do so at any time.

What happens if I don’t get featured one weekend?

Do not worry if your name doesn’t show up immediately. The process is random, which makes it very fair. There is a chance that your name won’t show up on a specific article, but then it may show up on multiple charts of a different post.

Can I opt out of being featured in a chart?

Of course. I came up with this idea to give my supporters public recognition, but I understand that some of you may have privacy concerns. If you don’t want your name to be included, I would recommend you to submit the name of a pet or similar since this way you’ll get something tangible in return for your suscription. If you still don’t want to submit a name, but still want to become a member, just let me know and I will not add your name or handle to the charts (but I will still be very grateful for your support).

Can I suggest a new phrase for the charts?

For sure. I already have a database with over 200 different phrases, but I’m sure there are many more that could be featured. If you want to submit a phrase, just use the Get in touch submission form.

I have a question that is not covered in this FAQ

Contact me using the Get in touch submission form or by clicking on the envelope icon at the top of the page to send me an email.

Thank you!

Thank you for reading, for your support, and for being part of the F1pace community. If you’d like to help keep this project running, consider joining the F1pace Supporters Program!.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 6)

Tue, 06 May 2025 00:00:00 +0000

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates

We’re already one quarter into the season. With 6 races, and 2 sprints, we now have more representative results. Just as a reminder, this season, we have the anomaly of Lawson switching places with Tsunoda after just three races, which makes our usual analysis a little trickier. Normally, I’d just use the median as the key metric of interest since it’s more robust to outliers. However, in this case, the median might skew the average delta between Lawson and Verstappen.

The results can change dramatically by switching between the mean and median as the key metric of interest. These numbers will eventually stabilize and converge as the season goes on.

Symmetric percent difference

Looking at the median symmetric percent difference, the biggest gap between teammates is at Red Bull Racing. Right now, Max Verstappen is beating Yuki Tsunoda by 0.968%. That’s now a larger difference than the one between Max and his previous teammate, Liam Lawson.

If we look at the mean symmetric percent difference instead, the largest gap shifts to Verstappen vs Lawson, with Max ahead by an average of 1.034%. I originally assumed that comparison would stay the biggest all season, but now I’m not so sure. Verstappen is currently outpacing Tsunoda by an average of 0.977%, which isn’t far off the gap to Lawson.

At the other end of the spectrum, the smallest delta is at McLaren—just 0.013% based on the median, or 0.109% based on the mean.

Overall, most teammate pairings have been fairly competitive so far, with a few exceptions—like Russell vs Antonelli, Hadjar vs Lawson, Gasly vs Doohan, and of course, Verstappen vs Lawson and Verstappen vs Tsunoda.

Delta in seconds

The results are pretty similar to the percent-based version, with just a few small changes. Looking at the median delta, then the results are very similar as the percent-based ones, with the smallest gap found at McLaren, at just 0.012 seconds. If we, instead, look at the mean delta in seconds, the smallest gap is at Williams, with Alex Albon leading Carlos Sainz by just 0.1 seconds on average.

The biggest deltas are again at Red Bull. Right now, Max Verstappen is ahead of Yuki Tsunoda by a median of 0.88 seconds—just over half a tenth more than the gap between Max and Lawson, and by a mean of 0.864 seconds.

Qualifying stage appearances

This chart was exported in very high resolution. If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the sixth race of the 2025 season, the biggest gap in qualifying performance is still at Alpine. Pierre Gasly has reached Q3 three times, while Jack Doohan hasn’t made it past Q2 yet.

Most of the other teammate battles have been pretty even so far. Oliver Bearman continues to hold his own against Esteban Ocon—a driver known for his strong one-lap pace. Matching Ocon in qualifying is no easy feat, which makes Bearman’s performance all the more impressive.

Privacy policy

Tue, 06 May 2025 00:00:00 +0000

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4. Your Rights and Choices

You can choose to disable cookies through your browser settings. However, this may affect how you interact with the website.

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F1pace.com may contain links to external websites. We are not responsible for the content or privacy practices of those sites.

6. Changes to This Policy

We may update this Privacy Policy from time to time. Any changes will be posted on this page with an updated effective date.

7. Contact Us

If you have any questions or concerns about this policy, feel free to contact us at: [email protected]" >[email protected]

2025 Miami GP: Race pace

Sun, 04 May 2025 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

Experimental feature: Added traffic data

Drivers are flagged as “in traffic” if they spend more than 33% of the lap (~1/3) within 2 seconds of any driver ahead (including backmarkers).

Still in testing — inaccuracies possible.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

This is a fun little chart that I started using for FP2. It shows all the laps from the race—as long as they were under green flag/yellow flag conditions—but in a single chart. I think of this as a lap cloud—if that’s even a thing. In any case, I like the way it looks. Make sure to zoom in to see the laps with more detail.

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Miami GP: Quali session

Sat, 03 May 2025 00:00:00 +0000

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Miami GP: Sprint

Sat, 03 May 2025 00:00:00 +0000

We have interactivity! The plot that shows the sprint laps of all of the drivers is interactive. Check it out below.

Sprint race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

For this race, some of the data, including tire selection, is wrong because the API published the wrong info.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Sprint race laps… all of them

Summarized sprint race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race pace delta

You can read the chart like this:

Choose a driver’s short name from the top or bottom of the plot (x-axis)
Choose a second driver from the left of the plot (y-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Miami GP: Sprint qualifying

Fri, 02 May 2025 00:00:00 +0000

Sprint quali session deltas

The sprint quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Hamilton on Q3 vs Verstappen on Q3. The delta is of -0.018 seconds. This means that Hamiltons’s best lap was 0.018 seconds faster than Verstappen’s.

The charts finally considers laps that have been deleted and therefore are not shown here =)

Tale of the session

A tire was considered to be new new only if it had a life of only 1 lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for the flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Sprint quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Sprint quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Sprint quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 F1 Season: Qualifying delta between teammates (rounds 1 - 5)

Tue, 22 Apr 2025 00:00:00 +0000

Analysis

Click to expand methodology

Methodology!

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

Quali delta between teammates (symmetric percent difference)

The results can change dramatically by switching between the mean and median as the key metric of interest. These numbers will eventually stabilize and converge as the season goes on.

After five races (and one sprint), the quali deltas have started to stabilize. This season, we have the anomaly of Lawson switching places with Tsunoda after just three races, which makes our usual analysis a little trickier. Normally, I’d just use the median as the key metric of interest since it’s more robust to outliers. However, in this case, the median might skew the average delta between Lawson and Verstappen.

Regardless of which metric we look at, the largest gap between teammates is found at Red Bull Racing. If we use the median symmetric percent difference, Max Verstappen is currently beating Yuki Tsunoda by a whopping 0.968%. If we go with the mean symmetric percent difference, then the largest gap shifts to Verstappen outpacing Liam Lawson by an average of 1.034%.

These numbers are pretty significant. For reference, in 2024, Verstappen outpaced Sergio Perez by a median of 0.543%, while the largest gap that season—at Sauber—was 0.602%. Right now, Verstappen is outpacing his teammates by around 80% more than that.

At the other end of the spectrum, the smallest delta is found either at McLaren (0.065% based on the median symmetric percent difference) or Haas (0.113% based on the mean symmetric percent difference).

It’s important to note that most teammate pairings have been fairly competitive so far, with a few exceptions—including Russell vs Antonelli, Gasly vs Doohan, and, of course, Verstappen vs Lawson and Verstappen vs Tsunoda.

Qualifying stage appearances

This chart was exported in very high resolution. If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Click to expand explanation

As of the fifth race of the 2025 season, the biggest difference in qualifying stage appearances is at Alpine. While Pierre Gasly has reached Q3 three times, Jack Doohan has yet to make it past Q2.

Another interesting contrast is at Aston Martin. Fernando Alonso has consistently made it to Q2 in every session he’s taken part in, while Lance Stroll has been knocked out in Q1 three times. That said, Stroll is also the only Aston Martin driver to make it to Q3 this season.

Most of the remaining teammate battles have been fairly even so far. One standout is Oliver Bearman, who’s been holding his own against Esteban Ocon—a driver known for his strong one-lap pace. Matching Ocon in qualifying is no easy task, which makes Bearman’s performance all the more impressive.

2025 Saudi Arabian GP: Race pace

Mon, 21 Apr 2025 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Saudi Arabian GP: Quali session

Sun, 20 Apr 2025 00:00:00 +0000

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Bahrain GP: Race pace

Sun, 13 Apr 2025 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Bahrain GP: Quali session

Sat, 12 Apr 2025 00:00:00 +0000

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Bahrain GP: FP2 session

Fri, 11 Apr 2025 00:00:00 +0000

We have interactivity! The plot that shows the FP2 laps of all of the drivers is interactive. Check it out below.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:44.000. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2025 Japanese GP: Race pace

Sun, 06 Apr 2025 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Japanese GP: FP3 session

Sat, 05 Apr 2025 00:00:00 +0000

We have interactivity! The plot that shows the FP3 laps of all of the drivers is interactive. Check it out below.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 4 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:38.000. Some drivers had laps above that threshold but I am not displaying them here.

This data is pretty unreliable. The teams haven’t been able to properly gather data during the practice sessions so their performance is unknown.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP3 race simulation laps… all of them

FP3 best sector times

I’ve decided to add some additional information about the FP3 section to this article. The two charts that I’m showing have very similar information about the best sector times of the session. The difference between them is as follows:

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP3 best lap times

FP3 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2025 Chinese GP: Race pace

Sun, 23 Mar 2025 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The chart shows every time a driver went through the pit lane, even if they didn’t change tires.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Chinese GP: Quali session

Sat, 22 Mar 2025 00:00:00 +0000

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

A tire was considered to be new new only if it had a life of only one lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for a flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Chinese GP: Sprint

Sat, 22 Mar 2025 00:00:00 +0000

We have interactivity! The plot that shows the sprint laps of all of the drivers is interactive. Check it out below.

Sprint race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Sprint race laps… all of them

Summarized sprint race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race pace delta

You can read the chart like this:

Choose a driver’s short name from the top or bottom of the plot (x-axis)
Choose a second driver from the left of the plot (y-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Chinese GP: Sprint qualifying

Fri, 21 Mar 2025 00:00:00 +0000

Sprint quali session deltas

The sprint quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Hamilton on Q3 vs Verstappen on Q3. The delta is of -0.018 seconds. This means that Hamiltons’s best lap was 0.018 seconds faster than Verstappen’s.

The charts finally considers laps that have been deleted and therefore are not shown here =)

Tale of the session

A tire was considered to be new new only if it had a life of only 1 lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for the flying lap.

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Sprint quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Sprint quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Sprint quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Australian GP: Race pace

Sun, 16 Mar 2025 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The chart shows every time a driver went through the pit lane, even if they didn’t change tires.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

The chart includes drivers that retired during the race.

2025 Australian GP: Quali session

Sat, 15 Mar 2025 00:00:00 +0000

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Hamilton on Q3. The delta is of -0.92 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Hamilton’s.

Tale of the session

Note

A tire was considered to be new new only if it had a life of only 1 lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for the flying lap.

Extra note

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2025 Australian GP: FP2 session

Fri, 14 Mar 2025 00:00:00 +0000

We have interactivity! The plot that shows the FP2 laps of all of the drivers is interactive. Check it out below.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:26.000. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2024 F1 Season: Simulations and the daunting task of matching Max Verstappen

Sun, 26 Jan 2025 00:00:00 +0000

One of the big talking points during the 2024 season was Red Bull’s second seat. Let’s be honest—Sergio Perez had a rough year, and eventually, he was replaced by rookie(ish) Liam Lawson. But let’s not sugarcoat it—driving alongside Max Verstappen is one of the toughest gigs in Formula 1. So, what exactly would qualify as a “good” season for Liam?

Dr. Helmut Marko has gone on record saying they expect Lawson to stay within three-tenths of Max’s lap times. According to him, that would be good enough to bring in solid points for the constructors’ championship.

This got me thinking: would three-tenths really cut it? To find out, I ran a simulation of the 2024 season. The twist? I replaced Sergio Perez with a fictional version of Liam Lawson, tweaking his pace to reflect different deltas to Verstappen. We know Sergio’s actual results weren’t enough to keep him in the seat, but would staying within three-tenths have been enough to save “Liam’s” job? Let’s dive into the numbers.

Methodology

I’ll walk you through my methodology step by step. While the concept is simple enough, explaining it in detail can get a bit tricky, so bear with me.

Testing different deltas
I tested six different average deltas to Max Verstappen in qualifying, ranging from 0 seconds (perfect match to Max) to 0.5 seconds (5 tenths) slower. I tested these in 0.1-second intervals. For example, let’s start with the 0.3-second (3 tenths) simulation.
Replacing Sergio with a simulated Liam Lawson
I removed Sergio Perez from all the 2024 qualifying sessions and replaced him with a simulated “Liam Lawson.”
Assigning Liam’s delta
Fake Liam’s delta to Max in each of the 30 qualifying sessions was determined using a normal distribution. For the 0.3s scenario, the mean delta was 0.3 seconds, and the standard deviation was 0.1 seconds. This means most of Liam’s deltas were close to 0.3s but could vary—he might be 0.2s slower in one session, 0.4s in another, or even have more extreme outliers occasionally.
Adjusting the grid
Based on Liam’s simulated qualifying times, I recalculated all the qualifying positions for each race. The other drivers kept their original qualifying times; only Liam’s times were adjusted.
Simulating multiple scenarios
I repeated this simulation 1,000 times for each of the six delta scenarios, resulting in a total of six scenarios (from 0 to 0.5 seconds slower, in 0.1-second intervals), each simulated 1,000 times.
Calculating the results
After running all the simulations, I calculated summary statistics for each scenario to get an average picture of how things played out across all the races.

Summary of Simulated Scenarios

0 tenths (+0.0s): Mean delta of 0.0s, standard deviation of 0.1s (most deltas between -0.1s and +0.1s). Simulated 1,000 times.
1 tenth (+0.1s): Mean delta of 0.1s, standard deviation of 0.1s (most deltas between 0.0s and +0.2s). Simulated 1,000 times.
2 tenths (+0.2s): Mean delta of 0.2s, standard deviation of 0.1s (most deltas between +0.1s and +0.3s). Simulated 1,000 times.
3 tenths (+0.3s): Mean delta of 0.3s, standard deviation of 0.1s (most deltas between +0.2s and +0.4s). Simulated 1,000 times.
4 tenths (+0.4s): Mean delta of 0.4s, standard deviation of 0.1s (most deltas between +0.3s and +0.5s). Simulated 1,000 times.
5 tenths (+0.5s): Mean delta of 0.5s, standard deviation of 0.1s (most deltas between +0.4s and +0.6s). Simulated 1,000 times.

Below, you can check out the chart showing how the deltas for each race were distributed across the simulations.

I’m leaving a FAQ at the end of the article to try to answer some of the most common questions that you may have about how this analysis was done.

Just as an example of how these simulations looked, check out the chart that shows five random simulations from the 3 tenths average scenario. In all these simulations, the simulated lap times were, on average, 3 tenths slower than Max Verstappen’s, but you can clearly see how the variation in the data plays out.

This variation introduces a wide range of outcomes across the final season results. For instance, being 3 tenths slower on average would’ve cost our simulated Liam a Q1 elimination in Monaco across all five simulations shown. Interestingly, in simulation 758, Liam also had a Q1 elimination—something we don’t see in the rest of the simulations shown.

This kind of variability highlights how even small differences in lap times can have big effects on qualifying results throughout the season.

Impact on Q1, Q2 and Q3 appearances

Now that we know how the simulations were made, let’s dive into the results. How would these deltas (0 to 5 tenths) impact simulated Liam during the 2024 season?

The simulations reveal that with an average delta of 0 tenths to Max, Liam would’ve had 29 Q3 appearances and just one single Q2 elimination. This Q2 elimination was unavoidable since Max himself failed to qualify for Q3 at the São Paulo GP. Interestingly, in this scenario, Liam would’ve outperformed Max in pole positions, with a score of 8-6. This is significant because having a teammate as strong as Max could have potentially cost him the World Drivers’ Championship (WDC) title by splitting critical points.

As the average delta increases, the number of Q1 and Q2 eliminations also grows. At an average delta of 0.1 seconds, the simulations show two additional Q2 eliminations. At 0.2 seconds, we see one more Q2 elimination and the first Q1 elimination.

At the expected 0.3 tenths delta, the numbers fall below what you’d expect from a Red Bull driver. The simulations show that an average delta of 0.3 seconds would’ve resulted in six Q2 eliminations and one Q1 elimination for fake Liam. Meanwhile, Verstappen’s season would remain unchanged, with 29 Q3 appearances and 13 pole positions.

What if things were even worse? At an average delta of 0.4 seconds to Max, Liam would’ve failed to reach Q3 nine times, compared to just once for Verstappen. If we increase the delta to 0.5 seconds, the results become even more dramatic, with eight Q2 and four Q1 eliminations for Liam.

These results highlight how critical even small performance gaps can be when competing at the top level in Formula 1.

Impact on final position

Instead of focusing on quali session appearances, let’s look at the final qualifying positions, which better emphasize how increasing the average delta to Max negatively impacts performance.

Based on the simulations, the only scenarios where you would reasonably expect Liam to achieve a pole position would be with an average delta of 0 or at most 1 tenth to Max. While a higher delta of 2 or even 3 tenths could occasionally result in a pole position, these instances would be anomalies and not the norm.

For example, in the +0.2s simulations, Liam would have started on the front row an average of 7 times (23% of the time). At +0.3s—the benchmark according to Dr. Helmut Marko—Liam would have managed only 5 front-row starts (17% of the time), compared to Max’s 19 front-row starts (63% of the time). As the delta increases to +0.4s and +0.5s, the numbers worsen significantly for Liam.

You can see the full results in the chart, but in my opinion, these numbers wouldn’t relieve the pressure on Verstappen’s teammate. Red Bull expects a consistent and reliable second driver, and these results suggest that even with a 0.3s average delta, Liam’s performance might fall short of expectations.

Best and worse case scenarios

One of the main advantages of running simulations is the ability to examine the variability within each scenario. Previously, we looked at average positions and the number of Q3 or Q2 appearances across the simulations, but the individual simulations often tell a more nuanced story, especially when we focus on specific races where our simulated driver struggled.

Let’s dive into the critical +0.3s scenario. I selected the best simulation (simulation 296) and the worst simulation (simulation 166) in terms of median finishing position, and the differences are striking. In both cases, simulated Liam failed to qualify for Q3 on 8 occasions, but the outcomes within those missed opportunities varied significantly.

In the best-case simulation, Liam qualified in 2nd place four times and in 3rd place nine times, showing a strong performance in the races where he reached Q3. Conversely, in the worst-case simulation, Liam managed only three 2nd-place starts and four 3rd-place starts. These subtle shifts add up—qualifying around 4th place on average might be considered a good season, but qualifying around 7th would undoubtedly create pressure on Liam’s seat at Red Bull.

The variability becomes even more pronounced in the +0.4s scenario. In the best-case simulation (simulation 73), Liam was eliminated 5 times in Q2 and 4 times in Q1. In the worst-case simulation (simulation 15), those numbers jumped to 9 Q2 eliminations and 6 Q1 eliminations, resulting in a total of 15 failures to reach Q3.

This highlights an important point: while the average scenario gives us the most likely outcome, the extremes within each test group reveal how fragile performance perception can be. A few bad results in critical moments can significantly alter how a season is viewed.

This is something that arguably happened to Checo during the 2024 season. His 4 Q1 eliminations in 16th place and 2 Q2 eliminations in 11th place created a poor impression. Had he converted just 2 or 3 of those into more favorable positions, his qualifying stats would have looked much better—perhaps 4 Q1 eliminations instead of 7, and 3 Q2 eliminations instead of 5. While the overall result still might not have been stellar, it could have been enough to strengthen his case to retain his Red Bull seat.

The advantages of running simulations

We’ve seen the averages, best and worst outcomes, and Q1, Q2, and Q3 appearances derived from our simulations. However, those numbers aren’t set in stone. Simulations inherently carry variability, meaning that even in our +0.0s delta scenario—where Liam was expected to qualify for Q3 in 29 sessions—it didn’t happen in every simulation.

Take a look at the chart below. Out of our 1,000 +0.0s delta simulations, roughly 40% resulted in Liam qualifying for Q3 28 times instead of 29, and in around 15%, he only made it to Q3 in 27 sessions. This demonstrates the impact of small margins, where a fraction of a second here or there can make a significant difference, including eliminations in Q1 or Q2.

The bottom six charts explore this concept further by focusing on the expected qualifying positions. For example, in the +0.0s delta simulations, Liam averaged 8 pole positions. But as the charts show, this number isn’t definitive. In some simulations, Liam secured as many as 14 pole positions, while in our best-case simulation, he only managed 11.

This highlights how additional simulations could, in theory, reveal even more extreme outcomes—perhaps even Lawson dominating the 2024 season. However, remember that such results would be anomalies, not something you’d realistically expect based on the given parameters.

Conclusion

So there we have it—an analysis of what we could’ve expected from our hypothetical Liam Lawson in 2024 based on different parameters. The results were pretty varied. The +0.0s version of Liam not only matched but beat Max Verstappen, while the +0.3s version would’ve done better than Sergio Perez but still failed to qualify for Q3 on seven occasions.

If the 2024 season is any indication of what 2025 will look like, would three tenths be enough for Liam? Based on my simple analysis, I’d say no. Sure, Lawson would outperform 2024 Sergio, but he’d still be far off Max and rarely fighting for wins or pole positions. I think it’s fair to say most of us don’t expect Liam to match Verstappen, but I doubt fans would be happy with him failing to make Q3 seven times in a season.

I’d argue that the minimum required from Liam would be an average delta of +0.2s to Max. Even then, we might see a few Q2 eliminations (or even Q1 on a bad day), but it would also significantly increase the number of times Liam could fight for a spot on the front row. Is this achievable for him? I’m not entirely sure, but I think failing to hit this target would put him under a lot of pressure during the season.

Final remarks

I hope you enjoyed this article! This simulation-based analysis took me quite a while to put together, as I went back and forth on how to handle the data and decide which charts to include.

I’ve been working on this project for years now, and I always strive to deliver insightful and detailed analysis. I’m not a fan of lazy journalism or quick 10-minute takes that rarely provide accurate or meaningful insights. Hopefully, those of you who visit my site appreciate the quality and effort I put into my content.

I know it’s the off-season, but if you have a moment, I’d really appreciate it if you could share this article. And if you’re able to, consider supporting me financially by clicking the Buy Me a Coffee button—it would mean a lot!

FAQ

Did you take into consideration the length of the track before adding the delta to “fake Liam?”
1. No, the delta was the same regardless of the track. While it is true that sometimes you find bigger gaps on longer tracks, that isn’t always the case. Sometimes teammates are very even on long tracks like Spa, while at times they are separated by many tenths on short tracks like Spielberg or Monaco.
Since you didn’t correct for track length, is your analysis wrong?
1. Yes and no. All analyses are wrong to a certain extent. We do what we can with the data available and with the time at our disposal. Simulations are basically hypothetical scenarios, so even though I didn’t consider track length for this analysis, it doesn’t necessarily mean that the results aren’t valid. They’re not perfect, but they’re still valid.
Why did you do 1,000 simulations, why not 10,000?
1. Time. With our 1,000 simulations for each scenario, we ended up with 6,000 simulations. In total this created a dataframe of over 8 million rows of data. Doing 10,000 simulations per scenario would’ve meant a final total of 60,000 simulations and over 80 million rows of data.

2024 F1 Season: Quali performance and trends

Thu, 19 Dec 2024 00:00:00 +0000

One of the reasons I rarely write about qualifying is because, well, it’s harder to analyze than it looks. Sure, you can do a simple head-to-head comparison in a table, but getting meaningful insights takes a lot more work. In 2024, we had 30 qualifying sessions, including sprint qualifying (SQ) events — that’s a lot to keep track of! Still, I decided to take on the challenge this time, so I hope you’ll enjoy this piece.

I do my best to get accurate data, but mistakes can happen. Analyzing data from the entire season is never easy! I’m hoping nothing is wrong here, but if there is, then … oops?

Quali delta between teammates (symmetric percent difference)

The closest qualifying battle of the season came at Ferrari, where Charles Leclerc outpaced Carlos Sainz by just 0.008%. Alpine had the tightest head-to-head in terms of qualifying victories, with Gasly edging out Ocon 15-14.

RB F1 Team featured two of the next closest gaps: Tsunoda narrowly beat Lawson 5-4 with a median gap of 0.043%, while Ricciardo outqualified Lawson 13-8 with a median difference of 0.123%.

On the opposite end, Sauber saw the biggest performance gap, with Valtteri Bottas leading Zhou Guanyu 25-4, showing a median percent difference of 0.602%. Red Bull wasn’t far behind, with Verstappen defeating Perez 29-1, though with a slightly smaller gap of 0.543%.

Rolling median quali delta between teammates (symmetric percent difference)

Another interesting perspective is how the qualifying delta evolved over the season. To track this, I used a rolling median, meaning I calculated the performance gap incrementally after each race. This helps visualize whether a driver got faster or slower compared to their teammate as the season progressed.

In most cases, the driver who started ahead in the qualifying battle maintained the lead through the season. However, there were notable exceptions. At Alpine, Ocon led the head-to-head battle for most of the year but lost the lead in the final race. Ferrari’s Sainz started the season strong, but Leclerc bounced back and held the lead until the end.

At RB F1 Team, Tsunoda dominated Ricciardo early on, but the gap narrowed as the season progressed. Against Lawson, Yuki initially trailed but turned things around by season’s end. At Williams, Albon struggled in the first few races against rookie Colapinto but eventually found his pace and emerged as the stronger qualifier.

Qualifying Stage Appearances

This chart was exported in very high resolution. If the text appears a bit too small, feel free to zoom in to see each individual bubble more clearly.

Each bubble represents a driver’s qualifying or sprint qualifying appearance throughout the 2024 F1 season. The bubble’s size reflects the driver’s final qualifying position, with larger bubbles indicating better results. The actual position is displayed as a big number inside the bubble, and the colors indicate the qualifying session reached.

No driver managed to qualify for all Q3 sessions this season. Max Verstappen came the closest, missing Q3 just once at the São Paulo GP. Similarly, Lando Norris missed Q3 only once, getting knocked out in Q1 at the Azerbaijan GP.

Only Verstappen and Ferrari’s Carlos Sainz and Charles Leclerc managed to escape Q1 every time. On the flip side, Logan Sargeant was the only driver never to reach Q3, while Zhou Guanyu managed it only once, at his home sprint qualifying during the Chinese GP.

Qualifying positions and distribution

Let’s wrap things up by looking at overall qualifying positions and how they were distributed across drivers this season.

The density chart visualizes the distribution of a driver’s qualifying positions throughout the season. The chart shows how often a driver finished in each qualifying position, with thicker areas representing positions where they qualified more frequently. For example, Max Verstappen has a high density around 1st place, reflecting his multiple pole positions throughout the season. The chart also shows how often he qualified in positions like 2nd to 4th, with those areas having a slightly lower density.

Max Verstappen topped the qualifying leaderboard with a median qualifying position of 2nd place. Lando Norris followed closely with a median of 3rd, while Sainz, Russell, Piastri, and Leclerc all managed a median of 5th. However, Ferrari’s drivers had a slightly better worst position (14th) compared to Russell and Piastri (17th). Lewis Hamilton and Sergio Perez rounded out the top eight, with median positions of 7th and 8.5, respectively.

Yuki Tsunoda edged out his newly promoted teammate Liam Lawson with a median qualifying position of 11th versus 12th, though Lawson only completed part of the season. Among rookies, Franco Colapinto had a challenging year. He showed promise when he joined the team but struggled as the season progressed, finishing with a median of 16.5—just ahead of Logan Sargeant and Zhou Guanyu.

Final remarks

As with most analyses, the conclusion is clear: Max Verstappen emerged victorious in 2024. He had the best race pace, the best quali pace, and is undeniably the rightful champion.

The rest of the field also showed strong competition, with the battle in quali sessions being tight this year. Six drivers managed to take pole position, with Hamilton and Perez coming close, securing their best finish in 2nd place. Fernando Alonso and Yuki Tsunoda also came close to the top spot, both achieving a season-best result of 3rd place.

I hope you’ve enjoyed this detailed end-of-season quali analysis. If you liked what you read, feel free to share it on social media. And if you’d like to support me, you can click the “Buy Me A Coffee” button below to leave a donation. Anything is greatly appreciated!

2024 F1 Season: The pit crew

Mon, 16 Dec 2024 00:00:00 +0000

The pit stop crew plays a crucial role in modern F1. With the current regulations making on-track overtakes tough, strategy-driven overtakes—like the undercut or overcut—have become key to gaining positions during a race. The pit stop crews work incredibly hard, race after race, to make each pit stop as fast as possible. So, how did the crews perform this year? Let’s take a look at the numbers.

Which stops should we analyze?

One of the main challenges when analyzing pit stops is distinguishing between botched pit stops and slow stops caused by penalties or damage. What I’m really interested in is understanding which crews were the most efficient during “typical” pit stops, not the ones affected by unusual circumstances. So, how do we filter out these “atypical” pit stops for our analysis?

A few years ago, I tackled a similar analysis by using a few different anomaly detection algorithms to remove outliers. The problem with that approach is that each algorithm could give different results, and there’s no clear way to know which one is right. To me, that felt like the wrong approach.

This year, I took a different route. I looked at the cumulative distribution, which means ordering all the pit stops from fastest to slowest and then seeing where the majority of them fall.

First, I looked at all the pit stops from the 2024 season, including the really slow ones. Then, I calculated percentile ranges—things like 0-10%, 10-20%, and so on. The 0-10% range represents the fastest 10% of pit stops, while the higher ranges, closer to 100%, contain the slowest stops of the season.

If we take a look at the first chart (full range), we can see that most pit stops are completed in 10 seconds or less. The final 5% of the slowest pit stops may take anywhere from 10 to 52 seconds, but these are likely due to penalties or damage.

To get a clearer view, I used a limited range chart that only shows pit stops up to around 10 seconds. Here, we can see that 90% of pit stops happen in 5 seconds or less (specifically 4.94 seconds). I decided to focus on the data up to the 90th percentile, so anything over 4.94 seconds was excluded from the following analyses.

Pit stop distribution

Percentile range

I decided to start by visualizing the data, dividing it into percentile ranges of 10%. The goal was to see which teams were more or less represented in each range, giving us a good idea of how efficient the pit crews were on average.

The results are pretty interesting. We can see that the Red Bull pit crew handled 36.6% of the pit stops in the fastest percentile range (0-10%, between 1.9 and 2.26 seconds). McLaren, Ferrari, and Alpine made up most of the rest of these fast stops, with Red Bull and Mercedes covering the final 15.5%. Neither Aston Martin, Haas, Sauber, nor Williams had any pit stops in this range.

On the opposite end, in the slowest percentile range (80-90%, 3.46 to 4.94 seconds), most of the pit stops were made by Sauber, followed by Haas and Williams. This gives us a solid idea of which teams were quicker in the pits and which ones were slower.

By team

Another way to look at the same data is by grouping it by team rather than by percentile range. This chart helps us see where most of each team’s pit stops fall in terms of percentile ranges.

The Red Bull crew was the fastest overall, with an average pit stop time of 2.46 seconds. A massive 38.8% of their pit stops were in the fastest 10% of the season. If we add the 10-20% and 20-30% percentile ranges to the fastest 0-10% range, we see that 64.1% of Red Bull’s pit stops were under 2.52 seconds. It’s safe to say their performance this season was nothing short of outstanding.

Ferrari’s mechanics were the second fastest, with an average of 2.57 seconds per stop. The main difference from Red Bull was that Ferrari had more stops in the 2.27-2.4 second range, while Red Bull dominated the 1.9-2.26 second range.

There wasn’t much separating Red Bull, Mercedes, and McLaren, with all three teams averaging 2.7 seconds per stop. It was interesting to see that despite differences in their pit stop distributions—for instance, Mercedes had fewer “top” stops but also fewer slow stops—these three teams ended up with the same average time after 24 races.

Alpine and Aston Martin followed behind, with average pit stop times of 2.78 and 2.89 seconds, respectively. Sauber, Haas, and Williams were the only teams whose average pit stop times exceeded the 3-second mark.

Pit stops (0 - 90th percentile)

So far, we’ve discretized the data, meaning we’ve categorized it into intervals. But we can also look at the stops in continuous space to see how they’re distributed. This chart shows all the stops analyzed, with background bands representing the 50% (darkest), 80%, and 95% interval ranges for each team. The 50% range shows where 50% of the stops around the average fall, while the 80% and 95% ranges show the same thing but with broader coverage.

You can clearly see that Red Bull was the fastest, though they were slightly less consistent than Ferrari. Meanwhile, the Alpine crew seemed to have a wider spread in their pit stop times, which suggests they weren’t as consistent as the other teams.

Haas is an interesting case. They were among the slowest, but surprisingly consistent. I guess there’s a silver lining there.

I also want to point out that Williams is a bit of a strange case. A few years ago, they had one of the best pit crews in F1 and were second only to Red Bull. I’m not sure what’s happened to them since. It’s hard to say whether they’ve lost talent to other teams or if there’s a mechanical issue, but it’s surprising to see them now as the slowest pit crew in current Formula 1.

Fastest pit stop for each team

Many people have shared or will share the fastest pit stops of the year, but this usually means highlighting laps mainly from Red Bull, Ferrari, and McLaren, while overlooking the fastest stops from other teams.

If we plot these in a simple bar chart, we see that in terms of maximum speed, McLaren and Red Bull were neck and neck, both hitting a fastest pit stop time of 1.90 seconds. Ferrari wasn’t far behind, with a 1.94-second stop at the Miami Grand Prix. RB F1 Team and Alpine came very close to a sub-2 second stop this year, both achieving a fastest time of 2.01 seconds.

When we look at the slowest stops, we see that Sauber, Aston Martin, Williams, and Haas never had a pit stop faster than 2.30 seconds. Haas’ fastest stop came in at 2.51 seconds. To put this into perspective, 62.6% of the representative pit stops done by Red Bull took less than 2.51 seconds.

Rolling average pit stop trend

We already know which teams were the fastest and slowest in the pit stop department, but how did their times change throughout the season? One way to figure this out is by calculating a rolling average for all the stops made in 2024 (as long as they were in the 0-90th percentile). This approach keeps adding stops to the average as the season progresses, showing us whether the stops got faster, slower, or stayed the same.

I find these results pretty exciting. One notable case is Sauber. We know they’ve struggled with their wheel guns over the past few years, and while they’re still among the slowest, we can see a clear improvement starting at the Miami Grand Prix. This trend continued throughout the season, which could motivate them to push for even more progress in 2025.

Alpine and McLaren also deserve a shout-out, as both showed strong improvement as the season went on—something that bodes well for next year.

One trend that surprised me was how average pit stop times seemed to slow down in the final rounds of the season. Teams like Red Bull, Ferrari, RB F1 Team, Alpine, Aston Martin, and Williams all got slightly slower in the final races. I can’t say for sure why this happened, but it wouldn’t be surprising if exhaustion after such a long season had an impact.

Final remarks

I think most people visiting this blog already knew that Red Bull had the best pit crew in the game, but it’s exciting to see this fact backed up by data. McLaren made great progress throughout the season, so it’ll be interesting to see them compete with Red Bull and Ferrari next year.

Sauber and Haas have struggled for years. While Haas didn’t show much improvement this season, Sauber was definitely on an upward trend. It would be great to see them fully solve their wheel gun issues next year and potentially make a leap forward.

Williams is such a strange case. They have struggled with on-track speed for many seasons now, but pit stops were not something that they struggled with. This season, though, they were dead last. It would be interesting to understand why they’ve regressed so much. If anyone knows why, drop a comment below!

This was another detailed end-of-season analysis, and it definitely took me more time and energy than the usual FP2 or race pace articles. If you liked what you read, feel free to share it on social media, and if you want to support me, you can click the “buy me a coffee” button below to leave a donation. Anything is greatly appreciated!

2024 F1 Season: Who was the fastest?

Sat, 14 Dec 2024 00:00:00 +0000

Introduction

The 2024 F1 season is over after 24 races around the world. I wanted to make a post that analyzed race pace throughout a season, so here we are. Who was the fastest by the end of the year? Let’s take a look at the data.

Methodology

One of the main challenges when comparing multiple races is that lap times can vary a lot between tracks. To make things more consistent, we can standardize the data by comparing each lap to a specific reference point—like the average lap time of a race.

For example, if a lap takes 1:30.000 and the average lap time for the race is also 1:30.000, the result is 0% (it’s right on the average). If a lap is 1:31.000, we divide 1:31.000 by 1:30.000, subtract 1, and multiply by 100. That gives us a result of 1.11%, meaning the lap was 1.11% slower than the average lap time. We can do this for every lap from every driver in each race, which will show us laps that are faster than average (negative numbers), equal to the average (0), or slower (positive numbers). This metric is called relative lap time, and we can calculate it for the entire season (overall/average relative lap time) or for each individual lap (relative lap time). By doing this for every race, we can compare performances across different tracks since each race has its own average lap time.

For the main analysis (season-long relative race pace), I used all laps from the whole season. For the secondary analysis (rolling relative race pace), I analyzed the data in a rolling manner. This means that for the first round, I only used data from race 1. For the second round, I used data from the first two races, and so on, all the way until race 24, which was the final race of the season. This approach helped me track how drivers’ race pace evolved throughout the season.

For both analyses, I only included laps I considered valid—so things like the first lap, pit stop laps, or laps under a safety car or VSC were excluded.

It’s important to note that I excluded Oliver Bearman and Jack Doohan from this analysis since they only raced sparingly during the season so their data wouldn’t really be comparable with that from the rest of the drivers.

Season-long relative race pace

After analyzing over 20,000 laps, the fastest driver was Max Verstappen, who narrowly beat Lando Norris by just 0.02%. Max and Lando were the only drivers with an overall relative lap time that was more than 1% faster than the average lap time. The next closest drivers with negative relative lap times were Oscar Piastri at -0.84%, George Russell at -0.75%, Charles Leclerc at -0.7%, Lewis Hamilton at -0.66%, Carlos Sainz at -0.64%, and Sergio Perez at -0.24%.

The midfield battle was pretty tight, but Fernando Alonso came out on top with an overall relative lap time of 0.12%. Nico Hulkenberg also deserves a shout-out, finishing with an average relative lap time of 0.23%. On the flip side, Zhou Guanyu and Logan Sargeant were the only drivers to end the season with an average relative lap time slower than 1% of the overall average.

Rolling relative race pace

Looking at how drivers performed throughout the season brings out some pretty interesting insights. One of the most notable ones is the development at McLaren. Both Norris and Piastri started the season well behind the Red Bull drivers but managed to improve their race pace as the year went on. Lando Norris kept getting faster, while Max Verstappen’s pace dipped. If the season had a couple more races, it’s likely that Lando would’ve ended up as the fastest driver of the year.

Mercedes had a slow start, gained some momentum in the middle of the season, but couldn’t keep that positive trend going and ended up a bit slower in the final races. On the other hand, Ferrari had a season full of ups and downs. Both Carlos and Charles started strong, slowed down mid-season, and then found some stability in the later races.

The y-axis scale for the top 4 and bottom 6 teams is different because the drivers from the top 4 teams were significantly faster than those from the midfield and bottom teams. The purpose of these two charts is to show trends, not to directly compare all drivers to one another.

Looking at the rolling relative race pace for the bottom 6 teams, we can see some interesting trends. Fernando Alonso started off strong for the first 5 races, but then slowed down and stabilized his performances. Nico Hulkenberg and Lance Stroll both had relatively stable performances, which is also true for Kevin Magnussen and Yuki Tsunoda.

Franco Colapinto had a solid start to his career in Italy, but struggled in the later races of the season. A similar story played out for Liam Lawson at Red Bull—he had a strong race at the Circuit of the Americas but finished the season off the pace.

Unfortunately for Sauber, neither Valtteri Bottas nor Zhou Guanyu were able to find the performance they needed. Zhou actually started the season with better pace than Bottas but ended up as the second slowest driver on the grid. Valtteri had a more consistent season but just couldn’t match the pace needed.

Final remarks

There you have it—the fastest driver was Max Verstappen. Personally, I think McLaren had the strongest package, but Max was the difference maker. He’s the best driver in the world, and it’s his talent and dedication that allowed him to still clinch the World Drivers’ Championship.

I hope you enjoyed this article! If you have any questions or comments, feel free to drop them in the comment section below.

It’s been a long season, but I hope you’ve enjoyed the content on this blog. If you liked what you read, please share it on social media, and if you want to support me, you can click the “buy me a coffee” button below to leave a donation. Anything is greatly appreciated!

2024 Abu Dhabi GP: Race pace

Sun, 08 Dec 2024 00:00:01 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Abu Dhabi GP: FP2 session

Sun, 08 Dec 2024 00:00:00 +0000

We have interactivity! The plot that shows the FP2 laps of all of the drivers is interactive. Check it out below.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:45.000. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2024 Qatar GP: Race pace

Mon, 02 Dec 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Qatar GP: Sprint

Sun, 01 Dec 2024 00:00:00 +0000

We have interactivity! The plot that shows the sprint laps of all of the drivers is interactive. Check it out below.

Sprint race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember

Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Sprint race laps… all of them

Summarized sprint race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race pace delta

You can read the chart like this:

Choose a driver’s short name from the top or bottom of the plot (x-axis)
Choose a second driver from the left of the plot (y-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Las Vegas GP: Was Sainz helping Verstappen?

Mon, 25 Nov 2024 00:00:00 +0000

This is a quick post so I will make it as straightforward as possible. After the 2024 Las Vegas GP there has been conflicting information on social media on whether or not Carlos Sainz was lifting before turn 12 to allow Verstappen to be within the one-second threshold to get DRS. Allowing so would be detrimental to the team’s aspirations of becoming World Constructors’ Champions, so accusing a driver of hampering his teammate is a big deal if you ask me. I wanted to see for myself if this is something that happened so I’m sharing my results with you. Enjoy.

Introduction

This analysis is only possible after extensive post-processing of the raw telemetry data. Other sources of this information use algorithms that, unfortunately, provide unreliable results. I post-process the data in a unique way that allows me to sync the throttle and speed traces much more accurately, allowing me to obtain higher-quality results.

My data won’t exactly match the data shown by other sources. I’ve extensively post-processed the raw data to get a dataset that is up to the task.

Throttle trace

In this chart, the faint lines represent the throttle trace for each lap, while the thick lines represent a calculated average for all of the laps of each individual subset of the data. The shaded section of the chart shows the area of interest.

I decided to divide Sainz’s data into two different subsets. The first one contains laps 33 to 40 when Carlos was chasing Verstappen, while the second one includes laps 43 to 47, just after Carlos managed to overtake Verstappen but before Leclerc managed to overtake Max. It’s pretty clear that Carlos would not have lifted earlier when he was chasing Verstappen, so this will be our reference data. It’s the second subset (laps 43 to 47) that we’re interested in. Do we see anything strange?

My answer is a simple no. It looks like Carlos was a bit more conservative with the throttle when he was chasing Max (laps 33 to 40), most likely due to the effect of the dirty air coming from the leading car. It’s also possible that he as doing a bit of lift and coast to conserve fuel and preserve battery energy before attacking Max on the main straight. On laps 43 to 47 Carlos lifted less than when he was chasing Max, and he also tended to lift a bit later when reaching T12. After navigating the turn he was just as aggressive at accelerating out of it before reaching the long straight between T13 and T14.

Speed trace

What about the actual speed? Maybe Carlos was using a less aggressive ERS deployment? Once again, I don’t see anything funny. Carlos had less speed when chasing Verstappen into T12, most likely conserving the tires and managing the dirty air caused by the leading car. His speed after coming out of T12 was pretty much the same, meaning that he was indeed doing his best to accelerate out of the corner and have an advantage while driving on the long straight between T13 and T14.

Final remarks

I don’t see any evidence of Carlos trying to keep Max Verstappen within DRS range. It is true that Carlos has done this in the past in Singapore and even on this same Las Vegas GP track, but the data doesn’t support the (conspiracy) theory that he was purposely hampering Charles Leclerc by helping Verstappen stay within DRS range.

Support me!

If you enjoyed this article, please support me and my project. Keeping this site going takes many hours of energy and effort. Trust me, maintaining a project like this one is no easy task. If you want to donate some money to the cause, please click on the BUY ME A COFFEE button. If you don’t have money to donate, please share the content with other people so that this project can keep growing. Thank you.

2024 Las Vegas GP: Race pace

Sun, 24 Nov 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Las Vegas GP: FP2 session

Fri, 22 Nov 2024 00:00:00 +0000

We have interactivity! The plot that shows the FP2 laps of all of the drivers is interactive. Check it out below.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:45.000. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2024 São Paulo GP: Race pace

Sun, 03 Nov 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 São Paulo GP: Sprint

Sat, 02 Nov 2024 00:00:00 +0000

We have interactivity! The plot that shows the sprint laps of all of the drivers is interactive. Check it out below.

Sprint race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember

Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Sprint race laps… all of them

Summarized sprint race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race pace delta

You can read the chart like this:

Choose a driver’s short name from the top or bottom of the plot (x-axis)
Choose a second driver from the left of the plot (y-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Mexico City GP: Race pace

Mon, 28 Oct 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 United States GP: Race pace

Mon, 21 Oct 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 United States GP: Sprint

Sun, 20 Oct 2024 00:00:00 +0000

We have interactivity! The plot that shows the sprint laps of all of the drivers is interactive. Check it out below.

Sprint race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember

Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Sprint race laps… all of them

Summarized sprint race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race pace delta

You can read the chart like this:

Choose a driver’s short name from the top or bottom of the plot (x-axis)
Choose a second driver from the left of the plot (y-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Singapore GP: Race pace

Mon, 23 Sep 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Singapore GP: FP2 session

Fri, 20 Sep 2024 00:00:00 +0000

We have interactivity! The plot that shows the FP2 laps of all of the drivers is interactive. Check it out below.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:45.000. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2024 Azerbaijan GP: Race pace

Sun, 15 Sep 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Azerbaijan GP: FP2 session

Fri, 13 Sep 2024 00:00:00 +0000

We have interactivity! The plot that shows the FP2 laps of all of the drivers is interactive. Check it out below.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:55.500. Some drivers had laps above that threshold but I am not displaying them here.

Esteban Ocon had two very slow laps during his stint. Those were removed for the chart and that’s why it looks like he was faster than he really was.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2024 Italian GP: Race pace

Sun, 01 Sep 2024 00:00:02 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Italian GP: FP2 session

Sun, 01 Sep 2024 00:00:01 +0000

We have interactivity! The plot that shows the FP2 laps of all of the drivers is interactive. Check it out below.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:20.500. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2024 Dutch GP: Race pace

Mon, 26 Aug 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Dutch GP: FP2 session

Sat, 24 Aug 2024 00:00:00 +0000

We have interactivity! The plot that shows the FP2 laps of all of the drivers is interactive. Check it out below.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:20.500. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2024 Belgian GP: Race pace

Sun, 28 Jul 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Belgian GP: Quali session

Sat, 27 Jul 2024 00:00:02 +0000

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Pérez on Q3. The delta is of -0.282 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Pérez’s.

Tale of the session

Note

A tire was considered to be new new only if it had a life of only 1 lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for the flying lap.

Extra note

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2024 Belgian GP: FP2 session

Sat, 27 Jul 2024 00:00:01 +0000

We have interactivity! The plot that shows the FP2 laps of all of the drivers is interactive. Check it out below.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:54.500. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2024 Hungarian GP: Race pace

Sun, 21 Jul 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Hungarian GP: FP2 session

Sat, 20 Jul 2024 00:00:00 +0000

We have interactivity! The plot that shows the FP2 laps of all of the drivers is interactive. Check it out below.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:29.000. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2024 British GP: Race pace

Sun, 07 Jul 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 British GP: Quali session

Sat, 06 Jul 2024 00:00:00 +0000

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Pérez on Q3. The delta is of -0.282 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Pérez’s.

Deleted laps

The charts finally considers laps that have been deleted and therefore are not shown here =)

Tale of the session

Note

A tire was considered to be new new only if it had a life of only 1 lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for the flying lap.

Extra note

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2024 British GP: FP2 session

Fri, 05 Jul 2024 00:00:00 +0000

We have interactivity! The plot that shows the FP2 laps of all of the drivers is interactive. Check it out below.

Some of the people reporting on simulation runs from FP2 should be ashamed of themselves. “X driver the slowest.” You’re literally removing 2 laps out of an already short run of 5 laps. When comparing very short runs show the damn distribution and get rid of point-estimates since they’re not representative in any shape, way or form. Please, for the people doing “data analyses,” be objective, stop trying to capitalize on hate, it’s embarrassing.

The numbers inside the dots represent the lap in which that particular time was done. Drivers who did their laps later in the session will tend to have faster times. Keep that in mind when comparing the lap times done by different drivers.
The box represents a traditional box plot, with the line in the middle representing the median time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The arrows represent the median time done by each driver. With this addition, you can visually compare the median time among the drivers.
The histogram on the right side of the plot is binning the lap times in intervals of 0.25 seconds. This histogram is on a 100% scale, meaning that each bin is independent of the others.
If you do not see a driver in the plot, it is because he did not do a proper race simulation stint during the practice session.
You may see fewer laps than the number displayed above the driver’s name. This is because some laps were slower than the maximum time represented in this chart.
For this particular plot, a race pace simulation stint was considered as long a driver had a stint of 5 or more laps.
Also for this particular plot, I decided to show laps with a maximum time of 1:37.000. Some drivers had laps above that threshold but I am not displaying them here.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

FP2 race simulation laps… all of them

FP2 best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

FP2 best lap times

FP2 top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

2024 Austrian GP: Race pace

Sun, 30 Jun 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Austrian GP: Race snapshot

Sun, 30 Jun 2024 00:00:00 +0000

We have interactivity! The plot that shows the race snapshot is interactive. Check it out below.

I have a new addition to the blog. I have always wanted to see a chart that shows a quick overview of a race, but this is pretty hard to do since each race has 20 drivers and dozens of laps. Due to the sheer magnitude of data, the plot tends to become messy and unreadable. However, I think I just came up with the right visualization chart that will allow me to show you a quick snapshot of a race.

Please note that this plot utilizes intermediate, predicted lap time data derived from sector times. This provides enhanced insights into each driver’s race pace. Lap times labeled with “S3” represent the actual recorded times by the official F1 electronic timer.

Race snapshot

This chart is better visualized on a computer.

The next plot is interactive. If you’re on a mobile device, tap and hold the chart to reveal the toolbar on the left. This toolbar enables zooming and scrolling. Click on the horizon plot area to see a tooltip with additional race information.

Static Interactive

Race snapshot

Horizon chart explanation

The horizon chart is a great way of displaying a quick overview of an event that contains a large amount of data. It’s hard to explain how a horizon plot is created, but the following image does a great job at showing how the computer creates a horizon plot.

A horizon chart is created by slicing the data into equal intervals, associating each interval with a colour, and then overlapping these segments into a single interval.

A horizon chart can be read the following way:

Select a driver
Choose the lap(s) that you are interested in and see which colour is displayed in the foreground of that lap(s).
The colour will determine the lap time interval, usually in 2-second intervals, but it depends on the plot.
If more than one colour is displayed, then remember that the colour that determines the interval is only the one in the foreground.
See what’s the height of the area based on the y-axis. The height can go from 0% to 100%.
Determine the approximate lap time from the combination of both the colour (lap time range) and area height (0 to 100%).
Example: If the foreground is dark purple and that corresponds to a lap time of 1:40.000 to 1:42.000, then we know right away that the lap had a time within that interval. If the area reaches the 50% mark, then we know that the lap time was 1:41.000 (50% of the higher-end of the range of 1:42.000)
If the area reached a height of 25% instead of 50%, then the lap time would’ve been 1:40.500.
If the area reached a height of 75% instead of 50%, then the lap time would’ve been 1:41.500.
This can be applied to any area. You won’t get millisecond precision when looking at the plot, but you this chart is not intended to give you that anyway.
The vertical, dashed-lines show pit stops.

The way you read a horizon chart is a bit unintuitive at first, but once you learn how to read it you’ll realize how much valuable information it can provide in a compact factor.

Note that this horizon chart has been modified to show the most valuable information, which is the regular lap times. Because of this, the lap times over a certain threshold will be displayed with the same colour regardless of the lap time. We lose a bit of detail here, but I find it worth it since this way we gain more granularity on the data that we’re interested in.

2024 Austrian GP: Quali session

Sat, 29 Jun 2024 00:00:00 +0000

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Pérez on Q3. The delta is of -0.282 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Pérez’s.

Deleted laps

The charts finally considers laps that have been deleted and therefore are not shown here =)

Tale of the session

Note

A tire was considered to be new new only if it had a life of only 1 lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for the flying lap.

Extra note

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2024 Austrian GP: Sprint

Sat, 29 Jun 2024 00:00:00 +0000

We have interactivity! The plot that shows the sprint laps of all of the drivers is interactive. Check it out below.

Sprint race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember

Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Sprint race laps… all of them

Summarized sprint race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race pace delta

You can read the chart like this:

Choose a driver’s short name from the top or bottom of the plot (x-axis)
Choose a second driver from the left of the plot (y-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Austrian GP: Sprint qualifying

Fri, 28 Jun 2024 00:00:00 +0000

Sprint quali session deltas

The sprint quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Pérez on Q3. The delta is of -0.282 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Pérez’s.

Deleted laps

The charts finally considers laps that have been deleted and therefore are not shown here =)

Tale of the session

Note

A tire was considered to be new new only if it had a life of only 1 lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for the flying lap.

Extra note

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Sprint quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Sprint quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Sprint quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2024 Spanish GP: Intra-team quali battles

Wed, 26 Jun 2024 00:00:00 +0000

The charts show the deltas for each driver for each sector and the entirety of the lap for the qualifying session of the previous race. Since sometimes a driver advances to the next quali session while his teammate doesn’t, I filtered the data to contain the maximum quali session in which both drivers from the same team participated.

The colour acts as a heatmap, with stronger colours showing a larger difference than fainter ones. In this case, the drivers who were faster will have a blue background, while drivers with slower times will have a red background.

I hope you like it.

2024 Spanish GP: Race pace

Tue, 25 Jun 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Spanish GP: Quali session

Sun, 23 Jun 2024 00:00:00 +0000

Is anyone still reading these? I’m not sure if it’s worth it to keep writing articles like this one.

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Pérez on Q3. The delta is of -0.282 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Pérez’s.

Deleted laps

The charts finally considers laps that have been deleted and therefore are not shown here =)

Tale of the session

Note

A tire was considered to be new new only if it had a life of only 1 lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for the flying lap.

Extra note

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2024 F1 Season: Qualifying delta between teammates (rounds 1 - 9)

Fri, 14 Jun 2024 00:00:00 +0000

We have interactivity! Check out the qualifying delta plot shown below.

Qualifying teammate battles seem to be of great interest to the current F1 community. While I’m a firm believer that Sundays are much more important than Saturdays, I will accept that qualifying has a great impact on a race, especially when you consider how hard it is to overtake with the latest generation F1 cars.

Analysis

Overall qualifying median delta

Static Interactive

Overall qualifying delta

This interactive chart is better visualized on a computer.

If you’re on a mobile device, tap and hold the chart to reveal the toolbar on the left. This toolbar enables zooming and scrolling. Hover over the data points to see a tooltip with additional information. On mobile devices you may need to click and hold the points to display the tooltip.

I don’t have much to say today. I’m pretty tired to be honest. I’m not sure it’s worth it to continue doing this. Anyways, I’ve switched the analysis to use the median instead of the mean just to avoid crazy outliers in rainy sessions. The median is more resistant to outliers so even if we have an extreme session here and there, it should stay true to reality.

Cumulative qualifying median delta

We can also visualize the cumulative difference between teammates by using a rolling average. This method involves adding each race’s data as the season progresses and then computing an average. The goal is to identify trends over time. This trend shows if a driver is getting closer, farther or at the same distance to his teammate.

Since I’m only interested in a 1v1 analysis between teammates, I decided to keep only the official race drivers for this analysis. This means that I removed the data from Oliver Bearman since I’m only interested in the Leclerc vs Sainz battle for the Ferrari team.

Methodology

I calculated the delta between teammates by using the symmetrical percent difference. To find out why I did this, check the “issues” section after the analysis.

For each race and for the highest qualifying session that both drivers from the same team reached I calculated the symmetric percent difference. Negative values mean that a driver was faster than his teammate, while positive values mean that the driver was slower than his teammate. A difference of 0% means that both drivers were just as fast.

Additionally, on the left-hand side of the chart next to the driver’s name, I also added the number of times a particular driver has been faster than his teammate in quali.

Finally, I added a gold-coloured diamond to show the median gap between teammates. This number will be equal to the overall value displayed on the left side of the plot, next to the team logo.

For the cumulative plot, I gathered data from each race and calculated a rolling median For example, for race 5, I used the qualifying delta from the first five races to compute the median As more races occur, additional data is incorporated into the rolling median, resulting in a more stable and accurate trend.

Issues!

$$ Percent\ difference = 100\times\frac{value1-value2}{value2} $$

The main problem is that this value is not symmetrical. This means that if I reverse the order of value 1 and value 2, the final percent difference will be different.

$$ Percent\ difference = 100\times\frac{80-90}{90}=-11.11\% $$ $$ Percent\ difference = 100\times\frac{90-80}{80}=12.5\% $$

You can see that the percentages are not reversible, even though in both cases we’ve changed the original value by 10 units.

One way we can solve this problem is by using the symmetric percent difference, which is calculated by using the following formula:

2024 Canadian GP: Intra-team quali battles

Tue, 11 Jun 2024 00:00:00 +0000

I hope you like it.

2024 Canadian GP: Race pace

Sun, 09 Jun 2024 00:00:00 +0000

We have interactivity! The plot that shows the race laps of all of the drivers is interactive. Check it out below.

Race pace

The main idea of these charts is to show the representative average race pace of each driver. Since plotting the raw data will not show accurate information, I did a bit of data pre-processing.

I removed the laps that were not raced under green or yellow flag conditions. This means that laps raced under Virtual Safety Car (VSC) or full Safety Car (SC) conditions were removed from the analysis.
The first lap is not consistent with the rest of the laps so it was removed from the analysis. Finally, laps in which a driver entered or left the pits are not “full laps,” so they were removed from the analysis as well.
If laps were done with the intermediate or wet tires, then they were considered for this analysis.
The box represents a modified box plot, with the line in the middle representing the mean (average) time done by each driver. The top and bottom lines respectively represent the 75% percentile and the 25% percentile.
The colors behind the box represent quantiles. There are 4 colours for each driver, with each colour representing a specific quantile. Quantiles with lighter colours include the fastest laps, while quantiles with darker colours include the slowest laps.
The jitter in the x-axis is mainly used to separate stints for each driver.
For example, if a driver stopped only once for new tires, then he had 2 stints during the race. Laps of the first stint will be aligned slightly more to the left side, while laps of the second stint will be aligned slightly more to the right.
Within each stint, the x-axis doesn’t represent anything. The points are jittered just to separate them and allow you to have a better view of each lap.
The scales are NOT necessarily the same for the fastest 10 and slowest 10 charts. I do this just to allow the package to separate the dots better and allow you to see each individual lap. For a fully representative chart of the data take a look at the 3rd chart (All drivers plot).
Finally, the column on the right side shows the percentage of laps done by each team in a 0.250 seconds bin. If you see for example a lot of red toward the bottom of the column, then it means that most of those laps in that time range were done by Ferrari.
Scenario: A Ferrari driver had the fastest lap of the race. It was a 1:20.300. That lap falls within the 1:20.250 to 1:20.500 bin. In that case, you would expect to see a fully red row on the column.
If another driver from another team—let’s say Red Bull—had done for example a lap with a time of 1:20.350, then then you would expect the row in that column to look half red and half blue.

Remember Drivers with more pit stops tend to have faster average times than drivers with fewer pit stops.

The next plot is interactive. Click on the toolbar on the top, left side of the plot to zoom in and scroll. Hover with the mouse over the data points to get additional information.

This chart is better visualized on a computer. To get additional information on a point while using a mobile device, click and hold the desired data point to get the proper tooltip displayed.

Summarized race pace

The chart looks pretty funky, but you can read it like this:

The number on the right side shows the average lap time for each driver.
This number corresponds to the big dot that is showed in each of the bars.
Each bar is divided by all the laps done by each driver. This is another way of showing the distribution instead of just the average (like a typical bar chart would do).
Longer “mini-bars” show laps in which a particular driver had a slower lap time. Shorter “mini-bars” represent laps in which a driver had a faster lap time.
If a driver’s name is red-colored, that means that the driver completed less than 70% of the race distance.

Race laps… all of them

Race pace delta

Similar to what I did with my latest quali post, I am now adding a new chart which shows the race pace deltas between all the drivers.

You can read the chart like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their mean/average race pace.
If you see negative numbers then that means that the driver on the left side was faster than the driver he’s being compared to.
If you see positive numbers then that means that the driver on the left side was slower than the driver he’s being compared to.

Note

The chart includes drivers that retired during the race.

2024 Canadian GP: Race snapshot

Sun, 09 Jun 2024 00:00:00 +0000

We have interactivity! The plot that shows the race snapshot is interactive. Check it out below.

Race snapshot

This chart is better visualized on a computer.

Static Interactive

Race snapshot

Horizon chart explanation

A horizon chart is created by slicing the data into equal intervals, associating each interval with a colour, and then overlapping these segments into a single interval.

A horizon chart can be read the following way:

Select a driver
Choose the lap(s) that you are interested in and see which colour is displayed in the foreground of that lap(s).
The colour will determine the lap time interval, usually in 2-second intervals, but it depends on the plot.
If more than one colour is displayed, then remember that the colour that determines the interval is only the one in the foreground.
See what’s the height of the area based on the y-axis. The height can go from 0% to 100%.
Determine the approximate lap time from the combination of both the colour (lap time range) and area height (0 to 100%).
Example: If the foreground is dark purple and that corresponds to a lap time of 1:40.000 to 1:42.000, then we know right away that the lap had a time within that interval. If the area reaches the 50% mark, then we know that the lap time was 1:41.000 (50% of the higher-end of the range of 1:42.000)
If the area reached a height of 25% instead of 50%, then the lap time would’ve been 1:40.500.
If the area reached a height of 75% instead of 50%, then the lap time would’ve been 1:41.500.
This can be applied to any area. You won’t get millisecond precision when looking at the plot, but you this chart is not intended to give you that anyway.
The vertical, dashed-lines show pit stops.

The way you read a horizon chart is a bit unintuitive at first, but once you learn how to read it you’ll realize how much valuable information it can provide in a compact factor.

2024 Canadian GP: Quali session - Russell vs Verstappen

Sat, 08 Jun 2024 00:00:02 +0000

Maybe you don’t remember, but when I was using my previous site (F1bythenumbers) I worked using the telemetry data. For a very long time I stopped working with that type of data mostly because, well, it has many quirks. Believe it or not, all of the analyses out there are using data that 1) isn’t properly synced and b) has many inaccurate interpolated values. In some cases, the distances calculated are wrong by as much as 100 meters or even more. In my opinion, this is completely unacceptable. The simple truth is that the raw data obtained by most sources out there is just not up to the task of analyzing runs that require millisecond precision.

It wasn’t until a few weeks ago that I decided to give telemetry data a chance. I worked hard for dozens of hours to properly process the raw data and obtain a dataset that was accurate enough for my high standards. As far as I’m aware, this is the first and only analysis out there that uses corrected data.

I will be using this newly corrected data to give you more detailed analyses that you can trust, so hopefully, you’ll enjoy them.

Introduction

This analysis is only possible after doing extensive post-processing of the raw telemetry data. The data points are properly synced and interpolation has been kept to a minimum. When interpolation was necessary it was done in a way that reduced the margin of error to the maximum by using complex statistical models that provide increased precision over simple interpolating techniques. I’m willing to put my head on the line here and assure you that this data is reliable and should provide proper, real, conclusions from what we saw in the qualifying session

For this analysis, I will be working with data from the qualifying session of the 2024 Canadian GP. In this analysis, I will compare the fastest lap done by George Russell (1:12.000) vs the fastest lap done by Max Verstappen (1:12.000).

My data won’t exactly match the data shown by other sources. I’ve extensively post-processed the raw data to get a dataset that is up to the task. Having said that, when two drivers have the exact same time up to the millisecond, there may be little issues here and there.

Time delta

The two charts shown in this section work in tandem. The time delta chart shows the time gap between both drivers at different points of the lap, while the map shows the same cumulative delta over the course of the lap. The time delta chart has higher precision than the map, but the map shows a good overview of how the lap developed for both drivers.

Do not confuse this map with the track dominance graphic provided by AWS. My chart shows the cumulative time delta throughout a lap, while their graphic shows who was faster on predetermined sections of the track.

Max had a strong start to the lap, achieving a delta of over 0.3 seconds ahead of Russell. He was particularly impressive between turns 5 and 6 (1000 to 1300 meters), where he managed to pull ahead of George. However, Russell had a much stronger performance in turn 7, which brought the delta back in his favor. Without video footage, it’s difficult to pinpoint exactly what happened, but Max lost a significant advantage in this section of the track.

The rest of the lap was quite even until the end of the Casino Straight, just before turn 12. The data shows that the Mercedes driver had a higher top speed, allowing him to turn a slight deficit (less than 0.1 seconds) into a strong lead of around 0.2 seconds. Verstappen, however, carried more speed through turn 12, giving him a fighting chance for pole position. In the end, it was a close battle, with both drivers finishing their laps at the same time.

Both of these charts use only positional data, with minimal interpolation required to perfectly match the position of both drivers. The precision of this analysis should be well under 1 hundredth of a second.

Speed telemetry

This chart interpolates some speed data, but the interpolation was done with statistical models that produce far more realistic data points than simpler algorithms. Although the telemetry’s low refresh rate causes some “blockiness”, the data should be smoother and more accurate than that provided by other sources.

The telemetry data reveals some significant differences between the two drivers. George and his F1 W15 were much faster than Max by the end of the Casino Straight. Although both drivers had similar acceleration, the Mercedes car had a higher top speed, suggesting that it was running with slightly less downforce than the Red Bull.

Max, on the other hand, was stronger in multiple corners. At the 700, 1200, and 3850 meter marks, Verstappen managed to carry more speed through the apex, allowing him to gain valuable time. However, he lost a significant amount of time between the 1200 and 1900 meter marks. It seems that Max had a poor turn 7, which cost him time from the exit of turn 7 up to the start of turn 8. While our data isn’t perfect, reviewing the onboard camera footage would help determine if Max had a moment or what exactly happened there. He lost approximately 0.3 seconds in that section of the track alone.

Final remarks

There was nothing to separate Max Verstappen and George Russell during the 2024 F1 Canadian GP qualifying session. Max looked slightly stronger in a few corners, but Russell made up ground on the straights, particularly between turns 7 and 8, as well as on the Casino Straight. It’s rare to see lap times so close, but this highlights just how skilled F1 drivers are and how even one-thousandth of a second can make a difference in a tight session like this.

Support me!

Hopefully, you have enjoyed this detailed analysis. I want to write more and create more personalized analyses, so I’m excited about the possibilities now that I have proper telemetry data. As I mentioned, my data won’t match the one provided by other sources. This data has been reworked to be more precise, allowing me to delve into the world of telemetry analysis. This process took at least 30 hours of hard work, at least based on rough estimates, but I think the result was worth it.

2024 Canadian GP: Quali session

Sat, 08 Jun 2024 00:00:01 +0000

Quali session deltas

The quali session deltas plot shows all the deltas between the different drivers for all of the 3 quali sessions of the day.

You can read the charts like this:

Choose a driver from the left side of the plot (y-axis)
Choose a second driver from the bottom of the plot (x-axis)
See where they intersect. The number that you see is the delta for their best lap in that quali session.
Negative numbers mean the driver on the left side was faster than the driver he’s being compared to.
Positive numbers mean the driver on the left side was slower than the driver he’s being compared to.
For example: Leclerc on Q3 vs Pérez on Q3. The delta is of -0.282 seconds. This means that Leclerc’s best lap was 0.282 seconds faster than Pérez’s.

Deleted laps

The charts finally considers laps that have been deleted and therefore are not shown here =)

Tale of the session

Note

A tire was considered to be new new only if it had a life of only 1 lap when starting the lap, meaning that the tire was only warmed up for one lap and then used for the flying lap.

Extra note

Session time isn’t perfect—it may be off by a minute or so due to data limitations—but the idea is just to show the order in which the drivers were sent out.

Quali best sector times

The first chart (Best sector times for each driver) shows the best sector times done by each driver, regardless of the lap those sector times were done. It could’ve been on a random lap and not necessarily on their best flying lap.
The second chart (Sector times from the best lap done by each driver) shows the sector times of each driver for their fastest lap of the session.

Quali top speeds

I think a lot of people like to see what were the top speeds of the session so I’m adding a chart for those too.

Quali session summary table

Just as before, I am adding a table that contains most of the information about the quali session.

2024 F1 Season: The case of shady data analysis

Sun, 02 Jun 2024 00:00:00 +0000

I posted my qualifying delta between teammates article not so long ago and it went on to have decent success. A day ago there was a post on Reddit that analyzed quali performances—specifically for Lewis vs Russell—but mentioned the gaps for the rest of the teams. The numbers didn’t make a lot of sense in my mind, so I decided to analyze them and see how the original author came up with them.

The article is Mark Hughes: The theories behind Hamilton’s qualifying defeats, posted by the digital-only motorsport channel The Race.

Before starting, I will make something very clear. I respect everyone in the media, especially those who do data analyses. Working with data is difficult and very time-consuming, and it’s very easy to make mistakes. Mark Hughes has posted technical articles for a long time. He’s one of the few journalists who still delves into technical data, which is something that I appreciate. Having said that, his article was conflicting for some reasons which will be discussed in the next few paragraphs.

Red flags

Lap time deltas

It has been generally accepted that using raw deltas to compare performances between tracks with different lengths and characteristics is a bad idea. Not adjusting for track length will usually bias the result, causing problems with the final data analysis. However, in many cases, these numbers will usually not be drastically different from the raw delta numbers. Why? Because the time deltas between drivers are usually pretty small, to begin with, so even if you adjust, the results will change once again by a very small margin. Still, this is a practice that in my opinion shouldn’t be accepted. At times you will get lucky with the results, but sometimes you will get spurious results that can alter public perception of events that have happened.

Adjusted for the various lap lengths

A very similar issue to the previous one. Adjusting for lap lengths is fine and is usually done by converting to percentages so that each delta is relative to the track in question. However, here we were presented with deltas on a time scale, not a percent scale. How were these deltas adjusted? There are many ways to standardize the data, but they all have downsides and can cause spurious results. It is my opinion that whenever data is being manipulated, mentioning the methodology is critical to validate the results. In this case, I have no idea how were these lengths “corrected,” which will always be a red flag in my book. Don’t do this.

Comparable sessions only

Another tricky one. Mentioning that only “comparable sessions” were used for the analysis means that some sessions were deemed as “not comparable,” therefore being removed from the final analysis. This is an issue since we don’t know which sessions were removed and why. What if they deem a session to be “not comparable” while I deem that same session as comparable? This will cause the final results to be different, and in some cases, will completely change their interpretation. They already know which sessions were removed from the analysis, so why not state them? It requires a simple disclaimer at the end saying which sessions were removed.

Conflicting information

The article focuses on the delta between Hamilton and Russell. If we consider only quali sessions, then the balance is 7 to 1 in favour of Russell. If we include the sprint quali sessions, then we would see Russell leading Hamilton 8 to 2.

In the text we see the author repeat that Russell is 7-1 in qualifying, and yet, the table with the deltas presents information that includes data from quali and sprint quali sessions. The text also mentions that “the Mercedes drivers are the second-most closely-matched pair on the grid,” but this includes sprint quali sessions as well. If only quali sessions are considered, then the delta must only include quali sessions. If sprint quali sessions are included, then Hamilton is not down 7-1, but 8-2. And yet, the flow of the text is based on data that includes quali and sprint quali. Just, why?

Analysis

Let’s start by comparing their data with the raw quali data. Since they mentioned “comparable sessions only,” I have to assume that they used the maximum quali session in each race in which both drivers from the same team participated.

Their data vs raw data

Raw data vs data provided by The Race

Team	Average delta	Average delta (The Race)	Delta between sources
McLaren	0.335	0.042	0.293
Mercedes	0.103	0.062	0.041
Alpine	0.133	0.104	0.029
RB F1 Team	0.141	0.104	0.037
Ferrari	0.038	0.156	-0.118
Aston Martin	0.116	0.218	-0.102
Sauber	0.556	0.265	0.291
Haas	0.300	0.276	0.024
Williams	0.286	0.277	0.009
Red Bull	0.312	0.302	0.010

Ok, something is weird. The raw average deltas are very different than the ones presented by The Race. Is this due to their “correction?” It’s extremely doubtful unless they made a catastrophic mistake. Most likely this is caused by their “comparable sessions only” adjustment. Let’s try to decipher what happened for each team.

First of all, the delta at McLaren is completely off. My best guess is that they removed the sprint quali session from the Chinese GP because apparently, Piastri had some gearbox issues. We’ll remove that session for the British team and recalculate the results.

At Sauber, we get something very similar. The delta between my data and their data is almost 3 tenths, which means that they must have removed at least one session. Their largest gap was found at the sprint quali of the Chinese GP, which was a rainy session. The gap between both drivers was of 2.5 seconds, but I couldn’t find anything regarding damage to Zhou’s car. I don’t believe this lap should be removed at all. Rain creates bigger gaps between drivers, but rain is a part of motorsport. Some drivers will adapt better than others, so in my opinion, this is a comparable session between both drivers. In any case, we’ll remove it to see if we can get closer to the numbers provided by The Race.

While doing research, I also found that apparently, Zhou picked up front wing damage at the quali session of the Australian GP. I will remove this data point too since apparently damage makes sessions “not comparable,” even though they’re also a part of racing.

Let’s recalculate the table before moving on.

Processed data after removing laps for Sauber and McLaren vs data provided by The Race

Team	Average delta	Average delta (The Race)	Delta between sources
McLaren	0.033	0.042	-0.009
Mercedes	0.103	0.062	0.041
Alpine	0.133	0.104	0.029
RB F1 Team	0.141	0.104	0.037
Ferrari	0.038	0.156	-0.118
Aston Martin	0.116	0.218	-0.102
Sauber	0.267	0.265	0.002
Haas	0.300	0.276	0.024
Williams	0.286	0.277	0.009
Red Bull	0.312	0.302	0.010

Alright, so the numbers are starting to make more sense. It seems we’re on the right track. Time to move on to the rest of the teams.

At Aston Martin, it appears that the writer has removed the quali from the Australian GP. Alonso messed up his lap, went up onto the gravel, and apparently picked up an “unknown problem”. I honestly don’t see how is this session not representative. He made a mistake, and even if he damaged the car, that’s on him. I don’t think it’s fair to punish Stroll and say that the session didn’t count because Fernando made a mistake. Still, even after removing this data point, it appears that the writer removed another session. I’m not really sure which session was this one, but it has to be one in which Stroll beat Alonso, otherwise the numbers won’t match.

At the Miami GP, Alonso said that they made some set-up changes but didn’t mention any problem whatsoever so that rules out removing that session. Alonso complained about traffic at the Monaco GP, but once again that’s part of racing so I don’t see why would anyone remove that session either. I couldn’t find anything related to car issues at the Emilia Romagna GP either, so that leaves us with very few options.

I have to assume that the article removes the Miami sprint quali. If we remove that data point then we get to an average of 0.210, which is very close to the 0.218 provided in the table. I don’t know why would we remove that data point though. Alonso did little running before the sprint quali session, but I mean, is that enough to say that it’s not a valid “comparable session?”

Finally, we have Ferrari. Out time is very different from the time provided by The Race so we know that they removed some laps. The Race has Leclerc beating Sainz by an average of 0.156 seconds, while we have him winning by just 0.038. Since we’re just focusing on the comparison between Leclerc and Sainz, we removed the Saudi Arabian GP since Sainz couldn’t participate due to health issues.

Leclerc mentioned that he didn’t maximize the performance at the Australian GP, but said nothing regarding car damage. Charles also said that he was just slow at the Japanese GP, so once again I don think that session was removed either. That only leaves us with the sprint quali session of the Chinese GP, and I guess the writer could say that the justification is that a rainy session isn’t comparable. I would completely disagree with this, but I don’t know how else can we get close to the numbers shown in the article. I will remove that data point and see what we get.

Processed data after removing laps for Sauber, McLaren, Aston Martin & Ferrari vs data provided by The Race

Team	Average delta	Average delta (The Race)	Delta between sources
McLaren	0.033	0.042	-0.009
Mercedes	0.103	0.062	0.041
Alpine	0.133	0.104	0.029
RB F1 Team	0.141	0.104	0.037
Ferrari	0.086	0.156	-0.070
Aston Martin	0.210	0.218	-0.008
Sauber	0.267	0.265	0.002
Haas	0.300	0.276	0.024
Williams	0.286	0.277	0.009
Red Bull	0.312	0.302	0.010

What about Mercedes? Our raw average delta has Russell leading Hamilton with a gap that is almost twice as big as the one provided by the race. Let’s take a look at the data.

The only round we can remove to make the delta remotely comparable to the one provided by Mr Hughes is round 5, which is the Chinese GP quali session. Lewis made a bad mistake during his last flying lap, locking up the tires before T14. This error was definitely expensive, but then again, it was a human error. I don’t see any way to justify this data point, but as I’ve said, this is the only session that can be removed to that the numbers match the ones provided on the original article.

Processed data after removing laps for Sauber, McLaren, Aston Martin, Ferrari & Mercedes vs data provided by The Race

Team	Average delta	Average delta (The Race)	Delta between sources
McLaren	0.033	0.042	-0.009
Mercedes	0.060	0.062	-0.002
Alpine	0.133	0.104	0.029
RB F1 Team	0.141	0.104	0.037
Ferrari	0.086	0.156	-0.070
Aston Martin	0.210	0.218	-0.008
Sauber	0.267	0.265	0.002
Haas	0.300	0.276	0.024
Williams	0.286	0.277	0.009
Red Bull	0.312	0.302	0.010

I was going to do this analysis for all of the teams, but I’m very tired and I just don’t have it in me anymore. The numbers don’t perfectly match, so perhaps a session here and there was removed, but without massive implications to the interpretation of the analysis.

Laps removed

It looks like at least the following laps were removed from the analysis provided by The Race:

Sauber: Australian GP quali (damage to Zhou’s car) and Chinese GP sprint quali (rain)
McLaren: Chinese GP sprint quali (Piastri with gearbox issues)
Aston Martin: Australian GP (unknown problem?) and Miami GP sprint quali (little run before sprint quali?)
Ferrari: Chinese GP sprint quali (rain?) and at least another lap, but I just couldn’t figure out which one.
Mercedes: Chinese GP quali (Lewis locking up in his final lap?)

I don’t agree with many of the laps that were most likely removed. Perhaps you could justify removing Piastri’s lap at the Chinese GP sprint quali, and Zhou’s lap at the Australian GP quali, but I think those are the only two that I could say were “anomalies.”

Rain caused a big delta between Bottas and Zhou at the Chinese GP sprint quali, but they were both driving under the same circumstances, with Bottas just doing better than Zhou. Regarding some of the other laps that were most likely removed, I also found little justification for their removal.

Even after doing extensive research and testing to see which laps could have been removed, I still can’t get a good match to the numbers provided by Mark Hughes. The fact that after at least 12 hours of research and testing, I still can’t seem to find how this data was calculated, is pretty concerning if you ask me.

Overall qualifying delta

This is the chart that I would normally use to display the overall qualifying deltas between different teammates. Note that I rarely remove laps because I think that over the season things eventually balance out.

It’s also important to mention that for my analyses I rarely use the sprint quali session information. My reasoning is in fact quite simple. I just don’t think sprint quali sessions are nearly as important as regular quali sessions. Sprint quali only allows you to gain positions for the sprint race, which only gives a maximum of 8 points. Most teams won’t be near the front, so I doubt many will be pushing to the limit to get the chance to maybe get 1 point. Drivers are very competitive by nature, but they still know that it’s better to fight for more points at the race than it is to bin the car at sprint quali and have the mechanics rebuild it from scratch.

Final (professional) remarks

I just find the whole article provided by The Race bizarre. The numbers don’t make a lot of sense, the text doesn’t match the stats provided in the table, and the whole article is in general quite shady. How hard was it to quickly mention which rounds were removed? I’m fine with not getting a ton of detail regarding how the laps were normalized, but at least tell me how these stats were calculated. Some of the results that they provide don’t match the original raw data at all. This can completely change the interpretation of the analysis, which is extremely concerning. You only need to take a look at at Mercedes’ delta. The gap balloons up from 0.060 to 0.103 when you don’t remove the Chinese GP lap. Was there any real reason to remove the lap? Is making a mistake during quali a good enough reason to say that the whole session is not comparable? This single session completely changes the interpretation of results.

Final (personal) remarks

I’ve started thinking about the article provided by The Race since I read it. The numbers just didn’t make sense to me and that’s why I decided to run my analysis. Spending 12+ hours working on this was exhausting, and even after all that work, I don’t know how some of these numbers were calculated. Some of the laps were removed for pretty much no reason, which seems incredibly questionable.

This blog has been a source of proper information for a long time. When I started this project in 2019 I had the objective of making this site educative, interesting, objective, and transparent. I’ve done this for a very long time, always doing my best to give you the best information available. I’ve spent hundreds and hundreds of hours working on this blog and I’m still not satisfied. And yet, articles like the one we just analyzed pop up and people read them, thinking that the data must be accurate. It’s so disappointing seeing something like this happen over and over again.