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CNET 5442 — Sports Analytics through Data and Networks (Spring 2026)

This repository contains the instructional materials for CNET 5442 (Spring 2026), including:

  • in-class notebooks
  • sample datasets
  • reusable helper code
  • [coming soon] intro resources for Python + scientific computing

Note: This repo is meant to be a living document throughout the semester. If something changes in class (schedule, links, datasets, etc.), the repo will be updated accordingly.


Course information

  • Course: CNET 5442 -- Sports Analytics through Data and Networks
  • Term: Spring 2026
  • Meeting time: Mon/Wed, 2:50–4:30pm
  • Location: Richards Hall #140
  • Instructor: Brennan Klein (https://brennanklein.com)

Syllabus: syllabus/CNET_5442_Syllabus_sp26.pdf


Repo map

  • notebooks/ — in-class notebooks
  • data/ — data access instructions + small samples (large data is not committed)
  • (future) utilities/ — reusable helper functions (e.g., plotting, IO, network helpers)
  • (future) resources/ — standalone primers (including a thorough Python bootcamp notebook)

Getting started

Python environment (CNET 5442)

We use a shared conda environment so the notebooks run consistently across machines.

# 1) clone the repo
git clone https://github.com/jkbren/cnet5442_sp26.git
cd cnet5442_sp26

# 2) create + activate the environment
conda env create -f environment.yml
conda activate cnet5442

# 3) register as a Jupyter kernel (recommended)
python -m ipykernel install --user --name cnet5442 --display-name "Python (cnet5442)"

# 4) launch Jupyter
jupyter lab

If environment.yml changes during the semester:

conda env update -f environment.yml --prune

Coursework and grading

  • Attendance and Participation: 10%
  • Weekly Assignments: 45% (six coding + analysis assignments)
  • Midterm Project Proposal and Presentation: 10%
  • Final Project Report and Presentation: 35%

Final project

The semester culminates in a group project applying course methods to a real sports dataset. Projects may analyze an existing dataset, scrape/collect new data, or extend methods introduced in class.

Deliverables:

  • Proposal & Intermediate Presentation (Week 7/8): short written description + 5–7 minute presentation
  • Final Report & Presentation (Finals Week): 8–12 page write-up + group presentation

Schedule → notebook mapping

Schedule and topics may be adjusted with reasonable notice.

Class Date (2026) Notebook(s) Topic
01 Wed Jan 07 no notebook Introduction — Sports as Complex Systems
02 Mon Jan 12 no notebook Data Types Across Sports
03 Wed Jan 14 no notebook Tournament Structures
Mon Jan 19 No class (MLK Day)
04 Wed Jan 21 class_04/class_04_distributions_odds_surprises.ipynb Distributions, Odds, & Surprises
Mon Jan 26 No class (Open Office Hours)
05 Wed Jan 28 class_05/class_05_regression_01_moneyball.ipynb Regression Pt. 1 — Moneyball Replication
06 Mon Feb 02 class_06/class_06_regression_02_expectation_likelihood.ipynb Regression Pt. 2 — Expectation & Likelihood
07 Wed Feb 04 class_07/class_07_regression_03_survival_logistic.ipynb Regression Pt. 3 — Survival + Logistic Regression
08 Mon Feb 09 class_08/class_08_bayesian_hot_hand.ipynb Regression Pt. 4 — Bayesian Statistics & the Hot Hand
09 Wed Feb 11 class_09/class_09_classification_clustering.ipynb Classification & Clustering
Mon Feb 16 No class (Presidents’ Day)
10 Wed Feb 18 class_10/class_10_multidimensional_embedding.ipynb Multidimensional Data & Embedding
11 Mon Feb 23 class_11/class_11_causality_01_intro.ipynb Causality Pt. 1 -- Introduction
12 Wed Feb 25 class_12/class_12_causality_02_applied.ipynb Causality Pt. 1 -- Applications
Mon Mar 02 No class (Spring Break)
Wed Mar 04 No class (Spring Break)
13 Mon Mar 09 class_13/class_13_ml_01_intro.ipynb Machine Learning Pt. 1 — Introduction
14 Wed Mar 11 class_14/class_14_ml_02_march_madness.ipynb Machine Learning Pt. 2 — March Madness
15 Mon Mar 16 class_15/class_15_spatiotemporal_hockey.ipynb Spatiotemporal Data Analysis: Hockey
16 Wed Mar 18 class_16/class_16_intro_network_science_through_sports.ipynb Intro to Network Science Through Sports
17 Mon Mar 23 class_17/class_17_soccer_passing_networks.ipynb Networks in Soccer — Passing Networks
18 Wed Mar 25 class_18/class_18_soccer_spatial_passing.ipynb Pitch Passing & Spatial Networks
19 Mon Mar 30 class_19/class_19_sequences_pt1.ipynb Sequences of Events Pt. 1
20 Wed Apr 01 class_20/class_20_sequences_pt2.ipynb Sequences of Events Pt. 2
21 Mon Apr 06 class_21/class_21_roles_motifs.ipynb Roles and Motifs
22 Wed Apr 08 class_22/class_22_transfer_trade_scouting.ipynb Transfer, Trade, and Scouting Networks
23 Mon Apr 13 TBD Information Theory or Ranking with Networks (TBD)
24 Wed Apr 15 no notebook Invited Speaker (TBD)
Mon Apr 20 No class (Patriot’s Day)
25 Wed Apr 22 no notebook Final Presentations

Academic integrity

All students are expected to follow Northeastern University’s Academic Integrity Policy. Proper citation is required for all external code, data, text, or ideas.

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Spring 2026 course materials for CNET 5442

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