Evidence-based AI coach for endurance training. Protocol-driven. Deterministic guidance for any LLM, with Intervals.icu integration.

Package for accessing historical and real-time NBA player injury data.

Based on NFL game data, we want to predict the success of a play. This can be used to insert different strategies before the play is called to determine the success probability.

An unofficial Python API wrapper for firstcycling.com

University Project ( Class: Database and Human-Computer Interaction )

Tools like Strava Summit and Training Peaks are great but can be inflexible when analyzing data. Other tools like elevate exist but are part extension of strava summit part application. The goal of this project is to propose different tools for analysising endurance sports and create a standalone containerized tool.

Professional Vertical Jump Analysis Tool.

Transformando datos deportivos en insights tácticos. Desarrollo de visualizaciones avanzadas y flujos automatizados para el análisis de rendimiento de equipos y jugadores mediante IA y Python

GPX+DEM ski run segmentation and training load fusion (Polar HR).

Live Tracking Server

CAVAPA: A tool for measuring physical activity of groups from video. Also, a tool in C# for easier manual/observational scoring of video

MacroCoach v2 — 科学健身/营养计划器（Streamlit）。按目标 %BW/周自动反推赤字，配合 PID 微调、EA 安全护栏与训练日碳水周期化；内置可视化报告，数据以 SQLite 持久化

This repository was created to showcase my skills and relevant Data Analysis / Sports Science projects

🔥 Advanced machine learning platform for accurate calorie burn prediction using comprehensive Kaggle fitness datasets. Features real-time predictions, professional analytics, and fitness industry integration capabilities.

Bioinformatics-driven Premier League evolution analysis (2000-2022). Applying computational biology frameworks to football analytics - evolutionary patterns, tactical systems, and competitive ecosystem dynamics.

Technical showcase production application demonstrating LLM integration, MCP pattern, RAG systems, and modern architecture.

Evidence-based sports injury prevention: load management, neuromuscular programs, ACL/hamstring/running injury protocols, and return-to-sport criteria. Peer-reviewed RCT evidence.

Evidence-based sleep science and recovery physiology for athletes: HRV, sleep extension, circadian rhythm, jet lag, and napping protocols. Peer-reviewed DOI citations.

An end-to-peer sports analytics system that utilizes the Acute:Chronic Workload Ratio (ACWR) and Machine Learning (Random Forest vs. Logistic Regression) to predict injury risk and provide clinical decision support for tennis coaches.

Decision-support tool for soccer coaches and analysts -- tactical analysis, physical load monitoring, and player movement intelligence from tracking data