An example of how to create a xG model using R and Wyscout event data
A football Expected Goals (xG) prediction model leveraging advanced machine learning techniques.
Python toolkit that turns football match-event data (WhoScored/Opta) into tactical visualizations, xG/xT models, and automated PDF match reports
Fun with Rocket League tracking data
An Expected Goals (xG) model built with StatsBomb open data. The project covers data scraping, cleaning, feature engineering, model training, and evaluation to estimate goal probabilities from shot events.
AI-powered Over/Under 2.5 football predictions. Runs automatically every day via GitHub Actions — predicts before the match, grades after. Full transparency, zero paywalls.
Football Analytics Intelligence Platform (FAIP)
A machine learning project that builds an Expected Goals (xG) model using StatsBomb event data. Includes feature engineering (distance, angle), logistic regression modeling, and visualizations such as xG heatmaps and player/team comparisons.
xG model on real StatsBomb data (4,309 shots, 3 competitions): tuned logistic regression vs gradient boosting, 5-fold CV, bootstrap significance testing, benchmarked against StatsBomb's official xG.
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