Cutting-edge football analytics, predictive modeling, and AI-driven insights the easy way.
Read the Blog Explore the codeRead in-depth articles on football analytics, machine learning models, and AI applications in sports.
Learn how to calculate Expected Threat (xT) in Python using linear algebra—bypassing the traditional convergence method. 🚀
Estimate goal expectancies from bookmaker's odds in Python — a step by step guide. ⚽
Comparing football goals models to see which predicts best and how to optimize them
An open-source Python package for advanced football analytics, predictive modeling, and betting market insights.
Gather football data from sources like FBRef, football-data, and Understat.
Use statistical and ML models to predict the odds for match outcomes.
Calculate probabilities for Asian handicaps, over/under goals, total goals and more.
Analyze and rank teams using Massey, Colley, Elo ratings and more.
Derive implied probabilities from bookmaker odds by removing the overround.
Optimize fantasy football team selection using mathematical models.
Interested in contributing, learning more, or exploring how we can work together? Check out the open-source repository or reach out.
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