Advanced Bayesian statistical modeling and open-source tools for professional football. Used by clubs, governing bodies, scouts, and analysts globally to deliver data-driven insights and improve decision-making.
Advanced Bayesian methods for incorporating uncertainty and prior knowledge into football analysis, providing probabilistic insights for decision-making.
Proven track record working with football clubs, governing bodies, player agencies, and professional scouts across global markets.
Development of match outcome models, player performance metrics, and tactical analysis systems using machine learning and statistical approaches.
Building production-ready data pipelines and open-source Python libraries used by analysts and researchers at all levels of professional football.
Why most finishing metrics are flawed and how a Bayesian approach gives us a truer picture of a player's finishing ability.
Comprehensive evaluation of different modeling approaches for football prediction, with performance metrics and optimization strategies.
A mathematical approach to calculating Expected Threat (xT) in Python using linear algebra, providing a more efficient alternative to the standard iterative method.
A comprehensive Python library for football analytics used by analysts, researchers, and consultants globally. Features advanced statistical models, data collection modules, betting market analysis tools, data visualization utilities, and implementations of cutting-edge metrics. The codebase has been adopted for professional work at all levels of the game.
If you're a club, researcher, or company working on an interesting problem, I'm always open to discussing new ideas and potential collaborations. Let's chat.