Technology Category
Reasoning under Uncertainty: Relational Probabilistic ModelsMachine Learning: Bayesian LearningIntelligent Robots: State Estimation
Bayesian weighted discrete-time dynamic models for association football prediction
🤖 AI Summary
Existing football prediction models typically assume static team offensive and defensive strengths, failing to capture dynamic strength evolution induced by events such as player transfers or managerial changes. To address this limitation, we propose a Bayesian weighted discrete-time dynamic model that incorporates a periodic exchangeable prior to adaptively modulate the weights governing strength evolution over time. Furthermore, we employ a spike-and-slab hyperprior to achieve time-varying precision control via adaptive shrinkage—balancing historical stability with responsiveness to abrupt performance shifts. The model integrates six goal-based statistical predictors and is evaluated on five recent seasons of Bundesliga, Premier League, and La Liga data. It significantly outperforms existing discrete dynamic approaches in predictive accuracy. All methodology and implementations are publicly available as the open-source R package *footBayes*.
Technology Category
Application Category
📝 Abstract
In recent years, great emphasis has been placed on the prediction of association football. Due to this, several studies have proposed different types of statistical models to predict the outcome of a football match. However, most existing approaches usually assume that the offensive and defensive abilities of teams remain static over time. We introduce a Bayesian dynamic approach for football goal based models that uses period-specific commensurate priors to flexibly weight the evolution of attacking and defensive abilities. Our approach assigns separate, time varying precisions for each ability and period, controlled via spike and slab hyperpriors. This adaptive shrinkage borrows information about teams' strength when past and current performance aligns and allows rapid adjustments when teams experience substantial changes (e.g., transfer windows or coaching changes). We integrate this framework into six standard goal based models evaluating predictive performance using data from the last five seasons of the German Bundesliga, English Premier League, and Spanish La Liga. Compared with the other discrete time dynamic models, our adaptive approach yields better predictive performance. The proposed methodology has also been implemented in the free and open source R package footBayes.
Problem
Research questions and friction points this paper is trying to address.
Predict football match outcomes with dynamic team abilities
Model time-varying attacking and defensive strengths adaptively
Improve accuracy over static-ability assumptions in existing models
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian dynamic model for football prediction
Time-varying team abilities with adaptive shrinkage
Open-source R package for implementation
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