Bayesian weighted discrete-time dynamic models for association football prediction

TL;DR

This paper introduces a Bayesian dynamic modeling approach for football match prediction that adaptively weights team abilities over time, improving predictive accuracy across major European leagues.

Contribution

The paper presents a novel Bayesian dynamic model with period-specific priors for football prediction, allowing flexible and rapid adaptation to team performance changes.

Findings

Outperforms existing static and dynamic models in predictive accuracy.

Effectively captures team ability fluctuations during transfer windows and coaching changes.

Implemented as an open-source R package for practical use.

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.