Combining historical data and bookmakers'odds in modelling football scores

TL;DR

This paper introduces a hierarchical Bayesian Poisson model that combines historical football data with bookmakers' odds to improve match outcome predictions, demonstrating enhanced accuracy over traditional models.

Contribution

The paper presents a novel hierarchical Bayesian approach that integrates betting odds with historical data to better model football scores and outcomes.

Findings

Improved predictive accuracy using combined data sources

Model validation through numerical and graphical checks

Application to European leagues over nine years

Abstract

Modelling football outcomes has gained increasing attention, in large part due to the potential for making substantial profits. Despite the strong connection existing between football models and the bookmakers' betting odds, no authors have used the latter for improving the fit and the predictive accuracy of these models. We have developed a hierarchical Bayesian Poisson model in which the scoring rates of the teams are convex combinations of parameters estimated from historical data and the additional source of the betting odds. We apply our analysis to a nine-year dataset of the most popular European leagues in order to predict match outcomes for their tenth seasons. In this paper, we provide numerical and graphical checks for our model.