Forecasting number of corner kicks taken in association football using compound Poisson distribution

TL;DR

This paper introduces a compound Poisson regression model, including a Bayesian approach, to accurately forecast corner kicks in football matches by capturing their batch nature and serial correlation, enhanced with betting odds data.

Contribution

It develops a novel compound Poisson regression framework with Bayesian implementation for modeling football corner kicks, incorporating market data to improve prediction accuracy.

Findings

Model effectively captures batch and serial correlation in corner kicks.

Bayesian approach with variable shape parameter improves forecast reliability.

Inclusion of betting odds enhances model predictability.

Abstract

This article presents a holistic compound Poisson regression model framework to forecast number of corner kicks taken in association football. Corner kick taken events are often decisive in the match outcome and inherently arrive in batch with serial clustering pattern. Providing parameter estimates with intuitive interpretation, a class of compound Poisson regression including a Bayesian implementation of geometric-Poisson distribution is introduced. With a varying shape parameter, the corner counts serial correlation between matches is handled naturally within the Bayesian model. In this study, information elicited from cross-market betting odds was used to improve the model predictability. Margin application methods to adjust market inefficiency in raw odds are also discussed.