An application of the generalized Poisson difference distribution to the Bayesian modelling of football scores

An application of the generalized Poisson difference distribution to the Bayesian modelling of football scores

0.00 Avg rating0 Votes
Article ID: iaor20162581
Volume: 70
Issue: 3
Start Page Number: 260
End Page Number: 273
Publication Date: Aug 2016
Journal: Statistica Neerlandica
Authors: ,
Keywords: statistics: general, statistics: distributions
Abstract:

The analysis of sports data, in particular football match outcomes, has always produced an immense interest among the statisticians. In this paper, we adopt the generalized Poisson difference distribution (GPDD) to model the goal difference of football matches. We discuss the advantages of the proposed model over the Poisson difference (PD) model, which was also used for the same purpose. The GPDD model, like the PD model, is based on the goal difference in each game that allows us to account for the correlation without explicitly modelling it. The main advantage of the GPDD model is its flexibility in the tails by considering shorter as well as longer tails than the PD distribution. We carry out the analysis in a Bayesian framework in order to incorporate external information, such as historical knowledge or data, through the prior distributions. We model both the mean and the variance of the goal difference and show that such a model performs considerably better than a model with a fixed variance. Finally, the proposed model is fitted to the 2012–2013 Italian Serie A football data, and various model diagnostics are carried out to evaluate the performance of the model.

Reviews

Required fields are marked *. Your email address will not be published.