Dynamic Bayesian forecasting models of football match outcomes with estimation of the evolution variance parameter

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13 Citations (Scopus)

Abstract

Statistical models of football (soccer) match outcomes have potential applications to areas such as the development of team rankings and football betting markets. Much of the published work in this context has typically focused on the use of generalized linear models, which are non-dynamic in the sense that the parameters in the model, which often represent the underlying abilities of each team, are assumed to remain constant over time. Dynamic generalized linear models (DGLMs) on the other hand allow the abilities of each team to vary over time. This paper illustrates the application of a DGLM in the context of football match outcome prediction and describes improvements on similar work previously presented by the author, in relation to the estimation of a parameter in the model, referred to as the evolution variance, which is crucial in terms of optimizing the predictive performance of these types of models. Match results data from the Scottish Premier League from 2003/2004 to 2005/2006 are used to show that the DGLM approach provides improved predictive probabilities of future match outcomes compared to the non-dynamic form of the model. DGLMs are also Bayesian in terms of their structure and so a Bayesian approach to parameter estimation is required. This paper therefore illustrates a practical implementation of the DGLM model that can easily be deployed using the freely available software WinBUGS.
Original languageEnglish
Pages (from-to)99-113
Number of pages15
JournalIMA Journal of Management Mathematics
Volume22
Issue number2
DOIs
Publication statusPublished - 20 Jan 2011

Fingerprint

Dynamic Linear Models
Generalized Linear Model
Forecasting
Model
WinBUGS
Potential Outcomes
Bayesian Approach
Statistical Model
Parameter Estimation
Bayesian forecasting
Generalized linear model
Football
Ranking
Vary
Software
Prediction
Parameter estimation

Bibliographical note

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Keywords

  • dynamic generalised linear models
  • Bayesian
  • evolution variance
  • football
  • Scottish Premier League

Cite this

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abstract = "Statistical models of football (soccer) match outcomes have potential applications to areas such as the development of team rankings and football betting markets. Much of the published work in this context has typically focused on the use of generalized linear models, which are non-dynamic in the sense that the parameters in the model, which often represent the underlying abilities of each team, are assumed to remain constant over time. Dynamic generalized linear models (DGLMs) on the other hand allow the abilities of each team to vary over time. This paper illustrates the application of a DGLM in the context of football match outcome prediction and describes improvements on similar work previously presented by the author, in relation to the estimation of a parameter in the model, referred to as the evolution variance, which is crucial in terms of optimizing the predictive performance of these types of models. Match results data from the Scottish Premier League from 2003/2004 to 2005/2006 are used to show that the DGLM approach provides improved predictive probabilities of future match outcomes compared to the non-dynamic form of the model. DGLMs are also Bayesian in terms of their structure and so a Bayesian approach to parameter estimation is required. This paper therefore illustrates a practical implementation of the DGLM model that can easily be deployed using the freely available software WinBUGS.",
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