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Dynamic bayesian forecasting models of football match outcomes with estimation of the evolution variance parameter
journal contribution
posted on 2011-10-11, 09:12 authored by Alun OwenStatistical 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 otherhand 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 parameterestimation 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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School
- Science
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- Mathematics Education Centre
Citation
OWEN, A., 2011. Dynamic bayesian forecasting models of football match outcomes with estimation of the evolution variance parameter. IMA Journal of Management Mathematics, 22, pp. 99-113.Publisher
© The authors 2011. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.Version
- AM (Accepted Manuscript)
Publication date
2011Notes
This article was published in the journal, IMA Journal of Management Mathematics [© The authors 2011. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved] and the definitive version is available at: http://dx.doi.org/10.1093/imaman/dpq018ISSN
1471-678X;1471-6798Publisher version
Language
- en
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