An exploration of predictive football modelling

Pearson Mitchell (), Jr Glen Livingston () and King Robert ()
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Pearson Mitchell: School of Mathematical and Physical Sciences, The University of Newcastle, Callaghan, Australia
Jr Glen Livingston: School of Mathematical and Physical Sciences, The University of Newcastle, Callaghan, Australia
King Robert: School of Mathematical and Physical Sciences, The University of Newcastle, Callaghan, Australia

Journal of Quantitative Analysis in Sports, 2020, vol. 16, issue 1, 27-39

Abstract: Predictive football modelling has become progressively popular over the last two decades. Due to this, numerous studies have proposed different types of statistical models to predict the outcome of a football match. This study provides a review of three different models published in the academic literature and then implements these on recent match data from the top football leagues in Europe. These models are then compared utilising the rank probability score to assess their predictive capability. Additionally, a modification is proposed which includes the travel distance of the away team. When tested on football leagues from both Australia and Russia, it is shown to improve predictive capability according to the rank probability score.

Keywords: bivariate Weibull count; Dixon-Coles; English Premier League; rank probability score; soccer (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1515/jqas-2019-0075

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