On the dependency of soccer scores – a sparse bivariate Poisson model for the UEFA European football championship 2016

Groll Andreas (), Kneib Thomas, Mayr Andreas and Schauberger Gunther
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Groll Andreas: Georg-August-Universität Göttingen, Department of Statistics and Econometrics, Göttingen, Niedersachsen, Germany; and Technische Universität Dortmund, Fakultät Statistik, Dortmund, Nordrhein-Westfalen, Germany
Kneib Thomas: Georg-August-Universität Göttingen, Department of Statistics and Econometrics, Göttingen, Niedersachsen, Germany
Mayr Andreas: Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Medizininformatik, Biometrie und Epidemiologie, Erlangen, Bavaria, Germany; and Institut für Medizinische Biometrie, Informatik und Epidemiologie, WG Statistical Methods in Epidemiology, University Hospital Bonn, Bonn, Germany
Schauberger Gunther: Ludwig-Maximilians-University, Department of Statistics, Munich, Bavaria, Germany; and Technical University of Munich, Department of Sport and Health Sciences, Chair of Epidemiology, Munich, Bavaria, Germany

Journal of Quantitative Analysis in Sports, 2018, vol. 14, issue 2, 65-79

Abstract: When analyzing and modeling the results of soccer matches, one important aspect is to account for the correct dependence of the scores of two competing teams. Several studies have found that, marginally, these scores are moderately negatively correlated. Even though many approaches that analyze the results of soccer matches are based on two (conditionally) independent pairwise Poisson distributions, a certain amount of (mostly negative) dependence between the scores of the competing teams can simply be induced by the inclusion of covariate information of both teams in a suitably structured linear predictor. One objective of this article is to analyze if this type of modeling is appropriate or if additional explicit modeling of the dependence structure for the joint score of a soccer match needs to be taken into account. Therefore, a specific bivariate Poisson model for the two numbers of goals scored by national teams competing in UEFA European football championship matches is fitted to all matches from the three previous European championships, including covariate information of both competing teams. A boosting approach is then used to select the relevant covariates. Based on the estimates, the tournament is simulated 1,000,000 times to obtain winning probabilities for all participating national teams.

Keywords: bivariate Poisson model; boosting; dependency of soccer scores; European championship 2016; variable selection (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1515/jqas-2017-0067

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