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Analysis of the importance of on-field covariates in the German Bundesliga

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  • Gunther Schauberger
  • Andreas Groll
  • Gerhard Tutz

Abstract

In modern football, various variables as, for example, the distance a team runs or its percentage of ball possession, are collected throughout a match. However, there is a lack of methods to make use of these on-field variables simultaneously and to connect them with the final result of the match. This paper considers data from the German Bundesliga season 2015/2016. The objective is to identify the on-field variables that are connected to the sportive success or failure of the single teams. An extended Bradley–Terry model for football matches is proposed that is able to take into account on-field covariates. Penalty terms are used to reduce the complexity of the model and to find clusters of teams with equal covariate effects. The model identifies the running distance to be the on-field covariate that is most strongly connected to the match outcome.

Suggested Citation

  • Gunther Schauberger & Andreas Groll & Gerhard Tutz, 2018. "Analysis of the importance of on-field covariates in the German Bundesliga," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1561-1578, July.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:9:p:1561-1578
    DOI: 10.1080/02664763.2017.1383370
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    References listed on IDEAS

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    1. Gerhard Tutz & Gunther Schauberger, 2015. "Extended ordered paired comparison models with application to football data from German Bundesliga," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(2), pages 209-227, April.
    2. Fiona Carmichael & Dennis Thomas & Robert Ward, 2000. "Team performance: the case of English Premiership football," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 21(1), pages 31-45.
    3. Alan Agresti, 1992. "Analysis of Ordinal Paired Comparison Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 287-297, June.
    4. D Dyte & S R Clarke, 2000. "A ratings based Poisson model for World Cup soccer simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(8), pages 993-998, August.
    5. Groll Andreas & Schauberger Gunther & Tutz Gerhard, 2015. "Prediction of major international soccer tournaments based on team-specific regularized Poisson regression: An application to the FIFA World Cup 2014," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(2), pages 97-115, June.
    6. Brian Francis & Regina Dittrich & Reinhold Hatzinger & Roger Penn, 2002. "Analysing partial ranks by using smoothed paired comparison methods: an investigation of value orientation in Europe," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(3), pages 319-336, July.
    7. Turner, Heather & Firth, David, 2012. "Bradley-Terry Models in R: The BradleyTerry2 Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i09).
    8. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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    Cited by:

    1. Riccardo Ievoli & Aldo Gardini & Lucio Palazzo, 2023. "The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 153-175, March.

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