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Comparing probabilistic predictive models applied to football

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  • Marcio Alves Diniz
  • Rafael Izbicki
  • Danilo Lopes
  • Luis Ernesto Salasar

Abstract

We propose two Bayesian multinomial-Dirichlet models to predict the final outcome of football (soccer) matches and compare them to three well-known models regarding their predictive power. All the models predicted the full-time results of 1710 matches of the first division of the Brazilian football championship and the comparison used three proper scoring rules, the proportion of errors and a calibration assessment. We also provide a goodness of fit measure. Our results show that multinomial-Dirichlet models are not only competitive with standard approaches, but they are also well calibrated and present reasonable goodness of fit.

Suggested Citation

  • Marcio Alves Diniz & Rafael Izbicki & Danilo Lopes & Luis Ernesto Salasar, 2019. "Comparing probabilistic predictive models applied to football," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(5), pages 770-782, May.
    • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:5:p:770-782
    DOI: 10.1080/01605682.2018.1457485
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    Cited by:

    1. Wheatcroft Edward, 2021. "Evaluating probabilistic forecasts of football matches: the case against the ranked probability score," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(4), pages 273-287, December.
    2. Guironnet, Jean-Pascal, 2023. "Competitive intensity and industry performance of professional sports," Economic Modelling, Elsevier, vol. 126(C).
    3. Raffaele Mattera, 2023. "Forecasting binary outcomes in soccer," Annals of Operations Research, Springer, vol. 325(1), pages 115-134, June.

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