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Bayesian hierarchical model for the prediction of football results

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  • Gianluca Baio
  • Marta Blangiardo

Abstract

The problem of modelling football data has become increasingly popular in the last few years and many different models have been proposed with the aim of estimating the characteristics that bring a team to lose or win a game, or to predict the score of a particular match. We propose a Bayesian hierarchical model to fulfil both these aims and test its predictive strength based on data about the Italian Serie A 1991-1992 championship. To overcome the issue of overshrinkage produced by the Bayesian hierarchical model, we specify a more complex mixture model that results in a better fit to the observed data. We test its performance using an example of the Italian Serie A 2007-2008 championship.

Suggested Citation

  • Gianluca Baio & Marta Blangiardo, 2010. "Bayesian hierarchical model for the prediction of football results," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 253-264.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:253-264
    DOI: 10.1080/02664760802684177
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    1. Chib, Siddhartha & Winkelmann, Rainer, 2001. "Markov Chain Monte Carlo Analysis of Correlated Count Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 428-435, October.
    2. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
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    7. Rose D. Baker & Ian G. McHale, 2015. "Time varying ratings in association football: the all-time greatest team is.," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(2), pages 481-492, February.
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    12. Robert C. Smit & Francesco Ravazzolo & Luca Rossini, 2020. "Dynamic Bayesian forecasting of English Premier League match results with the Skellam distribution," BEMPS - Bozen Economics & Management Paper Series BEMPS72, Faculty of Economics and Management at the Free University of Bozen.
    13. 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.
    14. Jacek Osiewalski & Jerzy Marzec, 2019. "Joint modelling of two count variables when one of them can be degenerate," Computational Statistics, Springer, vol. 34(1), pages 153-171, March.
    15. Anthony J. Vine, 2016. "Using Pythagorean Expectation to Determine Luck in the KFC Big Bash League," Economic Papers, The Economic Society of Australia, vol. 35(3), pages 269-281, September.
    16. Federico Fioravanti & Fernando Delbianco & Fernando Tohmé, 2023. "The relative importance of ability, luck and motivation in team sports: a Bayesian model of performance in the English Rugby Premiership," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 715-731, September.
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