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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. Constantinou Anthony Costa & Fenton Norman Elliott, 2012. "Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-14, March.
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    10. 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.
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    12. Andrés Ramírez Hassan & Johnatan Cardona Jiménez, 2014. "Which team will win the 2014 FIFA World Cup? A Bayesian approach for dummies," Documentos de Trabajo de Valor Público 10898, Universidad EAFIT.
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    14. Anna Bykova & Dennis Coates, 2022. "Professional team sporting success: do economic and personal freedom provide competitive advantages?," Economics of Governance, Springer, vol. 23(3), pages 323-358, December.
    15. Ruiz Francisco J. R. & Perez-Cruz Fernando, 2015. "A generative model for predicting outcomes in college basketball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 39-52, March.
    16. 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.
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    18. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.

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