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arXiv · 1705.04356

Comparing probabilistic predictive models applied to football

Marcio A. Diniz·Rafael Izbicki·Danilo Lopes·Luis Ernesto Salasar
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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.

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Marcio A. Diniz, Rafael Izbicki, Danilo Lopes, Luis Ernesto Salasar. 2017-05-11. Comparing probabilistic predictive models applied to football. https://doi.org/10.1080/01605682.2018.1457485

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