Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Predicting football scores via Poisson regression model: applications to the National Football League

  • Saraiva, Erlandson F. (Institute of Mathematics, Federal University of Mato Grosso do Sul) ;
  • Suzuki, Adriano K. (Department of Applied Mathematics and Statistics, University of Sao Paulo) ;
  • Filho, Ciro A.O. (Department of Applied Mathematics and Statistics, University of Sao Paulo) ;
  • Louzada, Francisco (Department of Applied Mathematics and Statistics, University of Sao Paulo)
  • Received : 2016.05.05
  • Accepted : 2016.05.30
  • Published : 2016.07.31
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Abstract

Football match predictions are of great interest to fans and sports press. In the last few years it has been the focus of several studies. In this paper, we propose the Poisson regression model in order to football match outcomes. We applied the proposed methodology to two national competitions: the 2012-2013 English Premier League and the 2015 Brazilian Football League. The number of goals scored by each team in a match is assumed to follow Poisson distribution, whose average reflects the strength of the attack, defense and the home team advantage. Inferences about all unknown quantities involved are made using a Bayesian approach. We calculate the probabilities of win, draw and loss for each match using a simulation procedure. Besides, also using simulation, the probability of a team qualifying for continental tournaments, being crowned champion or relegated to the second division is obtained.

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