Communications for Statistical Applications and Methods

  • 제23권4호
  • /
  • Pages.297-319
  • /
  • 2016
  • /
  • 2287-7843(pISSN)
  • /
  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

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)
  • 투고 : 2016.05.05
  • 심사 : 2016.05.30
  • 발행 : 2016.07.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

  1. Baio G and Blangiardo M (2010). Bayesian hierarchical model for the prediction of football result, Journal of Applied Statistics, 37, 253-264. https://doi.org/10.1080/02664760802684177
  2. Bastos LS and da Rosa JMC (2013). Predicting probabilities for the 2010 FIFA world cup games using a Poisson-Gamma model, Journal of Applied Statistics, 40, 1533-1544. https://doi.org/10.1080/02664763.2013.788619
  3. Brillinger DR (2008). Modelling game outcomes of the Brazilian 2006 series a championship as ordinal-valued, Brazilian Journal of Probability Statistics, 22, 89-104.
  4. Chib S and Greenberg E (1995). Understanding the Metropolis-Hastings algorithm, The American Statistician, 49, 327-335.
  5. Dixon MJ and Coles SG (1997). Modelling association football scores and inefficiencies in the foot-ball betting market, Journal of the Royal Statistical Society: Series C (Applied Statistics), 46, 265-280. https://doi.org/10.1111/1467-9876.00065
  6. Dyte D and Clarke SR (2000). A ratings based Poisson model for World Cup soccer simulation, Journal of the Operational Research Society, 51, 993-998. https://doi.org/10.1057/palgrave.jors.2600997
  7. Gelman A, Carlin JB, Stern HS, and Rubin DB (1995). Bayesian Data Analysis, Chapman and Hall, London.
  8. Gelman A, Sturtz S, Ligges U, Gorjane G, and Kerman J (2006). The R2WinBUGS Package Manual Version 2.0-4, Statistic Department Faculty, New York.
  9. Gilks WR, Richardson S, and Spiegelhalter DJ (1996). Markov Chain Monte Carlo in Practice, Chapman and Hall, London.
  10. Hastings WK (1970). Monte Carlo sampling methods using Markov Chains and their applications, Biometrika, 57, 97-109. https://doi.org/10.1093/biomet/57.1.97
  11. Karlis D and Ntzoufras I (2003). Analysis of sports data by using bivariate Poisson models, Journal of the Royal Statistical Society: Series D (The Statistician), 52, 381-393. https://doi.org/10.1111/1467-9884.00366
  12. Karlis D and Ntzoufras I (2009). Bayesian modelling of football outcomes: using the Skellam's distribution for the goal difference, IMA Journal of Management Mathematics, 20, 133-145.
  13. Keller JB (1994). A characterization of the Poisson distribution and the probability of winning a game, The American Statistician, 48, 294-298.
  14. Knorr-Held L (2000). Dynamic rating of sports teams, Journal of the Royal Statistical Society: Series D (The Statistician), 49, 261-276. https://doi.org/10.1111/1467-9884.00236
  15. Koopman SJ and Lit R (2015). A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League, Journal of the Royal Statistical Society: Series A (Statistics in Society), 178, 167-186. https://doi.org/10.1111/rssa.12042
  16. Lee AJ (1997). Modeling scores in the Premier League: is Manchester United really the best?, Chance, 10, 15-19.
  17. Maher MJ (1982). Modeling association football scores, Statistica Neerlandica, 36, 109-118. https://doi.org/10.1111/j.1467-9574.1982.tb00782.x
  18. R Development Core Team (2012). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.
  19. Spiegelhalter DJ, Thomas A, Best NG, and Lunn D (2003). WinBUGS User Manual (Version 1.4.1), MRC Biostatistics Unit, Cambridge, UK.
  20. Suzuki AK, Salasar LEB, Leite JG, and Louzada-Neto F (2010). A Bayesian approach for predicting match outcomes: the 2006 (Association) Football World Cup, Journal of the Operational Research Society, 61, 1530-1539. https://doi.org/10.1057/jors.2009.127
  21. Volf P (2009). A random point process model for the score in sport matches, IMA Journal of Management Mathematics, 20, 121-131.