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A probabilistic model for explaining the points achieved by a team in football competition: forecasting and regression with applications to the Spanish competition

  • Autores: Emilio Gómez Déniz Árbol académico, Nancy Dávila Cárdenes, José María Pérez Sánchez
  • Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 43, Nº. 1, 2019, págs. 95-112
  • Idioma: inglés
  • DOI: 10.2436/20.8080.02.81
  • Enlaces
  • Resumen
    • In the last decades, a lot of research papers applying statistical methods for analysing sports data have been published. Football, also called soccer, is one of the most popular sports all over the world organised in national championships in a round robin format in which the team reaching the most points at the end of the tournament wins the competition. The aim of this work is to develop a suitable probability model for studying the points achieved by a team in a football match. For this purpose, we built a discrete probability distribution taking values, zero for losing, one for a draw and three for a victory. We test its performance using data from the Spanish Football League (First division) during the 2013-14 season. Furthermore, the model provides an attractive framework for predicting points and incorporating covariates in order to study the factors affecting the points achieved by the teams.

  • Referencias bibliográficas
    • Baio, G. and Blangiado, M. (2010). Bayesian hierarchical model for the prediction of football result. Journal of Applied Statistics, 37, 253–264.
    • Brillinger, D. (2008). Modelling game outcome of the Brazilian 2006 series a championship as ordinalvalued. Brazilian Journal of Probability...
    • Dı́az, I. and Núñez, V. (2010). On the use of simulation methods to compute probabilities: application to the first division Spanish...
    • Fisher, R. (1934). The effects of methods of ascertainment upon the estimation of frequencies. Annals of Eugenics, 6, 13–25.
    • Greenhough, J., Birch, P., Chapman, S. and Rowlands, G. (2002). Football goal distributions and extremal statistics. Physica A, 316, 615–624.
    • Harandi, S.S. and Alamtsaz, M. (2013). Discrete alpha-skew-Laplace distribution. SORT, 39, 71–84.
    • Hon, L. and Parinduri, R. (2016). Does the three-point rule make soccer more exciting? evidence from a regression discontinuity design. Journal...
    • Johnson, N., Kemp, A. and Kotz, S. (2005). Univariate Discrete Distributions John Wiley, INC.
    • Karlis, D. and Ntzoufras, I. (2000). On modelling soccer data. Student, 3, 229–244.
    • Karlis, D. and Ntzoufras, I. (2003). Analysis of sports data by using bivariate Poisson models. Journal of the Royal Statistical Society....
    • Khatri, C. (1959). On certain properties of power-series distributions. Biometrika, 46, 486–490.
    • Lehmann, E. and G. Casella, G. (1998). Theory of Point Estimation Springer, New York.
    • Louzada, F., Suzuki, A. and Salasar, L.B. (2014). Predicting match outcomes in the English premier league: Which will be the final rank? Journal...
    • Noack, A. (1950). A class of random variables with discrete distributions. The Annals of Mathematical Statistics, 21, 127–132.
    • Patil, G. and Rao, C. (1978). Weighted distributions and size biased sampling with applications to wildlife populations and human families....
    • Pérez-Sánchez, J.M., Gómez-Déniz, E. and Dávila-Cárdenes, N. (2018). A comparative study of logistic models using an asymmetric...
    • Rue, H. and Salvesen, O. (2006). Prediction and retrospective analysis of soccer matches in a league. Journal of the Royal Statistical Society....
    • Ruskeepaa, H. (2009). Mathematica Navigator. Mathematics, Statistics, and Graphics. Third Edition Academic Press. USA.
    • Saraivaa, E., Suzuki, A., Ciro, A. and Luzadab, F. (2016). Predicting football scores via Poisson regression model: applications to the National...
    • Wolfram, S. (2003). The Mathematica Book Wolfram Media, Inc.

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