In this article an approach for the analysis and prediction of international soccer match results is proposed. It is based on a regularized Poisson regression model that includes various potentially influential covariates describing the national teams’ success in previous FIFA World Cups. Additionally, within the generalized linear model (GLM) framework, also differences of team-specific effects are incorporated. In order to achieve variable selection and shrinkage, we use tailored Lasso approaches. Based on preceding FIFA World Cups, two models for the prediction of the FIFA World Cup 2014 are fitted and investigated. Based on the model estimates, the FIFA World Cup 2014 is simulated repeatedly and winning probabilities are obtained for all teams. Both models favor the actual FIFA World Champion Germany.
Akaike, H. 1973. “Information Theory and the Extension of the Maximum Likelihood Principle.” Second International Symposium on Information Theory 267–281.
Dixon, M. J. and S. G. Coles. 1997. “Modelling Association Football Scores and Inefficiencies in the Football Betting Market.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 46:265–280.
Dobson, S. and J. Goddard. 2011. The Economics of Football. Cambridge: Cambridge University Press.
Dyte, D. and S. R. Clarke. 2000. “A Ratings Based Poisson Model for World Cup Soccer Simulation.” Journal of the Operational Research Society 51(8):993–998.
Elo, A. E. 2008. The Rating of Chess Players. Past and Present, San Rafael: Ishi Press.
Eugster, M. J. A., J. Gertheiss, and S. Kaiser. 2011. “Having the Second Leg at Home – Advantage in the UEFA Champions League Knockout Phase?” Journal of Quantitative Analysis in Sports 7(1).
Forrest, D. and R. Simmons. 2000. “Forecasting Sport: The Behaviour and Performance of Football Tipsters.” International Journal of Forecasting 16:317–331.
Groll, A. and J. Abedieh. 2013. “Spain Retains its Title and Sets a New Record – Generalized Linear Mixed Models on European Football Championships.” Journal of Quantitative Analysis in Sports 9:51–66.
Groll, A. and G. Tutz. 2014. “Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation.” Statistics and Computing 24:137–154.
Hoerl, A. E. and R. W. Kennard. 1970. “Ridge Regression: Biased Estimation for Nonorthogonal Problems.” Technometrics 12:55–67.
Karlis, D. and I. Ntzoufras. 2003. “Analysis of Sports Data by Using Bivariate Poisson Models.” The Statistician 52:381–393.
Karlis, D. and I. Ntzoufras. 2011. “Robust Fitting of Football Prediction Models,” IMA Journal of Management Mathematics 22:171–182.
Koopman, S. J. and R. Lit. 2015. “A Dynamic Bivariate Poisson Model for Analysing and Forecasting Match Results in the English Premier League.” Journal of the Royal Statistical Society, A 178:167–186.
Lee, A. J. 1997. “Modeling Scores in the Premier League: Is Manchester United Really the Best?” Chance 10:15–19.
Leitner, C., A. Zeileis, and K. Hornik. 2010a. “Forecasting Sports Tournaments by Ratings of (Prob)abilities: A Comparison for the EURO 2008.” International Journal of Forecasting 26:471–481.
Leitner, C., A. Zeileis, and K. Hornik. 2010b. “Forecasting the Winner of the FIFA World Cup 2010.” Report Series / Department of Statistics and Mathematics, 100. Institute for Statistics and Mathematics, WU Vienna.
Stoy, V., R. Frankenberger, D. Buhr, L. Haug, B. Springer, and J. Schmid. 2010. “Das Ganze ist mehr als die Summe seiner Lichtgestalten. Eine ganzheitliche Analyse der Erfolgschancen bei der Fußballweltmeisterschaft 2010.” Working Paper 46, Eberhard Karls University, Tübingen, Germany.
Tibshirani, R. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society, B 58:267–288.
Yuan, M. and Y. Lin. 2006. “Model Selection and Estimation in Regression with Grouped Variables.” Journal of the Royal Statistical Society, B 68:49–67.
Zeileis, A., C. Leitner, and K. Hornik. 2012. “History Repeating: Spain Beats Germany in the EURO 2012 final.” Working Paper, Faculty of Economics and Statistics, University of Innsbruck.
Zeileis, A., C. Leitner, and K. Hornik. 2014. “Home Victory for Brazil in the 2014 FIFA World Cup.” Working paper, Faculty of Economics and Statistics, University of Innsbruck.
JQAS, an official journal of the American Statistical Association, publishes research on the quantitative aspects of professional and collegiate sports. Articles deal with subjects as measurements of player performance, tournament structure, and the frequency and occurrence of records. Additionally, the journal serves as an outlet for professionals in the sports world to raise issues and ask questions that relate to quantitative sports analysis.