Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter March 3, 2017

On the importance of the probabilistic model in identifying the most decisive games in a tournament

Francisco Corona, Juan de Dios Tena Horrillo and Michael Peter Wiper

Abstract

Identifying the decisive matches in international football tournaments is of great relevance for a variety of decision makers such as organizers, team coaches and/or media managers. This paper addresses this issue by analyzing the role of the statistical approach used to estimate the outcome of the game on the identification of decisive matches on international tournaments for national football teams. We extend the measure of decisiveness proposed by Geenens (2014) in order to allow us to predict or evaluate the decisive matches before, during and after a particular game on the tournament. Using information from the 2014 FIFA World Cup, our results suggest that Poisson and kernel regressions significantly outperform the forecasts of ordered probit models. Moreover, we find that although the identification of the most decisive matches is independent of the model considered, the identification of other key matches is model dependent. We also apply this methodology to identify the favorite teams and to predict the most decisive matches in 2015 Copa America before the start of the competition. Furthermore, we compare our forecast approach with respect to the original measure during the knockout stage.

Acknowledgement

We are very grateful to the editor, two anonymous referees and the associate editor for incisive suggestions and to Ruud H.Koning for helpful comments to an early version of this paper.

References

Audas, R., S. Dobson, and J. Goddard. 2002. “The Impact of Managerial Change on Team Performance in Professional Sports.” Journal of Economics and Business 54(3):633–650.Search in Google Scholar

Bickel, J. E. 2007. “Some Comparisons Among Quadratic, Spherical, and Logarithmic Scoring Rules.” Decision Analysis 4(2):29–65.Search in Google Scholar

Boero, G., J. Smith, and K. F. Wallis. 2011. “Scoring Rules and Survey Density Forecast.” International Journal of Forecasting 27:379–393.Search in Google Scholar

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 C 46(2):265–280.Search in Google Scholar

Dyte, D. and S. R. Clarke. 2000. “A Ratings Based Poisson Model for World Cup Soccer Simulation.” Journal of the Operational Research Society 51:993–998.Search in Google Scholar

Geenens, G. 2014. “On the Decisiveness of a Game in a Tournament.” European Journal of Operational Research 232:156–168.Search in Google Scholar

Giacomini, R. and H. White. 2006. “Tests of Conditional Predictive Anility.” Econometrica 74:1545–1578.Search in Google Scholar

Gonzalez, I., P. G. P. Martin, S. Dejean, and A. Bacioni. 2008. “CCA: an R package to extend canonical correlation analysis.” Annals of Operations Research 23(12):1–14.Search in Google Scholar

Goossens, D., J. Beliën, and F. C. R. Spieksma. 2012. “Comparing League Formats with Respect to Match Importance in Belgian football.” Annals of Operations Research 191(1):223–240.Search in Google Scholar

Groll, A., G. Schauberger, and G. Tutz. 2015. “Prediction of a Major International Soccer Tournaments Based on Team-Specific Regularized Poisson Regression: An Application to the FIFA World Cup 2014.” Journal of Quantitative Analysis in Sports 11(2):97–115.Search in Google Scholar

Hilbe, J. 2014. Modeling Count Data. New York, NY: Cambridge University Press.Search in Google Scholar

Koning, R., M. Koolhaas, G. Renes, and G. Ridder. 2003. “A Simulation Model for Football Championships.” European Journal of Operational Research 142(2):268–276.Search in Google Scholar

Kuypers, T. 2000. “Information and Efficiency: An Empirical Study of a Fixed Odds Betting Market.” Applied Economics 32:1353–1363.Search in Google Scholar

Lesne, A. 2014. “Shannon Entropy: A Rigorous Notion at the Crossroads Between Probability, Information Theory, Dynamical Systems and Statistical Physics.” Mathematical Structures in Computer Science 24(3):e240311, 63 pages.Search in Google Scholar

Leurgans, S. E., R. A. Moyeed, and B. W. Silverman. 1993. “Canonical Correlation Analysis When the Data are Curves.” Journal of the Royal Statistical Society B 55(3):725–740.Search in Google Scholar

Maher, M. J. 1982. “Modelling Association Football Scores.” Statistica Neerlandica 36:109–118.Search in Google Scholar

McCullagh, P. 1980. “Regression Models for Ordinal Data.” Journal of the Royal Statistical Society. Series B (Methodological) 42(2):109–142.Search in Google Scholar

McHale, I. and S. Davies. 2007. Statistical Analysis of the FIFA World Rankings in R. Koning and J. Albert (eds.), Statistical Thinking in Sport. London: Chapman and Hall.Search in Google Scholar

Moroney, M. J. 1956. Facts from Figures. London: Penguin.Search in Google Scholar

Scarf, P. A. and X. Shi. 2008. “The Importance of a Match in a Tournament.” Computers and Operations Research 35:2406–2418.Search in Google Scholar

Schilling, M. F. 1994. “The Importance of a Game.” Mathematics Magazine 67:282–288.Search in Google Scholar

Suzuki, A. K., L. E. B. Salasar, J. G. Leite, and F. Lozada-Neto. 2010. “A Bayesian Approach for Predicting Match Outcomes: The 2006 (Association) Football World Cup.”. Journal of the Operational Research Society 61:1530–1539.Search in Google Scholar

Tena, J. D. and D. Forrest. 2007. “Within-season Dismissal of Football Coaches: Statistical Analysis of Causes and Consequences.” European Journal of Operational Research 181(1):362–373.Search in Google Scholar

Wand, M. P. and M. C. Jones. 1995. Kernel Smoothing. London: Chapman and Hall.Search in Google Scholar

Winkelmann, R. 2000. Econometric Analysis of Count Data. Berlin: Springer-Verlag.Search in Google Scholar

Zeileis, A., C. Leitner, and K. Hornik. 2014. “Home Victory for Brazil in the 2014 FIFA World Cup.” Working Papers in Economics and Statistics. University of Innsbruck 2014(17):1–18.Search in Google Scholar

Published Online: 2017-3-3
Published in Print: 2017-3-1

©2017 Walter de Gruyter GmbH, Berlin/Boston