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):633–650.10.1016/S0148-6195(02)00120-0Search in Google Scholar
Bickel, J. E. 2007. “Some Comparisons Among Quadratic, Spherical, and Logarithmic Scoring Rules.” Decision Analysis 4(2):29–65.10.1287/deca.1070.0089Search in Google Scholar
Boero, G., J. Smith, and K. F. Wallis. 2011. “Scoring Rules and Survey Density Forecast.” International Journal of Forecasting 27:379–393.10.1016/j.ijforecast.2010.04.003Search 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):265–280.10.1111/1467-9876.00065Search 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 51:993–998.10.1057/palgrave.jors.2600997Search in Google Scholar
Geenens, G. 2014. “On the Decisiveness of a Game in a Tournament.” European Journal of Operational Research 232:156–168.10.1016/j.ejor.2013.06.025Search in Google Scholar
Giacomini, R. and H. White. 2006. “Tests of Conditional Predictive Anility.” Econometrica 74:1545–1578.10.1111/j.1468-0262.2006.00718.xSearch 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):223–240.10.1007/s10479-010-0764-4Search 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):97–115.10.1515/jqas-2014-0051Search in Google Scholar
Hilbe, J. 2014. Modeling Count Data. New York, NY: Cambridge University Press.10.1017/CBO9781139236065Search 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):268–276.10.1016/S0377-2217(02)00683-5Search in Google Scholar
Kuypers, T. 2000. “Information and Efficiency: An Empirical Study of a Fixed Odds Betting Market.” Applied Economics 32:1353–1363.10.1080/00036840050151449Search 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.10.1017/S0960129512000783Search 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):725–740.10.1111/j.2517-6161.1993.tb01936.xSearch in Google Scholar
Maher, M. J. 1982. “Modelling Association Football Scores.” Statistica Neerlandica 36:109–118.10.1111/j.1467-9574.1982.tb00782.xSearch in Google Scholar
McCullagh, P. 1980. “Regression Models for Ordinal Data.” Journal of the Royal Statistical Society. Series B (Methodological) 42(2):109–142.10.1111/j.2517-6161.1980.tb01109.xSearch 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 35:2406–2418.10.1016/j.cor.2006.11.005Search in Google Scholar
Schilling, M. F. 1994. “The Importance of a Game.” Mathematics Magazine 67:282–288.10.1080/0025570X.1994.11996232Search 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 61:1530–1539.10.1057/jors.2009.127Search 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):362–373.10.1016/j.ejor.2006.05.024Search in Google Scholar
Wand, M. P. and M. C. Jones. 1995. Kernel Smoothing. London: Chapman and Hall.10.1007/978-1-4899-4493-1Search in Google Scholar
Winkelmann, R. 2000. Econometric Analysis of Count Data. Berlin: Springer-Verlag.10.1007/978-3-662-04149-9Search 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
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