Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Rigdon, Steve

Editorial Board Member: Glickman, PhD, Mark

4 Issues per year


CiteScore 2016: 0.44

SCImago Journal Rank (SJR) 2015: 0.288
Source Normalized Impact per Paper (SNIP) 2015: 0.358

Online
ISSN
1559-0410
See all formats and pricing
More options …

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

Francisco Corona / Juan de Dios Tena Horrillo
  • Corresponding author
  • University of Liverpool, Management School, Chatham Street, Liverpool L19 7ZH, UK, Tel.: + 44(0)1514752486
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Michael Peter Wiper
Published Online: 2017-03-03 | DOI: https://doi.org/10.1515/jqas-2016-0013

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.

Keywords: entropy; game importance; Kernel regression; ordered probit model; Poisson model

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.Google Scholar

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

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

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

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

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

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

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

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

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

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

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

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

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

About the article

Published Online: 2017-03-03

Published in Print: 2017-03-01


Citation Information: Journal of Quantitative Analysis in Sports, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2016-0013.

Export Citation

©2017 Walter de Gruyter GmbH, Berlin/Boston. Copyright Clearance Center

Comments (0)

Please log in or register to comment.
Log in