Modelling Penalty Cards in Football with Applications


Abstract


A compound Poisson distribution is used to study factors which can affect the showing of yellow and red cards in a football competition such as a national league, the FIFA World Cup or the UEFA Champi-ons League. The resulting model is applied to outcomes in the Spanish Football League during the season 2013–14, studying the partial and total effects on the home and away teams. It is shown that various factors, such as the victory of the away team, the goal difference be-tween the teams, the total number of fouls committed, the attacking play of the home team, whether the match is a derby or not, the stage reached in the league competition, the level of fair play, the age of the referee and his international experience or lack of it, can all influence the use of cards. The model works well, providing a simple tool which can be applied in this and other sports settings.


Keywords: Compound Distribution; Football; Yellow Card; Red Card

References


Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6):716–723.

Anders, A. and Rotthoff, K. (2011). Yellow cards: Do they matter? Journal of Quantitative Analysis in Sports, 7(1):1–12.

Boyko, R., Boyko, A., and Boyko, M. (2007). Referee bias contributes to home advantage in english premiership football. Journal of Sports Sciences, 25:1185–1194.

Cacaoullos, T. and Papageorgiou, P. (1982). Bivariate negative binomial-Poisson and negative binomial-Bernoulli models with an applicaton to ac-cident data. G. Kallianpur, P.R. Krishnaiah, J.K. Ghosh, eds. Statistics and Probability Essays in Honor of C.R. Rao, pages 155–168.

Cameron, C. and Trivedi, P. (1998). Regression Analysis of Count Data. Cambridge University Press.

Dawson, P. (2012). Experience, social pressure and preformance: the case of soccer officials. Applied Economic Letters, 19:883–886.

Dawson, P., Dobson, S., Goddard, J., and Wilson, J. (2007). Are football referees really biased and inconsistent? evidence from the english premier league. Journal of the Royal Statistical Society, Series A, 170:231–250.

Dohmen, T. (2008). The influence of social forces: Evidence from the behav-ior of football referees. Economic Inquiry, 46:411–424.

Greenhough, J., Birch, P., Chapman, S., and Rowlands, G. (2002). Football goal distributions and extremal statistics. Physica A, 316:615–624.

Kim, D. (2013). A simple zero in ated bivariate negative binomial regression model with different dispersion parameters. Journal of the Korean Data & Information Science Society, 24(4):895–900.

Leiter, R. and Hamdan, M. (1973). Some bivariate probability models appli-cable to traffic accidents and fatalities. Int. Stat. Rev., 41(1):87–100.

Lex, H., Pizzera, A., Kurtes, M., and Schack, T. (2015). Influence of play-ers’ vocalisations on soccer referees’ decisions. European Journal of Sport Science, 15(4):424–428.

Nevill, A., Balmer, N., and Williams, A. (2002). The influence of crowd noise and experience upon refereeing decisions in football. Psychology of Sport and Exercise, 3:261–272.

Reilly, B. and Witt, R. (2013). Red cards, referee home bias and social pressure: evidence from english premiership soccer. Applied Economic Letters, 20(7):710–714.

Ridder, G., Cramer, J., and Hopstaken, P. (1994). Down to ten: Estimating the effect of a red card in soccer. Journal of the American Statistical Association, 89(427):1124–1127.

Vecer, J., Kopriva, F., and Ichiba, T. (2009). Estimating the effect of the red cards in soccer: When to commit an ofense in exchange for preventing a goal opportunity. Journal of Quantitative Analysis in Sports, 18(1):1–20.

Wolfram, S. (2003). The Mathematica Book. Wolfram Media, Inc.


Full Text: pdf


Creative Commons License
This work is licensed under a Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License.