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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Glickman, PhD, Mark

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1559-0410
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Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries

Anthony Costa Constantinou
  • Corresponding author
  • Electronic Engineering and Computer Science, Queen Mary, University of London, CS332, RIM GROUP, EECS, Mile End, London E1 4NS, UK
  • Email:
/ Norman Elliott Fenton
  • Electronic Engineering and Computer Science, Queen Mary, University of London, CS435, RIM GROUP, EECS, Mile End, London E1 4NS, UK
Published Online: 2013-03-30 | DOI: https://doi.org/10.1515/jqas-2012-0036

Abstract

A rating system provides relative measures of superiority between adversaries. We propose a novel and simple approach, which we call pi-rating, for dynamically rating Association Football teams solely on the basis of the relative discrepancies in scores through relevant match instances. The pi-rating system is applicable to any other sport where the score is considered as a good indicator for prediction purposes, as well as determining the relative performances between adversaries. In an attempt to examine how well the ratings capture a team’s performance, we have a) assessed them against two recently proposed football ELO rating variants and b) used them as the basis of a football betting strategy against published market odds. The results show that the pi-ratings outperform considerably the widely accepted ELO ratings and, perhaps more importantly, demonstrate profitability over a period of five English Premier League seasons (2007/2008–2011/2012), even allowing for the bookmakers’ built-in profit margin. This is the first academic study to demonstrate profitability against market odds using such a relatively simple technique, and the resulting pi-ratings can be incorporated as parameters into other more sophisticated models in an attempt to further enhance forecasting capability.

Keywords: dynamic sports rating; ELO rating; football betting; football prediction; football ranking

References

  • Baio, G., and M. Blangiardo. 2010. “Bayesian Hierarchical Model for the Prediction of Football Results.” Journal of Applied Statistics 37(2):253–264. [Web of Science] [Crossref]

  • Buchner, A., W. Dubitzky, A. Schuster, P. Lopes, P. O’Doneghue, J. Hughes, D. A. Bell, K. Adamson, J. A. White, J. M. C. C. Anderson and M. D. Mulvenna. 1997. Corporate Evidential Decision Making in Performance Prediction Domains. Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI ’97). Providence, Rhode Island, USA: Brown University.

  • Clarke, S. R. and J. M. Norman. 1995. “Home Ground Advantage of Individual Clubs in English Soccer.” The Statistician 44:509–521. [Crossref]

  • Constantinou, A. C. and N. E. Fenton. 2012. Evidence of an (Intended) Inefficient Association Football Gambling Market.Under Review. Draft available at: http://constantinou.info/downloads/papers/evidenceofinefficiency.pdf.

  • Constantinou, A. C., N. E. Fenton, and M. Neil. 2012a. “pi-football: A Bayesian Network Model for Forecasting Association Football Match Outcomes”. Knowledge-Based Systems, 322–339. Draft available at: http://www.constantinou.info/downloads/papers/pi-model11.pdf.

  • Constantinou, A. C., N. E. Fenton, and M. Neil. 2012b. Profiting from an Inefficient Association Football Gambling Market: Prediction, Risk and Uncertainty Using Bayesian Networks.Under Review. Draft available at: http://www.constantinou.info/downloads/papers/pi-model12.pdf. [Web of Science]

  • Crowder, M., M. Dixon, A. Ledford and M. Robinson. 2002. “Dynamic Modelling and Prediction of English Football League Matches for Betting.” The Statistician 51:157–168.

  • Dixon, M., and S. Coles. 1997. “Modelling Association Football Scores and Inefficiencies in the Football Betting Market.” Applied Statistics 46:265–280.

  • Dixon, M., and P. Pope. 2004. “The Value of Statistical Forecasts in the UK Association Football Betting Market.” International Journal of Forecasting 20:697–711. [Crossref]

  • Dunning, E. 1999. Sport Matters: Sociological Studies of Sport, Violence and Civilisation. London: Routledge.

  • Dunning, E. G., A Joseph and R.E. Maguire. 1993. The Sports Process: A Comparative and Developmental Approach. p. 129. Champaign: Human Kinetics.

  • Elo, A. E. 1978. The Rating of Chess Players, Past and Present. New York: Arco Publishing.

  • Fenton, N. E. and M. Neil. 2012. Risk Assessment and Decision Analysis with Bayesian Networks. London: Chapman and Hall.

  • FIFA. 2012. FIFA. Retrieved March 27, 2012, from FIFA/Coca-Cola World Ranking Procedure: http://www.fifa.com/worldranking/procedureandschedule/menprocedure/index.html.

  • Football-Data. 2012. Football-Data.co.uk. Retrieved August 2, 2012, from Football Results, Statistics & Soccer Betting Odds Data: http://www.football-data.co.uk/englandm.php.

  • Forrest, D., J. Goddard and R. Simmons. 2005. “Odds-Setters as Forecasters: The Case of English Football.” International Journal of Forecasting 21:551–564. [Crossref]

  • Goddard, J. 2005. “Regression Models for Forecasting Goals and Match Results in Association Football.” International Journal of Forecasting 21:331–340. [Crossref]

  • Goddard, J. and I. Asimakopoulos. 2004. “Forecasting Football Results and the Efficiency of Fixed-odds Betting.” Journal of Forecasting 23:51–66. [Crossref]

  • Halicioglu, F. 2005a. “Can We Predict the Outcome of the International Football Tournaments?: The Case of Euro 2000.” Doğuş Üniversitesi Dergisi 6:112–122.

  • Halicioglu, F. 2005b. Forecasting the Professional Team Sporting Events: Evidence from Euro 2000 and 2004 Football Tournaments. 5th International Conference on Sports and Culture: Economic, Management and Marketing Aspects. Athens, Greece, pp. 30–31.

  • Harville, D. A. 1977. “The Use of Linear-model Methodology to Rate High School or College Football Teams.” Journal of American Statistical Association 72:278–289.

  • Hirotsu, N. and M. Wright. 2003. “An Evaluation of Characteristics of Teams in Association Football by Using a Markov Process Model.” The Statistician 52(4):591–602.

  • Hvattum, L. M. and H. Arntzen. 2010. “Using ELO Ratings for Match Result Prediction in Association Football.” International Journal of Forecasting 26:460–470. [Crossref] [Web of Science]

  • Joseph, A., N. Fenton and M. Neil. 2006. “Predicting Football Results Using Bayesian Nets and Other Machine Learning Techniques.” Knowledge-Based Systems 7:544–553.

  • Karlis, D. and I. Ntzoufras. 2000. “On Modelling Soccer Data.” Student 229–244.

  • Karlis, D. and I. Ntzoufras. 2003. “Analysis of Sports Data by Using Bivariate Poisson Models.” The Statistician 52(3): 381–393.

  • Knorr-Held, L. 1997. Hierarchical Modelling of Discrete Longitudinal Data, Applications of Markov Chain Monte Carlo. Munich: Utz.

  • Knorr-Held, L. 2000. “Dynamic Rating of Sports Teams.” The Statistician 49(2):261–276.

  • Koning, R. 2000. “Balance in Competition in Dutch Soccer.” The Statistician 49(3):419–431.

  • Koning, R., H. Koolhaas, M. Renes and G. Ridder. 2003. “A Simulation Model for Football Championships.” European Journal of Operational Research 148:268–276. [Crossref]

  • Kuonen, D. 1996. Statistical Models for Knock-Out Soccer Tournaments. Technical Report, Department of Mathematics, Ècole Polytechnique Federale de Lausanne.

  • Kuypers, T. 2000. “Information and Efficiency: an Empirical Study of a Fixed Odds Betting Market.” Applied Economics 32: 1353–1363. [Crossref]

  • Lee, A. J. 1997. “Modeling Scores in the Premier League: is Manchester United Really the Best?” Chance 10:15–19. [Web of Science]

  • Leitner, C., A. Zeileis and K. Hornik. 2010. “Forecasting Sports Tournaments by Ratings of (prob)abilities: A Comparison for the EURO 2008.” International Journal of Forecasting 26:471–481. [Crossref] [Web of Science]

  • Maher, M. J. 1982. “Modelling Association Football Scores.” Statististica Neerlandica 36:109–118. [Crossref]

  • Min, B., J. Kim, C. Choe, H. Eom, and R. B. McKay. 2008. “A Compound Framework for Sports Results Prediction: A Football Case Study.” Knowledge-Based Systems 21:551–562.

  • Mueller, F. O., R. C. Cantu and S. P. Camp. 1996. Catastrophic Injuries in High School and College Sports. Champaign: Human Kinetics, p. 57.

  • Murali, V. (2011, October 28) Bleacher Report. Retrieved March 28, 2012, from World Football: 40 Biggest Scandals in Football History: http://bleacherreport.com/articles/909932-world-football-40-biggest-scandals-in-football-history.

  • Poulter, D. R. 2009. “Home Advantage and Player Nationality in International Club Football.” Journal of Sports Sciences 27(8):797–805. [Web of Science] [Crossref]

  • Reid, D. A. and M. S. Nixon. 2011. “Using Comparative Human Descriptions for Soft Biometrics.” International Joint Conference on Biometrics (IJCB) 2011.

  • Rotshtein, A., M. Posner and A. Rakytyanska. 2005. “Football Predictions Based on a Fuzzy Model with Genetic and Neural Tuning.” Cybernetics and Systems Analysis 41(4):619–630.

  • Rue, H. and O. Salvesen. 2000. “Prediction and Retrospective Analysis of Soccer Matches in a League.” The Statistician 3:339–418.

  • Tsakonas, A., G. Dounias, S. Shtovba and V. Vivdyuk. 2002. Soft Computing-Based Result Prediction of Football Games. The First International Conference on Inductive Modelling (ICIM 2002). Lviv, Ukraine.

About the article

Corresponding author: Anthony Costa Constantinou, Electronic Engineering and Computer Science, Queen Mary, University of London, CS332, RIM GROUP, EECS, Mile End, London E1 4NS, UK


Published Online: 2013-03-30


Citation Information: Journal of Quantitative Analysis in Sports, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2012-0036. Export Citation

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