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.
Baio, G., and M. Blangiardo. 2010. “Bayesian Hierarchical Model for the Prediction of Football Results.” Journal of Applied Statistics 37(2):253–264.10.1080/02664760802684177)| false
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.
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.10.1016/j.knosys.2012.07.008)| false
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.10.1016/j.knosys.2013.05.008)| false
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 P. Pope. 2004. “The Value of Statistical Forecasts in the UK Association Football Betting Market.” International Journal of Forecasting 20:697–711.10.1016/j.ijforecast.2003.12.007)| false
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.
Halicioglu, F. 2005a. “Can We Predict the Outcome of the International Football Tournaments?: The Case of Euro 2000.” Doğuş Üniversitesi Dergisi 6:112–122.10.31671/dogus.2019.288)| false
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):591–602.10.1046/j.0039-0526.2003.00437.x)| false
Hvattum, L. M. and H. Arntzen. 2010. “Using ELO Ratings for Match Result Prediction in Association Football.” International Journal of Forecasting 26:460–470.
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 26:471–481.10.1016/j.ijforecast.2009.10.001)| false
Maher, M. J. 1982. “Modelling Association Football Scores.” Statististica Neerlandica 36:109–118.
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 21:551–562.10.1016/j.knosys.2008.03.016)| false
Mueller, F. O., R. C. Cantu and S. P. Camp. 1996. Catastrophic Injuries in High School and College Sports. Champaign: Human Kinetics, p. 57.
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):619–630.10.1007/s10559-005-0098-4)| false
Rue, H. and O. Salvesen. 2000. “Prediction and Retrospective Analysis of Soccer Matches in a League.” The Statistician 3:339–418.
Rue, H. and O. Salvesen. 2000. “Prediction and Retrospective Analysis of Soccer Matches in a League.” The Statistician 3:339–418.10.1111/1467-9884.00243)| false
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.
JQAS, an official journal of the American Statistical Association, publishes research on the quantitative aspects of professional and collegiate sports. Articles deal with subjects as measurements of player performance, tournament structure, and the frequency and occurrence of records. Additionally, the journal serves as an outlet for professionals in the sports world to raise issues and ask questions that relate to quantitative sports analysis.