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A simulation model for football championships

Author

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  • Koning, Ruud H.
  • Koolhaas, Michael
  • Renes, Gusta
  • Ridder, Geert

Abstract

In this paper we discuss a simulation/probability model that identifies the team that is most likely to win a tournament. The model can also be used to answer other questions like ‘which team had a lucky draw?’ or ‘what is the probability that two teams meet at some moment in the tournament?’. Input to the simulation/probability model are scoring intensities, that are estimated as a weighted average of goals scored. The model has been used in practice to write articles for the popular press, and seems to perform well.
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Suggested Citation

  • Koning, Ruud H. & Koolhaas, Michael & Renes, Gusta & Ridder, Geert, 2003. "A simulation model for football championships," European Journal of Operational Research, Elsevier, vol. 148(2), pages 268-276, July.
    • Handle: RePEc:eee:ejores:v:148:y:2003:i:2:p:268-276
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    References listed on IDEAS

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    1. Rick L. Wilson, 1995. "Ranking College Football Teams: A Neural Network Approach," Interfaces, INFORMS, vol. 25(4), pages 44-59, August.
    2. Koning, R.H., 2000. "An econometric evaluation of the firing of a coach on team performance," Research Report 00F40, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    3. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    4. repec:dgr:rugsom:00f40 is not listed on IDEAS
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    Cited by:

    1. Nicholas King & P. Dorian Owen & Rick Audas, 2012. "Playoff Uncertainty, Match Uncertainty and Attendance at Australian National Rugby League Matches," The Economic Record, The Economic Society of Australia, vol. 88(281), pages 262-277, June.
    2. Sumit Sarkar & Sooraj Kamath, 2023. "Does luck play a role in the determination of the rank positions in football leagues? A study of Europe’s ‘big five’," Annals of Operations Research, Springer, vol. 325(1), pages 245-260, June.
    3. László Csató, 2020. "Optimal Tournament Design: Lessons From the Men’s Handball Champions League," Journal of Sports Economics, , vol. 21(8), pages 848-868, December.
    4. Corona, Francisco & Forrest, David & Tena Horrillo, Juan de Dios & Wiper, Michael Peter, 2017. "Evaluating significant effects from alternative seeding systems : a Bayesian approach, with an application to the UEFA Champions League," DES - Working Papers. Statistics and Econometrics. WS 24521, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Hannes Rosenbusch & Jonas Röttger & David Rosenbusch, 2020. "Would Chuck Norris Certainly Win the Hunger Games? Simulating the Result Reliability of Battle Royale Games Through Agent-Based Models," Simulation & Gaming, , vol. 51(4), pages 461-476, August.
    6. Collingwood, James A.P. & Wright, Michael & Brooks, Roger J., 2023. "Simulating the progression of a professional snooker frame," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1286-1299.
    7. Ruud H. Koning & Ian G. McHale, 2012. "Estimating Match and World Cup Winning Probabilities," Chapters, in: Wolfgang Maennig & Andrew Zimbalist (ed.), International Handbook on the Economics of Mega Sporting Events, chapter 11, Edward Elgar Publishing.
    8. P. Dorian Owen & Nicholas King, 2015. "Competitive Balance Measures In Sports Leagues: The Effects Of Variation In Season Length," Economic Inquiry, Western Economic Association International, vol. 53(1), pages 731-744, January.
    9. Butler, David & Butler, Robert & Eakins, John, 2021. "Expert performance and crowd wisdom: Evidence from English Premier League predictions," European Journal of Operational Research, Elsevier, vol. 288(1), pages 170-182.
    10. Hofer, Vera & Leitner, Johannes, 2017. "Relative pricing of binary options in live soccer betting markets," Journal of Economic Dynamics and Control, Elsevier, vol. 76(C), pages 66-85.
    11. Dries Goossens & Jeroen Beliën & Frits Spieksma, 2012. "Comparing league formats with respect to match importance in Belgian football," Annals of Operations Research, Springer, vol. 194(1), pages 223-240, April.
    12. O'Leary, Daniel E., 2017. "Crowd performance in prediction of the World Cup 2014," European Journal of Operational Research, Elsevier, vol. 260(2), pages 715-724.
    13. Nicolau, Juan L., 2011. "The decision to raise firm value through a sports-business exchange: How much are Real Madrid's goals worth to its president's company's goals?," European Journal of Operational Research, Elsevier, vol. 215(1), pages 281-288, November.
    14. Corona, Francisco & Forrest, David & Tena, J.D. & Wiper, Michael, 2019. "Bayesian forecasting of UEFA Champions League under alternative seeding regimes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 722-732.
    15. Espitia-Escuer, Manuel A. & García-Cebrián, Lucía Isabel, 2012. "Diversificación en la configuración de los equipos de la primera división española de fútbol/Diversification in the Team Configuration of the Spanish Football First Division," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 30, pages 527-544, Agosto.
    16. Corona Francisco & Wiper Michael Peter & Horrillo Juan de Dios Tena, 2017. "On the importance of the probabilistic model in identifying the most decisive games in a tournament," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(1), pages 11-23, March.
    17. Dagaev Dmitry & Rudyak Vladimir Yu., 2019. "Seeding the UEFA Champions League participants: evaluation of the reforms," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(2), pages 129-140, June.
    18. Geenens Gery, 2010. "Who Deserved the 2008-2009 Belgian Football Champion Title? A Semiparametric Answer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(4), pages 1-31, October.
    19. Scarf, Phil & Parma, Rishikesh & McHale, Ian, 2019. "On outcome uncertainty and scoring rates in sport: The case of international rugby union," European Journal of Operational Research, Elsevier, vol. 273(2), pages 721-730.
    20. Constantinou Anthony Costa & Fenton Norman Elliott, 2013. "Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 37-50, March.
    21. J Bekker & W Lotz, 2009. "Planning Formula One race strategies using discrete-event simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(7), pages 952-961, July.
    22. repec:qut:auncer:2013_04 is not listed on IDEAS

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