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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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    1. 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).
    2. Rick L. Wilson, 1995. "Ranking College Football Teams: A Neural Network Approach," Interfaces, INFORMS, vol. 25(4), pages 44-59, August.
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    4. repec:dgr:rugsom:00f40 is not listed on IDEAS
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    7. 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.
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    12. 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.
    13. Corona Francisco & Horrillo Juan de Dios Tena & Wiper Michael Peter, 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.
    14. Corona, Francisco & Forrest, David, 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.
    15. 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.
    16. 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.
    17. 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.
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    19. 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.
    20. 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.
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