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A Bayesian approach for predicting match outcomes: The 2006 (Association) Football World Cup

Author

Listed:
  • A K Suzuki

    (Universidade Federal de São Carlos)

  • L E B Salasar

    (Universidade Federal de São Carlos)

  • J G Leite

    (Universidade Federal de São Carlos)

  • F Louzada-Neto

    (Universidade Federal de São Carlos)

Abstract

In this paper we propose a Bayesian methodology for predicting match outcomes. The methodology is illustrated on the 2006 Soccer World Cup. As prior information, we make use of the specialists’ opinions and the FIFA ratings. The method is applied to calculate the win, draw and loss probabilities at each match and also to simulate the whole competition in order to estimate classification probabilities in group stage and winning tournament chances for each team. The prediction capability of the proposed methodology is determined by the DeFinetti measure and by the percentage of correct forecasts.

Suggested Citation

  • A K Suzuki & L E B Salasar & J G Leite & F Louzada-Neto, 2010. "A Bayesian approach for predicting match outcomes: The 2006 (Association) Football World Cup," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1530-1539, October.
    • Handle: RePEc:pal:jorsoc:v:61:y:2010:i:10:d:10.1057_jors.2009.127
    DOI: 10.1057/jors.2009.127
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    References listed on IDEAS

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    1. Everson Phil & Goldsmith-Pinkham Paul S, 2008. "Composite Poisson Models for Goal Scoring," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(2), pages 1-17, April.
    2. D Dyte & S R Clarke, 2000. "A ratings based Poisson model for World Cup soccer simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(8), pages 993-998, August.
    3. Percy, David F., 2002. "Bayesian enhanced strategic decision making for reliability," European Journal of Operational Research, Elsevier, vol. 139(1), pages 133-145, May.
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    Cited by:

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    3. Chater, Mario & Arrondel, Luc & Gayant, Jean-Pascal & Laslier, Jean-François, 2021. "Fixing match-fixing: Optimal schedules to promote competitiveness," European Journal of Operational Research, Elsevier, vol. 294(2), pages 673-683.
    4. Sebastián Cea & Guillermo Durán & Mario Guajardo & Denis Sauré & Joaquín Siebert & Gonzalo Zamorano, 2020. "An analytics approach to the FIFA ranking procedure and the World Cup final draw," Annals of Operations Research, Springer, vol. 286(1), pages 119-146, March.
    5. 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.
    6. Hopfensitz, Astrid & Mantilla, Cesar, 2019. "Emotional expressions by sports teams: An analysis of World Cup soccer player portraits," Journal of Economic Psychology, Elsevier, vol. 75(PB).
    7. Guillermo Durán, 2021. "Sports scheduling and other topics in sports analytics: a survey with special reference to Latin America," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 125-155, April.
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    10. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.

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