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Alternative ranking measures to predict international football results

Author

Listed:
  • Roberto Macrì Demartino

    (University of Padova)

  • Leonardo Egidi

    (University of Trieste)

  • Nicola Torelli

    (University of Trieste)

Abstract

Over the last few years, there has been a growing interest in the prediction and modelling of competitive sports outcomes, with particular emphasis placed on this area by the Bayesian statistics and machine learning communities. In this paper, we have carried out a comparative evaluation of statistical and machine learning models to assess their predictive performance for the 2022 FIFA World Cup and the 2023 CAF Africa Cup of Nations by evaluating alternative summaries of past performances related to the involved teams. More specifically, we consider the Bayesian Bradley-Terry-Davidson model, which is a widely used statistical framework for ranking items based on paired comparisons that have been applied successfully in various domains, including football. The analysis was performed including in some canonical goal-based models both the Bradley-Terry-Davidson derived ranking and the widely recognized Coca-Cola FIFA ranking commonly adopted by football fans and amateurs.

Suggested Citation

  • Roberto Macrì Demartino & Leonardo Egidi & Nicola Torelli, 2025. "Alternative ranking measures to predict international football results," Computational Statistics, Springer, vol. 40(4), pages 1899-1917, April.
  • Handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-024-01585-z
    DOI: 10.1007/s00180-024-01585-z
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    References listed on IDEAS

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    1. Mark E. Glickman, 1999. "Parameter Estimation in Large Dynamic Paired Comparison Experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 377-394.
    2. Groll Andreas & Ley Cristophe & Van Eetvelde Hans & Schauberger Gunther, 2019. "A hybrid random forest to predict soccer matches in international tournaments," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 271-287, December.
    3. A. Springall, 1973. "Response Surface Fitting Using a Generalization of the Bradley‐Terry Paired Comparison Model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(1), pages 59-68, March.
    4. Gianluca Baio & Marta Blangiardo, 2010. "Bayesian hierarchical model for the prediction of football results," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 253-264.
    5. Siem Jan Koopman & Rutger Lit, 2015. "A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 167-186, January.
    6. Groll Andreas & Ley Cristophe & Schauberger Gunther & Van Eetvelde Hans, 2019. "A hybrid random forest to predict soccer matches in international tournaments," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 271-287, December.
    7. Leonardo Egidi & Nicola Torelli, 2021. "Comparing Goal-Based and Result-Based Approaches in Modelling Football Outcomes," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 801-813, August.
    8. Osei, Prince P. & Davidov, Ori, 2022. "Bayesian linear models for cardinal paired comparison data," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    9. Mark Glickman, 2001. "Dynamic paired comparison models with stochastic variances," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(6), pages 673-689.
    10. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
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