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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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