IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i4d10.1007_s00180-024-01585-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01585-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-024-01585-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Blaž Krese & Erik Štrumbelj, 2021. "A Bayesian approach to time-varying latent strengths in pairwise comparisons," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-17, May.
    3. Giovanni Angelini & Luca De Angelis, 2017. "PARX model for football match predictions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(7), pages 795-807, November.
    4. Rose D. Baker & Ian G. McHale, 2015. "Time varying ratings in association football: the all-time greatest team is.," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(2), pages 481-492, February.
    5. Lasek, Jan & Gagolewski, Marek, 2021. "Interpretable sports team rating models based on the gradient descent algorithm," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1061-1071.
    6. Robert C. Smit & Francesco Ravazzolo & Luca Rossini, 2020. "Dynamic Bayesian forecasting of English Premier League match results with the Skellam distribution," BEMPS - Bozen Economics & Management Paper Series BEMPS72, Faculty of Economics and Management at the Free University of Bozen.
    7. Alejandro Álvarez & Alejandro Cataldo & Guillermo Durán & Manuel Durán & Pablo Galaz & Iván Monardo & Denis Sauré, 2025. "Data science approach to simulating the FIFA World Cup Qatar 2022 at a website in tribute to Maradona," Computational Statistics, Springer, vol. 40(4), pages 2223-2247, April.
    8. Luke S. Benz & Michael J. Lopez, 2023. "Estimating the change in soccer’s home advantage during the Covid-19 pandemic using bivariate Poisson regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 205-232, March.
    9. 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.
    10. Kharrat, Tarak & McHale, Ian G. & Peña, Javier López, 2020. "Plus–minus player ratings for soccer," European Journal of Operational Research, Elsevier, vol. 283(2), pages 726-736.
    11. Florez Mauro & Guindani Michele & Vannucci Marina, 2025. "Bayesian bivariate Conway–Maxwell–Poisson regression model for correlated count data in sports," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 21(1), pages 51-71.
    12. Leonardo Egidi & Ioannis Ntzoufras, 2020. "A Bayesian quest for finding a unified model for predicting volleyball games," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1307-1336, November.
    13. Devlin Stephen & Treloar Thomas & Creagar Molly & Cassels Samuel, 2021. "An iterative Markov rating method," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 117-127, June.
    14. Ilias Leriou & Ioannis Ntzoufras, 2025. "Survival modeling of goal arrival times in English premier league," Computational Statistics, Springer, vol. 40(4), pages 2109-2133, April.
    15. Jiří LahviÄ ka, 2015. "The Impact of Playoffs on Seasonal Uncertainty in the Czech Ice Hockey Extraliga," Journal of Sports Economics, , vol. 16(7), pages 784-801, October.
    16. Holmes, Benjamin & McHale, Ian G., 2024. "Forecasting football match results using a player rating based model," International Journal of Forecasting, Elsevier, vol. 40(1), pages 302-312.
    17. Gavin A. Whitaker & Ricardo Silva & Daniel Edwards & Ioannis Kosmidis, 2021. "A Bayesian approach for determining player abilities in football," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 174-201, January.
    18. P. Gorgi & S. J. Koopman & R. Lit, 2023. "Estimation of final standings in football competitions with a premature ending: the case of COVID-19," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 233-250, March.
    19. Munđar Dušan & Šimić Diana, 2016. "Croatian First Football League: Teams' performance in the championship," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 2(1), pages 15-23, September.
    20. Riccardo Ievoli & Aldo Gardini & Lucio Palazzo, 2023. "The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 153-175, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-024-01585-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.