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Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models

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  • Constantinou Anthony Costa

    (Queen Mary, University of London)

  • Fenton Norman Elliott

    (Queen Mary, University of London)

Abstract

Despite the massive popularity of probabilistic (association) football forecasting models, and the relative simplicity of the outcome of such forecasts (they require only three probability values corresponding to home win, draw, and away win) there is no agreed scoring rule to determine their forecast accuracy. Moreover, the various scoring rules used for validation in previous studies are inadequate since they fail to recognise that football outcomes represent a ranked (ordinal) scale. This raises severe concerns about the validity of conclusions from previous studies. There is a well-established generic scoring rule, the Rank Probability Score (RPS), which has been missed by previous researchers, but which properly assesses football forecasting models.

Suggested Citation

  • Constantinou Anthony Costa & Fenton Norman Elliott, 2012. "Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-14, March.
    • Handle: RePEc:bpj:jqsprt:v:8:y:2012:i:1:n:12
    DOI: 10.1515/1559-0410.1418
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    6. Wheatcroft Edward, 2021. "Evaluating probabilistic forecasts of football matches: the case against the ranked probability score," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(4), pages 273-287, December.
    7. Lechner, Michael & Okasa, Gabriel, 2019. "Random Forest Estimation of the Ordered Choice Model," Economics Working Paper Series 1908, University of St. Gallen, School of Economics and Political Science.
    8. Gross, Johannes & Rebeggiani, Luca, 2018. "Chance or Ability? The Efficiency of the Football Betting Market Revisited," MPRA Paper 87230, University Library of Munich, Germany.
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    10. Wheatcroft, Edward, 2021. "Evaluating probabilistic forecasts of football matches: the case against the ranked probability score," LSE Research Online Documents on Economics 111494, London School of Economics and Political Science, LSE Library.
    11. Pearson Mitchell & Jr Glen Livingston & King Robert, 2020. "An exploration of predictive football modelling," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 27-39, March.
    12. Koopman, Siem Jan & Lit, Rutger, 2019. "Forecasting football match results in national league competitions using score-driven time series models," International Journal of Forecasting, Elsevier, vol. 35(2), pages 797-809.
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    15. Zachary J. Smith & J. Eric Bickel, 2020. "Additive Scoring Rules for Discrete Sample Spaces," Decision Analysis, INFORMS, vol. 17(2), pages 115-133, June.

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