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Evaluating probabilistic forecasts of football matches: the case against the ranked probability score

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  • Wheatcroft Edward

    (London School of Economics and Political Science, London, UK)

Abstract

A scoring rule is a function of a probabilistic forecast and a corresponding outcome used to evaluate forecast performance. There is some debate as to which scoring rules are most appropriate for evaluating forecasts of sporting events. This paper focuses on forecasts of the outcomes of football matches. The ranked probability score (RPS) is often recommended since it is ‘sensitive to distance’, that is it takes into account the ordering in the outcomes (a home win is ‘closer’ to a draw than it is to an away win). In this paper, this reasoning is disputed on the basis that it adds nothing in terms of the usual aims of using scoring rules. A local scoring rule is one that only takes the probability placed on the outcome into consideration. Two simulation experiments are carried out to compare the performance of the RPS, which is non-local and sensitive to distance, the Brier score, which is non-local and insensitive to distance, and the Ignorance score, which is local and insensitive to distance. The Ignorance score outperforms both the RPS and the Brier score, casting doubt on the value of non-locality and sensitivity to distance as properties of scoring rules in this context.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:jqsprt:v:17:y:2021:i:4:p:273-287:n:1
    DOI: 10.1515/jqas-2019-0089
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    References listed on IDEAS

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    1. 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.
    2. Forrest, David & Goddard, John & Simmons, Robert, 2005. "Odds-setters as forecasters: The case of English football," International Journal of Forecasting, Elsevier, vol. 21(3), pages 551-564.
    3. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.
    4. Schmidt, Carsten & Strobel, Martin & Volkland, Henning Oskar, 2008. "Accuracy, certainty and surprise : a prediction market on the outcome of the 2002 FIFA World Cup," Papers 08-13, Sonderforschungsbreich 504.
    5. Daniel Friedman, 1983. "Effective Scoring Rules for Probabilistic Forecasts," Management Science, INFORMS, vol. 29(4), pages 447-454, April.
    6. Wheatcroft, Edward, 2019. "Interpreting the skill score form of forecast performance metrics," International Journal of Forecasting, Elsevier, vol. 35(2), pages 573-579.
    7. 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.
    8. Marcio Alves Diniz & Rafael Izbicki & Danilo Lopes & Luis Ernesto Salasar, 2019. "Comparing probabilistic predictive models applied to football," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(5), pages 770-782, May.
    9. Victor Richmond R. Jose & Robert F. Nau & Robert L. Winkler, 2009. "Sensitivity to Distance and Baseline Distributions in Forecast Evaluation," Management Science, INFORMS, vol. 55(4), pages 582-590, April.
    10. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    11. Reinhard Selten, 1998. "Axiomatic Characterization of the Quadratic Scoring Rule," Experimental Economics, Springer;Economic Science Association, vol. 1(1), pages 43-61, June.
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