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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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