Evaluating probabilistic forecasts of football matches: The case against the Ranked Probability Score
Edward Wheatcroft

TL;DR
This paper critically evaluates scoring rules for football match forecasts, demonstrating that the ignorance score outperforms the ranked probability score and Brier score, questioning the value of sensitivity to outcome distance.
Contribution
It challenges the common preference for the ranked probability score by empirically showing the ignorance score's superior performance in football forecast evaluation.
Findings
Ignorance score outperforms RPS and Brier score in experiments.
Sensitivity to outcome distance may not be beneficial for forecast evaluation.
Local scoring rules can be more effective than non-local ones in this context.
Abstract
A scoring rule is a function of a probabilistic forecast and a corresponding outcome that is used to evaluate forecast performance. A wide range of scoring rules have been defined over time and there is some debate as to which are the most appropriate for evaluating the performance of 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, for example). In this paper, this reasoning is disputed on the basis that it adds nothing in terms of the actual aims of using scoring rules. A related property of scoring rules is locality. A scoring rule is local if it only takes the probability placed on the outcome into consideration. Two…
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