  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 simulation experiments are carried out in
the context of football matches 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 is found to outperform 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.
