  Scoring rules are an important tool for evaluating the performance of
probabilistic forecasting schemes. In the binary case, scoring rules (which are
strictly proper) allow for a decomposition into terms related to the resolution
and to the reliability of the forecast. This fact is particularly well known
for the Brier Score. In this paper, this result is extended to forecasts for
finite--valued targets. Both resolution and reliability are shown to have a
positive effect on the score. It is demonstrated that resolution and
reliability are directly related to forecast attributes which are desirable on
grounds independent of the notion of scores. This finding can be considered an
epistemological justification of measuring forecast quality by proper scores. A
link is provided to the original work of DeGroot et al (1982), extending their
concepts of sufficiency and refinement. The relation to the conjectured
sharpness principle of Gneiting et al (2005a) is elucidated.
