  Forecast of football outcomes in terms of Home Win, Draw and Away Win relies
largely on ex ante probability elicitation of these events and ex post
verification of them via computation of probability scoring rules (Brier,
Ranked Probability, Logarithmic, Zero-One scores). Usually, appraisal of the
quality of forecasting procedures is restricted to reporting mean score values.
The purpose of this article is to propose additional tools of verification,
such as score decompositions into several components of special interest.
Graphical and numerical diagnoses of reliability and discrimination and kindred
statistical methods are presented using different techniques of binning (fixed
thresholds, quantiles, logistic and iso regression). These procedures are
illustrated on probability forecasts for the outcomes of the UEFA Champions
League (C1) at the end of the group stage based on typical Poisson regression
models with reasonably good results in terms of reliability as compared to
those obtained from bookmaker odds and whatever the technique used. Links with
research in machine learning and different areas of application (meteorology,
medicine) are discussed.
