  Modelling football outcomes has gained increasing attention, in large part
due to the potential for making substantial profits. Despite the strong
connection existing between football models and the bookmakers' betting odds,
no authors have used the latter for improving the fit and the predictive
accuracy of these models. We have developed a hierarchical Bayesian Poisson
model in which the scoring rates of the teams are convex combinations of
parameters estimated from historical data and the additional source of the
betting odds. We apply our analysis to a nine-year dataset of the most popular
European leagues in order to predict match outcomes for their tenth seasons. In
this paper, we provide numerical and graphical checks for our model.
