  We show that the Brier game of prediction is mixable and find the optimal
learning rate and substitution function for it. The resulting prediction
algorithm is applied to predict results of football and tennis matches. The
theoretical performance guarantee turns out to be rather tight on these data
sets, especially in the case of the more extensive tennis data.
