  In this work, we draw attention to a connection between skill-based models of
game outcomes and Gaussian process classification models. The Gaussian process
perspective enables a) a principled way of dealing with uncertainty and b) rich
models, specified through kernel functions. Using this connection, we tackle
the problem of predicting outcomes of football matches between national teams.
We develop a player kernel that relates any two football matches through the
players lined up on the field. This makes it possible to share knowledge gained
from observing matches between clubs (available in large quantities) and
matches between national teams (available only in limited quantities). We
evaluate our approach on the Euro 2008, 2012 and 2016 final tournaments.
