  In basketball and hockey, state-of-the-art player value statistics are often
variants of Adjusted Plus-Minus (APM). But APM hasn't had the same impact in
soccer, since soccer games are low scoring with a low number of substitutions.
In soccer, perhaps the most comprehensive player value statistics come from
video games, and in particular FIFA. FIFA ratings combine the subjective
evaluations of over 9000 scouts, coaches, and season-ticket holders into
ratings for over 18,000 players. This paper combines FIFA ratings and APM into
a single metric, which we call Augmented APM. The key idea is recasting APM
into a Bayesian framework, and incorporating FIFA ratings into the prior
distribution. We show that Augmented APM predicts better than both standard APM
and a model using only FIFA ratings. We also show that Augmented APM
decorrelates players that are highly collinear.
