With game play data, empirical approaches to clustering are typically based solely on game outcomes, e.g. kills, deaths, and score for each player. In this paper, we investigate a method for clustering players based on how a player’s choices relate to outcomes, or equivalently the latent player styles exhibited by players. Our approach is based on a Bayesian semi-parametric clustering method which has several advantages: the number of clusters do not need to be specified a priori; the technique can work with a very compact representation of each match (e.g. consisting primarily of indicator variables for player choices); a player can belong to multiple clusters and hence can have a hybrid style; and the resulting clusterings often have a straight-forward interpretation. To demonstrate the approach, we apply our method to multiplayer match logs from Battlefield 3 consisting of over 1200 players and 500,000 matches.