  We consider clustering player behavior and learning the optimal team
composition for multiplayer online games. The goal is to determine a set of
descriptive play style groupings and learn a predictor for win/loss outcomes.
The predictor takes in as input the play styles of the participants in each
team; i.e., the various team compositions in a game. Our framework uses
unsupervised learning to find behavior clusters, which are, in turn, used with
classification algorithms to learn the outcome predictor. For our numerical
experiments, we consider League of Legends, a popular team-based role-playing
game developed by Riot Games. We observe the learned clusters to not only
corroborate well with game knowledge, but also provide insights surprising to
expert players. We also demonstrate that game outcomes can be predicted with
fairly high accuracy given team composition-based features.
