  The complexity of game play in online multiplayer games has generated strong
interest in modeling the different play styles or strategies used by players
for success. We develop a hierarchical Bayesian regression approach for the
online multiplayer game Battlefield 3 where performance is modeled as a
function of the roles, game type, and map taken on by that player in each of
their matches. We use a Dirichlet process prior that enables the clustering of
players that have similar player-specific coefficients in our regression model,
which allows us to discover common play styles amongst our sample of
Battlefield 3 players. This Bayesian semi-parametric clustering approach has
several advantages: the number of common play styles do not need to be
specified, players can move between multiple clusters, and the resulting
groupings often have a straight-forward interpretations. We examine the most
common play styles among Battlefield 3 players in detail and find groups of
players that exhibit overall high performance, as well as groupings of players
that perform particularly well in specific game types, maps and roles. We are
also able to differentiate between players that are stable members of a
particular play style from hybrid players that exhibit multiple play styles
across their matches. Modeling this landscape of different play styles will aid
game developers in developing specialized tutorials for new participants as
well as improving the construction of complementary teams in their online
matching queues.
