  Competitive online games use rating systems to match players with similar
skills to ensure a satisfying experience for players. In this paper, we focus
on the importance of addressing different aspects of playing behavior when
modeling players for creating match-ups. To this end, we engineer several
behavioral features from a dataset of over 75,000 battle royale matches and
create player models based on the retrieved features. We then use the created
models to predict ranks for different groups of players in the data. The
predicted ranks are compared to those of three popular rating systems. Our
results show the superiority of simple behavioral models over mainstream rating
systems. Some behavioral features provided accurate predictions for all groups
of players while others proved useful for certain groups of players. The
results of this study highlight the necessity of considering different aspects
of the player's behavior such as goals, strategy, and expertise when making
assignments.
