Although player performance in online games has been widely studied, few studies have considered thebehavioral preferences of players and how that impacts performance. In a competitive setting where playersmust cooperate with temporary teammates, it is even more crucial to understand how differences in playingstyle contribute to teamwork. Drawing on theories of individual behavior in teams, we describe a methodologyto empirically profile players based on the diversity and conformity of their gameplay styles. Applying thisapproach to a League of Legends dataset, we find three distinct types of players that align with our theoreticalframework:generalists,specialists, andmavericks. Importantly, the behavior of each player type remainsstable despite players becoming more experienced. Additionally, we extensively investigate the benefits anddrawbacks of each type of player by evaluating their individual performance, contribution to the team, andadaptation to changes in the game environment. We find that, overall, specialists tend to outperform others,while mavericks bear high risk but also potentially reap great rewards. Generalists are the most resilient toinstability in the environment (game patches). We discuss the implications of these findings in terms of gamedesign and community management, as well as team building in environments with varying levels of stability.