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One of the main goals of online competitive games is increasing playerengagement by ensuring fair matches. These games use rating systems forcreating balanced match-ups. Rating systems leverage statistical estimation torate players' skills and use skill ratings to predict rank before matchingplayers. Skill ratings of individual players can be aggregated to compute theskill level of a team. While research often aims to improve the accuracy ofskill estimation and fairness of match-ups, less attention has been given tohow the skill level of a team is calculated from the skill level of itsmembers. In this paper, we propose two new aggregation methods and compare themwith a standard approach extensively used in the research literature. Wepresent an exhaustive analysis of the impact of these methods on the predictiveperformance of rating systems. We perform our experiments using three popularrating systems, Elo, Glicko, and TrueSkill, on three real-world datasetsincluding over 100,000 battle royale and head-to-head matches. Our evaluationsshow the superiority of the MAX method over the other two methods in themajority of the tested cases, implying that the overall performance of a teamis best determined by the performance of its most skilled member. The resultsof this study highlight the necessity of devising more elaborated methods forcalculating a team's performance -- methods covering different aspects ofplayers' behavior such as skills, strategy, or goals.

Evaluating Team Skill Aggregation in Online Competitive Games