The latter is defined on the basis of three main players’ actions during the game: the number of kills (K), the number of assists (A) and the number of deaths (D). The former is defined as the fraction of matches during a session won by the player’s team. We measure performance at two levels: the overall team’s performance and the individual player’s performance. After segmenting matches by sessions-periods of game play activity without an extended break-we track the player’s performance over the course of the session. The data we study contain records of nearly 242 000 solo-queue matches played by 16 665 of the most active LoL players. RQ4 What factors predict a player’s choice to continue playing or end a given session? RQ3 If performance does change over a session, does experience mitigate its variation? RQ2 Are there notable changes in individual performance during the course of a single team-playing session? RQ1 Do players improve over time, as they acquire skills and experience through teamwork? To this aim, we study the performance of players in League of Legends (LoL), a popular MOBA game.ĭata from MOBA games like LoL enable us to explore the following four research questions: Understanding how individual and team performance change over time can then provide suitable insights on how to assemble successful teams. However, the performance of individuals within teams, and of the teams themselves, may evolve over time, as individuals improve and perfect their skills or learn how to work with others on a given shared task. There are several factors that affect the ad hoc team performance, such as communication , social ties , composition , etc. In addition, game designers dynamically assemble players to match the skill levels of opposing teams. Players must reach mutual understanding of the changing situations, work closely, continuously make new strategies together, build and maintain team cohesiveness, and deal with deviant players. Players understand that the way they interact with teammates affects collaboration, and thus they must discipline themselves to facilitate successful social interaction with their team. Previous studies showed that strangers collaborate in online games through communication and coordination, often trying to exert influence over their teammates. kill enemies, destroy towers and conquer the enemy base) to win the game. In this popular genre of games, two teams are assembled and face each other, with individuals collaborating with strangers to complete a series of complex, fast-paced tasks (e.g. An example of such ad hoc teams can be found in multiplayer online battle arena (MOBA) games. People are often brought together in temporary ad hoc teams to achieve a common goal before moving on to the next problem, likely with a different team. Solving today’s complex challenges increasingly calls for collaborating with others. Our findings suggest possible directions for individualized incentives aimed at steering the player’s behaviour and improving team performance. Modelling the short-term performance dynamics allows us to accurately predict when players choose to continue to play or end the session. We also find no significant long-term improvement in the individual performance of most players. Our analysis of player performance in successive matches of a gaming session demonstrates that a player’s success deteriorates over the course of the session, but this effect is mitigated by the player’s experience. Our work examines data from a popular MOBA game, League of Legends, to understand the evolution of individual performance within ad hoc teams. Many popular multiplayer online battle arena (MOBA) video-games adopt this team formation strategy and thus provide a natural environment to study ad hoc teams. Of special interest are ad hoc teams assembled to complete some task. Complex real-world challenges are often solved through teamwork.
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