VisuaLeague: player performance analysis using spatial-temporal data

In recent years, the phenomenon of eSports has been a growing trend and consequently, in addition to players, other groups of users, including coaches and analysts, took an interest in online video games and the data extracted from them. Among many types of video games, one of the most widely played is the MOBA (Multiplayer Online Battle Arena) League of Legend (LoL) game. Similary to traditional sports, players and coaches/analysts analyse all game events, such as, players’ movements, to understand how they play to define new strategies and improve their performance. Our main goal is to get a better understanding of which visualizations techniques are more adequate to handle this type of spatio-temporal information data, associated to player performance analysis in video games. To address this goal, we inquired players to identify the analytical questions they need to support for performance analysis and designed the VisuaLeague prototype for the visualization of in-game player trajectories, using animated maps, and events during a LoL match. This paper presents a user study to evaluate the adequacy of animated maps and the analytical strategies followed by players when using spatio-temporal data to analyse player performance. The results support the adequacy of using the animated maps technique to convey information to users in this context. Moreover, they also point out towards a high degree of importance given to the spatio-temporal components of the data for player performance analysis.