VisuaLeague: Visual Analytics of Multiple Games

Abstract

One of the most popular eSports (electronic sports) game types practiced is the Multiplayer Online Battle Arena (MOBA) genre, represented by one of the most popular competitive games, League of Legends (LoL). As in many traditional sports, to improve player and team performance, players and coaches analyze all the game events, such as, the positions and trajectories of the players, representing their movements, events and actions they performed during the game (spatial and temporal data). This paper presents VisuaLeague, a visualization tool for analysis of LoL matches for single player, teams, professional matches, and multiple games. The tool offers interaction with the visualizations, filtering and aggregation of data, and clustering to solve the common problems presented in analysis with voluminous amount of data, like cluttering and overlapping. We evaluated VisuaLeague through a user study covering the various types of analysis with two professional coaches. Results indicate that the tool was overall intuitive, useful, efficient and innovative and coaches show a particular interest in the analysis of professional training matches and multiple games as those provide visualizations that often lack in common tools, specially, regarding spatio-temporal data.