Matthias Schubert, Assistant Professor, LMU Munich
Tobias Mahlmann, Postdoctoral Researcher, University of Lund
Anders Drachen, Associate Professor, Aalborg University
Esports is computer games played in a competitive environment. As for any other type of sports competition, players and teams seek to improve their behavior to optimize their results. Thus, esports analytics is a new area identifying successful strategies and evaluating game play for computer games. Besides helping the players, esports analytics is also directed to help the game provider to ensure a fair and exciting gaming experience.
Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these games, teams of players fight against each other in enclosed arena environments, with a complex gameplay focused on tactical combat. The particular MOBA being examined in this paper, DOTA, had already more than 7.86 million active players monthly in 2013.
To win a match, a team has to develop its heroes by killing hostile units and buildings. This mostly happens during encounters involving players from both teams. Several encounters might occur simultaneously on different locations of the map. Thus, to evaluate game play, each fight has to be analyzed separately.
We present a technique for segmenting matches into spatio-temporally defined components representing these encounters which enables us to analyze player performance on a detailed level. We apply encounter-based analysis to match data from the popular esport game DOTA, and present win probability predictions based on encounters. Finally, metrics for evaluating team performance during match runtime are proposed.