Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2

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 arena environments, and the gameplay is focused on tactical combat. Mastering MOBAs requires extensive practice, as is exemplified in the popular MOBA Defence of the Ancients 2 (DotA 2). In this paper, we present three data-driven measures of spatio-temporal behavior in DotA 2: 1) Zone changes; 2) Distribution of team members and: 3) Time series clustering via a fuzzy approach. We present a method for obtaining accurate positional data from DotA 2. We investigate how behavior varies across these measures as a function of the skill level of teams, using four tiers from novice to professional players. Results indicate that spatio-temporal behavior of MOBA teams is related to team skill, with professional teams having smaller within-team distances and conducting more zone changes than amateur teams. The temporal distribution of the within-team distances of professional and high-skilled teams also generally follows patterns distinct from lower skill ranks.

[1]  Jean-Philippe Métivier,et al.  Mining Tracks of Competitive Video Games , 2014 .

[2]  Ben Medler,et al.  Player Dossiers: Analyzing Gameplay Data as a Reward , 2011, Game Stud..

[3]  Hans-Peter Kriegel,et al.  Managing and Mining Multiplayer Online Games , 2011, SSTD.

[4]  Hitoshi Mitomo,et al.  Leadership development through online gaming , 2012 .

[5]  Andreas M. Brandmaier,et al.  Permutation distribution clustering and structural equation model trees , 2011 .

[6]  Georgios N. Yannakakis Game AI revisited , 2012, CF '12.

[7]  Hannes Werthner,et al.  Ranking factors of team success , 2013, WWW.

[8]  Christian Bauckhage,et al.  Beyond heatmaps: Spatio-temporal clustering using behavior-based partitioning of game levels , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[9]  Michael Buro,et al.  Predicting Army Combat Outcomes in StarCraft , 2013, AIIDE.

[10]  Anders Drachen,et al.  Spatial game analytics and visualization , 2013, 2013 IEEE Conference on Computational Inteligence in Games (CIG).

[11]  Michael Mateas,et al.  A data mining approach to strategy prediction , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[12]  Yoshua Bengio,et al.  Beyond Skill Rating: Advanced Matchmaking in Ghost Recon Online , 2012, IEEE Transactions on Computational Intelligence and AI in Games.

[13]  Debanjan Saha,et al.  A long-term study of a popular MMORPG , 2007, NetGames '07.

[14]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[15]  Tim Fields,et al.  Social Game Design: Monetization Methods and Mechanics , 2011 .