Skill-based differences in spatio-temporal team behaviour in defence of the Ancients 2 (DotA 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 focussed on tactical combat. In this paper, we present three data-driven measures of spatio-temporal behaviour in Defence of the Ancients 2 (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 behaviour varies across these measures as a function of the skill level of teams, using four tiers from novice to professional players. Results from three analyses indicate that spatio-temporal behaviour of MOBA teams is highly related to team skill.

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

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

[3]  Alessandro Canossa,et al.  Evaluating motion: spatial user behaviour in virtual environments , 2011, Int. J. Arts Technol..

[4]  Brent E. Harrison,et al.  Identifying patterns in combat that are predictive of success in MOBA games , 2014, FDG.

[5]  Maneesh Agrawala,et al.  Visualizing competitive behaviors in multi-user virtual environments , 2004, IEEE Visualization 2004.

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

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

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

[9]  J. Srivastava,et al.  Analyzing Human Behavior from Multiplayer Online Game Logs-A Knowledge Discovery Approach - , 2010 .

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

[11]  Christian Bauckhage,et al.  Where am I ? – On Providing Gamebots with a Sense of Location Using Spectral Clustering of Waypoints , 2006 .

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

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

[14]  Christian Bauckhage,et al.  The Playtime Principle: Large-scale cross-games interest modeling , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[15]  D. Roberts,et al.  Extracting Human-readable Knowledge Rules in Complex Time-evolving Environments , 2013 .

[16]  Tim Fields,et al.  Mobile & Social Game Design: Monetization Methods and Mechanics, Second Edition , 2014 .

[17]  Tom Batsford Calculating Optimal Jungling Routes in DOTA2 Using Neural Networks and Genetic Algorithms , 2014 .

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

[19]  Anders Drachen,et al.  Spatial Game Analytics , 2013, Game Analytics, Maximizing the Value of Player Data.

[20]  Mitchell Harrop Truce in online games , 2009, OZCHI '09.

[21]  Charu C. Aggarwal,et al.  Time-Series Data Clustering , 2018, Data Clustering: Algorithms and Applications.

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

[23]  Jon Crowcroft,et al.  Group movement in World of Warcraft Battlegrounds , 2010, Int. J. Adv. Media Commun..

[24]  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.

[25]  Doktor der Naturwissenschaften,et al.  Permutation Distribution Clustering and Structural Equation Model Trees , 2011 .