TeamSkill: modeling team chemistry in online multi-player games

Authors:
Colin DeLong;Nishith Pathak;Kendrick Erickson;Eric Perrino;Kyong Shim;Jaideep Srivastava
Affiliations:
Department of Computer Science, University of Minnesota, Minneapolis, MN;Department of Computer Science, University of Minnesota, Minneapolis, MN;Department of Computer Science, University of Minnesota, Minneapolis, MN;Department of Computer Science, University of Minnesota, Minneapolis, MN;Department of Computer Science, University of Minnesota, Minneapolis, MN;Department of Computer Science, University of Minnesota, Minneapolis, MN
Venue:
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Year:
2011

Citing 2
Cited 3

Expectation Propagation for Rating Players in Sports Competitions

PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength

CG '08 Proceedings of the 6th international conference on Computers and Games

TeamSkill evolved: mixed classification schemes for team-based multi-player games

PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
TeamSkill and the NBA: applying lessons from virtual worlds to the real-world

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Evaluating simulation software components with player rating systems

Proceedings of the 6th International ICST Conference on Simulation Tools and Techniques

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we introduce a framework for modeling elements of "team chemistry" in the skill assessment process using the performances of subsets of teams and four approaches which make use of this framework to estimate the collective skill of a team. A new dataset based on the Xbox 360 video game, Halo 3, is used for evaluation. The dataset is comprised of online scrimmage and tournament games played between professional Halo 3 teams competing in the Major League Gaming (MLG) Pro Circuit during the 2008 and 2009 seasons. Using the Elo, Glicko, and TrueSkill rating systems as "base learners" for our approaches, we predict the outcomes of games based on subsets of the overall dataset in order to investigate their performance given differing game histories and playing environments. We find that Glicko and TrueSkill benefit greatly from our approaches (TeamSkill-AllK-EV in particular), significantly boosting prediction accuracy in close games and improving performance overall, while Elo performs better without them. We also find that the ways in which each rating system handles skill variance largely determines whether or not it will benefit from our techniques.