TeamSkill: Modeling team chemistry in online multi-player games

Colin DeLong, Nishith Pathak, Kendrick Erickson, Eric Perrino, Kyong Shim, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference, PAKDD 2011, Proceedings
PublisherSpringer Verlag
Number of pages13
EditionPART 2
ISBN (Print)9783642208461
StatePublished - 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6635 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Elo
  • Glicko
  • Player rating systems
  • TrueSkill
  • competitive gaming


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