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

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference, PAKDD 2011, Proceedings
Pages519-531
Number of pages13
EditionPART 2
DOIs
Publication statusPublished - 8 Jun 2011
Event15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011 - Shenzhen, China
Duration: 24 May 201127 May 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

Conference

Conference15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011
CountryChina
CityShenzhen
Period24/5/1127/5/11

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Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

DeLong, C., Pathak, N., Erickson, K., Perrino, E., Shim, K., & Srivastava, J. (2011). TeamSkill: Modeling team chemistry in online multi-player games. In Advances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference, PAKDD 2011, Proceedings (PART 2 ed., pp. 519-531). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6635 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-20847-8-43