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

5 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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages519-531
Number of pages13
Volume6635 LNAI
EditionPART 2
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011 - Shenzhen
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)03029743
ISSN (Electronic)16113349

Other

Other15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011
CityShenzhen
Period24/5/1127/5/11

Fingerprint

Chemistry
Game
Networks (circuits)
Modeling
Video Games
Subset
Gaming
Tournament
Boosting
Predict
Prediction
Evaluation
Estimate
Skills
Framework

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 6635 LNAI, 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

TeamSkill : Modeling team chemistry in online multi-player games. / DeLong, Colin; Pathak, Nishith; Erickson, Kendrick; Perrino, Eric; Shim, Kyong; Srivastava, Jaideep.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6635 LNAI PART 2. ed. 2011. p. 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).

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

DeLong, C, Pathak, N, Erickson, K, Perrino, E, Shim, K & Srivastava, J 2011, TeamSkill: Modeling team chemistry in online multi-player games. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 6635 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6635 LNAI, pp. 519-531, 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, Shenzhen, 24/5/11. https://doi.org/10.1007/978-3-642-20847-8-43
DeLong C, Pathak N, Erickson K, Perrino E, Shim K, Srivastava J. TeamSkill: Modeling team chemistry in online multi-player games. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 6635 LNAI. 2011. p. 519-531. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-20847-8-43
DeLong, Colin ; Pathak, Nishith ; Erickson, Kendrick ; Perrino, Eric ; Shim, Kyong ; Srivastava, Jaideep. / TeamSkill : Modeling team chemistry in online multi-player games. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6635 LNAI PART 2. ed. 2011. pp. 519-531 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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