Predicting MMO Player Gender from In-Game Attributes Using Machine Learning Models

Tracy Kennedy, Rabindra (Robby) Ratan, Komal Kapoor, Nishith Pathak, Dmitri Williams, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


What in-game attributes predict players’ offline gender? Our research addresses this question using behavioral logs of over 4,000 EverQuest II players. The analysis compares four variable sets with multiple combinations of character types (avatar characteristics or gameplay behaviors; primary or nonprimary character), three server types within the game (roleplaying, player-vs-player, and player-vs-environment), and three types of predictive machine learning models (JRip, J48, and Random Tree). Overall, the most highly predictive, interpretable model has an f-measure of 0.94 and suggests the primary character gender and number of male and female characters a player has provide the most prediction value, with players choosing characters to match their own gender. The results also suggest that female players craft, scribe recipes, and harvest items more than male players. While the strength of these findings varies by server type, they are generally consistent with previous research and suggest that players tend to play in ways that are consistent with their offline identities.

Original languageEnglish (US)
Title of host publicationSpringer Proceedings in Complexity
Number of pages16
StatePublished - 2014

Publication series

NameSpringer Proceedings in Complexity
ISSN (Print)2213-8684
ISSN (Electronic)2213-8692

Bibliographical note

Funding Information:
Acknowledgments The research reported herein was supported by the National Science Foundation (NSF) via award number: IIS-0729421, the Army Research Institute (ARI) via award number W91WAW-08-C-0106, Air Force Research Lab (AFRL) via Contract No: EA8650-10-C-7010 and the Army Research Lab (ARL) Network Science–Collaborative Technology Alliance (NSCTA) via BBN TECH/W911NF-09-2-0053. The data used for this research were provided by the SONY On-line Entertainment (SONY Corporation). We gratefully acknowledge all our sponsors. The findings presented do not in any way represent, either directly or through implication, the policies of these organizations.

Publisher Copyright:
© 2014, Springer International Publishing Switzerland.


  • Gender in MMOs
  • Gender prediction
  • MMOs
  • Machine learning models


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