Inferring player rating from performance data in massively multiplayer online role-playing games (MMORPGs)

Kyong Jin Shim, Muhammad Aurangzeb Ahmad, Nishith Pathak, Jaideep Srivastava

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

18 Scopus citations

Abstract

This paper examines online player performance in EverQuest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. The study uses the game's player performance data to devise performance metrics for online players. We report three major findings. First, we show that the game's point-scaling system overestimates performances of lower level players and underestimates performances of higher level players. We present a novel pointscaling system based on the game's player performance data that addresses the underestimation and overestimation problems. Second, we present a highly accurate predictive model for player performance as a function of past behavior. Third, we show that playing in groups impacts individual performance and that player-level characteristics alone are insufficient in explaining an individual's performance, which calls for a different set of performance metrics methods.

Original languageEnglish (US)
Title of host publicationProceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009 - 2009 IEEE International Conference on Social Computing, SocialCom 2009
Pages1199-1204
Number of pages6
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Social Computing, SocialCom 2009 - Vancouver, BC, Canada
Duration: Aug 29 2009Aug 31 2009

Publication series

NameProceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009
Volume4

Other

Other2009 IEEE International Conference on Social Computing, SocialCom 2009
CountryCanada
CityVancouver, BC
Period8/29/098/31/09

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