Inferring Player Rating from Performance Data in Massively Multiplayer Online Role-Playing Games (MMORPGs)

• Main Authors: SHIM, Kyong Jin, AHMAD, Muhammad Aurangzeb, PATHAK, Nishith, SRIVASTAVA, Jaideep
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/1501; https://ink.library.smu.edu.sg/context/sis_research/article/2500/viewcontent/3823f199.pdf
• Institution: Singapore Management University

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 point-scaling 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.