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

Abstract

In recent years, researchers have taken notice that virtual environments such as EverQuest II serve as a major mechanism for socialization. In particular, educational research has found virtual environments to be a sound venue for studying learning, collaboration, social participation, literacy in online space, and learning trajectory at the individual level as well as at the group level. The present research is concerned with learning in virtual environments and 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. The study reports three major findings. First, we show that the game's point scaling system over-estimates performances of lower level players and under-estimates performances of higher level players. We present a novel point scaling system based on the game's player performance data that addresses the under-estimation and over-estimation 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 has impact on player performance and that individual characteristics alone are not sufficient for explaining individual's performance, which calls for a different set of performance metrics methods. The discrepancy between the point scaling system in the game and observed player performance can be used as a guide to modify the existing system to better reflect the expected learning behaviors in different levels.