Player Performance Prediction in Massively Multiplayer Online Role-Playing Games (MMORPGs)

• Main Authors: SHIM, Kyong Jin, SHARAN, Richa, SRIVASTAVA, Jaideep
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/1524; https://ink.library.smu.edu.sg/context/sis_research/article/2523/viewcontent/10_003.pdf
• Institution: Singapore Management University

Recent years have seen an ever increasing number of people interacting in the online space. Massively multiplayer online role-playing games (MMORPGs) are personal computer or console-based digital games where thousands of players can simultaneously sign on to the same online, persistent virtual world to interact and collaborate with each other through their in-game characters. In recent years, researchers have 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. While many games today provide web and GUI-based reports and dashboards for monitoring player performance, we propose a more comprehensive performance management tool (i.e. player scorecards) for measuring and reporting operational activities of game players. This study uses performance data of game players in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models for game players. The prediction models provide a projection of player's future performance based on his past performance, which is expected to be a useful addition to existing player performance monitoring tools. First, we show that variations of PECOTA and MARCEL, two most popular baseball home run prediction methods, can be used for game player performance prediction. Second, we evaluate the effects of varying lengths of past performance and show that past performance can be a good predictor of future performance up to a certain degree. Third, we show that game players do not regress towards the mean and that prediction models built on buckets using discretization based on binning and histograms lead to higher prediction coverage.