Conference Proceeding Article
In this study, we propose a comprehensive performance management tool 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 forgame 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.
Lecture Notes in Computer Science, 2010, Volume 6119/2010, 71-80
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Advances in Knowledge Discovery and Data Mining: Proceedings of the 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24
City or Country
SHIM, Kyong Jin; SHARAN, R.; and SRIVASTAVA, J..
Player performance prediction in massively multiplayer online role-playing games (MMORPGs). (2010). Advances in Knowledge Discovery and Data Mining: Proceedings of the 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24. 6119, 71-80. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1490
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