Publication Type

Conference Proceeding Article

Publication Date

6-2010

Abstract

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 [2] and MARCEL [3], 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.

Keywords

Lecture Notes in Computer Science, 2010, Volume 6119/2010, 71-80

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

Advances in Knowledge Discovery and Data Mining: Proceedings of the 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24

Volume

6119

First Page

71

Last Page

80

ISBN

9783642136726

Identifier

10.1007/978-3-642-13672-6_8

Publisher

Springer Verlag

City or Country

Hyderabad, India

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1007/978-3-642-13672-6_8

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