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How players lose interest in playing a game: An empirical study based on distributions of total playing times

 
: Bauckhage, Christian; Kersting, Kristian; Sifa, Rafet; Thurau, Christian; Drachen, Anders; Canossa, Alessandro

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Postprint urn:nbn:de:0011-n-2249692 (439 KByte PDF)
MD5 Fingerprint: e65ad15348375835e7a4cb1ebc74fb7d
© 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Created on: 17.1.2013


IEEE Computational Intelligence Society:
IEEE Conference on Computational Intelligence and Games, CIG 2012 : 11-14 Sept. 2012, Granada, Spain
New York, NY: IEEE, 2012
ISBN: 978-1-4673-1193-9 (Print)
ISBN: 978-1-4673-1192-2 (Online)
pp.139-146
Conference on Computational Intelligence and Games (CIG) <2012, Granada>
English
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
playing time distribution; empirical study; game; player; data mining

Abstract
Analyzing telemetry data of player behavior in computer games is a topic of increasing interest for industry and research, alike. When applied to game telemetry data, pattern recognition and statistical analysis provide valuable business intelligence tools for game development. An important problem in this area is to characterize how player engagement in a game evolves over time. Reliable models are of pivotal interest since they allow for assessing the long-term success of game products and can provide estimates of how long players may be expected to keep actively playing a game. In this paper, we introduce methods from random process theory into game data mining in order to draw inferences about player engagement. Given large samples (over 250,000 players) of behavioral telemetry data from five different action-adventure and shooter games, we extract information as to how long individual players have played these games and apply techniques from lifetime analysis to identify common patterns. In all five cases, we find that the Weibull distribution gives a good account of the statistics of total playing times. This implies that an average players interest in playing one of the games considered evolves according to a non-homogeneous Poisson process. Therefore, given data on the initial playtime behavior of the players of a game, it becomes possible to predict when they stop playing.

: http://publica.fraunhofer.de/documents/N-224969.html