1 Department of Communication and Psychology, The Faculty of Humanities, Aalborg University, VBN2 Communication and Information Studies (CIS), The Faculty of Humanities, Aalborg University, VBN3 The Faculty of Humanities, Aalborg University, VBN4 The Faculty of Engineering and Science, Aalborg University, VBN5 Department of Architecture, Design and Media Technology, The Faculty of Engineering and Science, Aalborg University, VBN6 The Center for Applied Game Research, The Faculty of Engineering and Science, Aalborg University, VBN7 unknown8 Department of Architecture & Design, The Faculty of Engineering and Science, Aalborg University, VBN
An Empirical Study Based on Distributions of Total Playing Times
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 player’s 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.
Proceedings of Cig 2012, 2012, p. 139-146
Main Research Area:
IEEE Conference on Computational Intelligence And Games, 2012