Modeling Player Retention in Madden NFL 11

Video games are increasingly producing huge datasets available for analysis resulting from players engaging in interactive environments. These datasets enable investigation of individual player behavior at a massive scale, which can lead to reduced production costs and improved player retention. We present an approach for modeling player retention in Madden NFL 11, a commercial football game. Our approach encodes gameplay patterns of specific players as feature vectors and models player retention as a regression problem. By building an accurate model of player retention, we are able to identify which gameplay elements are most influential in maintaining active players. The outcome of our tool is recommendations which will be used to influence the design of future titles in the Madden NFL series.

[1]  David W. Aha,et al.  Improving Offensive Performance Through Opponent Modeling , 2009, AIIDE.

[2]  Michael Mateas,et al.  A requirements analysis for videogame design support tools , 2009, FDG.

[3]  Georg Zoeller Game Development Telemetry in Production , 2013, Game Analytics, Maximizing the Value of Player Data.

[4]  Douglas Stott Parker,et al.  Map-reduce-merge: simplified relational data processing on large clusters , 2007, SIGMOD '07.

[5]  Michael Mateas,et al.  LUDOCORE: A logical game engine for modeling videogames , 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.

[6]  Georgios N. Yannakakis,et al.  Real-Time Game Adaptation for Optimizing Player Satisfaction , 2009, IEEE Transactions on Computational Intelligence and AI in Games.

[7]  Nachiappan Nagappan,et al.  Data analytics for game development. , 2011, ICSE 2011.

[8]  Santiago Ontañón,et al.  ON‐LINE CASE‐BASED PLANNING , 2010, Comput. Intell..

[9]  Michael Mateas,et al.  A data mining approach to strategy prediction , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[10]  Georgios N. Yannakakis,et al.  Player modeling using self-organization in Tomb Raider: Underworld , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[11]  Georgios N. Yannakakis How to model and augment player satisfaction: a review , 2008, WOCCI.

[12]  J. Friedman Stochastic gradient boosting , 2002 .

[13]  Christian Bauckhage,et al.  Analyzing the Evolution of Social Groups in World of Warcraft® , 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.

[14]  Sabine Goutier,et al.  Extracting relevant features to explain electricity price variations , 2010, 2010 7th International Conference on the European Energy Market.