Using Player Type Models for Personalized Game Design – An Empirical Investigation

Abstract

Personalized games should provide a better player experience than one-size-fits-all games. As a method for personalization, player type models have been discussed recently. Player type models would be useful tools in the personalization of games, if they have a relationship to the players’ experience of specific game mechanics. However, this relationship has never been empirically investigated. To close this gap, we examine whether player types—as a specific appearance of personality traits—can significantly and reliably predict player experience. We investigate the predictive power of two player types (Mastermind, Seeker) of the BrainHex player type model. Results of a field study (n = 51) with a mobile game prototype tailored to the two player types Mastermind and Seeker suggest that player type models still need improvement: Player type scores do not significantly predict player experience of according game mechanics. We discuss possible explanations and a way to design personalized games that adapt to users gaming preferences with player type models.