Computer and videogames have been described using several formal systems - in this paper we consider them as Information Systems. In particular, we use a Decision Theoretic approach to model and analyse off-line, data from Pacman players. Our method attempts to calculate the optimal choices available to a player based on key utilities for a given game state. Our hypothesis in this approach is that observing a player's deviation from the optimal choices predicted can reveal their play preferences and skill, and thus form a basic player classifier. The method described builds on work done in [Cowley et al 2006], increasing the scope and sophistication of the model by decreasing reliance on supervision. The downside is a consequent performance hit, which prevents real-time execution of the modelling algorithm. In this paper we outline the basic principle of the Decision Theoretic approach and discuss the results of our evolution toward data-driven classification.