Player Modeling is becoming an important feature in Digital Games. It basically consists in understanding and modeling the player characteristics and behaviors during the game and has been mainly used to improve the games artificial intelligence, making games more adaptable to different players. In this paper, we try to characterize the preference of the players using a novel approach in games: we use mathematical regressions to characterize players behavior, looking for functions that best fit these behaviors. Using AI controlled players in Civilization IV as a testbed, this characterization is performed by extracting game data (score and resources, for example) at the end of each turn and generating functions that characterize the data evolution during the game. We were able to obtain models that distinguish the agents preferences showing the effectiveness of this approach.