Visual representation of logged game player data from both real and artificial agents can provide insight in order to discover patterns and behavior that would otherwise be hidden.

We explore the use of visual data mining and automated data processing to investigate the logged player data and present the results of finding five unique phenomena in our dataset of 3079 players.

These phenomena include pirouettes, flusters, jumpers, learning, and emergent behavior. Using the visual data mining process, we can find occurrences of such behavioral patterns in order to understand player behavior and improve the interactive experience.