  Modeling players' behaviors in games has gained increased momentum in the
past few years. This area of research has wide applications, including modeling
learners and understanding player strategies, to mention a few. In this paper,
we present a new methodology, called Interactive Behavior Analytics (IBA),
comprised of two visualization systems, a labeling mechanism, and abstraction
algorithms that use Dynamic Time Warping and clustering algorithms. The
methodology is packaged in a seamless interface to facilitate knowledge
discovery from game data. We demonstrate the use of this methodology with data
from two multiplayer team-based games: BoomTown, a game developed by Gallup,
and DotA 2. The results of this work show the effectiveness of this method in
modeling, and developing human-interpretable models of team and individual
behavior.
