With the growing importance of multi-agent teamwork, tools that can help humans analyze, evaluate, and understand team behaviors
are becoming increasingly important as well. To this end, we are creating ISAAC, a team analyst agent for post-hoc, off-line agentteam analysis. ISAAC's novelty stems from a key design constraint that arises in team analysis: multiple types of models of team
behavior are necessary to analyze different granularities of team events, including agent actions, interactions, and global
performance. These heterogeneous team models are automatically acquired via machine learning over teams' external behavior traces,
where the specific learning techniques are tailored to the particular model learned. Additionally, ISAAC employs multiple
presentation techniques that can aid human understanding of the analyses. This paper presents ISAAC's general conceptual
framework and its application in the RoboCup soccer domain, where ISAAC was awarded the RoboCup scientific challenge award.