With the development of AI technology, intelligent agents are expected to team with humans and adapt to their teammates in changing environments, as effective human team members would do. As an initial step towards adaptive agents, the present study examined individual's adaptive actions in a cooperative task. By analyzing the performance when participants paired with different partners, we were able to identify adaptations and isolate individual contributions to team performance. It is shown that the team performance is determined by factors at both individual and team levels. Using subjective similarity data collected on Amazon Mechanical Turk, we constructed high-dimensional embeddings of similarity distance between team trajectories. Results showed that team members who adapted most led to improved team performance. In current experiments we are extending our approach to examine the relation between teammate-likeness, sensitivity to social risk and performance in human-Agent teams.