The vast amount of data being collected by sensors and wearable devices in healthcare simulations has yet to be harnessed to improve our understanding of teamwork and coordination between teams. For instance, a recent paper argued that several constructs serve as essential indicators of the quality of between-team activities including coordination, boundary spanning, and adaptation (Lazzara, Keebler, Shuffler, Patzer, Smith, & Misasi, 2015). This is a valuable theoretical insight, but the key to unlocking the full potential for real-world application in training and simulation is dependent on our ability to find proxies to measure those phenomena. Sensors that record proximity, position (GPS), and speech pattern data have been used as proxies for coordination, communication, and other team processes, including task management, situational awareness, and decision-making (Feese, Burscher, Jonas, & Tröster, 2014; Rosen, Dietz, Yang, Priebe, & Pronovost, 2014). Generally, data are gathered throughout an entire simulation, without a focus on which team inflection points and performance episodes are most important to capture. Moreover, emergency response scenarios are often handled by a complex system of teams varying in their between-team interdependencies. These systems are referred to as multiteam systems (MTSs), which are made up of two or more teams that work together interdependently toward a common goal, while separately working toward more proximal goals (Mathieu, Marks, & Zaccaro, 2001). In this paper, we review the data sources being used to describe team behaviors, discuss how to make decisions about data collected during MTS scenarios, and the importance of the data validation process. Two case studies (one Healthcare and one Fire and Rescue scenario) are reported to demonstrate the use of various sensors during simulations. Finally, directions for reporting data in after action reviews and the implications for training using an event-based approach are provided.