Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach.

In recent years, cars have evolved from purely mechanical to veritable cyberphysical systems that generate large amounts of real-time data. These data are instrumental to the proper working of the vehicle itself, but make them amenable to a multitude of other uses. For instance, GPS information has recently been used for a large number of mobility studies in the academic community[1],[5], as well as to feed traffic apps such as Google Traffic and Waze. This use of vehicle data is already having a profound impact in science, industry, economy, and society at large. Now, imagine that instead of accessing one single source of vehicle-generated data (GPS), one can access the entire wealth of data exchanged on the controller area network (CAN) bus in near real timeamounting to over 4000 signals sampled at high frequency, corresponding to a few gigabytes of data per hour. What would be the implications, opportunities, and challenges sparked by this transition?