Visual Analytics for Cooperative and Competitive Behavior in Team Sports

Abstract

Automatic and interactive data analysis is instrumental in making use of increasing amounts of complex data. Owing to novel sensor modalities, analysis of data generated in professional team sport leagues such as soccer, handball, and basketball has recently become of concern, with high commercial and research interest. The analysis of team sports can serve many goals, for example, in coaching to understand the effects of strategies and tactics or to derive insights for improving performance. Also, it is often decisive for coaches and analysts to understand why a certain movement of a player or groups of players happened, and what the respective influencing factors were. We consider team sports as group movement including cooperation and competition of individuals following a specific set of rules. Analyzing team sports is a challenging problem as it involves joint understanding of heterogeneous data perspectives, including high-dimensional, video, and collective movement data, as well as considering team behavior and rules (constraints) given in the particular team sport. However, the discipline is in its infancy, largely restricted to commercial solutions developed out of necessity, while neglecting the movement context, with only a few academic contributions so far, and much room for improvement still exists. Consequently, the research in this dissertation happens at the intersection of several cutting-edge technologies, including computer vision and machine learning, data visualization, and human-computer interaction. All required research steps from data extraction and context enrichment to the visualization of cooperative and competitive behavior are covered in this thesis, enabling data acquisition and match analysis directly from existing video sources. The methods are capable of providing accurate analysis results both from a recording as well as in real time during a live match, improving and advancing the analytical possibilities of coaches and analysts in various invasive team sports. The impact of the presented methods is illustrated by highlighting how the application of proposed methods of this dissertation by the Austrian first league soccer club TSV Hartberg greatly improved their analysis process. Building on the foundations set by this dissertation will help to further revolutionize the way match analysis is being performed in the upcoming years. Ultimately, the progress enabled by research methods such as the introduced in-video visualization will not be limited to the domain of team sports analysis alone, but will have a general impact on how we visualize, see and perceive our data in the future.