Sports Analytics

Football through the Eyes of the Computer

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, our research in this domain 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, enabling data acquisition and match analysis directly from existing video sources.

Our methods aim to provide 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. Consequently, our research not only contributes to the theoretical body of knowledge, but also provides real-world benefits, for example, to soccer organizations who have utilized applications stemming from this work and plan to continue doing so in the foreseeable future. Building on these foundations will help to further revolutionize the way match analysis is being performed in the upcoming years.