In various different domains, the use of data has shown huge potential to support decision-making processes with aggregated insights from big amounts of data and for optimizing various processes. This is, independent of the domain, enabled through the availability of vast amounts of structured and unstructured data and due to the increasing affordability of computing power. To date, especially since new computer vision technologies enable teams and federations to acquire accurate digital reproductions of football games, numerous metrics aiming to quantify specific game situations in football based on positional data have been published. The overall goal of most of those approaches is to find key performance indicators (KPI's) to explain why individual players and teams perform better than others. Based on these developments, our goal is to survey the current state of the art literature and determine where and how a current research has been successfully integrated into practice. In addition to that it is our goal to either improve existing metrics or to develop new KPI's based on Machine Learning and Data-Science methods.

Research Line: Athletes, Teams and Performance.

Funding / Support: Sportec Solutions GmbH, DFB Academy.

Project Team: Oliver Höner, Augustin Kelava, Pascal Bauer, Gabriel Anzer

Publications:

• The Origins of Goals in the German Bundesliga. Published in the Journal of Sports Sciences. 
• A Goal Scoring Probability Model based on Synchronized Positional and Event Data. Published in Frontiers in Sports and Active Learning (Special Issue: Using Artificial Intelligence to Enhance Sport Performance).
• Expected Passes—Determining the Difficulty of a Pass in Football (Soccer) Using Spatio-Temporal Data. Published in Data Mining and Knowledge Discovery.
• Data-Driven Detection of Counterpressing in Professional Football—A Supervised Machine Learning Task based on Synchronized Positional and Event Data with Expert-Based Feature Extraction. Published in Data Mining and Knowledge Discovery.
• The Identification of Counterpressing in Football. Book chapter in Match Analysis.
• Toward Automatically Labeling Situations in Soccer. Published in Frontiers in Sports and Active Living.
• Machine Learning in Men’s Professional Football: Current Applications and Future Directions for improving Attacking Play. Published in the International Journal of Sports Science and Coaching.
• Constructing Spaces and Times for Tactical Analysis in Football. Published in IEEE Transactions on Visualization and Computer Graphics.