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 Vol. 35; no. 5; pp. 2009 - 2049
• Main Authors: Bauer, Pascal, Anzer, Gabriel
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021
• Subjects: Application Papers; Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Information Storage and Retrieval; Physics; Sports Analytics; Statistics for Engineering; Sports analytics; Football (Soccer); Applied machine learning; Positional and event data; Tactical performance analysis
• DOI: 10.1007/s10618-021-00763-7
• ISSN: 1384-5810

Detecting counterpressing is an important task for any professional match-analyst in football (soccer), but is being done exclusively manually by observing video footage. The purpose of this paper is not only to automatically identify this strategy, but also to derive metrics that support coaches with the analysis of transition situations. Additionally, we want to infer objective influence factors for its success and assess the validity of peer-created rules of thumb established in by practitioners. Based on a combination of positional and event data we detect counterpressing situations as a supervised machine learning task. Together, with professional match-analysis experts we discussed and consolidated a consistent definition, extracted 134 features and manually labeled more than 20, 000 defensive transition situations from 97 professional football matches. The extreme gradient boosting model—with an area under the curve of 87.4 % on the labeled test data—enabled us to judge how quickly teams can win the ball back with counterpressing strategies, how many shots they create or allow immediately afterwards and to determine what the most important success drivers are. We applied this automatic detection on all matches from six full seasons of the German Bundesliga and quantified the defensive and offensive consequences when applying counterpressing for each team. Automating the task saves analysts a tremendous amount of time, standardizes the otherwise subjective task, and allows to identify trends within larger data-sets. We present an effective way of how the detection and the lessons learned from this investigation are integrated effectively into common match-analysis processes.