Data-Driven (Tactical) Performance Analysis in Football

Modern technologies in image and video processing enable the collection of fine granular data in professional football (soccer). These data provide a digital reproduction of every match consisting of positional (i.e., time-dependent x/y-coordinates from all 22 players and the ball) and event data (e.g., a successful pass from player A to player B at a certain time point). In collaboration with the DFB-Akademie, Deutsche Fußball-Liga (DFL) and Sportec Solutions AG this project combines practitioners‘ expertise with research methodologies from sports and data-science to address three objectives, i.e. the synchronization of the two data sources, the quantification of offensive performance, and the automated detection of tactical patterns.

Based on the synchronization of positional and event data this project evaluates the application of Machine Learning (ML) techniques to objectively quantify and analyze tactical behaviors in football. We demonstrate that a large scale analysis of offensive actions, tactics, and strategies lead to a more accurate quantification of offensive performance than is possible with existing statistics (Anzer, 2022). Moreover, we develop and evaluate ML-based match analysis processes that detect tactical patterns (e.g., counterpressings, counterattacks) automatically and thus provide a basis for more time efficient processes in match analysis (Bauer, 2022).

Selected publications

Anzer, G., & Bauer, P. (2021). A Goal Scoring Probability Model based on Synchronized Positional and Event Data. Frontiers in Sports and Active Learning (Special Issue: Using Artificial Intelligence to Enhance Sport Performance), 3(0), 1–18. https://doi.org/10.3389/fspor.2021.624475
Anzer, G., & Bauer, P. (2022). Expected Passes—Determining the Difficulty of a Pass in Football (Soccer) Using Spatio-Temporal Data. Data Mining and Knowledge Discovery, Springer US. https://doi.org/10.1007/s10618-021-00810-3
Anzer, G., Bauer, P., & Brefeld, U. (2021, 2021/11/17). The origins of goals in the German Bundesliga. Journal of Sports Sciences, 39(22), 2525-2544. https://doi.org/10.1080/02640414.2021.1943981
Anzer, G., Bauer, P., Brefeld, U., & Fassmeyer, D. (2022). Detection of tactical patterns using semi-supervised graph neural networks. MIT Sloan Sports Analytics Conference, Boston, USA (Accepted for Research Paper Track 2022), 16, 1–3.
Anzer, G., Bauer, P., & Höner, O. (2022). Applying Machine Learning in Football. The Identification of Counterpressing in Football. In D. Memmert (Ed.), Match Analysis: How to Use Data in Professional Sport (p. 230-237): New York: Routledge.
Bauer, P., & Anzer, G. (2021). 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. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-021-00763-7
Bauer, P., Anzer, A., & Höner, O. (2022). Automatisierte Erkennung und Analyse taktischer Muster im Fußball mittels Spieldaten. In D. Memmert (Ed.), Spielanalyse im Sportspiel. Heidelberg, Berlin: Springer.
Bauer, P., Anzer, G., & Shaw, L. (2021). Putting Team Formations in Association Football into Context. IEEE Transactions on Big Data (Submitted).
Bauer, P., Anzer, G., & Smith, J. W. (2021). Individual role classification for players defending corners in football (soccer)—Categorisation of the defensive role for each player in a corner kick using positional data. Data Mining and Knowledge Discovery. https://www.frontiersin.org/articles/10.3389/fspor.2021.725431/abstract

PhDs

Bauer, P. (2022). Automated Detection of Complex Tactical Patterns in Football—Using Supervised Machine Learning Techniques to Classify Tactical Patterns. Dissertation Thesis, Eberhard Karls University Tübingen [pdf].
Anzer, G. (2022). Large Scale Analysis of Offensive Performance in Football—Using Synchronized Positional and Event Data to Quantify Offensive Actions, Tactics, and Strategy. Dissertation Thesis, Eberhard Karls University Tübingen [pdf].

Research overview