Steffen Lang
Research Associate
Tel +49.89.289.24503
Fax +49.89.289.24497
Function: Research Associate
Building: Campus D, Georg-Brauchle-Ring 60/62
Room: M206
Office Hours: by agreement
Email: steffen.lang@tum.de
Research Activities
Analysis of spatio-temporal tracking data in football using machine learning
- Quantification of individual performance
- Quantification of team performance
- Deriving match information
Support of BISp project "Performance Analysis in Beach-Volleyball"
Support of BISp project "Performance Analysis in Taekwondo"
Professional Career
- 2010-2013: B.A. sports science at Johannes-Gutenberg-University, Mainz
- 2013-2016: M.Sc. (TUM) Diagnostics & Training, Technical University of Munich
- 2015: student research assistant at the Chair of Performance Analysis and Sports Informatics, Technical University of Munich
- from 2019: graduate associate researcher
Publications
Lang, Steffen; Wild, R., Isenko, A., & Link, D., (2022). Predicting the in-game status in soccer with machine learning using spatiotemporal player tracking data. Scientific Reports, 12: 16291. doi: 10.1038/s41598-022-19948-1
Lang, Steffen; Haggenmüller, K., & Link, D. (2022). TaekViewer – Ein Softwaretool zur Auswertung von Wettkampfdaten im Taekwondo. BISP-Jahrbuch Forschungsförderung 2021/22 (S. 357-360). Köln: Strauß.
Lang, Steffen; Wenning, R., & Link, D. (2022). BeachCompiler – Das Softwaretool zur Unterstützung von Videotrainingseinheiten im Beach-Volleyball. BISP-Jahrbuch Forschungsförderung 2021/22 (S. 343-346). Köln: Strauß.
Lang, Steffen; Wenning, R., & Link, D. (2022). Spielverlaufsanalyse Beach-Volleyball. BISP-Jahrbuch Forschungsförderung 2021/22 (S. 199-202). Köln: Strauß.
Lang, Steffen (2019): Who will score next? About the predictive power of performance indicators in soccer. In M. Lames, A. Danilov, E. Timme & Y. Vassilevski (Eds.), Proceedings of the 12th International Symposium on Computer Science in Sport (IACSS 2019). Heidelberg: Springer.
Link, Daniel; Lang, St. (2019): How to find elementary football structures in positional data. In: Angel Ric und Raul Pelaez (Hg.): Football Analytics: Now and Beyond. A deep dive into the current state of advanced data analytics. Barcelona, S. 50–65.
Link, Daniel; Lang, St.; Wenning, R. (2019): Scouting-Software im Beach-Volleyball. Youtube Video. Bonn: Bundesinstitut für Sportwissenschaft. Online verfügbar unter https://www.youtube.com/watch?v=PcOdZCs-PRM
Link, Daniel; Lang, St., & Seidenschwarz, Ph. (2016). Real Time Quantification of Dangerousity in Football Using Spatiotemporal Tracking Data. PLOS ONE, 11(12): e0168768. doi:10.1371/journal.pone.0168768