Sports Analytics (Sportinformatik)

About me:

Juni.-Prof. Dr. phil. Pascal Bauer
Since 2019, I have been providing evidence to support decision-making by sports experts. With a background in mathematics and computer science, I gained industry experience as a Data Scientist at Fraunhofer IESE. In 2019, I joined the German Football Association (DFB) as a Data Scientist, where I now lead the Data Analytics and Research Department. I hold a PhD from Tübingen University and gained experience as a part-time data scientist at Zelus Analytics. I also hold a UEFA A-level coaching license and have 10 years of experience in semi-professional football (soccer). 

In October 2024, I returned to my alma mater to establish the Chair of Sports Analytics at Saarland University, while still holding my position at the DFB.
LinkedIn: https://www.linkedin.com/in/pascal-bauer-53b4ab139/
Google Scholar: scholar.google.com/citations

Research areas:

Focusing on ball sports and professional sports, we aim to use statistics and machine learning methods to analyse sports data. Our research seeks to provide evidence-based insights into hypotheses and trends in sports, ensuring  an objective foundation. The goal is for our findings to offer actionable insights, supporting and potentially automating processes in sports.
While we focus on football (soccer), we also aim to apply our findings to other ball sports, such as handball, tennis, golf, and more. Potential research topics within or across these sports include tactical analysis, data-driven load management, and the quantification of performance at both player and team levels.

Theses in Sports Analytics:

Students interested in sports analytics are welcome to reach out regarding theses. Please send your CV, a transcript of records, your preferred sport and/or broader topic, or, if available, a proposal describing the scope of your thesis in 1-2 sentences (including references) to  pascal.bauer(at)extern.uni-saarland.de. Please use the e-mail subject “Thesis Request Sports Analytics | Your Name | Desired Start of the Thesis”.
Based on your information, we will provide a list of potential thesis topics or feedback on your submitted proposal.
For all theses, at least a basic motivation to implement code (e.g., in Python or R) is desirable. Interdisciplinary projects, such as those involving the faculties of computer science and mathematics, are encouraged.
Students interested in writing a thesis are encouraged to join the reading group (see below) to gain a better understanding of potential research topics.

Office Hour:

Office hours will be held every Friday from 12:00 PM to 1:00 PM during the semester, preferably in person in the sports analytics office. Please provide some notes on the topic or question you would like to discuss by 10:00 AM the day before the office hour via pascal.bauer(at)extern.uni-saarland.de. Please use the e-mail subject “YYYY-MM-DD | Office Hour Sports Analytics | Your Name | Remote/Onsite” with the dater of the office hour you want to adress your question as well as an indication on whether you prefer to chat remote or onsite.

Reading Group:

For interested students and/or just interested people we host a reading group on every second Monday (8:00h – 9:00h; remote). Before each session, a scientific sports analytics paper will be shared as a pre-read. This paper will be discussed in an online meeting. When possible, peers from the sports analytics industry, preferably the authors of the discussed papers, will also be invited as discussion partners.

Feel free to reach out to pascal.bauer(at)extern.uni-saarland.de with 2-3 sentences describing your motivation to join. Please us the e-mail subject “Reading Group Sports Analytics | Your Name”.

Planned sessions for 2024 (subject to changes):

  • 07.10.2024, 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
  • 21.10.2024, Barnett, T, Reid , M., O’Shaughnessy, D., & McMurtrie, D. (2012). Game Theoretic Solutions to Tennis Serving Strategies. ITF Coaching & Sport Science Review, 20(56), 22–25. https://doi.org/10.52383/itfcoaching.v20i56.404
  • 11.11.2024, Swartz, Tim. (2009). A New Handicapping System for Golf. Journal of Quantitative Analysis in Sports. 5. 9-9. 10.2202/1559-0410.1168. https://www.researchgate.net/publication/46554857_A_New_Handicapping_System_for_Golf/citations
  • 25.11.2024, Adams, M., David, A., Hesse, M., & Ruckert, U. (2023). Expected Goals Prediction in Professional Handball using Synchronized Event and Positional Data. MMSports 2023 - Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports, Co-Located with: MM 2023, 83–91. https://doi.org/10.1145/3606038.3616152
  • 09.12.2024, Gutiérrez-Santiago, A.; Cidre-Fuentes, P.; Orío-García, E.; Silva-Pinto, A.J.; Reguera-López-de-la-Osa, X.; Prieto-Lage, I. Women’s Singles Tennis Match Analysis and Probability of Winning a Point. Appl. Sci. 2024, 14, 6761 https://doi.org/10.3390/app14156761
  • 23.12.2024, Davis, J., Robberechts, P., & Leuven, K. U. (n.d.). Biases in Expected Goals Models Confound Finishing Ability. https://www.espn.com/soccer/story/_/id/37577474