Prof. Dr. Daniel Memmert

Director of the Institute

  • Executive Editor of the Journal of Sport Science since 2025
  • Since 2023: Editor-in-Chief of the Journal of Applied Sport and Exercise Psychology
  • 2021–2025: Associate Editor of the International Journal of Sport and Exercise Psychology
  • 2017–2021: Co-editor of the Journal of Sport Psychology
  • 2016–2018: Co-Editor (Psychology) of the journal *Research Quarterly for Exercise and Sport*
  • 2012–2016: Editor of the Journal of Sport Science (Behavioral Science Section)
  • 2009–2016: Director of the Institute for Cognitive and Sports Game Research at the German Sport University Cologne
  • 2009–2013: Executive Director of the ASP (Association for Sport Psychology),
  • 2014: Visiting Professor at the University of Vienna
  • 2008–2022: Deputy Spokesperson for the dvs Commission on “Sports Games”
  • 2008: Habilitation at Heidelberg University (Award: DOSB Science Prize, Bronze)
  • 2003: Ph.D. from Heidelberg University (Award: dvs Young Researchers’ Prize, Bronze)

Benjamin Noel, Fabian Wunderlich, Dennis Redlich, Hans-Erik Scharfen, Michael Stügelmeier, Malte Albrecht, Dorothee Altmeier, Thomas Apitzsch, Mathias Bellinghausen, Frowin Fasold, Philip Furley, Andreas Grunz, Stephanie Hüttermann, Niels Kaffenberger, Matthias Kempe, Alexander Knyazev, Carina Kreitz, Stephan Nopp, Giulia Pugnaghi, Marco Rathschlag, Karsten Schul, Sebastian Schwab, Konstantinos Velentzas, Ben Low, Ashwin Phatak, Marc Garnica Caparrós, Marius Pokolm, Marcus Lödige (née Dodt), Ann-Kathrin Lobert, Dominik Raabe, Michel Brinkschulte. Henrik Biermann, Julian Henz, Max Klemp, David Smith, Manuel Bassek, Ju-Yi Huang

DFG Projects in the Field of Computer Science:

  • 2024–2028 DFG Funding: SportVid: A portal to support the search, analysis, and evaluation of videos in sports and exercise science
  • 2024–2027 DFG Funding: Implementation of floodlight e-Research technology for the analysis of spatiotemporal motion data in sports science [DFG]
  • 2022–2027 DFG Funding: Simulation of Interactive Action Sequences Using the Example of Elite Soccer [DFG]
  • 2019–2027 DFG Funding: A Theoretical Simulation Framework for Analyzing Predictive Rating Methods on Networks with Applications in Sports
  • since 2020 DFG - Funding: Data-driven approaches for analyzing soccer matches from an e-science perspective [DFG]
  • 2018–2020 DFG – Funding: Simulation of complex tasks using the example of elite soccer [DFG]
  • 2018–2020 DFG Funding: Simulation of Interaction Patterns and Simulative Effectiveness Analysis of Creative Plays in Sports Using Neural Networks [DFG]
  • 2014–2016 Simulation and Analysis of Creativity Using Neural Networks (Daniel Memmert & Jürgen Perl) (2nd renewal application)
  • 2011–2013 Simulation and Analysis of Creativity Using Neural Networks (Daniel Memmert & Jürgen Perl) (1st renewal application)
  • 2008–2011 Simulation and Analysis of Creativity Using Neural Networks (Daniel Memmert & Jürgen Perl) (Initial Grant Application)

DFG Projects in the Field of Psychology: 

  • 2016–2019 DFG Funding: Neural Mechanisms of Creative Solutions in Complex Tasks (Collaborator: Dr. Andreas Fink) [DFG]
  • 2016–2017 DFG Funding: Finding Appropriate and Original Solutions: A Research Program on the Influence of Personal and Situational Factors on Creative Motor Problem Solving [DFG]
  • 2014–2017 DFG Funding: Why Do We Aim Where We Do? An Investigation into the Simultaneous Involvement of Various Consciousness Processes in Laboratory and Field Settings [DFG]
  • 2013–2017 DFG Funding: Inattentional Blindness and Attention: Exploring the Mechanisms Underlying Failures of Awareness [DFG]

Projects supported by the BMBF:

  • 2022–2025 Funding for training initiatives and research projects in the field of machine learning as part of the funding program “Promotion of Artificial Intelligence in Higher Education”: A university-based teaching concept for AI in sports science (uLKIS)
  • 2020–2024 Multimodal Analysis for Sports Analytics: Intelligent Synchronization and Semantic Enrichment of Position and Video Data for the Analysis of Sports Game Data. “Application of Artificial Intelligence Methods in Practice” as part of the German Federal Government’s AI Strategy and the High-Tech Strategy 2025.

  1. Biermann, H., Yang, W., Wieland, F. G., Timmer, J., & Memmert, D. (2024). Quantification of Turnover Risk with xCounter. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds.) Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science, vol. 1784. Springer, Cham. 

  2. Biermann, H., Memmert, D., Petersen, N., & Raabe, D. (2025). Contextualization of soccer analysis with tactical periodization and machine learning. Data Mining and Knowledge Discovery, 39(3), 23. doi.org/10.1007/s10618-025-01092-9

  3. Raabe, D., Nabben, R., & Memmert, D. (2023). Graph representations for the analysis of multi-agent spatiotemporal sports data. Applied Intelligence, 53(4), 3783–3803.

  4. Wunderlich, F., Biermann, H., Yang, W., Bassek, M., Raabe, D., Elbert, N., ... & Garnica Caparrós, M. (2025). Assessing machine learning and data imputation approaches to address the issue of data sparsity in sports forecasting. Machine Learning, 114(2), 1-28.

  5. Garnica Caparrós, M., Memmert, D., & Wunderlich, F. (2022). The effects of scheduling network models in predictive processes in sports. Social Network Analysis and Mining, 12(1), 143.

  6. Garnica-Caparrós, M., Memmert, D., & Wunderlich, F. (2022). Artificial data in sports forecasting: a simulation framework for analyzing predictive models in sports. Information Systems and e-Business Management, 20(3), 551–580.

  7. Theiner, J., Gritz, W., Müller-Budack, E., Rein, R., Memmert, D., & Ewerth, R. (2022). Extraction of Positional Player Data from Broadcast Soccer Videos. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.

  8. Brefeld, U., Knauf, K., & Memmert, D. (2016). Spatio-Temporal Convolution Kernels. Machine Learning, 102(2), 247–273. DOI: 10.1007/s10994-015-5520-1

  9. Low, B., Coutinho, D., Gonçalves, B., Rein, R., Memmert, D., & Sampaio, J. (2020). A systematic review of collective tactical behaviors in soccer using positional data. Sports Medicine, 50, 343–385.

  10. Paul, Y., Klemp, M., Memmert, D. (2026). Beyond Outcome Bias: Incorporating Action Completion Probability and Risk-Return Into Soccer Evaluation Models. In: Rios-Neto, H., Robberechts, P., Van Roy, M., Zimmermann, A. (eds.) Machine Learning and Data Mining for Sports Analytics. MLSA 2025. Communications in Computer and Information Science, vol. 2833. Springer, Cham.