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Statistical Methods for Big Data
Department of Statistics
On blue background is drawn with white lines a brain and a network is shown from white dots. © Pixabay

Collaborative football analytics research with professional European football clubs

We have established a network consisting of 5 professional renowned European football clubs from 5 different countries and experts from academia to organize joint meetings and collaborate on hot topics in football analytics. 

Involved football clubs:

  • Borussia Dortmund; Prof. Dr. Mathias Kolodziej (Head of Sports Science), Alexander Gerharz (1st Team Data Scientist)
  • PSV Eindhoven; Ruud van Elk (Head of Sports Science & Analytics)
  • Athletic Club Bilbao; Jon Larruskain (Head of Sports Science)
  • FC Arsenal; Mikhail Zhilkin (1st Team Data Scientist)
  • Sport Lisboa e Benfica; Dr. Catarina Bajanca (Sports Scientist)
     
Researchers at BVB training grounds © Andreas Groll
Researchers at BVB training grounds © Dae-Jin Lee

Involved academic research groups:

Researchers at Athletic Club Bilbao © Andreas Groll
Researchers from Bilbao in Dortmund © Dae-Jin Lee

Exemplary research topics:

  • Identify joint problems & synergies between clubs
  • Model injuries
  • Measure fatigue
  • Improve performance
  • Usage of tracking data
     

We already organized 3 joint meetings (so far 2 in Bilbao, 1 in Dortmund) to work on those research topics.

Football training grounds at Athletic Club Bilbao © Andreas Groll
Football training grounds at BVB © Dae-Jin Lee
Researchers in front of the BVB stadium © Andreas Groll
Researchers in a restaurant © Andreas Groll

Publications:

  • Berendes, I., Gerharz, A., Groll, A., Kolodziej, M. (2025). Comparing machine learning and conventional statistical approaches for injury prediction in young professional soccer players. Electronic Journal of Applied Statistical Analysis, 18(1), 133-164.
  • Kolodziej, M., Groll, A., Nolte, K., Willwacher, S., Alt, T., Schmidt, M., Jaitner, T. (2023). Predictive modeling of lower extremity injury risk in male elite youth soccer players using least absolute shrinkage and selection operator regression. Scandinavian Journal of Medicine & Science in Sports, 33(6), 1021-1033.
  • Zumeta-Olaskoaga, L., Bender, A., & Lee, D. J. (2025). Flexible modelling of time-varying exposures and recurrent events to analyse training load effects in team sports injuries. Journal of the Royal Statistical Society Series C: Applied Statistics, 74(2), 391-405.
  • Zumeta-Olaskoaga, L., Weigert, M., Larruskain, J., Bikandi, E., Setuain, I., Lekue, J., Küchenhoff, H. & Lee, D.-J. (2023). Prediction of sports injuries in football: a recurrent time-to-event approach using regularized Cox models. AStA Advances in Statistical Analysis, 107(1-2), 101-126.