TSG ResearchLab

Smart Data

With many parameters being measured for each athlete, across each squad and year, the data can begin to accumulate. Understanding how to change big data into smart data is a process that we have taken to prevent paralysis by analysis. This is why big data must turn into smart data. Smart data can synthesize data across multiple domains (i.e., medical, physical, and psychological domains) that can turn into predictive patterns which can help in decision making. Smart data will be used to determine what performance criteria is required to achieve excellence in a football domain.

These criteria will further be used as a framework to help scout for talented players and inform best practice to help support talented youth players with their long-term transition into professional athletes.

Our models are also being used to better inform the staff as to why athletes have moved through the academy ranks of TSG Hoffenheim, reporting trends towards the preferred strengths and weaknesses of players. Smart data provides practitioners the likelihood of each player has with their unique skill set to progress within the academy based on the outcomes of players across the years. Furthermore, the uniqueness of the smart data provides a possibility to enter how much a player could improve their chances of reaching to the next level based on attainable goals.

Feature One

Turning big data into smart data to help advise practitioners on their decision-making within various environments.

Feature Two

Finding solutions via modern data analysis techniques (i.e. machine learning, deep learning algorithms).

Feature Three

Creating a user-friendly dashboard that creates individual reports on the strengths and weaknesses of individuals.

Scientific Journal Publications

Forcher, L., Forcher, L., Härtel, S., Jekauc, D., Wäsche, H., Woll, A., Groß, T., Altmann, S. (2022). Does Technical Match Performance in Professional Soccer Depend on the Positional Role or the Individuality of the Player? Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2022.813206

Forcher, L., Forcher, L., Wäsche, H., Jekauc, D., Woll, A., Altmann, S. (2022). The Influence of Tactical Formation on Physical and Technical Match Performance in Soccer: A Systematic Review. International journal of sports science & coaching. https://doi.org/10.1177/17479541221101363

Forcher, L., Altmann, S., Forcher, L., Jekauc, D., & Kempe, M. (2022). The use of player tracking data to analyze defensive play in professional soccer-A scoping review. International Journal of Sports Science & Coaching.

Forcher, L., Kempe, M., Altmann, S., Forcher, L., & Woll, A. (2021). The “Hockey” Assist Makes the Difference—Validation of a Defensive Disruptiveness Model to Evaluate Passing Sequences in Elite Soccer. Entropy, 23(12), 1607.

Altmann, S., Forcher, L., Ruf, L., Beavan, A., Groß, T., Lussi, P., ... & Härtel, S. (2021). Match-related physical performance in professional soccer: Position or player specific?. PloS one, 16(9), e0256695.

Altmann, S., Neumann, R., Härtel, S., Woll, A., & Buchheit, M. (2021). Using submaximal exercise heart rate for monitoring cardiorespiratory fitness changes in professional soccer players: a replication study. International Journal of Sports Physiology and Performance, 16(8), 1096-1102.

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