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.
Turning big data into smart data to help advise practitioners on their decision-making within various environments.
Finding solutions via modern data analysis techniques (i.e. machine learning, deep learning algorithms).
Creating a user-friendly dashboard that creates individual reports on the strengths and weaknesses of individuals.