SV Darmstadt 98 - Bundesliga club
Player analysis with AI technology
Starting point
- Increased analysis times
The coaching staff and scouts at Darmstadt 98 needed several days to access the performance data of players from the "Impect" dataset. - Manual queries
The IT department conducted manual queries, leading to bottlenecks and delays in decision-making regarding player acquisitions and game strategies. - Real-time data access
The club was looking for a more efficient solution to independently access data in real-time without IT support.
Approach
- By implementing an AI-powered solution with Amazon Bedrock and Amazon Athena, the "Impect" dataset can now be accessed in real-time.
- The newly developed generative AI agent allows the coaching staff and scouts to enter natural language queries such as "Who is the best substitute for an injured player?".
- The automation of data ingestion with AWS Lambda and the integrated performance monitoring ensure a fast response time while simultaneously increasing the system's reliability.
Features/Project outcome
- The data query time has been reduced from several days to less than 5 minutes, significantly improving efficiency and decision-making.
- The new, intuitive user interface enables even non-technical staff to independently and easily query complex datasets.
- The scalable architecture supports future expansions, such as predictive analysis of player performance, enabling continuous development.

Customer benefits
- Increase in operational efficiency
By eliminating IT bottlenecks, decisions can be made faster and more agilely. - Optimized decision accuracy
Real-time insights enable more precise decisions and enhance success in player acquisition. - High acceptance rate among scouts and the coaching staff
This promotes strategic agility and optimizes the overall performance of the team.

Lars Ritter | Senior Manager