Customer description
A sports association with national training programs for young athletes (13—18 years old), active in indoor sports such as basketball, volleyball and handball.
Challenge
Talent recognition was largely based on sight scouting, physical tests, and subjective assessments. Many late bloomers or atypical profiles fell by the wayside.
Solution
An AI model was developed that combined historical breakthrough information, test results, game insight and behavior to more objectively predict the potential of young athletes.
Approach
- Analysis of profiles of successful former talents
Dataset built with the physical, mental and tactical profile of successful internationals in the past. - Structure of test and observation data
Standardization of test batteries and rating scales for youth coaches and scouts. - Model training and forecasting
AI links individual results to success factors and calculates the chance of breakthrough in 3 to 5 years. - Scouting dashboard for regional coaches
Coaches see predicted potential for each player, remarkable strengths and advice for development direction.
Results
- More objectivity in talent selection
- Wider inflow at the bottom of the pyramid
- Early detection of 'invisible' growth brilliants
Learnings
AI is not a substitute for human knowledge, but it is a valuable addition. Enriching scouting data creates a fairer, more inclusive and more effective talent selection process.
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