Explainable machine learning for age-specific talent identification in Chinese youth football
Yan Tong, Jingyi Bi, Shixin Li, Luohang Chen, Chun Liu, Hengxin Chen · 2026
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Description
Yan Tong and five co-authors build a clear-eyed machine judge of young football talent in China, in a 2026 study. Scouts pick elite youth by eye, which can miss late bloomers and favor early growers. The team tested about 400 camp players aged 8 to 12 across body, fitness, skill, mind, and match measures. Separate models were built per age group, and a method that credits each factor explained every call.
The best models split picked from unpicked players with high accuracy. Dribbling speed and the standing long jump mattered at every age. Sprint speed weighed more for the youngest groups. At 12, left-foot shooting led for boys and match awareness for girls, while mind traits added little. Check runs on national picks agreed only in part, which the authors read as room for data to aid, not rule, scouts.
Catalog record
| CREATOR | Yan Tong, Jingyi Bi, Shixin Li, Luohang Chen, Chun Liu, Hengxin Chen |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 50 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |