The developmental activities of elite soccer players aged under-16 years from Brazil, England, France, Ghana, Mexico, Portugal and Sweden
Bibliographic record
Abstract
The developmental activities of 328 elite soccer players aged under-16 years from Brazil, England, France, Ghana, Mexico, Portugal and Sweden were examined using retrospective recall in a cross-sectional research design. The activities were compared to the early diversification, early specialisation, and early engagement pathways. Players started their involvement in soccer at approximately 5 years of age. During childhood, they engaged in soccer practice for a mean value of 185.7, s = 124.0 h · year(-¹), in soccer play for 186.0, s = 125.3 h · year(-¹), and in soccer competition for 37.1, s = 28.9 h · year(-¹). A mean value of 2.3, s = 1.6 sports additional to soccer were engaged in by 229 players during childhood. Players started their participation in an elite training academy at 11 to 12 years of age. During adolescence, they engaged in soccer practice for a mean value of 411.9, s = 184.3 h · year(-¹), in soccer play for 159.7, s = 195.0 h · year(-¹), and in soccer competition for 66.9, s = 48.8 h · year(-¹). A mean value of 2.5, s = 1.8 sports other than soccer were engaged in by 132 players during this period. There were some relatively minor differences between countries, but generally the developmental activities of the players followed a mixture of the early engagement and specialisation pathways, rather than early diversification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".