Performance characteristics of selected/deselected under 11 players from a professional youth football academy
Bibliographic record
Abstract
This study aimed to determine whether players selected for the under 11 team of a professional youth football academy outperform their deselected peers in physical, technical and gross motor coordination skills, or in psycho-social capacities. Of the young players active at different amateur clubs yearly 2% were scouted to participate at trainings and matches from an academy before the first objective baseline testing (season 1 n = 54 boys, season 2 n = 49, age: 9.25 ± 0.46). Most of the scouted players ( n = 103) were born in the first quarter of the year (47.6%) and started playing football at a young age (4.80 ± 0.84). Mann–Whitney U tests showed that the selected under 11 players ( n = 31) from the reduced pool outperformed their deselected peers ( n = 72) in the 30-m slalom sprint, dribble test and Loughborough soccer passing test, and on sport learning-, motor-, creative- and interpersonal capacity ( P < 0.05). A discriminant analysis resulted in a significant discriminant function (Wilks’ Λ = 0.673, df = 16 and P = 0.002) with 69.6% of players classified correctly. In sum, the current system, tends to scout 9-year old soccer players with multiple years of soccer experience, and well-developed motor skills, who are predominantly born in the first quarter of the year. Of those players, the ones with better physical and technical skills, who are believed to have most potential to become elite in the future are selected. However, 25 of the players with a high probability of being selected were deselected. Whether this system is appropriate serves a broader ethical discussion within contemporary society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".