The decision-making in dribbling: a video analysis study of U10 soccer players’ skills and coaches’ quality evaluation
Bibliographic record
Abstract
Introduction: Dribbling is an important soccer skill that, when effective, allows players to overcome opponents. It can provide a strong tactical advantage; for this reason, all of its components (sprint, speed, and ball control) are fundamental to the development of young players. Dribbling can also be considered a decision-making process, and due to its characteristics, it is not always easy to study ecologically. Using a video analysis study, this research aimed to determine whether dribbling skills, specifically dribbling choice (i.e., decision-making), were related to U10 soccer players' quality. Methods: Several outcomes measures, divided into three categories, were taken during video analyses: (i) measures related to the efficacy of dribbling skill; (ii) measures related to the ability of players without the ball to support the player in possession; and (iii) measures related to ball circulation. These data were retrospectively assessed to whether the coaches had formed the teams in training through an implicit knowledge of the players' dribbling skills. Results: < 0.05). In fact, this percentage was 12.9%, 24.0%, and 48.1% for the groups with lower, average, and higher technical skills, respectively. Conclusion: Overall, the results demonstrate that dribbling accuracy has an important weight in the coach's evaluation of the technical skills level of young soccer players.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".