Monitoring Matches and Small-sided Games in Elite Young Soccer Players
Bibliographic record
Abstract
The aim of this study was to compare the distances at various intensity in matches and small-sided games in elite-young soccer players using the metabolic power approach and running speed methods through fixed and individual speed zones. The second aim was to investigate the difference in high intensity external workload (% of total distances covered > 16 km/h or > 20 W/kg) between matches and small-sided games. Global positioning system data from 14 elite-youth players were analyzed during 13 matches and two types of small sided-games. Five intensity zones were used to compare the running distances between the metabolic power approach and the classic performance analysis. Metabolic power recorded more distances covered at high intensity than the running speed methods for every playing situations, except for the zone 5 of fixed speed (> 19 km/h) in matches (P<0.05). Smaller differences of external workload at high intensity were found when using the metabolic power approach compared to the traditional performance analysis. Our results highlight that the traditional analysis underestimates the athlete's high intensity efforts. The metabolic power approach seems more relevant to monitor matches and training situations but also to compare matches to small-sided games in elite-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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".