Motion Characteristics of Youth Women Soccer Matches: Female Athletes in Motion (FAiM) Study
Bibliographic record
Abstract
This study determined the locomotor characteristics for youth female soccer matches. 89 female soccer players (U-15-U-17) were assessed during a youth national championship or a talent identification camp using a Global Positioning System. Positional and age-group comparisons of locomotor characteristics were made for complete games, each half, differences between halves as well as sprint profiles using an ANCOVA adjusting for the differences in game or half durations, respectively. Midfielders covered greater distances (8 449 ± 170 m) than defenders (7 779 ± 114 m), mostly from more low- (2 553 ± 99 m vs. 2 151 ± 66 m) and moderate-speed running (1 389 ± 78 m vs. 1 142 ± 52 m). Forwards had more sprint distances (275 ± 42 m), sprints (15 ± 2) and greater maximum speed (26.7 ± 0.6 km · h(-1)) than midfielders (131 ± 24 m, 8 ± 1, 24.7 ± 0.4 km · h(-1), respectively). There was a tendency for increased distances within most velocity bands, workrate and sprints with increasing age. There was a greater increase in walking and jogging between the first and second half for forwards than defenders and midfielders. Youth female soccer players covered 6 500-9 000 m during matches with positional distinctions that are comparable to elite-standard women. These data provide novel insight into the physical demands of female youth soccer and should be used to establish appropriate age-group and positional strategies for training and development.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".