Relationship Between Physical Qualities and Minutes Played in International Women’s Rugby Sevens
Bibliographic record
Abstract
PURPOSE: To investigate the physical qualities that differentiate playing minutes in international-level women's rugby sevens players. METHODS: Twenty-four national-level female rugby sevens players underwent measurements of anthropometry, acceleration, speed, lower- and upper-body strength, lower-body power, and aerobic fitness. Playing minutes in international competition were used to differentiate players into 2 groups, a high- or low-playing-minutes group. Playing minutes were related to team selection, which was determined by the coaching staff. Playing minutes were therefore used to differentiate performance levels. RESULTS: Players in the high-playing-minutes group (≥70 min) were older (mean ± SD 24.3 ± 3.1 vs 21.2 ± 4.3 y, P = .05, effect size [ES] = 0.77 ± 0.66, 90% confidence limit) and had greater experience in a national-training-center environment (2.4 ± 0.8 vs 1.7 ± 0.9 y, P = .03, ES = 0.83 ± 0.65), faster 1600-m time (374.5 ± 20.4 vs 393.5 ± 29.8 s, P = .09, ES = -0.70 ± 0.68), and greater 1-repetition-maximum upper-body strength (bench press 68.4 ± 6.3 vs 62.2 ± 8.1 kg, P = .07, ES = 0.80 ± 0.70, and neutral-grip pull-up 84.0 ± 8.2 vs 79.1 ± 5.4 kg, P = .12, ES = 0.68 ± 0.72) than athletes who played fewer minutes. Age (rs = .59 ± ~.28), training experience (rs = .57 ± ~.29), bench press (r = .44 ± ~.36), and 1600-m time (r = -.43 ± ~.34) were significantly associated with playing minutes. Neutral-grip pull-up and bench press contributed significantly to a discriminant analysis. The average squared canonical correlation was .46. The discriminant analysis predicted 7 of 9 and 6 of 10 high- and low-playing-minutes athletes, respectively. CONCLUSIONS: Age, training experience, upper-body strength, and aerobic fitness differentiated athlete playing minutes in international women's rugby sevens.
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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 | 0.000 |
| 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) | 0.003 | 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".