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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".