A Comparison of the Application of Load Monitoring Metrics for Key Match Characteristics in Women’s Rugby Sevens
Bibliographic record
Abstract
In rugby sevens, multiple high-speed matches in quick succession make effective workload monitoring essential to support decision-making around athlete preparedness and competition strategy. Match characteristics like score differential, player’s competition experience, match type, and opponent may influence workload. The purpose of this investigation was to examine the relationships between match and player characteristics and three workload measures, session rating of perceived exertion (sRPE), mechanical work, and an alternative speed–deceleration–contact (SDC) model. Twenty-two female rugby sevens athletes were monitored across 103 international matches. Data from GNSS-derived playing times, speeds, accelerations, athlete mass, and self-reported ratings of perceived exertion were collected. sRPE and mechanical work were computed, and the SDC model produced predicted values. Associations between player experience, game category, opponent rank, and score differential with each workload measure were tested using ANOVAs with Tukey’s post hoc test. Player experience and match category were significant for all three workload measures. Opponent was significant associated with sRPE and the SDC model, and match outcome was only associated with sRPE. All three workload measures, sRPE, mechanical work, and the SDC model, are valuable but differ in response to contextual and experiential factors.
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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 | 0.006 |
| 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 | 0.001 | 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".