The Influence of Physical Contact on Athlete Load in International Female Rugby Sevens
Bibliographic record
Abstract
ABSTRACT: Epp-Stobbe, A, Tsai, M-C, Morris, C, and Klimstra, M. The influence of physical contact on athlete load in international female rugby sevens. J Strength Cond Res 37(2): 383-387, 2023-Although self-reported rate of perceived exertion (RPE) is a simple and popular metric for monitoring player loads, this holistic measure may not adequately represent the distinct contributing factors to athlete loading in team sports, such as contact load. The purpose of this investigation is to determine the relationship between the number of contacts experienced and playing time on RPE in elite women's rugby sevens athletes during competition. Additionally, we examine the contribution of the number of contacts and playing time to RPE. The data collected included RPE, playing time, and number of contacts from 1 team participating in 74 international women's sevens matches. The relationship was modeled using multiple linear regression. Results, including the coefficients for the number of contacts and playing time, were significant (p < 0.001), and R2adjusted was 0.3063. Because contacts are accounted for within the measure of RPE in the proposed model, this further supports the value of RPE as a global measure of athlete experience. However, this study has found a different relationship between RPE and playing time dependent on the number of contacts, such that the influence of playing time on RPE decreases as the number of contacts increase. Ultimately, this may mean that the weighting of individual salient factors affecting player loads, such as the number of contacts or playing time, depend on the levels of all known and potentially unknown factors experienced and may limit the use of RPE when contextualizing player load across athletes. Taken together, the findings suggest that the number of contacts, playing time, and RPE should be considered when monitoring athlete loads while further substantiating the need for more, and higher resolution, measures to better quantify competition loads in contact team sports.
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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 | 0.003 |
| 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".