Transfer Effect of Strength and Power Training to the Sprinting Kinematics of International Rugby Players
Bibliographic record
Abstract
Increasing lower-body strength is often considered to be important for improving the sprinting speed of rugby players. This concept was examined in a group (n = 40) of international rugby players in a 2-part study. The players were tested for body mass (BM), 1 repetition maximum power clean (PC), and front squat, as well as triple broad jump and broad jump. In addition, speed over 40 m was tested, with timing gates recording the 0- to 10-m and 30- to 40-m sections to assess acceleration and maximal velocity. Two video cameras recorded the 2 splits for later analysis of sprinting kinematics. The players were divided into a fast group (n = 20) and a slow group (n = 20) for both acceleration and maximal velocity. In the second part of the study, a group (n = 15) of players were tracked over a 1-year period to determine how changes in strength corresponded with changes in sprinting kinematics. The fast groups for both acceleration and maximal velocity showed greater levels of strength (d = 0.5-1.8), lower ground contact times (d = 0.8-2.1), and longer stride lengths (d = 0.5-1.3). There was a moderate improvement over 1 year in PC/BM (0.08 kg·kg, p = 0.008, d = 0.6), and this had a strong relationship with the change in maximal velocity stride length (r = 0.70). Acceleration stride length also had a large improvement over 1 year (0.09 m, p = 0.003, d = 0.81). Although increasing lower-body strength is likely important for increasing sprinting speed of players with low training backgrounds, it may not have the same effect with highly trained players.
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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 | 0.004 | 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".