Efficacy of Horizontal Jumping Tasks as a Method for Talent Identification of Female Rugby Players
Bibliographic record
Abstract
The purpose of this study was to explore the relationship between horizontal jumping tasks (standing long jump [SLJ] and standing triple jump [STJ]) and sprint speed (initial sprint speed [ISS] and maximum sprint speed [MSS]) in elite female rugby athletes. Data were collected from provincial, under 20 international fifteens players, in addition to senior sevens international level female rugby athletes (n = 114). Body weight, SLJ, STJ, 10-m sprint speed (ISS), 30- to 40-m sprint speed (MSS), initial sprint momentum, and maximal sprint momentum were analyzed. When categorized by horizontal jumping ability, there was a significant difference in sprint speeds (p < 0.001) between the top 50% and bottom 50% groups. Examining the relationship between horizontal jumping tasks and sprinting speed revealed a stronger correlation in the slowest 50% of athletes compared with the fastest 50%. A linear regression developed from STJ and body weight adequately predicted ISS (r = 0.645, p < 0.001) and MSS (r = 0.761, p < 0.001). In conclusion, horizontal jumping tasks can be used as a valuable performance test to identify differences of sprinting ability in elite female rugby players. However, the relationship between horizontal jumping tasks and sprinting speed seems to decrease in faster athletes. Further, STJ and body weight can be used to predict both ISS and MSS. Based on these data, it is suggested that only STJ be collected when identifying potential sprinting talent in female rugby athletes and caution be used when generalizing results across varying levels of athletes.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".