Which Measure of Drop Jump Performance Best Predicts Sprinting Speed?
Bibliographic record
Abstract
The purpose of this study was to evaluate which measure of a drop jump (DJ) has the highest correlation with sprinting speed over 60 m. For use of comparison, maximal leg strengths in a front squat, countermovement jump, and squat jump were also assessed. The subjects in the study were all high-caliber female university rugby players. Subjects did DJs from 0.12, 0.24, 0.36, 0.48, 0.60, 0.72, and 0.84 m. Jump height and reactive strength index (RSI) were calculated at each drop height. Pearson correlations were used to analyze the relationship between the strength and jumping measures with sprinting speed. The DJ height from 0.84 m had the highest negative correlation with 0- to 10-m split (r = -0.66), the 10- to 30-m split (r = -0.86) and 30- to 60-m split (r = -0.86). The use of RSI is questioned as a measurement of DJ performance. It is suggested that maximal height achieved in a DJ is the most important DJ measure. If it is desired to measure ground contact time, then it may be more useful to use a second test where the jump height for the athlete is set by having the athlete jump onto a box or touch a target overhead set at a standard height and measure the ground contact time with a switch mat or force plate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".