The Relationship Between Horizontal Jumping and Sprinting Ability Across Sexes in Young Active Adults
Bibliographic record
Abstract
Purpose: The purpose of this research was to investigate the relationship between horizontal jump distance, 10 m time, and 30–40 m time in multi-sport athletes separated by sex and sprint speed. Methods: A total of 1352 athletes (742 males and 610 females) performed 40 m sprints, standing broad jumps (SBJs), and standing triple jumps (STJs). Data were separated by sex and then grouped as fast, average, and slow using the K-Means algorithm in three conditions (acceleration, max speed, and combined). Results: Regression models explained 84.01% of the variance (F(7,757) = 574.5, p < 0.001) for the 10 m times with mass, speed group, and sex as significant predictors and 88.51% of the variance (F = (7,757 = 841.6, p < 0.001) for the 30–40 m times with SBJ, STJ, speed group, sex, and the interaction of sex and group as significant predictors. Conclusions: These results suggest that when examining general athlete physical performance, horizontal jump tests and max speed sprint times can be used equivalently to stratify athletes. However, it is important to group athletes by speed and sex before being able to predict sprint ability from horizontal jump tests. Further, athlete mass is a significant factor in the prediction of acceleration ability but not maximum speed, and horizontal jumps were significant factors in the prediction of max speed but not acceleration.
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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) | 0.000 | 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) | 0.002 | 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".