Do Lower-Body Dimensions and Body Composition Explain Vertical Jump Ability?
Bibliographic record
Abstract
Caia, J, Weiss, LW, Chiu, LZF, Schilling, BK, Paquette, MR, and Relyea, GE. Do lower-body dimensions and body composition explain vertical jump ability? J Strength Cond Res 30(11): 3073-3083, 2016-Vertical jump (VJ) capability is integral to the level of success attained by individuals participating in numerous sport and physical activities. Knowledge of factors related to jump performance may help with talent identification and/or optimizing training prescription. Although myriad variables are likely related to VJ, this study focused on determining if various lower-body dimensions and/or body composition would explain some of the variability in performance. Selected anthropometric dimensions were obtained from 50 university students (25 men and 25 women) on 2 occasions separated by 48 or 72 hours. Estimated body fat percentage (BF%), height, body weight, hip width, pelvic width, bilateral quadriceps angle (Q-angle), and bilateral longitudinal dimensions of the feet, leg, thigh, and lower limb were obtained. Additionally, participants completed countermovement VJs. Analysis showed BF% to have the highest correlation with countermovement VJ displacement (r = -0.76, p < 0.001). When examining lower-body dimensions, right-side Q-angle displayed the strongest association with countermovement VJ displacement (r = -0.58, p < 0.001). Regression analysis revealed that 2 different pairs of variables accounted for the greatest variation (66%) in VJ: (a) BF% and sex and (b) BF% and body weight. Regression models involving BF% and lower-body dimensions explained up to 61% of the variance observed in VJ. Although the variance explained by BF% may be increased by using several lower-body dimensions, either sex identification or body weight explains comparatively more. Therefore, these data suggest that the lower-body dimensions measured herein have limited utility in explaining VJ performance.
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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 | 0.002 | 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".