Contributing Factors to Performance of a Medicine Ball Explosive Power Test: A Comparison Between Jump and Nonjump Athletes
Bibliographic record
Abstract
The present study examined the factors contributing to performance of a backward overhead medicine ball throw (B-MBT) across 2 types of athletes. Twenty male volleyball players (jump athletes) and 20 wrestlers (nonjump athletes) were evaluated on 4 measures of power, including B-MBT, chest medicine ball throw (C-MBT), countermovement vertical jump (CMJ), and power index (PI). The athletes also completed 3 measures of strength: a 1-repetition-maximum (1RM) bench press (BP), a 1RM leg press (LP), and combined BP + LP strength. Jump athletes demonstrated greater absolute scores for CMJ, C-MBT, and B-MBT (p < 0.05), whereas nonjump athletes demonstrated greater strength scores for BP and for BP + LP (p < 0.05). When performances were examined on a relative basis, jump athletes achieved superior scores for C-MBT (p < 0.05), whereas nonjump athletes had greater scores for BP, LP, and BP + LP (p < 0.05). For both groups, B-MBT had strong correlations with PI (r = 0.817 [jump] and 0.917 [nonjump]), whereas for C-MBT, only nonjump athletes demonstrated a strong correlation (r = 0.842). When expressed in relative terms, B-MBT was strongly correlated with C-MBT (r = 0.762 [jump] and 0.835 [nonjump]) and CMJ (r = 0.899 [jump] and 0.945 [nonjump]). Only nonjump athletes demonstrated strong correlations with strength for absolute LP (r = 0.801) and BP + LP (r = 0.810) strength. The interaction of upper- and lower-body strength and power in the performance of a B-MBT appears complex, with the contributing factors differing for athletes with divergent skill sets and performance demands.
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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 | 0.000 | 0.002 |
| 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".