The magnitude of correlation between deadlift 1RM and jumping performance is sports dependent
Bibliographic record
Abstract
Introduction Based on the assumption of maximal strength as a basic ability, several studies show a high influence of maximum strength on jumping performance in several sport athletes. However, there is a wide range of correlations from r = 0.17–0.9 between squat 1RM and jumping performance in different sports. Additionally, there are only a few studies investigating the influence of deadlift one repetition maximum (1RM) on jumping performance. Thus, this study aimed to investigate the correlations between 1RM in the deadlift on jumping performance using the countermovement jump height (CMJ) and squat jump height (SJ) considering different sports. Methods 103 athletes with experience in the deadlift from soccer, basketball, American football, powerlifting as well as participants from different sports without any deadlift experience (control group) were included to this study. Results Overall statistics showed a significant moderate influence of deadlift 1RM (r = 0.301–0.472) on jumping performance. However, subgroup analysis showed no significant correlation between deadlift 1RM and jumping performance in control participants, while moderate correlations could be detected in powerlifters (r = 0.34–0.39), soccer players (r = 0.437–0.46), American football players (0.584–0.62) and high correlations in basketball players (r = 0.809–0.848) showing significant influence of type of sport on correlations between deadlift maximum strength and jumping performance. Discussion Presented results underline movement velocity- and task specificity of strength training routines which is discussed in the light of the respective sports.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".