The Relationship Between Physical Characteristics and Maximal Strength in Men Practicing the Back Squat, the Bench Press and the Deadlift
Bibliographic record
Abstract
This study was designed to quantify the relationships between physical characteristics and maximal strength in the back squat, the bench press and the deadlift on powerlifters and football players. Eighteen male junior drug-tested classic powerlifters and seventeen NCAA Division II American football players' anthropometric measurements were taken to compare them with maximal strength results from either a powerlifting meet or testing from their supervised strength and conditioning program. Pearson's bivariate correlations analysis revealed (statistical significance was set at p<0.05) that individuals with a greater (Wilks points) back squat, generally presented a higher Bodyweight (BW) (r=0.37), Body Mass Index (BMI) (r=0.45), Bodyfat Percentage (BF%) (r=0.36), Hip (r=0.41), Waist (r=0.35) and Torso (r=0.41) Circumference (C), Hip C/Height (r=0.46), Waist C/Height (r=0.39) and Torso C/Height (r=0.45) ratios. The individuals with a greater bench press generally presented a higher BMI (r=0.37), Lean Body Weight (LBW) (r=0.36), Hip C (r=0.39) and Hip C/Height ratio (r=0.39). On the other hand, individuals with a greater deadlift were generally older (r=0.34), shorter (r=-0.41), had shorter thighs (r=-0.52) and trunks (r=-0.36), smaller Thigh Length (L)/Height ratio (r=-0.44), Waist C/Hip C (r=-0.41) and Thigh L/Lower Leg L (r=-0.53) ratios, but a higher Lower Leg L/Height ratio (r=-046). The results of this study should be utilized by strength and conditioning coaches to deepen their comprehension of their athletes' physical characteristics in order to help them develop strength through their advantages. Further research should focus on evaluating how physical characteristics affect performance in different squat, bench, and deadlift stances.
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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 | 0.000 | 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) | 0.002 | 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".