Lower Limb Maximal Dynamic Strength and Agility Determinants in Elite Basketball Players
Bibliographic record
Abstract
The aims of this study were to examine the relationship between squat 1 repetition maximum (1RM) and basketball-relevant tests and the variables that influence agility (T-test) in elite male professional basketball players (n = 14, age 23.3 +/- 2.7 years, height 195.6 +/- 8.3 cm, body mass 94.2 +/- 10.2 kg). T-test performance was significantly related to body mass (r = 0.58, p = 0.03) and to percentage of body fat (r = 0.80, p < 0.001). A significant negative correlation was observed between t-test and 5-jump test performance (r = -0.61, p = 0.02). Squat 1RM was significantly related to 5-, 10-, and 30-m sprint times. Stepwise correlation analysis showed percentage of body fat was the best single predictor factor (p < 0.05) of agility. Squat 1RM performance was the best single predictor of 5-m and 10-m sprint times (p < 0.05). In light of the present study's findings, agility should be regarded as a per se physiological ability for elite basketball players. Consequently, basketball-specific agility drills should be stressed in elite basketball training. Given the association between squat 1RM performance and short sprint times, squat exercises should be a major component of basketball conditioning.
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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 | 0.001 |
| 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".