Relationships between maximal strength of lower limb, anthropometric characteristics and fundamental explosive performance in handball players
Bibliographic record
Abstract
Abstract Background The purpose of this study was to examine relationships between lower body muscular strength, anthropometric characteristics and several measures of explosive performance in elite team-handball players. Methods 22 male elite team-handball players (age: 19.1 ± 1.7 years) were studied during the competitive season. Standard anthropometric and body composition measures included body mass index, lower limb and thigh muscle volume, and body fat percentage. Maximal leg strength was determined by a one-repetition maximum (1-RM) half back-squat. Vertical jump performance was assessed using a squat jump (SJ) and a counter movement jump (CMJ). Repeated shuttle-sprint ability (RSA) was tested by 6 (2 × 15 m) shuttle sprints with 20 s of active recovery intervals. The best time in a single shuttle sprint (30m; RSAbest), fastest total time (RSATT) and RSA test performance decrement (RSAdec) were recorded. Agility was measured using a modified T-half test (MAT). Throwing velocities of jump shooting and 3-step throwing were recorded by digital video camera. Results The explained variance of 1-RM half-back-squats ranged from 0.2 % (RSA% Fatigue Index) to 70.1 % (CMJ). Four out of 8 variables (RSA Best Time, CMJ, SJ, throwing velocity of jump shoot) demonstrated an r2 > 0.5. Jump performances seemed closely related to 1-RM half-back-squats. Furthermore, 1-RM half-back-squats were positively correlated with leg and thigh muscle volumes (r = 0.652, r = 0.768). Conclusion The anthropometric characteristics and some physical performance tests are closely related to the maximal strength performance of handball players. Coaches should focus on maximal strength training programs for the lower limbs when seeking improvements in the throwing velocity and jump performance of handball players.
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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) | 0.003 | 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".