Effects of Short-Term In-Season Weightlifting Training on the Muscle Strength, Peak Power, Sprint Performance, and Ball-Throwing Velocity of Male Handball Players
Bibliographic record
Abstract
Hermassi, S, Schwesig, R, Aloui, G, Shephard, RJ, and Chelly, MS. Effects of short-term in-season weightlifting training on the muscle strength, peak power, sprint performance, and ball-throwing velocity of male handball players. J Strength Cond Res 33(12): 3309-3321, 2019-This study analyzed the effects of in-season weightlifting training, conducted biweekly for 12 weeks. Twenty-two male handball players were divided into experimental (age: 20.3 ± 0.5 years, height: 1.85 ± 0.04 m, and body mass: 86.3 ± 9.4 kg) and control (age: 20.1 ± 0.5 years, height: 1.81 ± 0.05 m, and body mass: 83.9 ± 10.3 kg) groups, and performance was assessed before and after the intervention. Peak power was determined by a cycle ergometer force-velocity test, a vertical squat jump, and a countermovement jump. Measures of 1 repetition maximal strength included bench press, back half-squats, snatch, and clean and jerk. Handball-throwing velocity was assessed by standing, running, and jump throws. The change of direction T-half test and sprint times over 5, 15, and 30 m were recorded by paired photocells. The intraclass correlation coefficient of all parameters exceeded 0.75. Significant training effects were seen for all sprint (3/3) and throwing (3/3) measures, but only 7/14 strength parameters and 3/10 jump parameters. The largest increases of performance were for snatch (η = 0.627; d = 2.85) and 15-m sprinting (η = 0.852; d = 2.73). Countermovement jump force showed a negative response (d = -0.62). Three other parameters (V0 power for the upper and lower limbs, countermovement jump power) showed only small effect sizes (d = 0.45, d = 0.31, and d = 0.23, respectively). We conclude that 12 weeks of biweekly in-season weight training improved the peak power, maximal strength, sprinting, and throwing in handball players, but that their jump performance did not increase with this kind of intervention.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".