Effects of Elastic Band Plyometric Training on Physical Performance of Team Handball Players
Bibliographic record
Abstract
This project investigated the effect of incorporating 8 weeks of biweekly upper limb loaded plyometric training (using elastic bands) into the in-season regimen of handball players. Participants were randomly allocated to a control group (CG) (n = 15, age = 18.1 ± 0.5 years, body mass = 73.7 ± 13.9 kg), or an experimental group (EG) (n = 14, age = 17.7 ± 0.3 years, body mass = 76.8 ± 10.7 kg). The measurements obtained pre- and post-intervention included a cycle ergometer force–velocity test, ball throwing velocity in three types of throwing, one-repetition maximum (1-RM) bench press and pull-over, and anthropometric estimates of the upper limb muscle volumes. The EG improved in absolute muscle power (W) (Δ23.3%; interaction effect p = 0.032 more than pre-intervention), relative muscle power (W·kg−1) (Δ22.3%; interaction effect p = 0.024), and all three types of ball throwing (Δ18.6%, interaction effect p = 0.019 on a jumping shot; Δ18.6%, interaction effect p = 0.017 on a three-step running throw; and Δ19.1%, interaction effect p = 0.046 on a standing throw). There was no interaction effect for the 1-RM bench press and pull-over performance. The upper limb muscle volumes remained unchanged in both groups. We concluded that adding biweekly elastic band plyometric training to standard training improves the muscle power and throwing velocity. Accordingly, such exercises should be adopted as a part of a pragmatic approach to handball training.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".