The effects of different multidirectional plyometric sequences on shooting, balance, and neuromuscular performance in professional male basketball players
Bibliographic record
Abstract
OBJECTIVE: Repeated multidirectional jumps are highly specific movements in basketball, which may be important to integrate the training routine. Accordingly, this study aimed to assess the effects of three different multidirectional plyometric jumping sequences on shooting, balance, and neuromuscular performance in professional, male basketball players. METHODS: Eighteen players underwent familiarization and baseline performance assessments in an initial session. Players then completed 21 visits in a randomized, counterbalanced manner. Each visit involving 10 repetitions of one of three different multidirectional plyometric protocols followed by a single performance test (countermovement jump (CMJ), squat jump (SJ), five jump (FJT), change-of-direction T-test (CoD T), Stork balance (SBT), Y-balance (YBT), three-point shooting (3pts) tests). The first plyometric protocol (P1) consisted of a combination of vertical and horizontal jumps, the second protocol (P2) consisted of a combination of drop, lateral, and vertical jumps, and the third protocol (P3) consisted of a combination of drop, single-leg step-up, and horizontal jumps. Each session was separated by at least 48 h of rest and recovery time between repetitions was 20 s. RESULTS: Neuromuscular (CMJ, SJ, FJT and CoD T), body balance (SBT and YBT), and Shooting performances were significantly worse following each plyometric protocol compared to baseline (p < 0.001; d = 0.23-2.21). In addition, the majority of the measured performances were significantly lower following P3 compared to P1 and P2. CONCLUSIONS: Basketball-specific performance was adversely influenced by repeated multidirectional plyometric routines. Incorporating these particular strategies into the training regimen to counteract the induced tiredness will be intriguing.
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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) | 0.000 | 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) | 0.001 | 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".