Effects of Lower-Limb Strength Training on Agility, Repeated Sprinting With Changes of Direction, Leg Peak Power, and Neuromuscular Adaptations of Soccer Players
Bibliographic record
Abstract
Hammami, M, Negra, Y, Billaut, F, Hermassi, S, Shephard, RJ, and Chelly, MS. Effects of lower-limb strength training on agility, repeated sprinting with changes of direction, leg peak power, and neuromuscular adaptations of soccer players. J Strength Cond Res 32(1): 37-47, 2018-We examined the effects on explosive muscular performance of incorporating 8 weeks strength training into the preparation of junior male soccer players, allocating subjects between an experimental group (E, n = 19) and a matched control group (C, n = 12). Controls maintained their regular training program, but the experimental group replaced a part of this schedule by strength training. Performance was assessed using running times (5, 10, 20, 30 and 40 m), a sprint test with 180° turns (S180°), a 9-3-6-3-9 m sprint with backward and forward running (SBF), a 4 × 5 m sprint test with turns, repeated shuttle sprinting, repeated changes of direction, squat (SJ) and counter-movement (CMJ) jumping, back half-squatting, and a force-velocity test. Electromyographic (EMG) activity of the vastus lateralis (VL), vastus medialis (VM), and rectus femoris (RF) muscles was recorded during jumping. Two-way ANOVA showed significant gains in E relative to C during the straight sprint (all distances). Scores of E increased substantially (p ≤ 0.01) on S4 × 5 and SBF and moderately on S180°. Leg peak power, SJ, and CMJ were also enhanced, with significant increases in EMG activity. However, repeated-sprint parameters showed no significant changes. We conclude that biweekly strength training improves key components of performance in junior soccer players relative to standard in-season 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".