The role of stretching protocols in post-fatigue performance and flexibility among soccer players
Bibliographic record
Abstract
This study aimed to compare the efficacy of static stretching (SS), slow dynamic stretching (SDS), and fast dynamic stretching (FDS) on restoring flexibility, balance, and performance following soccer-specific fatigue. Forty male soccer players (age: 21.0 ± 2.4 years) completed the study. Participants performed a soccer-specific fatigue protocol followed by one of four conditions (SS, SDS at 50 bpm, FDS at 100 bpm, and a control condition (CC)). Measures included knee range of motion (ROM) (Modified Thomas and passive knee extension tests), dynamic balance (Y-Balance Test), biomechanics during a countermovement jump (CMJ) (assessed via OpenCap), 20-m sprint speed, and Illinois agility test performance. The findings indicate that the SS condition showed the greatest improvements in knee flexion (d = 0.51-0.98) and extension (d = 0.45-0.52) ROM. The SDS condition demonstrated superior performance in CMJ jump (highest knee flexion increase, lowest knee valgus, fastest take-off time; d = 0.43-1.89), sprint speed (d = 0.57-0.71), and agility (d = 0.80-0.92). Although dynamic balance improved over time, there were no significant differences between the stretching conditions (p > 0.05). Additionally, the FDS protocol resulted in the smallest gains across all measured outcomes, particularly under fatigued conditions. SDS is most effective for enhancing knee joint mechanics, sprint, and agility recovery post-fatigue, while SS is optimal for ROM restoration. Stretching protocol selection should be contingent on the intended recovery outcome. These findings support incorporating such targeted interventions to optimize athletic performance.
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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 | 0.001 | 0.001 |
| 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".