Difference Asymmetry between Preferred Dominant and Non-Dominant Legs in Muscular Power and Balance among Sub-Elite Soccer Players in Qatar
Bibliographic record
Abstract
The objective of this study was to determine and compare leg asymmetry between preferred dominant and non-dominant legs in muscular power and balance among sub-elite soccer (football) players in Qatar. Thirty-two professional local soccer players from the Qatar Stars League (Second Division) participated in the study (23.1 ± 6.1 years). They were classified according to their preferred dominant leg (preferred leg to kick the ball). Twenty-two players had a right dominant leg, and the remaining ten had a left dominant leg. Countermovement jump (CMJ) was used to measure unilateral and bilateral vertical jump performances. The Y-balance test (YBT) was used to assess dynamic balance. No significant differences were found between the dominant and non-dominant leg for CMJ flight height (p > 0.05; asymmetry index (AI) = 1.83 ± 11.46) or the relative and absolute reach distance derived from the YBT (p > 0.05; AI (relative) = −0.45 ± 9.68, AI (absolute) = −0.60 ± 12.3). Bilateral asymmetry in dynamic balance was not significant for any of the anterior, posteromedial, or the posterolateral reaching directions (p > 0.05). The selected football players demonstrated an acceptable level of leg symmetry for power and dynamic balance. These findings may prove helpful for the assessment and evaluation of talents and should help to develop and optimize training regimes.
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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 | 0.001 | 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".