The Effects of Functional Training on Some Biomotor Abilities and Physiological Characteristics in Elite Soccer Players
Bibliographic record
Abstract
Soccer game or a soccer match includes functional movements that require complex muscular balance including stopping, walking, jogging, sprint with dribbling, shooting and passing (Eniseler, 1994; Boyle, 2004). Functional training contribute to the different systems of the body through functional exercises performed in different parts of the body (covering the whole body, improving universal motor skills, applied in multiple motion planes) through intensive, short and constantly changing sessions. The aim of this study was to investigate the effects of functional training on some physiological and Biomotor Abilities in elite soccer players. Athletes were randomly divided into two groups as Traditional Training Group (TTG) and Functional Training Group (FTG). For eight weeks, TTG athletes were trained five days per week for classical soccer training while FTG athletes were trained with functional training two days a week in addition to this training. Table 4 shows that the effect of pre-test from post-test and present an adjusted post-test mean and determine the difference of the adjusted post-test mean of two groups. According to the ANCOVA results, differences were not found statistically significant (p>0.05). As a result, in this study, which aimed to investigate the effects of functional training on some physiological and bio-motor properties in elite soccer players, it was determined that functional training method had a positive effect on some physiological and bio-motor properties of pre- and post-test values in soccer players, however to determine the difference of the adjusted post-test mean of two groups, ANCOVA results show that differences were not found statistically significant.
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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) | 0.000 | 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) | 0.002 | 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".