Physiological Correlates of Golf Performance
Bibliographic record
Abstract
Golf is now a sport where physical training is an integral component of elite players' practice and contributes to the ability to play at a high level consistently and without injury. Relationships between physical conditioning and golf performance have not been reported. Therefore, the objective of this research was to identify physiological correlates of golf performance in elite golfers under laboratory (ball speed and distance) and tournament conditions (average score, greens in regulation, short game measures, and putting accuracy).The correlation analysis revealed significant associations between mass, height, body mass index, sit height, arm length, and predicted Vo2max and golf measures. Significant correlations were noted between anterior abdominal muscle endurance and driver carry distance (r = 0.38; P = 0.04) and average putt distance after a chip shot (r = -0.44; P = 0.03), between dominant side abdominal muscle endurance and average putt distance after a chip shot (r = -0.43; P = 0.03), and between nondominant-side abdominal muscle endurance and average putt distance after a sand shot (r = -0.59; P = 0.001). Further correlations were found among sit and reach and driver carry distance (r = -0.36; P = 0.04), 5-iron ball speed (r = - 0.41; P = 0.02), 5-iron carry distance (r = -0.44; P = 0.01), and score (r = 0.43; P = 0.03). Correlation analysis revealed significant associations among peripheral muscle test results, golf driver results, 5-iron ball measures, score, and putting efficacy.These results may be important for developing training programs based on sound physiological rationale and for the development of talent identification programs. Results suggest that core strength and stability, flexibility, balance, and peripheral muscle strength are correlated with golf performance and should be included in golf training programs.
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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) | 0.001 | 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".