Ocular Dominance and Handedness in Golf Putting
Bibliographic record
Abstract
PURPOSE: In golf, the impact of eye-hand dominance on putting performance has long been debated. Eye-hand dominance is thought to impact how golfers judge the alignment of the ball with the target and the club with the ball, as well as how golfers visualize the line of the putt when making decisions about the force needed to hit the ball. Previous studies have all measured ocular dominance in primary gaze only, despite golfers spending a significant amount of their time in a putting stance (bent at the hips, head tilted down). Thus, the purpose of this study was to assess ocular dominance in both primary gaze (aligning the ball with the target) and putting gaze (addressing the ball and aligning the club). METHODS: This study investigated measuring pointing ocular dominance in both primary and putting gaze positions on 31 golfers (14 amateur, 7 club professionals, and 10 top professionals). All players were right-handed golfers, although one reported having no hand dominance and one reported being strongly left hand dominant. RESULTS: The results showed that (1) primary and putting gaze ocular dominances are not equal, nor are they predictive of each other; (2) the magnitude of putting ocular dominance is significantly less than the magnitude of primary gaze ocular dominance; (3) ocular dominance is not correlated with handedness in either primary or putting gaze; and (4) eye-hand dominance is not associated with increased putting skill, although ocular dominance may be associated with increased putting success. CONCLUSIONS: It is important that coaches assess golfers' ocular dominance in both primary and putting gaze positions to ensure they have the most accurate information upon which to base their vision strategy decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".