Where do golf driver swings go wrong? Factors influencing driver swing consistency
Bibliographic record
Abstract
One of the challenging skills in golfing is the driver swing. There have been a large number of studies characterizing golf swings, yielding insightful instructions on how to swing well. As a result, achieving a sub-18 handicap is no longer the top problem for golfers. Instead, players are now most troubled by a lack of consistency during swing execution. The goal of this study was to determine how to consistently execute good golf swings. Using 3D motion capture and full-body biomechanical modeling, 22 experienced golfers were analysed. For characterizing both successful and failed swings, 19 selected parameters (13 angles, 4 time parameters, and 2 distances) were used. The results showed that 14 parameters are highly sensitive and/or prone to motor control variations. These parameters sensitized five distinct areas of swing to variation: (a) ball positioning, (b) transverse club angle, (c) transition, (d) wrist control, and (e) posture migration between takeaway and impact. Suggestions were provided for how to address these five distinct problem areas. We hope our findings on how to achieve consistency in golf swings will benefit all levels of golf pedagogy and help maintain/develop interests to involve more golf/physical activity for a healthy lifestyle.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".