The Effects of Custom-Fitted Clubs versus “Placebo” Clubs on Golf-Swing Characteristics
Bibliographic record
Abstract
While the use of custom-fitted equipment is on the rise in the game of golf, empirical data as to its benefits and/or pitfalls is lacking. The current study sought to determine the effectiveness of custom-fitted clubs in the golf swing performance of both novice and experienced golfers. A launch monitor system was used to obtain measures of clubhead speed, clubface angle, and tempo to quantify baseline performance and any subsequent change in performance following the experimental procedures. Following a pre-test, which involved recording 12 swings with a standard 6-iron, participants were custom fit for club length, shaft flex, and lie angle according to the Henry-Griffitts TotalTest® system. Post-tests were administered following a 10-minute break. During the post-test, participants unknowingly used either a properly fitted club, a purposely ill-fitted (i.e., ‘placebo’ club), or the same standard club used in the pre-test. Our data suggest that custom club fitting does improve the characteristics of the golf swing, although often in different ways depending upon the skill level of the player.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".