On the Use and Misuse of Video Analysis
Bibliographic record
Abstract
The popularity of video analysis in sports in general, and golf in particular, has recently risen. However, research in the area of video analysis has lagged well behind these trends in current coaching practice. The current study was designed to assess changes in performance as a result of using video feedback as part of an instructional session. Forty-eight golfers (24 novices; 24 skilled players) performed a pre-test in which twelve swings were recorded using an indoor launch monitor system. The participants were then randomly assigned to a lesson in one of three groups: 1) Verbal coaching (V), 2) Verbal +Video coaching (V+V), and 3) Self-Guided (SG) practice. All groups were then retested to determine the extent to which the various training conditions impacted overall swing characteristics. The results indicated that the positive effects of video feedback were: A) limited in scope, and b) observed to a greater extent in more skilled performers. The results suggest that while more skilled players were able to glean useful timing information from video feedback, these same conditions may in fact impede the learning process in novice performers.
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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 | 0.025 | 0.227 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".