Anthropometric and kinematic influences on release speed in men’s fast-medium bowling
Bibliographic record
Abstract
The main aim of this study was to identify significant relationships between selected anthropometric and kinematic variables and ball release speed. Nine collegiate fast-medium bowlers (mean +/- s: age 21.0 +/- 0.9 years, body mass 77.2 +/- 8.1 kg, height 1.83 +/- 0.1 m) were filmed and reconstructed three-dimensionally. Ball release speeds were measured by a previously validated Speedchek Personal Sports Radar (Tribar Industries, Canada). Relationships between selected anthropometric variables and ball release speed and between kinematic variables and ball release speed were investigated using Pearson's product-moment correlation coefficients (r). A significant relationship was found between the horizontal velocity during the pre-delivery stride (r = 0.728, P < 0.05) and ball release speed (31.5 +/- 1.9 m(-1) s(-1)). We believe that the high correlation was due to the bowlers using techniques that allowed them to contribute more of the horizontal velocity created during the run-up to ball release speed. We also found that the angular velocity (40.6 +/- 3.4 rad x s(-1)) of the right humerus had a low correlation (r = 0.358, P > 0.05) with ball release speed. Although the action of the wrist was not analysed because of an inadequate frame rate, we found high correlations between ball release speed and shoulder-wrist length (661 +/- 31 mm; r = 0.626, P < 0.05) and ball release speed and total arm length (860 +/- 36 mm; r = 0.583, P < 0.05). We conclude that the variance in release speed within this group may be accounted for by the difference in radial length between the axis of rotation at the glenohumeral joint and the release point.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".