Softball Pitchers’ Body Segment Girths Are Associated with Varied Biomechanics at Ball Release of the Pitch
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the relationships between pitcher trunk and throwing arm girth with kinematics at ball release of the softball pitch. Softball pitchers can be of various shapes and sizes; however, it is unknown if segment girth might influence pitch biomechanics. Because of the importance of body positioning at ball release of the windmill pitch, this study examined the relationship between segment girth and pitch biomechanics at ball release. METHODS: Forty-two pitchers volunteered to participate (1.706 ± 0.061 m, 74.98 ± 15.94 kg, 15.5 ± 1.7 yr). Participants' throwing upper arm, chest, waist, and hip girth were measured. Participants then pitched fastballs to a catcher (43 ft) while an electromagnetic motion capture system synced with a force plate and motion analysis software collected kinematic data (sampled at 240 Hz). RESULTS: Linear regressions revealed that upper arm, chest, and waist girth were associated with throwing arm shoulder horizontal abduction. Greater segment girth was associated with less horizontal abduction at ball release of the pitch. CONCLUSIONS: Segment girth is related to pitch biomechanics and may influence body positions at crucial softball pitch events such as ball release. CLINICAL RELEVANCE: Understanding biomechanical differences according to body segment girth is important for safe and optimal player development and performance.
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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 | 0.001 |
| 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) | 0.005 | 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".