Ground Reaction Forces and Peak Throwing Shoulder Forces in Softball High School Pitchers
Bibliographic record
Abstract
Abstract To optimize pitching performance, pitchers must generate substantial ground reaction forces to aid pitch velocity while minimizing the forces experienced in their throwing shoulder. Extremely high shoulder forces are generally thought to be injurious for softball pitchers. Therefore, this study aimed to identify the relationship between ground reaction forces during the propulsion phase of the pitch and peak shoulder forces during the pitch. Thirty-two high school softball pitchers (1.70±0.06 m, 76.09±17.50 kg, and 15±1 y) pitched fastballs for strikes. Kinematic and kinetic data from the three fastest pitches were averaged for analysis. The relationships between ground reaction forces during pitch propulsion and peak shoulder kinetics during the propulsion and acceleration phases were examined via multiple regressions and correlations. A vertical ground reaction force was significantly associated with a peak resultant shoulder force (t=–3.176 and p=0.003). The rate of ground reaction force development was correlated with the peak shoulder distraction force (r=–0.367 and p=0.033) and the resultant force during propulsion (r=–0.439 and p=0.009). These observations underscore the potential significance of lower body contributions and kinetic chain sequencing in reducing shoulder forces during the early stages of the pitch, which may have implications for injury risk as ground reaction forces during pitch propulsion may decline with fatigue.
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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) | 0.004 | 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".