Decreased Shoulder and Elbow Joint Loads During the Changeup Compared With the Fastball and Curveball in NCAA Division I Collegiate Softball Pitchers
Bibliographic record
Abstract
Background: Baseball leagues have implemented pitch count and pitch type restrictions based on biomechanical concepts associated with pitch type. Softball has not yet adopted these practices, although softball pitchers continue to pitch at a high volume and learn multiple pitches at a young age. Purpose: To examine shoulder and elbow kinetics between the fastball, curveball, and changeup, as well as to provide descriptive upper extremity pain data in National Collegiate Athletic Association (NCAA) softball pitchers. Study Design: Descriptive laboratory study. Methods: Study participants consisted of 27 female NCAA Division I softball pitchers (age, 20.2 ± 1.9 years; height, 175.7 ± 5.7 cm; weight, 83.6 ± 12.7 kg). The participants pitched 3 balls of each pitch type, and kinetic data were recorded. A one-way within-participants repeated-measures multivariate analysis of variance was used to determine significant differences in kinetics and pitch speed between pitch types. Results: Results revealed a statistically significant main effect for pitch type (Wilks λ = .087; F = 36.523; P < .001). Post hoc testing showed that the changeup produced less anterior elbow force compared with the fastball ( P < .001) and the curveball ( P = .012). In addition, the changeup produced less shoulder distraction force compared with the fastball ( P < .001) and the curveball ( P = .001). Additionally, there was a significant difference in pitch speed between all 3 pitch types ( P = .006). The curveball revealed no statistically significant kinetic differences compared with the fastball. Conclusion: The fastball and curveball placed similar stress on the upper extremity in collegiate softball pitchers. However, in comparison with the changeup, the fastball and curveball placed increased stress on the upper extremity. More research is needed to fully explain the differences seen between pitch type and injury risk. Clinical Relevance: Sports medicine professionals, coaches, and athletes should use the current study results to note these differences in shoulder distraction and elbow anterior forces between softball pitch types. The study results can be used as a reference and basis for future research investigating kinetic differences across varying pitch types.
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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 | 0.002 |
| 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) | 0.003 | 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".