Effects of Increasing Pitch Count on Pitch Type Ball Metrics and Release Height in High School Softball Pitchers
Bibliographic record
Abstract
BACKGROUND: Softball research has investigated changes in physical characteristics, mechanics, and ball speed as elements of fatigue. However, the influence of pitch volume on ball metrics is unclear. The purpose of this study was to investigate the influence of pitch volume on ball performance and release metrics in softball pitchers across different pitch types. HYPOTHESIS: As pitch volume increased, there would be a decrease in ball metrics of the fastball and changes in breaking pitches would be observed earlier than the fastball or changeup. STUDY DESIGN: Descriptive laboratory study. LEVEL OF EVIDENCE: Level 5. METHODS: A total of 21 (15.4 ± 1.6 years; 1.6 ± 0.2 m; 76.0 ± 17.2 kg) softball pitchers participated. Procedures consisted of participants pitching a simulated game consisting of 100 pitches, taking a 30-minute break, and then throwing 12 pitches to simulate the first inning of a doubleheader. Participants randomly threw each pitch type (fastball, changeup, curveball, or dropball). Ball performance and release metrics were measured using a Rapsodo portable pitch tracker. RESULTS: A 3 (time) by 4 (pitch type) multivariate analysis of variance revealed that pitch speed was significantly higher in the first inning compared with the last inning and the doubleheader inning. The fastball, curveball, and dropball revealed a significant difference in pitch speed between timepoints. Specifically, the curveball and dropball first-inning pitch speed was significantly greater than the last and doubleheader inning. Alternatively, the fastball had a significant increase in pitch speed from the last inning to the doubleheader inning. CONCLUSION: The typical 30-minute break given between games for doubleheaders may be sufficient recovery time for the fastball but not for the curveball and dropball. CLINICAL RELEVANCE: The Rapsodo device is an accessible method of tracking ball performance and pitch release metrics and could be helpful in identifying when a pitcher may be experiencing performance detriments in response to increasing pitch count.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".