Fastball Velocity Trends in Short-Season Minor League Baseball
Bibliographic record
Abstract
Diminishing baseball velocities are objective measures to delineate pitching fatigue. Yet, velocity changes over the course of a competitive season vs. a single game remain unknown. This study examined fastball velocity (FBV) trends of minor league pitchers over an 8-game span. We assumed that accumulation of pitches would cause similar velocity decreases within games to produce velocity decreases between games pitched. Retrospective analysis of major league-affiliated pitching charts indicated mean FBVs, game pitches thrown, game innings pitched, rest days, and pitching work to rest ratios (PWRRs) for 12 pitchers over 8 games. Regression analyses (p < 0.05) were performed using the ordinary least squares method. The FBV was the dependent variable, where the explanatory variable was the game number (representing cumulative workload). Further analyses were performed on ball velocity differences predicted by days rest and PWRRs. The FBV increased linearly for the first 8 games of the season (R = 0.91, F(1,7) = 64.67, p < 0.001). Over the 8 - game period, mean FBVs increased 0.25 m/s (0.56 mph) with the greatest velocity increase occurring between the first and eighth game at 1.97 m/s (4.4 mph). Days rest and PWRRs did not impact FBV differences. When compared with previous research, minor league pitchers at the Class A Short Season level did not show similar exertion responses to cumulative workloads (pitches and innings pitched). Recovery factors (rest days, PWRRs, and training) also did not impact FBVs. Velocity increases may be attributable to biomechanical compensations, skill development, strength and conditioning regimens, multistarter rotations, and other performance-related factors. Strength and conditioning professionals should be aware of ball velocity trends, as apparent changes may infer neuromuscular fatigue and increased injury susceptibility, which require in-season training modifications.
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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) | 0.001 | 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".