An Inferential and Descriptive Statistical Examination of the Relationship Between Cumulative Work Metrics and Injury in Major League Baseball Pitchers
Bibliographic record
Abstract
In Major League Baseball (MLB), games pitched, total innings pitched, total pitches thrown, innings pitched per game, and pitches thrown per game are used to measure cumulative work. Often, pitchers are allocated limits, based on pitches thrown per game and total innings pitched in a season, in an attempt to prevent future injuries. To date, the efficacy in predicting injuries from these cumulative work metrics remains in question. It was hypothesized that the cumulative work metrics would be a significant predictor for future injury in MLB pitchers. Correlations between cumulative work for pitchers during 2002-07 and injury days in the following seasons were examined using regression analyses to test this hypothesis. Each metric was then "binned" into smaller cohorts to examine trends in the associated risk of injury for each cohort. During the study time period, 27% of pitchers were injured after a season in which they pitched. Although some interesting trends were noticed during the binning process, based on the regression analyses, it was found that no cumulative work metric was a significant predictor for future injury. It was concluded that management of a pitcher's playing schedule based on these cumulative work metrics alone could not be an effective means of preventing injury. These findings indicate that an integrated approach to injury prevention is required. This approach will likely involve advanced cumulative work metrics and biomechanical assessment.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".