Major League Baseball pace-of-play rules and their influence on predicted muscle fatigue during simulated baseball games
Bibliographic record
Abstract
Major League Baseball (MLB) has proposed rule changes to speed up baseball games. Reducing the time between pitches may impair recovery from fatigue. Fatigue is a known precursor to injury and may jeopardise joint stability. This study examined how fatigue accumulated during baseball games and how different pace of play initiatives may influence fatigue. Pitcher data were retrieved from a public database. A predictive model of muscle fatigue estimated muscle fatigue in 8 arm muscles. A self-selected pace (22.7 s), 12 s pace (Rule 8.04 from the MLB) and a 20 s rest (a pitch clock examined in the 2014 Arizona Fall League (AFL)) were examined. Significantly more muscle fatigue existed in both the AFL and Rule 8.04 conditions, when compared to the self-selected pace condition (5.01 ± 1.73%, 3.95 ± 1.20% and 3.70 ± 1.10% MVC force lost, respectively). Elevated levels of muscle fatigue are predicted in the flexor-pronator mass, which is responsible for providing elbow stability. Reduced effectiveness of the flexor-pronator mass may reduce the active contributions to joint rotational stiffness, increasing strain on the ulnar collateral ligament (UCL) and possibly increasing injury risk.
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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 | 0.006 |
| 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 | 0.000 |
| 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".