Changes in Physical Size Among Major League Baseball Players and Its Attribution to Elite Offensive Performance
Bibliographic record
Abstract
Major League Baseball (MLB) players have not been longitudinally examined for changes in physical size. Height, weight, and body mass indices (BMIs) were examined among offensive league leaders (OLL) and MLB reference cohorts at 1970, 1990, and 2010. Anthropometric values were expected to increase successively, where OLL were expected to be larger at each respective time point. A Mixed Model analysis of variance (p ≤ 0.05) examined anthropometric differences over time within and between groups. Mass and BMI increased over successive years with the largest effect seen between 1990 and 2010 (p < 0.001). A significant height reduction was shown for OLL from 1970 to 1990 (p ≤ 0.05), being the only significant decrease in physical size; yet, leaders were heavier and taller compared with the MLB reference population (p < 0.014). Results show that physical size has evolved in MLB, with the OLL being the largest players shown at each year in succession. Professional baseball scouts may have been influenced by greater offensive prowess shown by larger athletes; yet, increased secular anthropometrics must also be factored in greater heights, weights, BMIs shown over time in MLB. It is possible that greater participation in strength and conditioning programs at an earlier age, advances in sport nutrition, and potential abuse of anabolic drugs are factors perpetuating growth rates at present.
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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 | 0.001 |
| 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) | 0.002 | 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".