Major League Baseball Managers: Do They Matter?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smart and Wolfe (2003) assessed the concurrent contribution of leadership and human resources to Major League Baseball (MLB) team performance. They found that player resources (defense/pitching and offence/batting) explained 67% of the variance in winning percentage, whereas leadership explained very little (slightly more than 1%) of the variance. In discussing the minimal contribution of leadership to their results, the authors suggested that future studies expand their operationalization of leadership. That is what is done in this study. Finding that the expanded operationalization has limited effect in explaining the contribution of leadership, we take an alternative tack in attempting to understand leadership in MLB. In addition, we estimate a production frontier (based on offensive and defensive resources), determine the efficiency of MLB managers relative to that frontier, and investigate the extent to which manager efficiency can be explained by manager characteristics. Finally, manager characteristics are related to manager compensation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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 it