Seeing Stars: Matthew Effects and Status Bias in Major League Baseball Umpiring
Bibliographic record
Abstract
This paper tests the assumption that evaluators are biased to positively evaluate high-status individuals, irrespective of quality. Using unique data from Major League Baseball umpires' evaluation of pitch quality, which allow us to observe the difference in a pitch's objective quality and in its perceived quality as judged by the umpire, we show that umpires are more likely to overrecognize quality by expanding the strike zone, and less likely to underrecognize quality by missing pitches in the strike zone for high-status pitchers. Ambiguity and the pitcher's reputation as a “control pitcher” moderate the effect of status on umpire judgment. Furthermore, we show that umpire errors resulting from status bias lead to actual performance differences for the pitcher and team. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1967 . This paper was accepted by Jesper Sørensen, organizations.
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".