Verbal Aggressions Against Major League Baseball Umpires Affect Their Decision Making
Bibliographic record
Abstract
Excessively criticizing a perceived unfair decision is considered to be common behavior among people seeking to restore fairness. However, the effectiveness of this strategy remains unclear. Using an ecological environment where excessive criticism is rampant—Major League Baseball—we assess the impact of verbal aggression on subsequent home-plate umpire decision making during the 2010 to 2019 seasons ( N = 153,255 pitches). Results suggest a two-sided benefit of resorting to verbal abuse. After being excessively criticized, home-plate umpires ( N = 110 adults, employed in the United States) were less likely to call strikes to batters from the complaining team and more prone to call strikes to batters on the opposing team. A series of additional analyses lead us to reject an alternative hypothesis, namely that umpires, after ejecting the aggressor, seek to compensate for the negative consequences brought on by the loss of a teammate. Rather, our findings support the hypothesis that, under certain conditions, verbal aggression may offer an advantage to complainants.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".