The Intentional Base-on-ball Phenomenon in Baseball:\nA Statistical Analysis and Strategic Recommendations
Bibliographic record
Abstract
The game of baseball is amenable to a variety of strategies that affect short-term outcomes. This paper employs regression analysis, simulation, and cognitive analysis of mental biases to analyze the strategic scenario known as the "Intentional Base-on-Balls" and proposes a model to explain that strategy and predict its effectiveness. <br /><br /> The results of this study suggest that managers are prone to Type II errors, that is, issuing an Intentional Base-on-Ball in a situation where objective analysis suggests otherwise. Results further suggest that the ratio of Type I errors to Type II errors is disproportional to the ratio of their respective costs. This imbalance points to a subjective component to the decision-making process, one that can be explained by biases and cognitive errors. <br /><br /> The results and model described in this paper may allow managers to avoid future mistakes and improve their decision-making ability.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".