The Athletic Intelligence Quotient and Performance Outcomes in Professional Baseball
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
The focus on quantifiable data in sport performance has led to incremental advantages in baseball and has played an important role in the development of new hitting, pitching, fielding, and coaching strategies. Recently, researchers and team representatives have considered the impact of additional factors in baseball, including cognitive functioning. In this study, predictive validity for the Athletic Intelligence Quotient (AIQ) was examined vis-à-vis performance outcomes in professional baseball. Specifically, AIQ scores were obtained from 149 Minor League Baseball (MiLB) players prior to the 2014 baseball season and their subsequent performance was assessed through traditional and newly emphasized baseball statistics. Using hierarchical multiple regression, it was demonstrated that the AIQ predicted statistically significant relationships with hitting and pitching statistics, after controlling for other variables. Given the recent impact of analytics in professional sports, the potential importance of the AIQ in the selection and coaching process was discussed.
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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) | 0.000 | 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 it