Association Between Performance on Visual Tests and Batting Performance Indicators in Highly Trained Baseball Players
Bibliographic record
Abstract
Visual perception is crucial for successfully executing motor skills in interceptive sports like baseball. Understanding the contribution of visual skills (VS) to baseball performance is essential for talent selection and development. Some evidence suggests that performance on visual tests is predictive of batting performance, though these findings are not consistently replicated across studies. Therefore, the aim of this study is to associate a broad spectrum of visual performance indicators with a set of batting performance variables in highly trained baseball players by combining different methodological approaches used in previous studies. Forty-five highly trained male baseball players from the same club, aged between 15 and 19 years old ( mean = 17.25), underwent a thorough battery of visual tests under standardized conditions. Twenty-one variables of VS were collected and associated with ten performance indicators, including game statistics, players’ ranking, age, years of practice, and position. Frequentist correlations and t -tests revealed that 17 out of the 210 associations (8.09%) reached our unadjusted threshold level and thus indicated a positive and statistically significant association. Bayesian analyses identified 34 associations (16.19%) that supported a positive association between VS and performance indicators, but only two of them (0.95%) revealed a moderate level of evidence in favor of the positive association. Therefore, this study provides limited support to the hypothesis that performance on visual tests predicts batting performance. The homogeneity of the sample and potential non-linear relations between visual and batting performance may account for these findings.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".