What are the Individual Characteristics or Skills Associated with Baseball Batting Performance? A Scoping Review
Bibliographic record
Abstract
BACKGROUND: In baseball, batting performance can be measured using game and advanced statistics as well as hitting metrics. To date, the core set of individual characteristics or skills associated with superior batting performance remains to be identified. The aim of this scoping review was to identify and classify the individual characteristics or skills associated with baseball batting performance indicators and describe the methods used to assess these individual characteristics or skills and batting performance indicators. METHODS: A scoping review design was chosen to conduct a systematic literature search. Electronic searches of MEDLINE, SPORTDiscus, and PsycINFO databases were undertaken from inception to August 2024. Cross-sectional studies that investigated the relationship between batting performance indicators and individual characteristics or skills in male or female baseball batters were selected. RESULTS: Twenty-two cross-sectional studies investigating potential individual characteristics or skills of baseball batting performance met the inclusion criteria. The primary baseball batting performance indicators were grouped into three categories: game statistics, advanced statistics and hitting metrics. Anthropometric measures (height, weight), physical fitness tests (1-RM bench and squat, grip strength, jumps, medicine ball throws, sprint, trunk flexibility, etc.), visual skills (visual acuity, contrast sensitivity, etc.), perceptual skills (anticipation, visual recognition, etc.) and visuomotor skills (eye-hand coordination, reaction time, etc.) were the individual characteristics or skills associated with either game statistics, advanced statistics or hitting metrics. CONCLUSIONS: Based on the studies included in this scoping review, the results show that several anthropometrics, physical, perceptual-cognitive, and visual skills were associated with superior game statistics, advanced statistics or hitting metrics. Greater height, weight, upper- and lower-body muscle strength, power, and speed, as well as oculomotor skills, visual system characteristics, anticipation, visual recognition, and visuomotor skills corresponded to better batting performance.
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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 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".