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Record W4416757166 · doi:10.1186/s40798-025-00947-1

What are the Individual Characteristics or Skills Associated with Baseball Batting Performance? A Scoping Review

2025· article· en· W4416757166 on OpenAlexafffund
Mathieu Tremblay, Bastien Couëpel, Jacques Abboud, Martin Descarreaux

Bibliographic record

VenueSports Medicine - Open · 2025
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEye trackingVisual perceptionPerceptionVisual methods

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.273
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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