Evidence map›Paper›PMID 42266236›Full record

ArticleFrontiers in physiology2026

Correlation between sports visual ability and catching performance in elite collegiate baseball infielders: a position-specific analysis.

Yuankun Long, Zhao Zhang, Dexin Wang, Qiang Wei, Youlin Xiao, Chao Chen, Xinbo Zhang

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Article in Frontiers in physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yuankun LongCollege of Physical Education, Dalian University, Dalian, China.
Zhao ZhangCollege of Physical Education, Xi 'an University of Architecture and Technology, Xi 'an, China.
Dexin WangSchool of Athletic Performance, Shanghai University of Sport, Shanghai, China.
Qiang WeiCollege of Physical Education, Dalian University, Dalian, China.
Youlin XiaoCollege of Physical Education, Dalian University, Dalian, China.
Chao ChenCollege of Physical Education, Dalian University, Dalian, China.
Xinbo ZhangSchool of Athletic Performance, Shanghai University of Sport, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To explore the relationship between sports visual ability and specific performance of 24 elite collegiate baseball infielders (including first basemen, second basemen, third basemen, and shortstops) from the Dalian University School of Physical Education. Methods: This study employed a cross-sectional correlational design to examine the relationship between sports visual abilities and catching performance among 24 infielders from the baseball team of the Dalian University School of Physical Education. The sports visual ability indices of 24 infielders from the baseball team of the Dalian University School of Physical Education were evaluated using the Senaptec system. The specific performance was reflected by the correct catch rate (success rate for 50 random pitches). Multiple linear stepwise regression analysis was used to analyze the predictive utility of visual abilities for correlation between the two. Results: Key findings revealed that depth perception (stereoscopic vision) demonstrated the strongest negative association with catching performance (R = -0.844, P < 0.001), followed by eye-hand coordination average reaction time and perceptual span (R = -0.729 and R = 0.800, respectively; all P < 0.001). Additional visual abilities, including near/far quickness, target capture, and multi-object tracking, also exhibited moderate to strong associations with performance (absolute R values ranging from 0.467 to 0.573; all P < 0.001). Multiple linear stepwise regression identified depth perception, eye-hand coordination metrics, Go/No go total score, and perceptual span as significant predictors, collectively predicting 93.2% of the variance in catching performance (adjusted R² = 0.932, P < 0.001). A leave-one-out cross-validation (LOOCV) was conducted to assess model generalizability, yielding a cross-validated R² (Q²) of 0.87, which confirms that the model is not severely over-fitted despite the modest sample size. Conclusion: In this cross-sectional study of 24 collegiate baseball infielders, depth perception, perceptual span, eye-hand coordination, reaction time, near/far quickness, and target capture were found to be significantly associated with laboratory-based catching performance. These findings suggest that these visual abilities may be important correlates of catching accuracy in this controlled setting; however, their direct causal role in on-field performance enhancement requires further investigation through longitudinal and ecologically valid studies.

Indexed as

catching accuracycross-validationeye-hand coordinationSenaptec assessmentstereoscopic vision

Identifiers

PMID42266236
PMCPMC13243017

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.