Review of Machine Learning in Baseball Analytics for Performance Improvement and Future Directions

Authors

• Daniel D. Wu, Georgia College & State University
• Yu Hsiang Huang, Georgia College & State University
• Jenq Foung Yao, Georgia College & State University

Document Type

Conference Proceeding

Publication Date

1-1-2025

Publication Title

Conference Proceedings IEEE SOUTHEASTCON

Abstract

The application of machine learning (ML) in baseball analytics has revolutionized the way teams and players approach performance improvement. This literature review explores the intersection of ML and baseball analytics, focusing on key areas such as player evaluation, injury prevention, game strategy, and skill development. Highlighting advancements in data collection, algorithmic approaches, and real-world applications, this review synthesizes findings from existing research to identify trends, challenges, and opportunities for future innovation.

First Page

1038

Last Page

1043

DOI

10.1109/SoutheastCon56624.2025.10971442

Recommended Citation

Wu, Daniel D.; Huang, Yu Hsiang; and Yao, Jenq Foung, "Review of Machine Learning in Baseball Analytics for Performance Improvement and Future Directions" (2025). Faculty and Staff Works. 934.
https://kb.gcsu.edu/fac-staff/934