Citation

Mizels J, Erickson B, Chalmers P. Curr. Rev. Musculoskelet. Med. 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s12178-022-09763-6

PMID

35486325

Abstract

PURPOSE OF REVIEW: Baseball has become one of the largest data-driven sports. In this review, we highlight the historical context of how big data and sabermetrics began to transform baseball, the current methods for data collection and analysis in baseball, and a look to the future including emerging technologies. RECENT FINDINGS: Machine learning (ML), artificial intelligence (AI), and modern motion-analysis techniques have shown promise in predicting player performance and preventing injury. With the advent of the Health Injury Tracking System (HITS), numerous studies have been published which highlight the epidemiology and performance implications for specific injuries. Wearable technologies allow for the prospective collection of kinematic data to improve pitching mechanics and prevent injury. Data and analytics research has transcended baseball over time, and the future of this field remains bright.

Language: en

Keywords

Machine learning; Artificial intelligence; Baseball; Motion analysis; Sabermetrics