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Current State of Data and Analytics Research in Baseball

  • Injuries in Overhead Athletes (J Dines and C Camp, Section Editors)
  • Published:
Current Reviews in Musculoskeletal Medicine Aims and scope Submit manuscript

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.

Summary

Data and analytics research has transcended baseball over time, and the future of this field remains bright.

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Authors and Affiliations

  1. Department of Orthopaedic Surgery, University of Utah, 590 Wakara Way, Salt Lake City, UT, 84108, USA

    Joshua Mizels & Peter Chalmers

  2. Rothman Orthopaedic Institute, New York, NY, USA

    Brandon Erickson

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  1. Joshua Mizels
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  2. Brandon Erickson
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  3. Peter Chalmers
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Correspondence to Peter Chalmers.

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All reported studies/experiments with human or animal subjects performed by the authors have been previously published and complied with all applicable ethical standards (including the Helsinki declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).

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This article is part of the Topical Collection on Injuries in Overhead Athletes

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Mizels, J., Erickson, B. & Chalmers, P. Current State of Data and Analytics Research in Baseball. Curr Rev Musculoskelet Med 15, 283–290 (2022). https://doi.org/10.1007/s12178-022-09763-6

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