Background The application of artificial intelligence (AI) opens an interesting perspective for
predicting injury risk and performance in team sports. A better understanding of the …

Purpose Injuries are common in sports and can have significant physical, psychological and
financial consequences. Machine learning (ML) methods could be used to improve injury …

Background An increasing number of musculoskeletal injury prediction models are being
developed and implemented in sports medicine. Prediction model quality needs to be …

Supervised learning is the most common form of machine learning utilized in medical
research. It is used to predict outcomes of interest or classify positive and/or negative cases …

Context: Quantifying external workload is fundamental to training prescription in sport. In
tennis, global positioning data are imprecise and fail to capture hitting loads. The current …

Abstract Purpose of Review This review examines recent literature on injury epidemiology
and prevention in the sport of baseball from youth to professional levels. Recent Findings …

Context High loads in the elbow during baseball pitching can lead to serious injuries,
including injuries to the ulnar collateral ligament. These injuries have substantial …

Background: Machine learning (ML) allows for the development of a predictive algorithm
capable of imbibing historical data on a Major League Baseball (MLB) player to accurately …

Context The National Collegiate Athletic Association has supported men's baseball
championships since 1947. Since its inception, the number of participating teams and …

Background: Publicly obtained data (POD) have recently been utilized frequently by sports
medicine researchers to describe injury patterns, risk factors, and outcomes in elite athletes …