What question did this study set out to answer?

The study aims to assess injury risk and classify injuries in elite youth football players using machine learning.

A Machine Learning Approach to Assess Injury Risk in Elite Youth Football Players

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Randomized trial assesses injury risk in elite youth football players, suggesting effective injury management strategies.

The study aims to assess injury risk and classify injuries in elite youth football players using machine learning.
Followed 734 players aged U10 to U15 for one season.
Collected preseason anthropometric measurements and conducted physical performance tests.
Applied extreme gradient boosting algorithms (XGBoost) to predict injury based on preseason results.
368 out of 734 players sustained at least one injury during the season.
Algorithm achieved 85% precision, recall, and accuracy in identifying injured players.
Injuries classified as overuse or acute were predicted with 78% precision, recall, and accuracy.

Rommers et al. (2020) studied this question.

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