Predicting noncontact injuries of professional football players using machine learning.

Noncontact injuries are prevalent among professional football players. Yet, most research on this topic is retrospective, focusing solely on statistical correlations between Global Positioning System (GPS) metrics and injury occurrence, overlooking the multifactorial nature of injuries. This study introduces an automated injury identification and prediction approach using machine learning, leveraging GPS data and player-specific parameters.

The Relationship between Different Large-Sided Games and Official Matches on Professional Football Players' Locomotor Intensity.

Large-sided games (LSG) are commonly used in the training contexts for providing either technical/tactical or locomotor/physiological stimuli. Despite natural similarities with the official match, the locomotor profile seems to be different, which must be considered by the coaches to identify compensatory strategies for achieving the ideal dose of training. The aim of this study was two-fold: (1) to investigate the locomotor demands imposed by LSGs and the official matches; and (2) to compare the effect of different pitch sizes’ LSG conditions in the locomotor demands.

Speed and Agility Predictors among Adolescent Male Football Players.

This study aimed to examine the associations between lower-body explosive strength (squat jump-SJ, and countermovement jump-CMJ), speed (10- and 35-m linear sprints), and agility (-test) capacities, after controlling for crucial predictors such as chronological age (CA) and body composition. The sample was composed of 164 adolescent male football players from under 19, under 17, and under 15 age groups. Body fat percentage (BF%) was significantly and positively related to body mass, speed, and agility.