Hamstring Strain Injury Risk in Soccer: An Exploratory, Hypothesis-Generating Prediction Model.

ABSTRACT: Hamstring strain injuries (HSI) are common in soccer and remain challenging to predict, as traditional risk factors often fail to capture the multifactorial nature of injury susceptibility. This prospective cohort study aimed to develop and internally validate a machine learning-assisted logistic regression model for predicting hamstring injuries in amateur soccer players using preseason clinical and strength-related variables. A total of 120 male players were followed for one competitive season (30 weeks). Baseline predictors included age, body mass index, previous injury, and bilateral isometric hip and knee strength measured via handheld dynamometry. Twenty initial predictors were reduced to ten through symmetrical uncertainty feature ranking before training a logistic regression model

SUBMITTER: Kekelekis A 

PROVIDER: S-EPMC12641714 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature