ArticleMuscles (Basel, Switzerland)2025
Hamstring Strain Injury Risk in Soccer: An Exploratory, Hypothesis-Generating Prediction Model.
Article in Muscles (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning models for injury risk prediction in football players: A systematic review of predictors, performance, practical applications, and limitations.The Journal of international medical research · 2026Pooled it
- Development and validation of a machine learning model for non-contact injury prediction based on lower limb strength asymmetry in professional football.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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 with elastic-net regularization (training set:
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