ArticleSports (Basel, Switzerland)2021
Predictive Analytic Techniques to Identify Hidden Relationships between Training Load, Fatigue and Muscle Strains in Young Soccer Players.
Article in Sports (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The application of machine learning in the prediction of sports injuries: systematic review and meta-analysis.Scientific reports · 2026Pooled it
- A Systematic Review of the Relationship between Workload and Injury Risk of Professional Male Soccer Players.International journal of environmental research and public health · 2022Pooled it
- Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives.Annals of medicine · 2026Review
- Injury Prediction and Risk Modelling in Team Sports Using Artificial Intelligence and Sensor-Based Monitoring: A Scoping Review.Journal of functional morphology and kinesiology · 2026Review
- Multidisciplinary prediction of running-related injuries using machine learning.NPJ digital medicine · 2026Article
- Article
- The effect of contextualised match variables on the metabolic power of elite soccer players during English Premier League match-play.Biology of sport · 2026Article
- The loading impact of training and match-play on non-contact muscle injuries in elite male soccer players. A seasonal analysis.Biology of sport · 2026Article
- Article
- Article
- Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis.British journal of sports medicine · 2025Article
- Assessment Methods of Sport-Induced Neuromuscular Fatigue: A Scoping Review.International journal of sports physical therapy · 2025Article
- Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Management of proximal rectus femoris injuries - do we know what we're doing?: A systematic review.Biology of sport · 2023Article
- Effects of Playing Position and Contextual Factors on Internal Match Loads, Post-Match Recovery and Well-Being Responses of Elite Male Water Polo Players.Journal of functional morphology and kinesiology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This study aimed to analyze different predictive analytic techniques to forecast the risk of muscle strain injuries (MSI) in youth soccer based on training load data. Twenty-two young soccer players (age: 13.5 ± 0.3 years) were recruited, and an injury surveillance system was applied to record all MSI during the season. Anthropometric data, predicted age at peak height velocity, and skeletal age were collected. The session-RPE method was daily employed to quantify internal training/match load, and monotony, strain, and cumulative load over the weeks were calculated. A countermovement jump (CMJ) test was submitted before and after each training/match to quantify players' neuromuscular fatigue. All these data were used to predict the risk of MSI through different data mining models: Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM). Among them, SVM showed the best predictive ability (area under the curve = 0.84 ± 0.05). Then, Decision tree (DT) algorithm was employed to understand the interactions identified by the SVM model. The rules extracted by DT revealed how the risk of injury could change according to players' maturity status, neuromuscular fatigue, anthropometric factors, higher workloads, and low recovery status. This approach allowed to identify MSI and the underlying risk factors.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.