Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/103500
Title: Predictive Analytic Techniques to Identify Hidden Relationships between Training Load, Fatigue and Muscle Strains in Young Soccer Players
Authors: Mandorino, Mauro
Figueiredo, António J. 
Cima, Gianluca
Tessitore, Antonio 
Keywords: youth soccer; workload; injury; fatigue; predictive analytics
Issue Date: 24-Dec-2021
Publisher: MDPI
Serial title, monograph or event: Sports
Volume: 10
Issue: 1
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.
URI: http://hdl.handle.net/10316/103500
ISSN: 2075-4663
DOI: 10.3390/sports10010003
Rights: openAccess
Appears in Collections:I&D CIDAF - Artigos em Revistas Internacionais

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