Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis.
Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in high-intensity sports like soccer. This study used machine learning algorithms to assess non-contact injury risk in professional male soccer players from physiological and mechanical load variables. Twe...
| Publicado en: | Journal of Sports Science & Medicine Vol. 23; no. 3; pp. 537 - 548 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Hakan Gur, Journal of Sports Science & Medicine
Sep2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179686698&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179686698 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13032968 FYN jtl: Journal of Sports Science & Medicine issn: 13032968 maglogo: N pubinfo: dt: Sep2024 vid: 23 iid: 3 pid: 26030 pub: Hakan Gur, Journal of Sports Science & Medicine artinfo: ui: 179686698 179686698 179686698 10.52082/jssm.2024.537 179686698 ppf: 537 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis. aug: au: Tsilimigkras, Theodoros Kakkos, Ioannis Matsopoulos, George K. Bogdanis, Gregory C. affil: Biomedical Engineering Laboratory, National Technical University of Athens, Athens, Greece sug: subj: Soccer Injuries Risk Factors Athletic Training Evaluation Muscle, Skeletal Physiology Machine Learning Athletes, Professional Athletes, Male Risk Assessment Biomechanics Human Resistance Training Acceleration Heart Rate Exercise Intensity Workload Measurement Sprinting Descriptive Statistics Walking Speed Sensitivity and Specificity Coaches, Athletic Exertion ab: Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in high-intensity sports like soccer. This study used machine learning algorithms to assess non-contact injury risk in professional male soccer players from physiological and mechanical load variables. Twenty-five professional male soccer players with a first-time, non-contact muscle injury were included in this study. Recordings of external load (speed, distance, and acceleration/deceleration data) and internal load (heart rate) were obtained during all training sessions and official matches over a 4-year period. Machine learning model training and evaluation features were calculated for each of nine different metrics for a 28-day period prior to the injury and an equal-length baseline epoch. The acute surge in the values of each workload metric was quantified by the deviation of maximum values from the average, while the variations of cumulative workload over the last four weeks preceding injury were also calculated. Seven features were selected by the model as prominent estimators of injury incidence. Three of the features concerned acute load deviations (number of sprints, training load score-incorporating heart rate and muscle load- and time of heart rate at the 90 - 100% of maximum). The four cumulative load features were (total distance, high speed and sprint running distance and training load score). The accuracy of the muscle injury risk assessment model was 0.78, with a sensitivity of 0.73 and specificity of 0.85. Our model achieved high performance in injury risk detection using a limited number of training load variables. The inclusion, for the first time, of heart rate related variables in an injury risk assessment model highlights the importance of physiological overload as a contributor to muscle injuries in soccer. By identifying the important parameters, coaches may prevent muscle injuries by controlling surges of training load during training and competition. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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