Predicting Injury and Illness with Machine Learning in Elite Youth Soccer: A Comprehensive Monitoring Approach over 3 Months

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • Nils Haller
  • Stefan Kranzinger
  • Christina Kranzinger
  • Julia C Blumkaitis
  • Tilmann Strepp
  • Perikles Simon
  • Aleksandar Tomaskovic
  • James O'Brien
  • Manfred Duering
  • Thomas Stoeggl
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:001052850100001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.52082/jssm.2023.475
Externe Identifier
  • Clarivate Analytics Document Solution ID: P8AT3
ISSN
1303-2968
Zeitschrift
JOURNAL OF SPORTS SCIENCE AND MEDICINE
Schlüsselwörter
  • Football
  • artificial intelligence
  • injury prevention
  • load management
  • load monitoring
Datum der Veröffentlichung
2023
Status
Published
Titel
Predicting Injury and Illness with Machine Learning in Elite Youth Soccer: A Comprehensive Monitoring Approach over 3 Months
Sub types
  • Article
  • Early Access

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Abstract
<jats:p>The search for monitoring tools that provide early indication of injury and illness could contribute to better player protection. The aim of the present study was to i) determine the feasibility of and adherence to our monitoring approach, and ii) identify variables associated with up-coming illness and injury. We incorporated a comprehensive set of monitoring tools consisting of external load and physical fitness data, questionnaires, blood, neuromuscular-, hamstring, hip abductor and hip adductor performance tests performed over a three-month period in elite under-18 academy soccer players. Twenty-five players (age: 16.6 ± 0.9 years, height: 178 ± 7 cm, weight: 74 ± 7 kg, VO2max: 59 ± 4 ml/min/kg) took part in the study. In addition to evaluating adherence to the monitoring approach, data were analyzed using a linear support vector machine (SVM) to predict illness and injuries. The approach was feasible, with no injuries or dropouts due to the monitoring process. Questionnaire adherence was high at the beginning and decreased steadily towards the end of the study. An SVM resulted in the best classification results for three classification tasks, i.e., illness prediction, illness determination and injury prediction. For injury prediction, one of four injuries present in the test data set was detected, with 96.3% of all data points (i.e., injuries and non-injuries) correctly detected. For both illness prediction and determination, there was only one illness in the test data set that was detected by the linear SVM. However, the model showed low precision for injury and illness prediction with a considerable number of false-positives. The results demonstrate the feasibility of a holistic monitoring approach with the possibility of predicting illness and injury. Additional data points are needed to improve the prediction models. In practical application, this may lead to overcautious recommendations on when players should be protected from injury and illness.</jats:p>
Autoren
  • Nils Haller
  • Stefan Kranzinger
  • Christina Kranzinger
  • Julia C Blumkaitis
  • Tilmann Strepp
  • Perikles Simon
  • Aleksandar Tomaskovic
  • James O’Brien
  • Manfred Düring
  • Thomas Stöggl
DOI
10.52082/jssm.2023.475
eISSN
1303-2968
Zeitschrift
Journal of Sports Science and Medicine
Sprache
en
Online publication date
2023
Paginierung
475 - 486
Status
Published online
Herausgeber
Journal of Sports Science and Medicine
Herausgeber URL
http://dx.doi.org/10.52082/jssm.2023.475
Datum der Datenerfassung
2023
Titel
Predicting Injury and Illness with Machine Learning in Elite Youth Soccer: A Comprehensive Monitoring Approach over 3 Months

Datenquelle: Crossref

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