Authors:	Haller, Nils Kranzinger, Stefan Blumkaitis, Julia C. Strepp, Tilmann Simon, Perikles Tomaskovic, Aleksandar O'Brien, James Düring, Manfred Stöggl, Thomas
Title:	Predicting injury and illness with machine learning in elite youth soccer : a comprehensive monitoring approach over 3 month
Online publication date:	24-Nov-2023
Year of first publication:	2023
Language:	english
Abstract:	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 per- formed 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 noninjuries) 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.
DDC:	610 Medizin 610 Medical sciences 796 Sport 796 Athletic and outdoor sports and games
Institution:	Johannes Gutenberg-Universität Mainz
Department:	FB 02 Sozialwiss., Medien u. Sport
Place:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-9702
Version:	Published version
Publication type:	Zeitschriftenaufsatz
Document type specification:	Scientific article
License:	CC BY-NC-ND
Information on rights of use:	https://creativecommons.org/licenses/by-nc-nd/4.0/
Journal:	Journal of Sports Science and Medicine 22
Pages or article number:	476 487
Publisher:	Department of Sports Medicine, Medical Faculty of Uludag University
Publisher place:	Bursa, Turkey
Issue date:	2023
ISSN:	1303-2968
Publisher DOI:	10.52082/jssm.2023.476
Appears in collections:	DFG-491381577-G