Gutenberg Open Science: Predicting injury and illness with machine learning in elite youth soccer : a comprehensive monitoring approach over 3 month

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9702

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DC Field	Value	Language
dc.contributor.author	Haller, Nils	-
dc.contributor.author	Kranzinger, Stefan	-
dc.contributor.author	Blumkaitis, Julia C.	-
dc.contributor.author	Strepp, Tilmann	-
dc.contributor.author	Simon, Perikles	-
dc.contributor.author	Tomaskovic, Aleksandar	-
dc.contributor.author	O'Brien, James	-
dc.contributor.author	Düring, Manfred	-
dc.contributor.author	Stöggl, Thomas	-
dc.date.accessioned	2023-11-24T10:17:22Z	-
dc.date.available	2023-11-24T10:17:22Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9720	-
dc.description.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.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY-NC-ND	*
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.subject.ddc	796 Sport	de_DE
dc.subject.ddc	796 Athletic and outdoor sports and games	en_GB
dc.title	Predicting injury and illness with machine learning in elite youth soccer : a comprehensive monitoring approach over 3 month	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9702	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 02 Sozialwiss., Medien u. Sport	de
jgu.organisation.number	7910	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Journal of Sports Science and Medicine	de
jgu.journal.volume	22	de
jgu.pages.start	476	de
jgu.pages.end	487	de
jgu.publisher.year	2023	-
jgu.publisher.name	Department of Sports Medicine, Medical Faculty of Uludag University	de
jgu.publisher.place	Bursa, Turkey	de
jgu.publisher.issn	1303-2968	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
jgu.subject.ddccode	796	de
jgu.publisher.doi	10.52082/jssm.2023.476	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Geistes- und Sozialwissenschaften	de
Appears in collections:	DFG-491381577-G

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