Predictive Modeling of Injury Risk Based on Body Composition and Selected Physical Fitness Tests for Elite Football Players
Injuries are one of the most significant issues for elite football players. Consequently, elite football clubs have been consistently interested in having practical, interpretable, and usable models as decision-making support for technical staff. This study aimed to analyze predictive modeling of in...
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| Veröffentlicht in: | Journal of clinical medicine 2022-08, Vol.11 (16), p.4923 |
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| creator | Martins, Francisco Przednowek, Krzysztof França, Cíntia Lopes, Helder de Maio Nascimento, Marcelo Sarmento, Hugo Marques, Adilson Ihle, Andreas Henriques, Ricardo Gouveia, Élvio Rúbio |
| description | Injuries are one of the most significant issues for elite football players. Consequently, elite football clubs have been consistently interested in having practical, interpretable, and usable models as decision-making support for technical staff. This study aimed to analyze predictive modeling of injury risk based on body composition variables and selected physical fitness tests for elite football players through a sports season. The sample comprised 36 male elite football players who competed in the First Portuguese Soccer League in the 2020/2021 season. The models were calculated based on 22 independent variables that included players’ information, body composition, physical fitness, and one dependent variable, the number of injuries per season. In the net elastic analysis, the variables that best predicted injury risk were sectorial positions (defensive and forward), body height, sit-and-reach performance, 1 min number of push-ups, handgrip strength, and 35 m linear speed. This study considered multiple-input single-output regression-type models. The analysis showed that the most accurate model presented in this work generates an error of RMSE = 0.591. Our approach opens a novel perspective for injury prevention and training monitorization. Nevertheless, more studies are needed to identify risk factors associated with injury prediction in elite soccer players, as this is a rising topic that requires several analyses performed in different contexts. |
| doi_str_mv | 10.3390/jcm11164923 |
| format | Article |
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Consequently, elite football clubs have been consistently interested in having practical, interpretable, and usable models as decision-making support for technical staff. This study aimed to analyze predictive modeling of injury risk based on body composition variables and selected physical fitness tests for elite football players through a sports season. The sample comprised 36 male elite football players who competed in the First Portuguese Soccer League in the 2020/2021 season. The models were calculated based on 22 independent variables that included players’ information, body composition, physical fitness, and one dependent variable, the number of injuries per season. In the net elastic analysis, the variables that best predicted injury risk were sectorial positions (defensive and forward), body height, sit-and-reach performance, 1 min number of push-ups, handgrip strength, and 35 m linear speed. This study considered multiple-input single-output regression-type models. The analysis showed that the most accurate model presented in this work generates an error of RMSE = 0.591. Our approach opens a novel perspective for injury prevention and training monitorization. Nevertheless, more studies are needed to identify risk factors associated with injury prediction in elite soccer players, as this is a rising topic that requires several analyses performed in different contexts.</description><identifier>ISSN: 2077-0383</identifier><identifier>EISSN: 2077-0383</identifier><identifier>DOI: 10.3390/jcm11164923</identifier><identifier>PMID: 36013162</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Body composition ; Clinical medicine ; Coaches & managers ; Data collection ; Decision making ; Injury prevention ; Machine learning ; Physical fitness ; Physical fitness tests ; Professional football ; Sports injuries ; Statistical analysis ; Variables</subject><ispartof>Journal of clinical medicine, 2022-08, Vol.11 (16), p.4923</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Consequently, elite football clubs have been consistently interested in having practical, interpretable, and usable models as decision-making support for technical staff. This study aimed to analyze predictive modeling of injury risk based on body composition variables and selected physical fitness tests for elite football players through a sports season. The sample comprised 36 male elite football players who competed in the First Portuguese Soccer League in the 2020/2021 season. The models were calculated based on 22 independent variables that included players’ information, body composition, physical fitness, and one dependent variable, the number of injuries per season. In the net elastic analysis, the variables that best predicted injury risk were sectorial positions (defensive and forward), body height, sit-and-reach performance, 1 min number of push-ups, handgrip strength, and 35 m linear speed. This study considered multiple-input single-output regression-type models. The analysis showed that the most accurate model presented in this work generates an error of RMSE = 0.591. Our approach opens a novel perspective for injury prevention and training monitorization. Nevertheless, more studies are needed to identify risk factors associated with injury prediction in elite soccer players, as this is a rising topic that requires several analyses performed in different contexts.</description><subject>Body composition</subject><subject>Clinical medicine</subject><subject>Coaches & managers</subject><subject>Data collection</subject><subject>Decision making</subject><subject>Injury prevention</subject><subject>Machine learning</subject><subject>Physical fitness</subject><subject>Physical fitness tests</subject><subject>Professional football</subject><subject>Sports injuries</subject><subject>Statistical analysis</subject><subject>Variables</subject><issn>2077-0383</issn><issn>2077-0383</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpdkV9LHDEUxUNpqbL1qV8g0JdC2Tb_djJ5KdTFbQXFxdrnkEnuaLaZyTbJCINf3ogi1vtyL_f-ONzDQegjJV85V-Tbzg6U0kYoxt-gQ0akXBLe8rcv5gN0lPOO1Gpbwah8jw54QyinDTtEd9sEztvibwGfRwfBj9c49vh03E1pxpc-_8XHJoPDccTH0c14HYd9zL74ujCjw78hgC0V2N7M2VsT8MaXEXLGV5BLxn1M-CT4AngTY-lMCHgbzAwpf0DvehMyHD31BfqzObla_1qeXfw8Xf84W1ohSFkqo1riVpY2LVk5BYJLK3oOSgjgtnG1dUI541atIp2TohMdE51tJZPcmY4v0PdH3f3UDeAsjCWZoPfJDybNOhqv_7-M_kZfx1utBFGy4VXg85NAiv-m6koPPlsIwYwQp6yZJLIhktcQFujTK3QXpzRWew9Uw-iKMVqpL4-UTTHnBP3zM5Toh1z1i1z5PXSqlUE</recordid><startdate>20220822</startdate><enddate>20220822</enddate><creator>Martins, Francisco</creator><creator>Przednowek, Krzysztof</creator><creator>França, Cíntia</creator><creator>Lopes, Helder</creator><creator>de Maio Nascimento, Marcelo</creator><creator>Sarmento, Hugo</creator><creator>Marques, Adilson</creator><creator>Ihle, Andreas</creator><creator>Henriques, Ricardo</creator><creator>Gouveia, Élvio Rúbio</creator><general>MDPI AG</general><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-2128-4116</orcidid><orcidid>https://orcid.org/0000-0001-8364-6832</orcidid><orcidid>https://orcid.org/0000-0002-1388-9473</orcidid><orcidid>https://orcid.org/0000-0003-0927-692X</orcidid><orcidid>https://orcid.org/0000-0002-3577-3439</orcidid><orcidid>https://orcid.org/0000-0001-8681-0642</orcidid><orcidid>https://orcid.org/0000-0003-4838-4931</orcidid><orcidid>https://orcid.org/0000-0001-9850-7771</orcidid></search><sort><creationdate>20220822</creationdate><title>Predictive Modeling of Injury Risk Based on Body Composition and Selected Physical Fitness Tests for Elite Football Players</title><author>Martins, Francisco ; 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Consequently, elite football clubs have been consistently interested in having practical, interpretable, and usable models as decision-making support for technical staff. This study aimed to analyze predictive modeling of injury risk based on body composition variables and selected physical fitness tests for elite football players through a sports season. The sample comprised 36 male elite football players who competed in the First Portuguese Soccer League in the 2020/2021 season. The models were calculated based on 22 independent variables that included players’ information, body composition, physical fitness, and one dependent variable, the number of injuries per season. In the net elastic analysis, the variables that best predicted injury risk were sectorial positions (defensive and forward), body height, sit-and-reach performance, 1 min number of push-ups, handgrip strength, and 35 m linear speed. This study considered multiple-input single-output regression-type models. 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| subjects | Body composition Clinical medicine Coaches & managers Data collection Decision making Injury prevention Machine learning Physical fitness Physical fitness tests Professional football Sports injuries Statistical analysis Variables |
| title | Predictive Modeling of Injury Risk Based on Body Composition and Selected Physical Fitness Tests for Elite Football Players |
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