Prediction of football injuries using GPS-based data in Iranian professional football players: a machine learning approach
IntroductionThe study aims to assess and compare the predictive effectiveness of football-related injuries using external load data and a decision tree classification algorithm by unidimensional approach.MethodsThe sample consisted of 25 players from one of the 16 teams participating in the Persian...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Sports and Active Living |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspor.2025.1425180/full |
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| author | Reza Saberisani Amir Hossein Barati Mostafa Zarei Paulo Santos Armin Gorouhi Luca Paolo Ardigò Hadi Nobari |
| author_facet | Reza Saberisani Amir Hossein Barati Mostafa Zarei Paulo Santos Armin Gorouhi Luca Paolo Ardigò Hadi Nobari |
| author_sort | Reza Saberisani |
| collection | DOAJ |
| description | IntroductionThe study aims to assess and compare the predictive effectiveness of football-related injuries using external load data and a decision tree classification algorithm by unidimensional approach.MethodsThe sample consisted of 25 players from one of the 16 teams participating in the Persian Gulf Pro League during the 2022--2023 season. Player injury data and raw GPS data from all training and competition sessions throughout the football league season were gathered (214 training sessions and 34 competition sessions). The acute-tochronic workload ratio was calculated separately for each variable using a ratio of 1:3 weeks. Finally, the decision tree algorithm with machine learning was utilised to assess the predictive power of injury occurrence based on the acute-to-chronic workload ratio.ResultsThe results showed that the variable of the number of decelerations had the highest predictive power compared to other variables [area under the curve (AUC) = 0.91, recall = 87.5%, precision = 58.3%, accuracy = 94.7%].ConclusionAlthough none of the selected external load variables in this study had high predictive power (AUC > 0.95), due to the high predictive power of injury of the number of deceleration variables compared with other variables, the necessity of attention and management of this variable as a risk factor for injury occurrence is essential for preventing future injuries. |
| format | Article |
| id | doaj-art-2addbf84878f48f68dd518bec59f597b |
| institution | Kabale University |
| issn | 2624-9367 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Sports and Active Living |
| spelling | doaj-art-2addbf84878f48f68dd518bec59f597b2025-01-31T06:40:07ZengFrontiers Media S.A.Frontiers in Sports and Active Living2624-93672025-01-01710.3389/fspor.2025.14251801425180Prediction of football injuries using GPS-based data in Iranian professional football players: a machine learning approachReza Saberisani0Amir Hossein Barati1Mostafa Zarei2Paulo Santos3Armin Gorouhi4Luca Paolo Ardigò5Hadi Nobari6Department of Health and Sport Rehabilitation, Faculty of Sport Sciences and Health, Shahid Beheshti University, Tehran, IranDepartment of Health and Sport Rehabilitation, Faculty of Sport Sciences and Health, Shahid Beheshti University, Tehran, IranDepartment of Health and Sport Rehabilitation, Faculty of Sport Sciences and Health, Shahid Beheshti University, Tehran, IranFaculty of Sports, University of Porto, Porto, PortugalDepartment of Health Sciences, Doctoral Program in Health and Human Motor Skill, University of A Coruña, Coruña, SpainDepartment of Teacher Education, NLA University College, Oslo, NorwayLaboratorio de Fisiología del Esfuerzo (LFE), Department of Health and Human Performance, Faculty of Physical Activity and Sport Science (INEF), Universidad Politécnica de Madrid, Madrid, SpainIntroductionThe study aims to assess and compare the predictive effectiveness of football-related injuries using external load data and a decision tree classification algorithm by unidimensional approach.MethodsThe sample consisted of 25 players from one of the 16 teams participating in the Persian Gulf Pro League during the 2022--2023 season. Player injury data and raw GPS data from all training and competition sessions throughout the football league season were gathered (214 training sessions and 34 competition sessions). The acute-tochronic workload ratio was calculated separately for each variable using a ratio of 1:3 weeks. Finally, the decision tree algorithm with machine learning was utilised to assess the predictive power of injury occurrence based on the acute-to-chronic workload ratio.ResultsThe results showed that the variable of the number of decelerations had the highest predictive power compared to other variables [area under the curve (AUC) = 0.91, recall = 87.5%, precision = 58.3%, accuracy = 94.7%].ConclusionAlthough none of the selected external load variables in this study had high predictive power (AUC > 0.95), due to the high predictive power of injury of the number of deceleration variables compared with other variables, the necessity of attention and management of this variable as a risk factor for injury occurrence is essential for preventing future injuries.https://www.frontiersin.org/articles/10.3389/fspor.2025.1425180/fullinjury predictiontraining loadGPSfootballmachine learning |
| spellingShingle | Reza Saberisani Amir Hossein Barati Mostafa Zarei Paulo Santos Armin Gorouhi Luca Paolo Ardigò Hadi Nobari Prediction of football injuries using GPS-based data in Iranian professional football players: a machine learning approach Frontiers in Sports and Active Living injury prediction training load GPS football machine learning |
| title | Prediction of football injuries using GPS-based data in Iranian professional football players: a machine learning approach |
| title_full | Prediction of football injuries using GPS-based data in Iranian professional football players: a machine learning approach |
| title_fullStr | Prediction of football injuries using GPS-based data in Iranian professional football players: a machine learning approach |
| title_full_unstemmed | Prediction of football injuries using GPS-based data in Iranian professional football players: a machine learning approach |
| title_short | Prediction of football injuries using GPS-based data in Iranian professional football players: a machine learning approach |
| title_sort | prediction of football injuries using gps based data in iranian professional football players a machine learning approach |
| topic | injury prediction training load GPS football machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fspor.2025.1425180/full |
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