SoccerGuard: Investigating Injury Risk Factors for Professional Soccer Players with Machine Learning
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913577764388864 |
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| author | Bartels, Finn Xing, Lu Midoglu, Cise Boeker, Matthias Kirsten, Toralf Halvorsen, Pål |
| author_facet | Bartels, Finn Xing, Lu Midoglu, Cise Boeker, Matthias Kirsten, Toralf Halvorsen, Pål |
| contents | We present SoccerGuard, a novel framework for predicting injuries in women's soccer using Machine Learning (ML). This framework can ingest data from multiple sources, including subjective wellness and training load reports from players, objective GPS sensor measurements, third-party player statistics, and injury reports verified by medical personnel. We experiment with a number of different settings related to synthetic data generation, input and output window sizes, and ML models for prediction. Our results show that, given the right configurations and feature combinations, injury event prediction can be undertaken with considerable accuracy. The optimal results are achieved when input windows are reduced and larger combined output windows are defined, in combination with an ideally balanced data set. The framework also includes a dashboard with a user-friendly Graphical User Interface (GUI) to support interactive analysis and visualization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08901 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SoccerGuard: Investigating Injury Risk Factors for Professional Soccer Players with Machine Learning Bartels, Finn Xing, Lu Midoglu, Cise Boeker, Matthias Kirsten, Toralf Halvorsen, Pål Human-Computer Interaction Machine Learning We present SoccerGuard, a novel framework for predicting injuries in women's soccer using Machine Learning (ML). This framework can ingest data from multiple sources, including subjective wellness and training load reports from players, objective GPS sensor measurements, third-party player statistics, and injury reports verified by medical personnel. We experiment with a number of different settings related to synthetic data generation, input and output window sizes, and ML models for prediction. Our results show that, given the right configurations and feature combinations, injury event prediction can be undertaken with considerable accuracy. The optimal results are achieved when input windows are reduced and larger combined output windows are defined, in combination with an ideally balanced data set. The framework also includes a dashboard with a user-friendly Graphical User Interface (GUI) to support interactive analysis and visualization. |
| title | SoccerGuard: Investigating Injury Risk Factors for Professional Soccer Players with Machine Learning |
| topic | Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2411.08901 |