Can machine learning distinguish between elite and non-elite rowers?
A major challenge for sports coaches and analysts is to identify critical elements of athletes’ movement patterns. A potentially relevant tool is machine learning, useful because of its ability to extract patterns from data. In the current study, we employed various deep learning frameworks, includi...
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| Format: | Article |
| Language: | English |
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Sciendo
2025-05-01
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| Series: | International Journal of Computer Science in Sport |
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| Online Access: | https://doi.org/10.2478/ijcss-2025-0007 |
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| _version_ | 1849733470372233216 |
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| author | Orten Kristine Fjellkårstad Helgesen Sander Elias Magnussen Chen Bihui Baselizadeh Adel Torresen Jim Herrebrøden Henrik |
| author_facet | Orten Kristine Fjellkårstad Helgesen Sander Elias Magnussen Chen Bihui Baselizadeh Adel Torresen Jim Herrebrøden Henrik |
| author_sort | Orten Kristine Fjellkårstad |
| collection | DOAJ |
| description | A major challenge for sports coaches and analysts is to identify critical elements of athletes’ movement patterns. A potentially relevant tool is machine learning, useful because of its ability to extract patterns from data. In the current study, we employed various deep learning frameworks, including Gated Recurrent Unit networks (GRUs), Convolutional Neural Networks (CNNs), and Multi-Layer Perceptrons (MLPs), to search for differences between elite and non-elite rowers using a rowing ergometer. The MLP model achieved an accuracy of 100% when using all input features, indicating that the problem is suitable as a machine learning task. Our research focused on using a limited amount of the data. Despite using fewer input features, the models managed to classify skill levels with reasonable precision, reaching a best performance of 77% accuracy for the model combining GRU and CNN architectures, 78% for the GRU model, and 94% for the MLP model. From a rowing perspective, the results suggest that movement coordination between upper and lower body limbs, as represented by different feature combinations, is informative in distinguishing between elites and non-elites. The current work suggests that machine learning may supplement human experts in sports coaching, analytics, and talent identification. |
| format | Article |
| id | doaj-art-e3cf24f8a2fa4acd8de86d29a7be44f9 |
| institution | DOAJ |
| issn | 1684-4769 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Computer Science in Sport |
| spelling | doaj-art-e3cf24f8a2fa4acd8de86d29a7be44f92025-08-20T03:08:01ZengSciendoInternational Journal of Computer Science in Sport1684-47692025-05-0124111813210.2478/ijcss-2025-0007Can machine learning distinguish between elite and non-elite rowers?Orten Kristine Fjellkårstad0Helgesen Sander Elias Magnussen1Chen Bihui2Baselizadeh Adel3Torresen Jim4Herrebrøden Henrik5Department of Informatics, University of Oslo, Oslo, NorwayDepartment of Informatics, University of Oslo, Oslo, NorwayDepartment of Informatics, University of Oslo, Oslo, NorwayDepartment of Informatics, University of Oslo, Oslo, NorwayDepartment of Informatics, University of Oslo, Oslo, NorwayDepartment of Psychology, Pedagogy and Law, School of Health Sciences, Kristiania University College, Oslo, NorwayA major challenge for sports coaches and analysts is to identify critical elements of athletes’ movement patterns. A potentially relevant tool is machine learning, useful because of its ability to extract patterns from data. In the current study, we employed various deep learning frameworks, including Gated Recurrent Unit networks (GRUs), Convolutional Neural Networks (CNNs), and Multi-Layer Perceptrons (MLPs), to search for differences between elite and non-elite rowers using a rowing ergometer. The MLP model achieved an accuracy of 100% when using all input features, indicating that the problem is suitable as a machine learning task. Our research focused on using a limited amount of the data. Despite using fewer input features, the models managed to classify skill levels with reasonable precision, reaching a best performance of 77% accuracy for the model combining GRU and CNN architectures, 78% for the GRU model, and 94% for the MLP model. From a rowing perspective, the results suggest that movement coordination between upper and lower body limbs, as represented by different feature combinations, is informative in distinguishing between elites and non-elites. The current work suggests that machine learning may supplement human experts in sports coaching, analytics, and talent identification.https://doi.org/10.2478/ijcss-2025-0007sports analysisrowingmachine learningneural networksdeep learninggated recurrent unitconvolutional neural networkmulit-layer perceptron |
| spellingShingle | Orten Kristine Fjellkårstad Helgesen Sander Elias Magnussen Chen Bihui Baselizadeh Adel Torresen Jim Herrebrøden Henrik Can machine learning distinguish between elite and non-elite rowers? International Journal of Computer Science in Sport sports analysis rowing machine learning neural networks deep learning gated recurrent unit convolutional neural network mulit-layer perceptron |
| title | Can machine learning distinguish between elite and non-elite rowers? |
| title_full | Can machine learning distinguish between elite and non-elite rowers? |
| title_fullStr | Can machine learning distinguish between elite and non-elite rowers? |
| title_full_unstemmed | Can machine learning distinguish between elite and non-elite rowers? |
| title_short | Can machine learning distinguish between elite and non-elite rowers? |
| title_sort | can machine learning distinguish between elite and non elite rowers |
| topic | sports analysis rowing machine learning neural networks deep learning gated recurrent unit convolutional neural network mulit-layer perceptron |
| url | https://doi.org/10.2478/ijcss-2025-0007 |
| work_keys_str_mv | AT ortenkristinefjellkarstad canmachinelearningdistinguishbetweeneliteandnoneliterowers AT helgesensandereliasmagnussen canmachinelearningdistinguishbetweeneliteandnoneliterowers AT chenbihui canmachinelearningdistinguishbetweeneliteandnoneliterowers AT baselizadehadel canmachinelearningdistinguishbetweeneliteandnoneliterowers AT torresenjim canmachinelearningdistinguishbetweeneliteandnoneliterowers AT herrebrødenhenrik canmachinelearningdistinguishbetweeneliteandnoneliterowers |