A Framework for Spatio-Temporal Graph Analytics In Field Sports
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929425963024384 |
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| author | Antonini, Valerio Scriney, Michael Mileo, Alessandra Roantree, Mark |
| author_facet | Antonini, Valerio Scriney, Michael Mileo, Alessandra Roantree, Mark |
| contents | The global sports analytics industry has a market value of USD 3.78 billion in 2023. The increase of wearables such as GPS sensors has provided analysts with large fine-grained datasets detailing player performance. Traditional analysis of this data focuses on individual athletes with measures of internal and external loading such as distance covered in speed zones or rate of perceived exertion. However these metrics do not provide enough information to understand team dynamics within field sports. The spatio-temporal nature of match play necessitates an investment in date-engineering to adequately transform the data into a suitable format to extract features such as areas of activity. In this paper we present an approach to construct Time-Window Spatial Activity Graphs (TWGs) for field sports. Using GPS data obtained from Gaelic Football matches we demonstrate how our approach can be utilised to extract spatio-temporal features from GPS sensor data |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_13109 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Framework for Spatio-Temporal Graph Analytics In Field Sports Antonini, Valerio Scriney, Michael Mileo, Alessandra Roantree, Mark Computers and Society Machine Learning The global sports analytics industry has a market value of USD 3.78 billion in 2023. The increase of wearables such as GPS sensors has provided analysts with large fine-grained datasets detailing player performance. Traditional analysis of this data focuses on individual athletes with measures of internal and external loading such as distance covered in speed zones or rate of perceived exertion. However these metrics do not provide enough information to understand team dynamics within field sports. The spatio-temporal nature of match play necessitates an investment in date-engineering to adequately transform the data into a suitable format to extract features such as areas of activity. In this paper we present an approach to construct Time-Window Spatial Activity Graphs (TWGs) for field sports. Using GPS data obtained from Gaelic Football matches we demonstrate how our approach can be utilised to extract spatio-temporal features from GPS sensor data |
| title | A Framework for Spatio-Temporal Graph Analytics In Field Sports |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2407.13109 |