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A Framework for Spatio-Temporal Graph Analytics In Field Sports

Antonini, Valerio orcid logoORCID: 0009-0004-2607-9623, Scriney, Michael orcid logoORCID: 0000-0001-6813-2630, Mileo, Alessandra orcid logoORCID: 0000-0002-6614-6462 and Roantree, Mark orcid logoORCID: 0000-0002-1329-2570 (2024) A Framework for Spatio-Temporal Graph Analytics In Field Sports. The 26th International Conference on Big Data Analytics and Knowledge Discovery (DAWAK 2024) . pp. 1-11.

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.
Item Type:Article (Submitted)
Subjects:Computer Science > Algorithms
Computer Science > Information retrieval
Computer Science > Machine learning
Medical Sciences > Sports sciences
DCU Faculties and Centres:UNSPECIFIED
Official URL:https://www.dexa.org/dawak2024
Copyright Information:Authors
Funders:This work was supported by Science Foundation Ireland through the Insight Centre for Data Analytics (SFI/12/RC/2289_P2) and the SFI Centre for Research Training in Machine Learning (18/CRT/6183)
ID Code:30059
Deposited On:11 Jun 2024 13:55 by Valerio Antonini . Last Modified 11 Jun 2024 13:55

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