Abstract
In modern fixed-field team sports, computer vision techniques have been commonly applied in analyzing team strategies and tactics. This research presents a combination approach using object detection, multi-object tracking, and social network analysis (SNA) to investigate the dynamics of ice hockey strategies. Specifically, we utilize YOLOv8 object detection algorithm to detect players and ByteTrack to track their movements. The passing information between players is used to construct a network representation of the team's strategy. By using weighted-edges and modularity network community detection, this research demonstrates the team roles of each player in community analysis and captures the impact of team strategies. The goal of this research is to promote teamwork, strategic analysis, and the development of innovative knowledge of sports rules and strategies in sports education.
Author Information
Boyang Zhang, Wapice, Finland
Tommy Löwendahl, Wapice, Finland
Paper Information
Conference: ECE2023
Stream: Design
This paper is part of the ECE2023 Conference Proceedings (View)
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To cite this article:
Zhang B., & Löwendahl T. (2023) Exploring the Dynamics of Ice Hockey Strategies Using YOLOv8 and Gephi in Sports Education ISSN: 2188-1162 The European Conference on Education 2023: Official Conference Proceedings https://doi.org/10.22492/issn.2188-1162.2023.66
To link to this article: https://doi.org/10.22492/issn.2188-1162.2023.66
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