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Player Activity and Load Profiling with Hidden Markov Models: A Novel Application in Rugby League
Neil Watson1, Sharief Hendricks1,2, Dan Weaving2,3,4
1University of Cape Town.
Research Quarterly for Exercise and Sport
|July 23, 2024
Summary
Hidden Markov models (HMMs) effectively categorize complex rugby league player movement into distinct states. These models reveal how score difference and time influence player activity, differentiating training from match loads.
Area of Science:
- Sports Science
- Biomechanics
- Data Science
Background:
- Player movement in rugby league is complex and spatiotemporal, making robust activity and load measurement challenging.
- Existing models often overlook time-varying factors and the integration of diverse movement dimensions.
- Simultaneously categorizing activity states and analyzing transition influences is underexplored.
Purpose of the Study:
- To demonstrate Hidden Markov Models (HMMs) for data-driven categorization of multi-variable player movement states.
- To investigate the impact of score difference and elapsed match time on player activity states.
- To compare player activity and load profiles between training and match contexts.
Main Methods:
- Applied HMMs to Global Positioning System (GPS), accelerometer, and heart rate data from a professional rugby league team.
- Analyzed data from 60 training sessions and 35 matches.
- Identified distinct player activity states and transition influences.
Main Results:
- HMMs successfully categorized complex player movement into distinct activity states for both training and matches.
- Score difference and elapsed match time significantly influenced transitions between player activity states during matches.
- Significant differences were observed in activity and load profiles between training and match play.
Conclusions:
- HMMs provide a robust, data-driven method for profiling player activity and load in rugby league.
- This approach accounts for the complexity and time-varying nature of player movement.
- HMMs offer potential for advancing research in sports science and performance analysis across various disciplines.
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