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Sequential movement pattern-mining (SMP) in field-based team-sport: A framework for quantifying spatiotemporal data

Ryan White1,2, Anna Palczewska1,3, Dan Weaving1,2

  • 1Carnegie Applied Rugby Research (Carr) Centre, Carnegie School of Sport, Leeds Beckett University, Leeds, UK.

Journal of Sports Sciences
|September 27, 2021
PubMed
Summary

This study introduces a new framework using GPS data to analyze athlete movement sequences in team sports. This method enhances training specificity and quantifies movement patterns for better performance insights.

Keywords:
Global positioning systemsperformance analysissport analyticsteam sportstime-motion analysis

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Area of Science:

  • Sports Science
  • Biomechanics
  • Data Analytics

Background:

  • Athlete external load is traditionally measured using distance, speed, and time.
  • Previous frameworks for movement sequences relied on radio frequency data.
  • A need exists for analyzing complex movement patterns in team sports.

Purpose of the Study:

  • To develop and validate a novel framework for identifying sequential movement patterns (SMP) using GPS-derived spatiotemporal data in team sports.
  • To establish the stability and robustness of this new framework.
  • To enable novel quantification of movement similarities and dissimilarities between athletes and playing positions.

Main Methods:

  • Utilized GPS-derived spatiotemporal data (latitude, longitude) from thirteen rugby league players during a match.
  • Applied techniques to determine player turning angles and improve movement descriptor assignment.
  • Developed Sequential Movement Pattern-mining (SMP) to classify frequent movement sequences and condense sub-sequences.

Main Results:

  • Successfully identified frequent sequential movement patterns (SMP) in field-based team-sport athletes using GPS data.
  • Demonstrated the framework's ability to condense movement sub-sequences, removing repetitions.
  • Established the robustness and stability of the SMP framework for analyzing athlete movement.

Conclusions:

  • The SMP framework offers a novel and stable method for analyzing GPS-derived data in team sports.
  • It enables the quantification of similarities and differences in movement patterns among players and positions.
  • Application of this framework can optimize training specificity for field-based team-sport athletes.