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Validity of a Microsensor-Based Algorithm for Detecting Scrum Events in Rugby Union.

Ryan M Chambers, Tim J Gabbett, Michael H Cole

    International Journal of Sports Physiology and Performance
    |July 25, 2018
    PubMed
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    This study developed an algorithm using wearable microsensors to automatically detect scrum events in rugby union. The algorithm showed high accuracy in distinguishing scrums during training and matches.

    Keywords:
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    Area of Science:

    • Sports Science
    • Biomechanics
    • Rugby Union Performance Analysis

    Background:

    • Wearable microtechnology devices are crucial for quantifying rugby union demands.
    • Existing technologies include accelerometers, gyroscopes, magnetometers, and GPS.

    Purpose of the Study:

    • To investigate the development of an algorithm for automatic scrum event detection in rugby union.
    • To assess the efficacy of data from wearable microsensors for this purpose.

    Main Methods:

    • Collected data from 30 elite rugby players using Catapult OptimEye S5 devices.
    • Utilized random forest machine learning to train and validate the scrum detection algorithm.
    • Validated the algorithm on 310 files from training and match-play sessions.

    Main Results:

    • The algorithm achieved 91% sensitivity and 91% specificity for detecting scrums across all player positions.
    • Accuracy was higher for match-play events (93.6%) compared to training events (87.6%).
    • Detection performance was optimal for scrums involving 5 or more players.

    Conclusions:

    • The developed scrum algorithm accurately detects scrum events in rugby union.
    • Practitioners should use recommended confidence levels per position to minimize false positives.
    • The algorithm is not suitable for detecting 3-player scrums (e.g., rugby sevens) and requires complementary algorithms for full contact analysis.