Abstract
Objectives
To automate the detection of ruck and tackle events in rugby union using a specifically-designed
algorithm based on microsensor data.
Design
Cross-sectional study.
Methods
Elite rugby union players wore microtechnology devices (Catapult, S5) during match-play.
Ruck (n = 125) and tackle (n = 125) event data was synchronised with video footage compiled from international rugby
union match-play ruck and tackle events. A specifically-designed algorithm to detect
ruck and tackle events was developed using a random forest classification model. This
algorithm was then validated using 8 additional international match-play datasets
and video footage, with each ruck and tackle manually coded and verified if the event
was correctly identified by the algorithm.
Results
The classification algorithm’s results indicated that all rucks and tackles were correctly
identified during match-play when 79.4 ± 9.2% and 81.0 ± 9.3% of the random forest decision trees agreed with the video-based determination
of these events. Sub-group analyses of backs and forwards yielded similar optimal
confidence percentages of 79.7% and 79.1% respectively for rucks. Sub-analysis revealed
backs (85.3 ± 7.2%) produced a higher algorithm cut-off for tackles than forwards (77.7 ± 12.2%).
Conclusions
The specifically-designed algorithm was able to detect rucks and tackles for all positions
involved. For optimal results, it is recommended that practitioners use the recommended
cut-off (80%) to limit false positives for match-play and training. Although this
algorithm provides an improved insight into the number and type of collisions in which
rugby players engage, this algorithm does not provide impact forces of these events.
Keywords
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Article info
Publication history
Published online: January 06, 2019
Accepted:
January 1,
2019
Received in revised form:
November 12,
2018
Received:
June 23,
2018
Identification
Copyright
Crown Copyright © 2019 Published by Elsevier Ltd on behalf of Sports Medicine Australia. All rights reserved.