Validity of a microsensor-based algorithm for detecting scrum events in rugby union

Chambers, Ryan M. and Gabbett, Tim J. and Cole, Michael H. (2019) Validity of a microsensor-based algorithm for detecting scrum events in rugby union. International Journal of Sports Physiology and Performance, 14 (2). pp. 176-182. ISSN 1555-0265

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PURPOSE: Commercially available microtechnology devices containing accelerometers, gyroscopes, magnetometers, and global positioning technology have been widely used to quantify the demands of rugby union. This study investigated whether data derived from wearable microsensors can be used to develop an algorithm that automatically detects scrum events in rugby union training and match play.

METHODS: Data were collected from 30 elite rugby players wearing a Catapult OptimEye S5 (Catapult Sports, Melbourne, Australia) microtechnology device during a series of competitive matches (n = 46) and training sessions (n = 51). A total of 97 files were required to 'train' an algorithm to automatically detect scrum events using random forest machine learning. A further 310 files from training (n = 167) and match-play (n = 143) sessions were used to validate the algorithm's performance.

RESULTS: Across all positions (front row, second row, and back row), the algorithm demonstrated good sensitivity (91%) and specificity (91%) for training and match-play events when the confidence level of the random forest was set to 50%. Generally, the algorithm had better accuracy for match-play events (93.6%) than for training events (87.6%).

CONCLUSIONS: The scrum algorithm was able to accurately detect scrum events for front-row, second-row, and back-row positions. However, for optimal results, practitioners are advised to use the recommended confidence level for each position to limit false positives. Scrum algorithm detection was better with scrums involving >/=5 players and is therefore unlikely to be suitable for scrums involving 3 players (eg, rugby sevens). Additional contact- and collision-detection algorithms are required to fully quantify rugby union demands.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Institute for Resilient Regions
Faculty/School / Institute/Centre: Current - Institute for Resilient Regions
Date Deposited: 02 Sep 2019 07:29
Last Modified: 17 Oct 2019 01:20
Uncontrolled Keywords: contact detection, machine learning, microtechnology, team sport
Fields of Research (2008): 11 Medical and Health Sciences > 1106 Human Movement and Sports Science > 110699 Human Movement and Sports Science not elsewhere classified
Fields of Research (2020): 42 HEALTH SCIENCES > 4207 Sports science and exercise > 420799 Sports science and exercise not elsewhere classified
Socio-Economic Objectives (2008): C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
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