Classifying Winning Performances in International Women’s Rugby Union

Scott, Georgia; Edwards, Ollie; Bezodis, Neil; Waldron, Mark; Roberts, Eifion; Pyne, David B.; Mara, Jocelyn; Cook, Christian; Mason, Laura; Brown, Rowan; Kilduff, Liam

doi:10.1123/ijspp.2023-0086

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Classifying Winning Performances in International Women’s Rugby Union

Georgia Scott, Ollie Edwards, Neil Bezodis

, Mark Waldron

, Eifion Roberts, David B. Pyne

, Jocelyn Mara

, Christian Cook

, Laura Mason

, Rowan Brown

, Liam Kilduff

International Journal of Sports Physiology and Performance, Volume: 18, Issue: 9, Pages: 1 - 7

Swansea University Authors: Georgia Scott, Neil Bezodis , Mark Waldron , Laura Mason , Rowan Brown , Liam Kilduff

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DOI (Published version): 10.1123/ijspp.2023-0086

Abstract

Purpose: The efficacy of isolated and relative performance indicators (PIs) has been compared within Rugby Union; the latter more effective at discerning match outcomes. However, this methodology has not been applied within women’s rugby. The aim of this study was to identify PIs that maximize predi...

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Published in:	International Journal of Sports Physiology and Performance
ISSN:	1555-0265 1555-0273
Published:	Human Kinetics
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa63875
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first_indexed	2023-07-12T12:23:20Z
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However, this methodology has not been applied within women’s rugby. The aim of this study was to identify PIs that maximize prediction accuracy of match outcome, from isolated and relative datasets, in Women’s Rugby Union. Methods: Twenty-six PIs were selected from 110 women’s international rugby matches between 2017-2022 to form an isolated dataset, with relative datasets determined by subtracting corresponding opposition PIs. Random forest classification was completed on both datasets, and feature selection and importance used to simplify models and interpret key PIs. Models were used in prediction on the 2021 World Cup to evaluate performance on unseen data. Results: The isolated full model correctly classified 75% of outcomes (CI (65%, 82%)), whereas the relative full model correctly classified 78% (CI (69%, 86%)). Reduced respective models correctly classified 74% (CI (65%, 82%)) and 76% (CI (67%, 84%)). Reduced models correctly predicted 100% and 96% of outcomes for isolated and relative test datasets, respectively. No significant difference in accuracy was found between datasets. Within the relative reduced model, metres made, clean breaks, missed tackles, lineouts lost, carries and kicks from hand were significant. Conclusions: Increased relative metres made, clean breaks, carries, kicks from hand, and decreased relative missed tackles and lineouts lost were associated with success. 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spelling	v2 63875 2023-07-12 Classifying Winning Performances in International Women’s Rugby Union e6170934bdc5ac51306b5aebecfe9aba Georgia Scott Georgia Scott true false 534588568c1936e94e1ed8527b8c991b 0000-0003-2229-3310 Neil Bezodis Neil Bezodis true false 70db7c6c54d46f5e70b39e5ae0a056fa 0000-0002-2720-4615 Mark Waldron Mark Waldron true false ef88a9ba99af7706e3e80e418f482e0a 0000-0002-9679-7063 Laura Mason Laura Mason true false d7db8d42c476dfa69c15ce06d29bd863 0000-0003-3628-2524 Rowan Brown Rowan Brown true false 972ed9a1dda7a0de20581a0f8350be98 0000-0001-9449-2293 Liam Kilduff Liam Kilduff true false 2023-07-12 FGSEN Purpose: The efficacy of isolated and relative performance indicators (PIs) has been compared within Rugby Union; the latter more effective at discerning match outcomes. However, this methodology has not been applied within women’s rugby. The aim of this study was to identify PIs that maximize prediction accuracy of match outcome, from isolated and relative datasets, in Women’s Rugby Union. Methods: Twenty-six PIs were selected from 110 women’s international rugby matches between 2017-2022 to form an isolated dataset, with relative datasets determined by subtracting corresponding opposition PIs. Random forest classification was completed on both datasets, and feature selection and importance used to simplify models and interpret key PIs. Models were used in prediction on the 2021 World Cup to evaluate performance on unseen data. Results: The isolated full model correctly classified 75% of outcomes (CI (65%, 82%)), whereas the relative full model correctly classified 78% (CI (69%, 86%)). Reduced respective models correctly classified 74% (CI (65%, 82%)) and 76% (CI (67%, 84%)). Reduced models correctly predicted 100% and 96% of outcomes for isolated and relative test datasets, respectively. No significant difference in accuracy was found between datasets. Within the relative reduced model, metres made, clean breaks, missed tackles, lineouts lost, carries and kicks from hand were significant. Conclusions: Increased relative metres made, clean breaks, carries, kicks from hand, and decreased relative missed tackles and lineouts lost were associated with success. This information can be utilized to inform physical and tactical preparation and direct physiological studies in women’s rugby. Journal Article International Journal of Sports Physiology and Performance 18 9 1 7 Human Kinetics 1555-0265 1555-0273 Game Statistics, Decision Modelling, Multivariate Analysis, Team Sports, Women’s Sports. 0 0 0 0001-01-01 10.1123/ijspp.2023-0086 http://dx.doi.org/10.1123/ijspp.2023-0086 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2023-09-05T15:58:18.6152926 2023-07-12T13:20:55.5953425 Faculty of Science and Engineering School of Engineering and Applied Sciences - Sport and Exercise Sciences Georgia Scott 1 Ollie Edwards 2 Neil Bezodis 0000-0003-2229-3310 3 Mark Waldron 0000-0002-2720-4615 4 Eifion Roberts 5 David B. Pyne 0000-0003-1555-5079 6 Jocelyn Mara 0000-0003-2091-2608 7 Christian Cook 0000-0001-9677-0306 8 Laura Mason 0000-0002-9679-7063 9 Rowan Brown 0000-0003-3628-2524 10 Liam Kilduff 0000-0001-9449-2293 11 63875__28092__ae348e6f5799490b8f93545e951765ec.pdf 63875.pdf 2023-07-12T13:23:19.0791707 Output 279972 application/pdf Accepted Manuscript true true eng
title	Classifying Winning Performances in International Women’s Rugby Union
spellingShingle	Classifying Winning Performances in International Women’s Rugby Union Georgia Scott Neil Bezodis Mark Waldron Laura Mason Rowan Brown Liam Kilduff
title_short	Classifying Winning Performances in International Women’s Rugby Union
title_full	Classifying Winning Performances in International Women’s Rugby Union
title_fullStr	Classifying Winning Performances in International Women’s Rugby Union
title_full_unstemmed	Classifying Winning Performances in International Women’s Rugby Union
title_sort	Classifying Winning Performances in International Women’s Rugby Union
author_id_str_mv	e6170934bdc5ac51306b5aebecfe9aba 534588568c1936e94e1ed8527b8c991b 70db7c6c54d46f5e70b39e5ae0a056fa ef88a9ba99af7706e3e80e418f482e0a d7db8d42c476dfa69c15ce06d29bd863 972ed9a1dda7a0de20581a0f8350be98
author_id_fullname_str_mv	e6170934bdc5ac51306b5aebecfe9aba_*_Georgia Scott 534588568c1936e94e1ed8527b8c991b__Neil Bezodis 70db7c6c54d46f5e70b39e5ae0a056fa__Mark Waldron ef88a9ba99af7706e3e80e418f482e0a__Laura Mason d7db8d42c476dfa69c15ce06d29bd863__Rowan Brown 972ed9a1dda7a0de20581a0f8350be98_*_Liam Kilduff
author	Georgia Scott Neil Bezodis Mark Waldron Laura Mason Rowan Brown Liam Kilduff
author2	Georgia Scott Ollie Edwards Neil Bezodis Mark Waldron Eifion Roberts David B. Pyne Jocelyn Mara Christian Cook Laura Mason Rowan Brown Liam Kilduff
format	Journal article
container_title	International Journal of Sports Physiology and Performance
container_volume	18
container_issue	9
container_start_page	1
institution	Swansea University
issn	1555-0265 1555-0273
doi_str_mv	10.1123/ijspp.2023-0086
publisher	Human Kinetics
college_str	Faculty of Science and Engineering
hierarchytype
hierarchy_top_id	facultyofscienceandengineering
hierarchy_top_title	Faculty of Science and Engineering
hierarchy_parent_id	facultyofscienceandengineering
hierarchy_parent_title	Faculty of Science and Engineering
department_str	School of Engineering and Applied Sciences - Sport and Exercise Sciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Sport and Exercise Sciences
url	http://dx.doi.org/10.1123/ijspp.2023-0086
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description	Purpose: The efficacy of isolated and relative performance indicators (PIs) has been compared within Rugby Union; the latter more effective at discerning match outcomes. However, this methodology has not been applied within women’s rugby. The aim of this study was to identify PIs that maximize prediction accuracy of match outcome, from isolated and relative datasets, in Women’s Rugby Union. Methods: Twenty-six PIs were selected from 110 women’s international rugby matches between 2017-2022 to form an isolated dataset, with relative datasets determined by subtracting corresponding opposition PIs. Random forest classification was completed on both datasets, and feature selection and importance used to simplify models and interpret key PIs. Models were used in prediction on the 2021 World Cup to evaluate performance on unseen data. Results: The isolated full model correctly classified 75% of outcomes (CI (65%, 82%)), whereas the relative full model correctly classified 78% (CI (69%, 86%)). Reduced respective models correctly classified 74% (CI (65%, 82%)) and 76% (CI (67%, 84%)). Reduced models correctly predicted 100% and 96% of outcomes for isolated and relative test datasets, respectively. No significant difference in accuracy was found between datasets. Within the relative reduced model, metres made, clean breaks, missed tackles, lineouts lost, carries and kicks from hand were significant. Conclusions: Increased relative metres made, clean breaks, carries, kicks from hand, and decreased relative missed tackles and lineouts lost were associated with success. This information can be utilized to inform physical and tactical preparation and direct physiological studies in women’s rugby.
published_date	0001-01-01T15:58:20Z
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Classifying Winning Performances in International Women’s Rugby Union

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