Performance indicators associated with match outcome within the United Rugby Championship

Scott, Georgia; Bezodis, Neil; Waldron, Mark; Bennett, Mark; Church, Simon; Kilduff, Liam; Brown, Rowan

doi:10.1016/j.jsams.2022.11.006

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Performance indicators associated with match outcome within the United Rugby Championship

Georgia Scott, Neil Bezodis

, Mark Waldron

, Mark Bennett, Simon Church, Liam Kilduff

, Rowan Brown

Journal of Science and Medicine in Sport, Volume: 26, Issue: 1, Pages: 63 - 68

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

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DOI (Published version): 10.1016/j.jsams.2022.11.006

Abstract

ObjectivesThe aims of this study were to: i) identify performance indicators (PIs) associated with match outcomes in the United Rugby Championship to; ii) compare efficacy of isolated data and data relative to opposition in predicting match outcome; and iii) investigate whether reduced PI statistica...

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Published in:	Journal of Science and Medicine in Sport
ISSN:	1440-2440
Published:	Elsevier BV 2023
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URI:	https://cronfa.swan.ac.uk/Record/cronfa62102
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first_indexed	2022-12-01T12:11:20Z
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Random forest classification (RFC) was completed on isolated and relative datasets, using a binary match outcome (win/lose). Maximum relevance and minimum redundancy PI selection was utilised to reduce models. In addition, models were tested on 53 matches from the 2021-22 season to ascertain prediction accuracy. ResultsWithin the 2020-21 datasets, the full models correctly classified 83% (CI 77%-88%) of match performances for the relative dataset and 64% (CI 56%-70%) for isolated data. When models were reduced, these values were 85% (CI 79%-90%) and 66% (CI 58%-72%). In prediction on the 21-22 season, the reduced relative model successfully classified 90% of match performances (CI 82%-95%). Within the reduced relative model, five PIs were significant for match outcome: kicks from hand, metres made, clean breaks, turnovers conceded and scrum penalties. ConclusionsRelative PIs were more effective in predicting match outcomes than isolated data. Reducing features used in random forest classification did not degrade prediction accuracy, whilst also simplifying interpretation for practitioners. Increased kicks from hand, metres made, and clean breaks compared to the opposition, as well as fewer scrum penalties and turnovers conceded were all indicators of winning match outcomes within the United Rugby Championship.</abstract><type>Journal Article</type><journal>Journal of Science and Medicine in Sport</journal><volume>26</volume><journalNumber>1</journalNumber><paginationStart>63</paginationStart><paginationEnd>68</paginationEnd><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1440-2440</issnPrint><issnElectronic/><keywords>Game Statistics, Decision Modelling, Multivariate Analysis, Sports Performance, Team Sports.</keywords><publishedDay>1</publishedDay><publishedMonth>1</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-01-01</publishedDate><doi>10.1016/j.jsams.2022.11.006</doi><url/><notes/><college>COLLEGE NANME</college><department>Science and Engineering - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGSEN</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>This work was supported by the ESPRC DTP (EP/EGF1069/; EP/T517987/1) and Ospreys Rugby.</funders><projectreference/><lastEdited>2023-01-10T10:59:42.0701274</lastEdited><Created>2022-12-01T11:39:26.8424367</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Biomedical Engineering</level></path><authors><author><firstname>Georgia</firstname><surname>Scott</surname><order>1</order></author><author><firstname>Neil</firstname><surname>Bezodis</surname><orcid>0000-0003-2229-3310</orcid><order>2</order></author><author><firstname>Mark</firstname><surname>Waldron</surname><orcid>0000-0002-2720-4615</orcid><order>3</order></author><author><firstname>Mark</firstname><surname>Bennett</surname><order>4</order></author><author><firstname>Simon</firstname><surname>Church</surname><order>5</order></author><author><firstname>Liam</firstname><surname>Kilduff</surname><orcid>0000-0001-9449-2293</orcid><order>6</order></author><author><firstname>Rowan</firstname><surname>Brown</surname><orcid>0000-0003-3628-2524</orcid><order>7</order></author></authors><documents><document><filename>62102__26225__69540ce342444cbdab3d36028560c6f4.pdf</filename><originalFilename>62102.pdf</originalFilename><uploaded>2023-01-10T10:57:57.9389926</uploaded><type>Output</type><contentLength>615550</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© 2022 The Authors. 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spelling	2023-01-10T10:59:42.0701274 v2 62102 2022-12-01 Performance indicators associated with match outcome within the United Rugby Championship 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 bd632dd19f7ba6391670f261d0a5a242 Mark Bennett Mark Bennett true false 972ed9a1dda7a0de20581a0f8350be98 0000-0001-9449-2293 Liam Kilduff Liam Kilduff true false d7db8d42c476dfa69c15ce06d29bd863 0000-0003-3628-2524 Rowan Brown Rowan Brown true false 2022-12-01 FGSEN ObjectivesThe aims of this study were to: i) identify performance indicators (PIs) associated with match outcomes in the United Rugby Championship to; ii) compare efficacy of isolated data and data relative to opposition in predicting match outcome; and iii) investigate whether reduced PI statistical models can reproduce predictive accuracy.MethodsTwenty-seven PIs were selected from 96 matches (2020-21 United Rugby Championship). Random forest classification (RFC) was completed on isolated and relative datasets, using a binary match outcome (win/lose). Maximum relevance and minimum redundancy PI selection was utilised to reduce models. In addition, models were tested on 53 matches from the 2021-22 season to ascertain prediction accuracy. ResultsWithin the 2020-21 datasets, the full models correctly classified 83% (CI 77%-88%) of match performances for the relative dataset and 64% (CI 56%-70%) for isolated data. When models were reduced, these values were 85% (CI 79%-90%) and 66% (CI 58%-72%). In prediction on the 21-22 season, the reduced relative model successfully classified 90% of match performances (CI 82%-95%). Within the reduced relative model, five PIs were significant for match outcome: kicks from hand, metres made, clean breaks, turnovers conceded and scrum penalties. ConclusionsRelative PIs were more effective in predicting match outcomes than isolated data. Reducing features used in random forest classification did not degrade prediction accuracy, whilst also simplifying interpretation for practitioners. Increased kicks from hand, metres made, and clean breaks compared to the opposition, as well as fewer scrum penalties and turnovers conceded were all indicators of winning match outcomes within the United Rugby Championship. Journal Article Journal of Science and Medicine in Sport 26 1 63 68 Elsevier BV 1440-2440 Game Statistics, Decision Modelling, Multivariate Analysis, Sports Performance, Team Sports. 1 1 2023 2023-01-01 10.1016/j.jsams.2022.11.006 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University SU Library paid the OA fee (TA Institutional Deal) This work was supported by the ESPRC DTP (EP/EGF1069/; EP/T517987/1) and Ospreys Rugby. 2023-01-10T10:59:42.0701274 2022-12-01T11:39:26.8424367 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Georgia Scott 1 Neil Bezodis 0000-0003-2229-3310 2 Mark Waldron 0000-0002-2720-4615 3 Mark Bennett 4 Simon Church 5 Liam Kilduff 0000-0001-9449-2293 6 Rowan Brown 0000-0003-3628-2524 7 62102__26225__69540ce342444cbdab3d36028560c6f4.pdf 62102.pdf 2023-01-10T10:57:57.9389926 Output 615550 application/pdf Version of Record true © 2022 The Authors. This is an open access article under the CC BY license true eng http://creativecommons.org/licenses/by/4.0/
title	Performance indicators associated with match outcome within the United Rugby Championship
spellingShingle	Performance indicators associated with match outcome within the United Rugby Championship Georgia Scott Neil Bezodis Mark Waldron Mark Bennett Liam Kilduff Rowan Brown
title_short	Performance indicators associated with match outcome within the United Rugby Championship
title_full	Performance indicators associated with match outcome within the United Rugby Championship
title_fullStr	Performance indicators associated with match outcome within the United Rugby Championship
title_full_unstemmed	Performance indicators associated with match outcome within the United Rugby Championship
title_sort	Performance indicators associated with match outcome within the United Rugby Championship
author_id_str_mv	e6170934bdc5ac51306b5aebecfe9aba 534588568c1936e94e1ed8527b8c991b 70db7c6c54d46f5e70b39e5ae0a056fa bd632dd19f7ba6391670f261d0a5a242 972ed9a1dda7a0de20581a0f8350be98 d7db8d42c476dfa69c15ce06d29bd863
author_id_fullname_str_mv	e6170934bdc5ac51306b5aebecfe9aba_*_Georgia Scott 534588568c1936e94e1ed8527b8c991b__Neil Bezodis 70db7c6c54d46f5e70b39e5ae0a056fa__Mark Waldron bd632dd19f7ba6391670f261d0a5a242__Mark Bennett 972ed9a1dda7a0de20581a0f8350be98__Liam Kilduff d7db8d42c476dfa69c15ce06d29bd863_*_Rowan Brown
author	Georgia Scott Neil Bezodis Mark Waldron Mark Bennett Liam Kilduff Rowan Brown
author2	Georgia Scott Neil Bezodis Mark Waldron Mark Bennett Simon Church Liam Kilduff Rowan Brown
format	Journal article
container_title	Journal of Science and Medicine in Sport
container_volume	26
container_issue	1
container_start_page	63
publishDate	2023
institution	Swansea University
issn	1440-2440
doi_str_mv	10.1016/j.jsams.2022.11.006
publisher	Elsevier BV
college_str	Faculty of Science and Engineering
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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 - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
document_store_str	1
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description	ObjectivesThe aims of this study were to: i) identify performance indicators (PIs) associated with match outcomes in the United Rugby Championship to; ii) compare efficacy of isolated data and data relative to opposition in predicting match outcome; and iii) investigate whether reduced PI statistical models can reproduce predictive accuracy.MethodsTwenty-seven PIs were selected from 96 matches (2020-21 United Rugby Championship). Random forest classification (RFC) was completed on isolated and relative datasets, using a binary match outcome (win/lose). Maximum relevance and minimum redundancy PI selection was utilised to reduce models. In addition, models were tested on 53 matches from the 2021-22 season to ascertain prediction accuracy. ResultsWithin the 2020-21 datasets, the full models correctly classified 83% (CI 77%-88%) of match performances for the relative dataset and 64% (CI 56%-70%) for isolated data. When models were reduced, these values were 85% (CI 79%-90%) and 66% (CI 58%-72%). In prediction on the 21-22 season, the reduced relative model successfully classified 90% of match performances (CI 82%-95%). Within the reduced relative model, five PIs were significant for match outcome: kicks from hand, metres made, clean breaks, turnovers conceded and scrum penalties. ConclusionsRelative PIs were more effective in predicting match outcomes than isolated data. Reducing features used in random forest classification did not degrade prediction accuracy, whilst also simplifying interpretation for practitioners. Increased kicks from hand, metres made, and clean breaks compared to the opposition, as well as fewer scrum penalties and turnovers conceded were all indicators of winning match outcomes within the United Rugby Championship.
published_date	2023-01-01T04:21:27Z
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score	11.036706

Performance indicators associated with match outcome within the United Rugby Championship

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