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The Application of Data Analytics in Match and Kicking Performance in Elite Men’s Rugby Union / GEORGIA SCOTT

Swansea University Author: GEORGIA SCOTT

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DOI (Published version): 10.23889/SUThesis.69769

Abstract

Performance indicators are a key measure in analysing successful performance within Rugby Union. Their use has been documented in many different global competitions, including at the international level; however, research is scarce within the United Rugby Championship.There is a lack of general under...

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Published: Swansea University, Wales, UK 2025
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Bezodis, N.E., Waldron, M., Church, S., Kilduff, L.P., and Brown, M.R.
URI: https://cronfa.swan.ac.uk/Record/cronfa69769
first_indexed 2025-06-19T11:34:06Z
last_indexed 2025-06-20T04:59:20Z
id cronfa69769
recordtype RisThesis
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spelling 2025-06-19T12:38:57.9642364 v2 69769 2025-06-19 The Application of Data Analytics in Match and Kicking Performance in Elite Men’s Rugby Union b5738fa3f654dc5c5330e661b3420419 GEORGIA SCOTT GEORGIA SCOTT true false 2025-06-19 Performance indicators are a key measure in analysing successful performance within Rugby Union. Their use has been documented in many different global competitions, including at the international level; however, research is scarce within the United Rugby Championship.There is a lack of general understanding of match performance at more detailed levels such as the sequence and action level across all competitions. Chapter 4 aimed to understand the associations between key performance indicators and match outcomes through the use of relative data and simplified modelling strategies. Results identified that increased relative kicking, metres made, and clean breaks, and decreased relative turnovers and scrum penalties conceded were associated with successful match outcomes. It was also established that relative data improved prediction accuracy, and simplification in model design did not degrade model accuracy. Chapter 5 aimed to interpret how relative kicking influences matches at the sequence level. This chapter established that in most sequences, a team only makes one additional kick than their opposition, confirming that relative kick values are built across many sequences within a single match. In Chapter 6, the aim was to investigate whether differences in kicking tactics exist between winning and losing teams.Results identified that despite kicking more, the distribution across the field and kick types was similar between winning and losing teams. Winning teams benefited from improved sequence outcomes when they utilised kicks in the red zone of the field. Chapter 7 aimed to interpret the spatiotemporal characteristics of kicks utilising K - Means clustering. Four key clusters emerged, which can be contextualised into ′′fast′′ and ′′slow′′ contestable kicks, and ′′fast′′ and ′′slow′′ territorial kicks. These studies combine to give a holistic understanding of kicking performance at the match, sequence, and action level, which can inform technical, tactical and physical performance. E-Thesis Swansea University, Wales, UK Rugby Union, Data Analytics, Team Sport. 21 3 2025 2025-03-21 10.23889/SUThesis.69769 A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information. COLLEGE NANME COLLEGE CODE Swansea University Bezodis, N.E., Waldron, M., Church, S., Kilduff, L.P., and Brown, M.R. Doctoral Ph.D EPSRC, Ospreys Rugby EPSRC, Ospreys Rugby 2025-06-19T12:38:57.9642364 2025-06-19T12:23:41.1933132 Faculty of Science and Engineering School of Engineering and Applied Sciences - Sport and Exercise Sciences GEORGIA SCOTT 1 69769__34519__45f976830f804cc78c89839d01efd688.pdf 2025_Scott_G.final.69769.pdf 2025-06-19T12:32:58.8524476 Output 5975705 application/pdf E-Thesis – open access true Copyright, The author, Georgia Scott, 2025 true eng
title The Application of Data Analytics in Match and Kicking Performance in Elite Men’s Rugby Union
spellingShingle The Application of Data Analytics in Match and Kicking Performance in Elite Men’s Rugby Union
GEORGIA SCOTT
title_short The Application of Data Analytics in Match and Kicking Performance in Elite Men’s Rugby Union
title_full The Application of Data Analytics in Match and Kicking Performance in Elite Men’s Rugby Union
title_fullStr The Application of Data Analytics in Match and Kicking Performance in Elite Men’s Rugby Union
title_full_unstemmed The Application of Data Analytics in Match and Kicking Performance in Elite Men’s Rugby Union
title_sort The Application of Data Analytics in Match and Kicking Performance in Elite Men’s Rugby Union
author_id_str_mv b5738fa3f654dc5c5330e661b3420419
author_id_fullname_str_mv b5738fa3f654dc5c5330e661b3420419_***_GEORGIA SCOTT
author GEORGIA SCOTT
author2 GEORGIA SCOTT
format E-Thesis
publishDate 2025
institution Swansea University
doi_str_mv 10.23889/SUThesis.69769
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
document_store_str 1
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description Performance indicators are a key measure in analysing successful performance within Rugby Union. Their use has been documented in many different global competitions, including at the international level; however, research is scarce within the United Rugby Championship.There is a lack of general understanding of match performance at more detailed levels such as the sequence and action level across all competitions. Chapter 4 aimed to understand the associations between key performance indicators and match outcomes through the use of relative data and simplified modelling strategies. Results identified that increased relative kicking, metres made, and clean breaks, and decreased relative turnovers and scrum penalties conceded were associated with successful match outcomes. It was also established that relative data improved prediction accuracy, and simplification in model design did not degrade model accuracy. Chapter 5 aimed to interpret how relative kicking influences matches at the sequence level. This chapter established that in most sequences, a team only makes one additional kick than their opposition, confirming that relative kick values are built across many sequences within a single match. In Chapter 6, the aim was to investigate whether differences in kicking tactics exist between winning and losing teams.Results identified that despite kicking more, the distribution across the field and kick types was similar between winning and losing teams. Winning teams benefited from improved sequence outcomes when they utilised kicks in the red zone of the field. Chapter 7 aimed to interpret the spatiotemporal characteristics of kicks utilising K - Means clustering. Four key clusters emerged, which can be contextualised into ′′fast′′ and ′′slow′′ contestable kicks, and ′′fast′′ and ′′slow′′ territorial kicks. These studies combine to give a holistic understanding of kicking performance at the match, sequence, and action level, which can inform technical, tactical and physical performance.
published_date 2025-03-21T05:27:49Z
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score 11.089572

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