E-Thesis 444 views 1052 downloads
The Application of Data Analytics in Match and Kicking Performance in Elite Men’s Rugby Union / GEORGIA SCOTT
Swansea University Author: GEORGIA SCOTT
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...
| 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 |
| 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 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. |
|---|---|
| Item Description: |
A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information. |
| Keywords: |
Rugby Union, Data Analytics, Team Sport. |
| College: |
Faculty of Science and Engineering |
| Funders: |
EPSRC, Ospreys Rugby |