Performance Analysis in Rugby Union: a Critical Systematic Review

Abstract

Background: Performance analysis in rugby union has become an integral part of the coaching process. Although performance analysis research in rugby and data collection has progressed, the utility of the insights is not well understood. The primary objective of this review is to consider the current state of performance analysis research in professional rugby union and consider the utility of common methods of analysing performance and the applicability of these methods within professional coaching practice. Methods: SPORTDiscus electronic database was searched for relevant articles published between 1 January 1997 and 7 March 2019. Professional, male 15-aside rugby union studies that included relevant data on tactical and performance evaluation, and statistical compilation of time-motion analysis were included. Studies were categorised based on the main focus and each study was reviewed by assessing a number of factors such as context, opposition analysis, competition and sample size. Results: Forty-one studies met the inclusion criteria. The majority of these studies measured performance through the collection and analysis of performance indicators. The majority did not provide context relating to multiple confounding factors such as field location, match location and opposition information. Twenty-nine performance indicators differentiated between successful match outcomes; however, only eight were commonly shared across some studies. Five studies considered rugby union as a dynamical system; however, these studies were limited in analysing lower or national-level competitions. Conclusions: The review highlighted the issues associated with assessing isolated measures of performance, lacking contextual information such as the opposition, match location, period within match and field location. A small number of studies have assessed rugby union performance through a dynamical systems lens, identifying successful characteristics in collective behaviour patterns in attacking phases. Performance analysis in international rugby union can be advanced by adopting these approaches in addition to methods currently adopted in other team sports.

Sanctioning of breakdown infringements during the knockout stage of the 2019 rugby world cup

“…Players are required to follow the laws of the game assigned by World Rugby, the global governing body for rugby union, put in place to allow players to play within the spirit of the game and more importantly, to protect the welfare of players. 4 As rugby union is a relatively ‘new’ sport, turning professional in 1995, the game is constantly evolving to make the sport safer for participants. This encourages World Rugby to review the laws regularly and make rule amendments where they see fit.…”

Training Design, Performance Analysis, and Talent Identification—A Systematic Review about the Most Relevant Variables through the Principal Component Analysis in Soccer, Basketball, and Rugby

“…Similar to the tactical dimension, the most relevant technical variables were not extracted. Therefore, although some actions such as collisions/tackle and passes are highly considered in rugby [ 66 , 67 ], PCA is necessary to extract the most critical information from technical actions.…”

“…This task could be based on worst-case scenarios to ensure players’ performance in these critical situations [ 66 ]. In these cases, high-speed running efforts and the importance of tackle success is important [ 67 ]. As a consequence of the high-frequency collisions, especially the head/face collisions, complementary training programs are essential to reduce injury incidence [ 68 , 69 ].…”

Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union

“…M. T. Hughes et al (2012) suggested that given rugby is a complex, dynamic and interactive sport, the analysis of frequencies alone (e.g., through PIs) cannot be expected to solve difficult multivariate problems. The necessity for more advanced methods in rugby union was also highlighted by Watson et al (2017) and Colomer, Pyne, Mooney, McKune, and Serpell (2020), who suggested that methods from dynamical systems, machine learning, social network analysis, interpersonal distance and group behaviour, which have been applied in basketball and soccer, remain largely unused in rugby. Machine learning models have been used for the prediction of results in rugby (Mosey & Mitchell, 2019;O'Donoghue & Williams, 2004;O'Donoghue, Ball, Eustace, McFarlan, & Nisotaki, 2016;Reed & O'Donoghue, 2005), while Croft, Lamb, and Middlemas (2015) and Lamb and Croft (2016) used Self-Organising Maps (Kohonen, 1997) to identify important PIs and effective playing styles in New Zealand provincial rugby.…”