The application of self-organising maps to performance analysis data in rugby union

Croft, Hayden; Lamb, Peter; Middlemas, Simon

doi:10.1080/24748668.2015.11868849

Cited by 19 publications

(12 citation statements)

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“…There are some notable studies that have explored the performance processes in rugby union. Recently, researchers have used clustering approaches to identify important patterns in match data associated with certain game outcomes [35,42]. These methods are useful for reducing large volumes of high-dimensional data to visualisable, lowdimensional output maps or identifying key playing patterns.…”

Section: Advancing Rugby Performance Analysismentioning

confidence: 99%

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Performance Analysis in Rugby Union: a Critical Systematic Review

et al. 2020

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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.

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Section: Advancing Rugby Performance Analysismentioning

confidence: 99%

“…A small number of studies have started to progress the field of performance analysis in rugby union [26,27,32,35,42]. However, compared to various other team sports, the field of dynamical systems analysis in rugby remains largely unexplored.…”

Section: Future Directionmentioning

confidence: 99%

Performance Analysis in Rugby Union: a Critical Systematic Review

et al. 2020

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“…This paper builds on earlier work (Croft et al, 2015;Lamb & Croft, 2016) by not only applying SOMs to a different sport, but also introducing a further development around coach focused variable selection and introducing a live dashboard to track a strategic finding from the SOM analysis. This process is one that other sports could adopt and provides an alternative to the traditional performance indicator approach first described by M. Hughes and Bartlett (2002).…”

Section: Resultsmentioning

confidence: 99%

“…Self-organising maps (SOMs) present an opportunity to characterise the highdimensional interaction between sports teams (see Croft, Lamb, & Middlemas, 2015;Lamb & Croft, 2016, for applications in rugby union). SOMs are a type of neural network useful for clustering and visualising high-dimensional information on a lowdimensional output map (Kohonen, 2013).…”

Section: Introductionmentioning

confidence: 99%

Using performance data to identify styles of play in netball: an alternative to performance indicators

Wilcox

Croft

Lamb

2017

International Journal of Performance Analysis in Sport

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The advent of sports technology has led to large, high-dimensional, performance data sets, which pose decision-making challenges for coaches and performance analysts. If large data sets are managed poorly inaccurate and biased decision-making may actually be enabled. This paper outlines a process for capturing, organising, and analysing a large performance data set in professional netball. 250 ANZ Championship matches, from the 2012-2015 seasons, where analysed. Self-organising maps and a k-means clustering algorithm were used to describe seven games-styles, which were used in a case study to devise a strategy for an upcoming opponent. The team implemented a centre-pass (CP) defence strategy based on the opponent's previous successful and unsuccessful performances. This strategy involved allowing the oppositions Wing-attack to receive the CP while allowing their Goal-attack to take the second pass. The strategy was monitored live by the coaches on a tablet computer via a custom-built dashboard, which tracks each component of the strategy. The process provides an alternative to use of conventional performance indicators and demonstrates a method for handling large high-dimensional performance data sets. Further work is needed to identify an ecologically valid method for variable selection.

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“…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. Sasaki, Yamamoto, Miyao, Katsuta, and Kono (2017) applied network centrality to identify tactical and leadership structures and to improve the description of complex passages of play at the 2015 RWC, while (Coughlan, Mountifield, Sharpe, & Mara, 2019) applied K-modes cluster analysis to identify particular patterns of play that led to tries in the 2018 Super Rugby season.…”

Section: Related Workmentioning

confidence: 99%

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

Bunker

Fujii

Hanada

et al. 2020

Preprint

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Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent sub-sequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad outcomes, which is often of greater interest to coaches. In this study, we apply a supervised sequential pattern mining algorithm called safe pattern pruning (SPP) to 490 labelled event sequences representing passages of play from one rugby team’s matches from the 2018 Japan Top League, and then evaluate the importance of the obtained sub-sequences to points-scoring outcomes. Linebreaks, successful lineouts, regained kicks in play, repeated phase-breakdown play, and failed opposition exit plays were identified as important patterns of play for the team scoring. When sequences were labelled with points scoring outcomes for the opposition teams, opposition team linebreaks, errors made by the team, opposition team lineouts, and repeated phase-breakdown play by the opposition team were identified as important patterns of play for the opposition team scoring. By virtue of its supervised nature and pruning properties, SPP obtained a greater variety of generally more sophisticated patterns than the well-known unsupervised PrefixSpan algorithm.

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The application of self-organising maps to performance analysis data in rugby union

Cited by 19 publications

References 1 publication

Performance Analysis in Rugby Union: a Critical Systematic Review

Performance Analysis in Rugby Union: a Critical Systematic Review

Using performance data to identify styles of play in netball: an alternative to performance indicators

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

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