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How they scored the tries: applying cluster analysis to identify playing patterns that lead to tries in super rugby

Author

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  • Molly Coughlan
  • Charles Mountifield
  • Stirling Sharpe
  • Jocelyn K. Mara

Abstract

Rugby union is a complex sport, involving the interaction of a number of behaviours which combine to form overall playing patterns. Previous research that has analysed key characteristics of scoring tries in rugby union has tended to report single variables in isolation from other related behaviours. The aim of this study was to identify patterns of play that lead to scoring tries in rugby union, with the purpose of demonstrating a holistic and process-oriented approach to analysing multiple performance variables simultaneously. Data related to the match context and events leading to tries were collected. K-modes cluster analysis was used to conduct a multidimensional data analysis and identify common playing patterns that preceded a try. It was found that plays following line-outs, scrums and kick receipts were highlighted as common approaches to scoring tries. In particular, the line-out–maul combination commencing from the attacking 22-m zone was found to be the most prominent pattern identified from the cluster analysis. Coaches and analysts can use the information presented by the cluster analysis as a framework to plan and implement effective set-play and open-play strategies.

Suggested Citation

  • Molly Coughlan & Charles Mountifield & Stirling Sharpe & Jocelyn K. Mara, 2019. "How they scored the tries: applying cluster analysis to identify playing patterns that lead to tries in super rugby," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 19(3), pages 435-451, May.
    • Handle: RePEc:taf:rpanxx:v:19:y:2019:i:3:p:435-451
    DOI: 10.1080/24748668.2019.1617018
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    Cited by:

    1. Rory Bunker & Keisuke Fujii & Hiroyuki Hanada & Ichiro Takeuchi, 2021. "Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-19, September.
    2. Alexandru Nicolae Ungureanu & Corrado Lupo & Paolo Riccardo Brustio, 2021. "A Machine Learning Approach to Analyze Home Advantage during COVID-19 Pandemic Period with Regards to Margin of Victory and to Different Tournaments in Professional Rugby Union Competitions," IJERPH, MDPI, vol. 18(23), pages 1-8, December.

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