Explaining match outcome and ladder position in the National Rugby League using team performance indicators

Abstract

A unique combination of primarily attacking performance indicators provided the greatest explanation of match outcome and ladder position in the NRL. These results could be used by NRL coaches and analysts as a basis for the development of practice conditions and game strategies that may increase their teams' likelihood of success. Beyond rugby league, this study presents analytical techniques that could be applied to other sports when examining the relationships between performance indicators and match derivatives.

Game-play characteristics differ between the European Super League and the National Rugby League: Implications for coaching and talent recruitment

“…Performance indicators from each team participating in the 2016 NRL and ESL seasons were extracted from publically available sources (http://www.nrl.com/stats; http://www.rugbyleague.com/superleague/stats) ( Table 1). The indicators were chosen given their utility in the explanation of match outcome in RL [4], and availability within both competitions at the time of analysis. Seasonal averages of each performance indicator were used to account for the different lengths of each respective season.…”

“…The NRL, founded in 1998, consists of 16 teams (15 from Australia and one from New Zealand) who compete within a 26-week 'Premiership' season. The premise of this season is to rank teams on a ladder based on the number of wins they accrue, with the highest ranked eight teams at its conclusion then competing within a four-week finals series in an attempt to win the NRL Premiership [3,4]. The ESL, founded in 1996, consists of 12 teams (11 from England and one from France) who compete within a 23-week 'home and away' season.…”

Multifactorial analysis of factors influencing elite australian football match outcomes: a machine learning approach

“…A general solution is to exclude opposition data in the analysis in a long format with each game (and variable) being represented from the perspective of both teams (Leicht, Gomez, & Woods, 2017;Robertson, Gupta, & McIntosh, 2016;Robertson & Joyce, 2015;Woods, Sinclair, & Robertson, 2017). Nevertheless, this approach still presents challenges, as for any given game two different predictions can be made from the analysis (i.e., one for each team).…”

Using principal component analysis to develop performance indicators in professional rugby league

“…It has been conducted in various sports such as soccer (Castellano and Casamichana & Lago, 2012;Jones, James & Mellalieu, 2004;Lago-Peñas, Lago-Ballesteros & Rey, 2011), rugby union (Bishop & Barnes, 2013;Hughes, Hughes, Williams, James, Vučković & Locke, 2012;Villarejo, Palao, Ortega, Gomez-Ruano & Kraak, 2015) and basketball (Csataljay, O'Donoghue, Hughes & Dancs, 2009;Gómez, Lorenzo, Ibañez & Sampaio, 2013;Puente, Coso & Salinero, 2015). However, few papers have analysed performance indicators in rugby league (Parmar, James, Hughes, Jones and Hearne, 2017;Woods, Leicht, Jones and Till, 2018;Woods, Sinclair & Robertson, 2017).…”

“…Within rugby league, Woods et al (2017) analysed three hundred and seventysix team observations taken from a publicly available statistics website, using thirteen team performance indicators to assess their effect on match outcome and final league position in the 2016 Australian National Rugby League (NRL) using ordinal regression and conditional interference classification decision trees. Try assists, all run metres, offloads, line breaks and dummy half runs were retained within the classification tree detecting 66% of the losses and 91% of the wins.…”