“…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. Recently, Watson, Hendricks, Stewart, and Durbach (2020) used convolutional and recurrent neural networks to predict the outcomes (territory gain, retaining possession, scoring a try, and conceding/being awarded a penalty) of sequences of play, based on event order and their on-field locations.…”