Performance indicators that predict wins and losses in the United Rugby Championship - Scott, G.; Brown, M. R.; Kilduff, L. P.; Bezodis, N.; Waldron, M. (27th Annual Congress of the European College of Sport Science (ECSS), Sevilla, 30. Aug - 2. Sep 2022, 2022)
INTRODUCTION: In recent years, advanced data analytic techniques have been utilised to determine performance indicators in professional rugby union. Typically, these studies report 20+ key PI`s that collectively can be used to predict match outcome. Whilst, acknowledgement of the PI`s is strategically important, their high number can impede the deployment of actionable interventions by practitioners. No key research has investigated the use of variable selection methods to build and validate simplified models capable of predicting successful performances. Equally, there has also been no evidence of research within the United Rugby Championship (URC). Therefore, the primary aim of this study was to investigate team level PI`s associated with winning performances within the URC, through machine learning and feature selection methods. Secondary aims included to test differences between individual team PI`s (isolated data) and team PI`s relative to their opposition (relative data) and to examine whether variable selection can be used to simplify modelling processes.