Tim J Mosey1, Lachlan J G Mitchell2. 1. Queensland Academy of Sport, Australia. Electronic address: tim.mosey@npsr.qld.gov.au. 2. Queensland Academy of Sport, Australia.
Abstract
OBJECTIVES: The primary aim of this study was to determine which key performance indicators (PIs) were most important to success in sub-elite rugby union, and whether the analysis of absolute or relative data sets as a method for determining match outcome was stronger than the other. METHODS: Data was taken from 17 PIs from 76 matches across the 2018 Queensland Premier Rugby Union season. A random forest classification model was created using these data sets based on win/loss outcomes. RESULTS: The randomForest model classified 53 from 73 losses (72.6%) and 53 from 73 wins for an overall percentage accuracy of 72.6%. The randomForest model based on the relative data set classified 57 from 73 losses (78.1%) and 57 from 73 wins for an overall percentage accuracy of 78.1%. McNemar's value of p=0.84 confirmed that the relative data model did not outperform the absolute data set. There were positive associations between match outcome and relative number of kicks in play, meters carried, turnovers conceded and initial clean breaks. CONCLUSIONS: Outcomes in Queensland Premier Rugby can be predicted using relative and absolute data sets, though the difference between absolute and relative set usage was not as substantial as in professional rugby. Absolute and relative data sets can be used to create match strategies and assess match performance. A game plan based around an out of hand kicking game and accumulating more metres than the opposition, whilst minimising turnovers when in possession were key to success.
OBJECTIVES: The primary aim of this study was to determine which key performance indicators (PIs) were most important to success in sub-elite rugby union, and whether the analysis of absolute or relative data sets as a method for determining match outcome was stronger than the other. METHODS: Data was taken from 17 PIs from 76 matches across the 2018 Queensland Premier Rugby Union season. A random forest classification model was created using these data sets based on win/loss outcomes. RESULTS: The randomForest model classified 53 from 73 losses (72.6%) and 53 from 73 wins for an overall percentage accuracy of 72.6%. The randomForest model based on the relative data set classified 57 from 73 losses (78.1%) and 57 from 73 wins for an overall percentage accuracy of 78.1%. McNemar's value of p=0.84 confirmed that the relative data model did not outperform the absolute data set. There were positive associations between match outcome and relative number of kicks in play, meters carried, turnovers conceded and initial clean breaks. CONCLUSIONS: Outcomes in Queensland Premier Rugby can be predicted using relative and absolute data sets, though the difference between absolute and relative set usage was not as substantial as in professional rugby. Absolute and relative data sets can be used to create match strategies and assess match performance. A game plan based around an out of hand kicking game and accumulating more metres than the opposition, whilst minimising turnovers when in possession were key to success.