Queensland Academy of Sport, Australia.
Queensland Academy of Sport, Australia.
J Sci Med Sport. 2020 Jan;23(1):35-40. doi: 10.1016/j.jsams.2019.08.014. Epub 2019 Aug 22.
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.
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.
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.
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.
本研究的主要目的是确定哪些关键绩效指标（PI）对次精英级别的橄榄球联盟的成功最为重要，以及分析绝对或相对数据集作为确定比赛结果的方法是否比另一种方法更强。
从 2018 年昆士兰总理橄榄球联盟赛季的 76 场比赛中的 17 个 PI 中获取数据。使用这些数据集基于胜负结果创建了一个随机森林分类模型。
随机森林模型将 73 场失利中的 53 场（72.6%）和 73 场胜利中的 53 场进行了分类，整体准确率为 72.6%。基于相对数据集的随机森林模型将 73 场失利中的 57 场（78.1%）和 73 场胜利中的 57 场进行了分类，整体准确率为 78.1%。McNemar 值 p=0.84 证实相对数据集模型并未优于绝对数据集。比赛结果与相对踢球次数、跑动米数、失误次数和初始干净突破次数之间存在正相关关系。
可以使用相对和绝对数据集来预测昆士兰总理橄榄球的结果，尽管绝对和相对数据集的使用差异并不像职业橄榄球那样显著。绝对和相对数据集可用于制定比赛策略和评估比赛表现。基于手抛球比赛和积累比对手更多的米数，同时在控球时尽量减少失误的比赛计划是成功的关键。
