Applied Sports Technology Exercise and Medicine Research Centre (A-STEM), Faculty of Science and Engineering, Swansea University, Swansea,United Kingdom.
Welsh Institute of Performance Science (WIPS), Swansea University, Swansea,United Kingdom.
Int J Sports Physiol Perform. 2023 Aug 18;18(9):1072-1078. doi: 10.1123/ijspp.2023-0086. Print 2023 Sep 1.
The efficacy of isolated and relative performance indicators (PIs) has been compared in rugby union; the latter more effective at discerning match outcomes. However, this methodology has not been applied in women's rugby. The aim of this study was to identify PIs that maximize prediction accuracy of match outcome, from isolated and relative data sets, in women's rugby union.
Twenty-six PIs were selected from 110 women's international rugby matches between 2017 and 2022 to form an isolated data set, with relative data sets determined by subtracting corresponding opposition PIs. Random forest classification was completed on both data sets, and feature selection and importance were used to simplify models and interpret key PIs. Models were used in prediction on the 2021 World Cup to evaluate performance on unseen data.
The isolated full model correctly classified 75% of outcomes (CI, 65%-82%), whereas the relative full model correctly classified 78% (CI, 69%-86%). Reduced respective models correctly classified 74% (CI, 65%-82%) and 76% (CI, 67%-84%). Reduced models correctly predicted 100% and 96% of outcomes for isolated and relative test data sets, respectively. No significant difference in accuracy was found between data sets. In the relative reduced model, meters made, clean breaks, missed tackles, lineouts lost, carries, and kicks from hand were significant.
Increased relative meters made, clean breaks, carries, and kicks from hand and decreased relative missed tackles and lineouts lost were associated with success. This information can be utilized to inform physical and tactical preparation and direct physiological studies in women's rugby.
在橄榄球联盟中比较了孤立和相对绩效指标（PIs）的功效；后者更能准确区分比赛结果。然而，这种方法尚未应用于女子橄榄球。本研究旨在确定在女子橄榄球联盟中，从孤立和相对数据集最大程度提高比赛结果预测准确性的 PIs。
从 2017 年至 2022 年的 26 场女子国际橄榄球比赛中选择了 26 个 PIs，组成了一个孤立数据集，通过减去相应的对手 PIs 来确定相对数据集。对两个数据集都完成了随机森林分类，并使用特征选择和重要性来简化模型并解释关键 PIs。在 2021 年世界杯上使用模型进行预测，以评估在未见数据上的性能。
孤立的完整模型正确分类了 75%的结果（CI，65%-82%），而相对完整模型正确分类了 78%（CI，69%-86%）。相应的简化模型分别正确分类了 74%（CI，65%-82%）和 76%（CI，67%-84%）。简化模型正确预测了孤立和相对测试数据集的 100%和 96%的结果。两个数据集之间的准确性没有显著差异。在相对简化模型中，米数、干净突破、漏接、失去边线、携带球和手抛球都是重要的因素。
相对米数、干净突破、携带球和手抛球的增加以及相对漏接和失去边线的减少与成功相关。这些信息可用于为女子橄榄球提供物理和战术准备，并指导生理学研究。
