Centre for Sport Research, Deakin University, Geelong, Australia.
School of Information Technology, Deakin University, Geelong, Australia.
J Sci Med Sport. 2019 Apr;22(4):467-471. doi: 10.1016/j.jsams.2018.09.235. Epub 2018 Oct 10.
To identify novel insights about performance in Australian Football (AF), by modelling the relationships between player actions and match outcomes. This study extends and improves on previous studies by utilising a wider range of performance indicators (PIs) and a longer time frame for the development of predictive models.
Observational.
Ninety-one team PIs from the 2001 to 2016 Australian Football League seasons were used as independent variables. The categorical Win-Loss and continuous Score Margin match outcome measures were used as dependent variables. Decision tree and Generalised Linear Models were created to describe the relationships between the values of the PIs and match outcome.
Decision tree models predicted Win-Loss and Score Margin with up to 88.9% and 70.3% accuracy, respectively. The Generalised Linear Models predicted Score Margin to within 6.8 points (RMSE) and Win-Loss with up to 95.1% accuracy. The PIs that are most predictive of match outcome include; Turnovers Forced score, Inside 50s per shot, Metres Gained and Time in Possession, all in their relative (to opposition) form. The decision trees illustrate how combinations of the values of these PIs are associated with match outcome, and they indicate target values for these PIs.
This work used a wider range of PIs and more historical data than previous reports and consequently demonstrated higher prediction accuracies and additional insights about important indicators of performance. The methods used in this work can be implemented by other sport analysts to generate further insights that support the strategic decision-making processes of coaches.
通过建立球员行为与比赛结果之间的关系模型，来确定澳大利亚足球（AF）比赛表现的新见解。本研究通过利用更广泛的绩效指标（PIs）和更长的时间框架来开发预测模型，对以往的研究进行了扩展和改进。
观察性研究。
使用 2001 年至 2016 年澳大利亚足球联赛赛季的 91 个团队 PI 作为自变量。分类胜负和连续得分差距的比赛结果测量作为因变量。创建决策树和广义线性模型来描述 PI 值与比赛结果之间的关系。
决策树模型预测胜负和得分差距的准确率分别高达 88.9%和 70.3%。广义线性模型预测得分差距的准确率为 6.8 分（RMSE），预测胜负的准确率高达 95.1%。最能预测比赛结果的 PI 包括：被迫失误得分、每射门进入 50 码次数、米数和控球时间，均以相对（对阵方）形式表示。决策树说明了这些 PI 值的组合如何与比赛结果相关联，并指出了这些 PI 值的目标值。
这项工作使用了比以往报告更广泛的 PI 和更多的历史数据，因此表现出更高的预测准确性和对重要绩效指标的额外见解。本工作中使用的方法可以被其他体育分析师实施，以生成支持教练战略决策过程的进一步见解。
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