Centre for Sport Research, Deakin University, Geelong, Australia.
Sport and Exercise Science, La Trobe University, Melbourne, Australia.
PLoS One. 2020 Oct 22;15(10):e0240992. doi: 10.1371/journal.pone.0240992. eCollection 2020.
Knowledge of optimal technical performance is used to determine match strategy and the design of training programs. Previous studies in men's soccer have identified certain technical characteristics that are related to success. These studies however, have relative limited sample sizes or limited ranges of performance indicators, which may have limited the analytical approaches that were used. Research in women's soccer and our understanding of optimal technical performance, is even more limited (n = 3). Therefore, the aim of this study was to identify technical determinants of match outcome in the women's game and to compare analytical approaches using a large sample size (n = 1390 team performances) and range of variables (n = 450). Three different analytical approaches (i.e. combinations of technical performance variables) were used, a data-driven approach, a rational approach and an approach based on the literature in men's soccer. Match outcome was modelled using variables from each analytical approach, using generalised linear modelling and decision trees. It was found that the rational and data-driven approaches outperformed the literature-driven approach in predicting match outcome. The strongest determinants of match outcome were; scoring first, intentional assists relative to the opponent, the percentage of shots on goal saved by the goalkeeper relative to the opponent, shots on goal relative to the opponent and the percentage of duels that are successful. Moreover the rational and data-driven approach achieved higher prediction accuracies than comparable studies about men's soccer.
对最佳技术表现的了解用于确定比赛策略和训练计划的设计。之前的男子足球研究已经确定了与成功相关的某些技术特征。然而,这些研究的样本量相对较小或性能指标的范围有限,这可能限制了所使用的分析方法。女子足球的研究和我们对最佳技术表现的理解甚至更加有限(n = 3)。因此,本研究的目的是确定女子比赛中比赛结果的技术决定因素,并使用较大的样本量(n = 1390 支球队表现)和较多的变量范围(n = 450)比较分析方法。使用三种不同的分析方法(即技术性能变量的组合),一种是数据驱动方法,一种是理性方法,另一种是基于男子足球文献的方法。使用广义线性模型和决策树,根据每个分析方法的变量对比赛结果进行建模。结果发现,理性和数据驱动的方法在预测比赛结果方面优于文献驱动的方法。比赛结果的最强决定因素是:先得分、相对于对手的故意助攻、守门员相对于对手的射门得分率、射门得分率和成功决斗的百分比。此外,理性和数据驱动的方法比关于男子足球的可比研究具有更高的预测准确性。