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Technical determinants of success in professional women’s soccer: A wider range of variables reveals new insights

机译:专业女性足球成功的技术决定因素:更广泛的变量揭示了新的见解

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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.
机译:了解最佳技术性能的知识用于确定匹配策略和培训计划的设计。以前的男子足球研究已经确定了与成功有关的某些技术特征。然而,这些研究具有相对限制的样本尺寸或有限的性能指标范围,这可能限制了所使用的分析方法。妇女足球的研究和我们对最佳技术性能的理解,更有限(n = 3)。因此,本研究的目的是识别女性游戏中匹配结果的技术决定因素,并使用大型样本大小(n = 1390队演奏)和变量范围(n = 450)进行比较分析方法。使用了三种不同的分析方法(即技术性能变量的组合),一种数据驱动方法,理性方法以及基于男子足球的文献的方法。使用广义的线性建模和决策树,使用来自每个分析方法的变量建模匹配结果。发现理性和数据驱动方法优于预测匹配结果的文献驱动方法。匹配结果最强的决定因素是;第一个,故意协助相对于对手的助攻,守门员相对于对手的目标拍摄的百分比,相对于对手的目标和成功的二元的百分比射击。此外,Rational和Data-Drive的方法可以实现比关于男子足球的可比性研究更高的预测准确性。

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