Department of Science of Physical Activity and Sport, Catholic University of Valencia "San Vicente Mártir", 46900 Valencia, Spain.
Department of Social Psychology and Quantitative Psychology, University of Barcelona, 08001 Barcelona, Spain.
Int J Environ Res Public Health. 2021 Mar 19;18(6):3176. doi: 10.3390/ijerph18063176.
The use of principal component analysis (PCA) provides information about the main characteristics of teams, based on a set of indicators, instead of displaying individualized information for each of these indicators. In this work we have considered reducing an extensive data matrix to improve interpretation, using PCA. Subsequently, with new components and with multiple linear regression, we have carried out a comparative analysis between the best and bottom teams of LaLiga. The sample consisted of the matches corresponding to the 2015/16, 2016/17 and 2017/18 seasons. The results showed that the best teams were characterized and differentiated from bottom teams in the realization of a greater number of successful passes and in the execution of a greater number of dynamic offensive transitions. The bottom teams were characterized by executing more defensive than offensive actions, showing fewer number of goals and a greater ball possession time in the final third of the field. Goals, ball possession time in the final third of the field, number of effective shots and crosses are the main discriminating performance factors of football. This information allows us to increase knowledge about the key performance indicators (KPI) in football.
主成分分析（PCA）的使用提供了基于一组指标的团队主要特征的信息，而不是显示每个指标的个性化信息。在这项工作中，我们考虑使用 PCA 来简化大量数据矩阵以提高解释能力。随后，使用新的组件和多元线性回归，我们对西甲最佳和最差球队进行了对比分析。样本包括 2015/16、2016/17 和 2017/18 赛季的比赛。结果表明，最佳球队的特点是成功传球次数较多，执行动态进攻转换的次数也较多，而最差球队的特点是执行的防守动作多于进攻动作，进球数较少，在最后三分之一的场地上控球时间较长。进球、最后三分之一场地上的控球时间、有效射门次数和传中次数是足球比赛的主要区分表现因素。这些信息使我们能够增加对足球关键绩效指标（KPI）的了解。
