RESUMEN
The aims of this study were to create a composite index to measure the overall players' physical performance in professional soccer matches and analyze the effect of individual playing time and positional differences on this composite index. A total of 830 official matches from LaLiga men's first division and Spanish Copa del Rey were analyzed, which resulted in 24,980 match observations collected from 1138 male players (forwards, n = 286; midfielders, n = 441; defenders, n = 411). The physical performance variables, which represent the locomotor demands, were collected using electronic performance tracking systems. A Partial Least-Squares Structural Equation Model (PLS-SEM) was used to measure performance. The PLS-SEM output had three significant latent components, which explained 95% of the initial variability, that were related to the acceleration-specific performance (component 1), high-intensity running-related variables (component 2), and medium intensity actions variables (component 3). Also, a linear regression analysis was used to explore relationships between playing activity time (hours-X axis) and the composite index (10-point scale-Y axis), in which a strong and positive correlation was observed between individual playing time and the composite index (r = 0.76; p < 0.001; R2 = 0.58). Also, significant positive correlations were observed in forwards (r = 0.85; p < 0.001; R2 = 0.74), midfielders (r = 0.80; p < 0.001; R2 = 0.64), and defenders (r = 0.67; p < 0.001; R2 = 0.45). However, significant differences between playing positions with a small effect size (p < 0.05; eta-squared = 0.01) were found. From a practical perspective, this study may serve as a reference for sports performance practitioners to create a composite index that measures the overall players' physical performance. The instructions to create this index are available in the manuscript.
Asunto(s)
Rendimiento Atlético , Fútbol , Fútbol/fisiología , Humanos , Masculino , Rendimiento Atlético/fisiología , Carrera/fisiología , Adulto , Rendimiento Físico Funcional , AtletasRESUMEN
Nowadays, data science is applied in several areas of daily life. There have been many applications to sports. In this context, the attention will be focused on football (i.e. 'soccer' for Americans): the making of strategic choices, whether by the scouting department of the football club, or the technical staff, up to the management, is crucial. It has been measured and monitored football players' performance in the season 2018/2019, for the top five European Leagues, using data provided by Electronic Arts (EA) experts and available on the Kaggle data science platform. For this purpose, with the help of football experts, a third-order partial least-squares path model (PLS-PM) approach was adopted to the sofifa key performance indices in order to compute a composite indicator differentiated by role and compare it with the well-known overall indicator from EA Sports. It has been taken into account players' observed heterogeneity (i.e. roles and leagues), since often experts refer to differences in these features, and so the objective is to verify their importance scientifically. The results are very consistent with this because they underline how some sub-areas of performance have different significance weights depending on the role.