Exploring essential variables for successful and unsuccessful football teams in the "Big Five'' with multivariate supervised techniques


Abstract


Esta investigación propone técnicas multivariantes para descubrir las acciones de juego que contribuyen a la clasificación final de los equipos de fútbol. Este estudio utiliza datos pertenecientes a los equipos "Big Five" que compitieron en Primera División de la Bundesliga, Premier League, LaLiga, Ligue 1 y Serie A en la temporada 2018-2019. El análisis de componentes principales se utiliza para la detección de valores atípicos y para proporcionar una visión general preliminar. Las acciones de juego estadísticamente significativas de los equipos superior e inferior se estudiaron utilizando tres técnicas multivariadas supervisadas, a saber, el análisis discriminante de mínimos cuadrados parciales, el bosque aleatorio y la regresión logística. El modelo de análisis discriminante de mínimos cuadrados parciales identifica mejor las variables con la contribución estadísticamente más significativa para el éxito o el fracaso de un equipo. Los resultados se compararon con los obtenidos utilizando pruebas univariadas de dos muestras (como la prueba t de Student o la prueba de Mann-Whitney), demostrando las ventajas de los enfoques multivariados sobre los enfoques univariados. Los resultados indican que los mejores equipos tienen potencia tanto ofensiva como defensiva, y destacan el alto número de acciones de ataque; en cambio, los colistas tienen defensas débiles y pocas acciones ofensivas.

DOI Code: 10.1285/i20705948v15n1p249

Keywords: multivariate methods, two-sample tests, partial least squares discriminant analysis (PLS-DA), random forest (RF), logistic regression (RL), game actions

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