¿La demanda presente en el juego es la misma para todos los equipos? Análisis de la temporada 2021-22 de la Bundesliga (Is the demand in the game the same for all teams? Analysis of the 2021-22 Bundesliga season)
DOI:
https://doi.org/10.47197/retos.v51.100429Palabras clave:
Análisis de Juego; Rendimiento; Análisis explicativo; Fútbol; Demandas del juego.Resumen
El objetivo es identificar el grado de explicación de la diferencia de goles para los diferentes equipos de la Bundesliga 2021-2022. Se recogieron un total de 306 partidos, 34 de los cuales fueron partidos de equipo. Entre las variables de bola, 15 son simples y 4 son compuestas. La organización de los datos ocurrió por equipo y en la competencia completa. Se aplicó la Regresión Lineal Múltiple como tratamiento estadístico en el modelo de regresión. El conjunto de variables explicativas se seleccionó identificando el valor R2 ajustado más alto y VIF menor a 10. Los grados de asociación apuntan valores altos en todos los escenarios (<0,750), y todos los equipos analizados específicamente muestran valores más altos cuando se comparan a plena competencia. Entre las variables identificadas como explicativas, los goles/saques de saque destacan en el 95% de los análisis. Los tiros desde dentro del área y los saques/pases se dan en el 63% de los análisis, destacando el bloqueo/patada al contrario (53%) y la defensa del portero (47%) son las acciones de momento defensivo. Se concluye que el conjunto de variables explicativas es propio de cada club, indicando así su singularidad en las exigencias del juego que enfrentan los equipos. También destaca la necesidad de un análisis de juego específico por equipo para caracterizar mejor las demandas del juego.
Palabras clave: Análisis de Juego; Rendimiento; Análisis explicativo; Fútbol; Demandas del juego.
Abstract. The objective is to identify the degree of explanation of goal difference for different teams in the 2021-2022 Bundesliga season. A total of 306 matches were collected, with 34 matches per team. Among the ball-related variables, there are 15 simple and 4 composite variables. The data organization was done by team and in the complete competition. Multiple Linear Regression with backward model was applied as a statistical treatment. The set of explanatory variables was selected by identifying the highest adjusted R-squared value and VIF less than 10. The association degrees indicate high values in all scenarios (<0.750), with all teams analyzed individually showing higher values compared to the complete competition. Among the variables identified as explanatory, goals/shots stands out in 95% of the analyses. Shots from inside the box and shots/pass occur in 63% of the analyses, while opponent's block/shot (53%) and goalkeeper's save (47%) are the highlights in defensive actions. It is concluded that the set of explanatory variables is specific to each club, indicating their uniqueness in the game demands faced by the teams. The need for a specific game analysis for each team is also emphasized in order to better characterize the game demands.
Keywords: Game Analysis; Performance; Explanatory Analysis; Football, Game demands.
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