Discovering Synergic Association by Feature Clustering from Soccer Players

Abstract

This study applies big data analysis techniques to analyze soccer managers' tactics and formations. For each playing position, the Boruta algorithm (a feature engineering algorithm) is applied to select the important features. K-means clustering was performed using the selected features, enabling the definition of the detailed roles of each position, such as holding midfielder and deep-lying playmaker. The analysis was conducted by dividing the CL (Champions League Level), EL (Europa League Level), ML (Middle Level) and RL (Relegation Level) to identify the differences in the tactics and formation patterns of the managers according to the level of opponent. Moreover, to include synergy between the players, weighted association rule mining was performed using the rating data as the weight to detect the strategy for each club. This implies that a manager establishes formations and tactics according to the level of the opponent.