A Longitudinal Study on the Evolution of the Four Main Football Leagues Using Artificial Intelligence: Analysis of the Differences in English Premier League Teams

Purpose: The aim of this study was to analyze the evolution of the four most important leagues and to identify if there are differences between the English Premier League and the rest of the European leagues. Methods: Each team was characterized according to a set of 52 variables including offensive, defensive, and buildup 10 variables that were computed from OPTA's on-ball event records of the matches for main national leagues between the 2014 and 2018 seasons. To test the evolution of leagues, the t-SNE dimensionality reduction technique was used. To better understand the differences between leagues and teams, the most discriminating variables were obtained as a set of rules discovered by RIPPER, a machine learning algorithm. Results: The evolution of playing styles has meant that teams in the major European leagues seem to 15 be approaching homogeneity of technical-tactical behavior. Despite this, a distinction can be seen between the English teams concerning the rest of the teams in the other leagues, determined by fewer free kicks, fewer long passes but more vertical, more errors in ball control but greater success in dribbling. Conclusions: These results provide important knowledge and practical applications because of the study of the different variables and performance indicators among the best football championships

Automated summary

The aim of this study was to analyze the evolution of the four most important leagues and identify differences between the English Premier League and other European leagues. The results showed that playing styles have become more homogeneous across the major European leagues, but there are still distinctions between English teams and teams from other leagues in terms of free kicks, long passes, ball control errors, and dribbling ability.