What Is the Relevance in the Passing Action between the Passer and the Receiver in Soccer? Study of Elite Soccer in La Liga

Analysis of player speed and angle toward the ball in soccer

The study analyzes how the magnitude and angle of the speed of soccer players change according to the distance to the ball and the phases of the game, namely the defensive and attacking phases. We observed how the role played in the team (goalkeeper, defender, midfielder, or forward) strongly determines the speed pattern of players. As a general trend, the speed's modulus is incremented as their position is closer to the ball, however, it is slightly decreased when arriving at it. Next, we studied how the angle of the speed with the direction to the ball is related to the distance to the ball and the game phases. We observed that, during the defensive phase, goalkeepers are the players that run more parallel to the ball, while forwards are the ones running more directly to the ball position. Importantly, this behavior changes dramatically during the attacking phase. Finally, we show how the proposed methodology can be used to analyze the speed-angle patterns of specific players to understand better how they move on the pitch according to the distance to the ball.

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.

Training Design, Performance Analysis, and Talent Identification-A Systematic Review about the Most Relevant Variables through the Principal Component Analysis in Soccer, Basketball, and Rugby

Since the accelerating development of technology applied to team sports and its subsequent high amount of information available, the need for data mining leads to the use of data reduction techniques such as Principal Component Analysis (PCA). This systematic review aims to identify determinant variables in soccer, basketball and rugby using exploratory factor analysis for, training design, performance analysis and talent identification. Three electronic databases (PubMed, Web of Science, SPORTDiscus) were systematically searched and 34 studies were finally included in the qualitative synthesis. Through PCA, data sets were reduced by 75.07%, and 3.9 ± 2.53 factors were retained that explained 80 ± 0.14% of the total variance. All team sports should be analyzed or trained based on the high level of aerobic capacity combined with adequate levels of power and strength to perform repeated high-intensity actions in a very short time, which differ between team sports. Accelerations and decelerations are mainly significant in soccer, jumps and landings are crucial in basketball, and impacts are primarily identified in rugby. Besides, from these team sports, primary information about different technical/tactical variables was extracted such as (a) soccer: occupied space, ball controls, passes, and shots; (b) basketball: throws, rebounds, and turnovers; or (c) rugby: possession game pace and team formation. Regarding talent identification, both anthropometrics and some physical capacity measures are relevant in soccer and basketball. Although overall, since these variables have been identified in different investigations, further studies should perform PCA on data sets that involve variables from different dimensions (technical, tactical, conditional).