Who are the best passing players in professional soccer? A machine learning approach for classifying passes with different levels of difficulty and discriminating the best passing players.

The present study aimed to assess the use of technical-tactical variables and machine learning (ML) classifiers in the automatic classification of the passing difficulty (DP) level in soccer matches and to illustrate the use of the model with the best performance to distinguish the best passing players. We compared eight ML classifiers according to their accuracy performance in classifying passing events using 35 technical-tactical variables based on spatiotemporal data. The Support Vector Machine (SVM) algorithm achieved a balanced accuracy of 0.70 ± 0.04%, considering a multi-class classification. Next, we illustrate the use of the best-performing classifier in the assessment of players. In our study, 2,522 pass actions were classified by the SVM algorithm as low (53.9%), medium (23.6%), and high difficulty passes (22.5%). Furthermore, we used successful rates in low-DP, medium-DP, and high-DP as inputs for principal component analysis (PCA). The first principal component (PC1) showed a higher correlation with high-DP (0.80), followed by medium-DP (0.73), and low-DP accuracy (0.24). The PC1 scores were used to rank the best passing players. This information can be a very rich performance indication by ranking the best passing players and teams and can be applied in offensive sequences analysis and talent identification.

Classification and determinants of passing difficulty in soccer: a multivariate approach.

INTRODUCTION: Usually, the players' or teams' efficiency to perform passes is measured in terms of accuracy. The degree of difficulty of this action has been overlooked in the literature. OBJECTIVES: The present study aimed to classify the degree of passing difficulty in soccer matches and to identify and to discuss the variables that most explain the passing difficulty using spatiotemporal data. RESULTS: The data used corresponds to 2,856 passes and 32 independent variables. The Fisher Discriminant Analysis presented 72.0% of the original grouped cases classified correctly. The passes analyzed were classified as low (56.5%), medium (22.6%), and high difficulty (20.9%), and we identified 16 variables that best explain the degree of passing difficulty related to the passing receiver, ball trajectory, pitch position and passing player. CONCLUSIONS: The merit and ability of the player to perform passes with high difficulty should be valued and can be used to rank the best players and teams.In addition, the highlighted variables should be looked carefully by coaches when analyzing profiles, strengths and weaknesses of players and teams, and talent identification context. PRACTICAL IMPLICATIONS: The values found for each variable can be used as a reference for planning training, such as small side games, and in future research.

Relationship between children physical activity, inflammatory mediators and lymphocyte activation: possible impact of social isolation (COVID-19).

OBJECTIVE: Lifestyle and body composition may be simultaneously responsible for immune response modulation. This study aimed to compare plasmatic adipokines concentration and lymphocyte cytokine production in children with different daily steps (DS) range, as well as to discuss the potential negative impact of the social isolation during COVID-19 pandemic in this context. DS can be a useful and low-cost way of monitoring children's health status. STUDY DESIGN: Fifty children were classified into clusters based in DS measured by pedometer: Sedentary Group (DS = 9338 ± 902 steps) and Active Group (DS = 13,614 ± 1003 steps). Plasma and lymphocytes were isolated and cultured to evaluate cytokine production. RESULTS: Sedentary group presented lower adiponectin (7573 ± 232 pg/mL), higher leptin (16,250 ± 1825 pg/mL) plasma concentration, and higher lymphocyte production of IL-17, IFN-gamma, TNF-, IL-2 in relation to active group, suggesting predominance of Th1 response. Otherwise, the active group presented higher lymphocyte supernatant concentration of IL-10 and higher regulatory T cell (Treg) percentage. CONCLUSION: These results indicate that lymphocytes of children performing higher DS have an anti-inflammatory profile, especially of Treg. Besides, the prolonged social isolation in children during the COVID-19 pandemic, limiting physical mobility and exercise, reduces DS and increases adiposity, which could impair the immune system function and raise the susceptibility to inflammatory diseases.

Exploring the determinants of success in different clusters of ball possession sequences in soccer.

The purpose of this study was two-step: (1) classify ball possession (BP) according to the duration and number of passes and (2) identify which tactical variables most discriminate the different BP. We obtained 527 BPs from four official matches of the Brazilian Soccer Championship 2016. Forty-one "notational", "space occupation", and "displacement synchronization" predictor variables were used. The BPs were classified into three groups: short (11.07 ± 4.49 s, 1.93 ± 0.99 passes), medium (26.83 ± 7.33 s, 5.41 ± 1.84 passes), long (55.50 ± 14.97 s, 12.11 ± 4.61 passes). Discriminant analysis identified the five most relevant variables to describe each group: coefficient of variation (CV) of the defensive team's synchronization-Y, CV defensive team´s synchronization-X, successful pass last third, CV distance between offensive team's centroid and target, mean of the offensive team's width. The approach highlights important variables and could benefit the description of offensive and defensive game sequences to provide precise knowledge on the process.