Individual-based Creatine Kinase Reference Values in Response to Soccer Match-play

The aim of the present study was to determine the creatine kinase reference limits for professional soccer players based on their own normal post-match response. The creatine kinase concentration was analyzed in response to official matches in 25 players throughout a 3-year period. Samples were obtained between 36–43 hours following 70 professional soccer matches and corresponded to 19.1±12.1 [range: 6–49] samples per player. Absolute reference limits were calculated as 2.5th and 97.5th percentile of the samples collected. Creatine kinase values were also represented as a percentage change from the individual’s season mean and represented by 90th, 95th and 97.5th percentiles. The absolute reference limits for creatine kinase concentration calculated as 97.5th and 2.5th percentiles were 1480 U.L−1 and 115.8 U.L−1, respectively. The percentage change from the individual’s season mean was 97.45±35.92% and players were in the 90th, 95th and 97.5th percentiles when the percentages of these differences were 50.01, 66.7, and 71.34% higher than player’s season mean response, respectively. The data allowed us to determine whether the creatine kinase response is typical or if it is indicative of a higher than normal creatine kinase elevation and could be used as a practical guide for detection of muscle overload, following professional soccer match-play.

Machine Learning on Soccer Players Position

During the last few years, sports analytics has been growing rapidly. The main usage of this discipline is the prediction of soccer match results, even if it can be applied with interesting results in different areas, such as analysis based on the player position information. In this paper, we propose an approach aimed to recognize the player position in a soccer match, predicting the specific zone in which the player is located in a specific moment. Similar objectives have never been considered yet with our best knowledge. We consider supervised machine learning techniques by considering a dataset obtained through video capturing and tracking system. The data analyzed refer to several professional soccer games captured at the Alfheim Stadium in Tromso, Norway. The approach can be used in real-time, in order to verify if a player is playing according to the guidelines of the coach. In the experimental analysis, three different types of classification have been performed, i.e., three different divisions of the field, reaching the best results with Random Tree Algorithm.

Simulations of Nearest Teammate-Based Soccer Match-Plays with Different Formations

Formation in soccer is among the most important tactical choices for a successful match.Herein, the simulations of 420000 match-plays have been performed varying the formation, the number of opponents that are actively pressing the team, the speed of the opponents in attempting a pass interception. Dribbling has been neglected. The match-play ends either with a successful series of passes from a central back to the line of the strikers or with the opponents that steal the ball. In this work, I demonstrate that 3-4-3 formation, which is among the most employed formations, relates to the highest probability of success.