چكيده به لاتين
Big data analytics in today's world plays an imporatant role, because we can access the information that was not possible in the past. Since the volume of data is increasing every day, it is necessary to think intellectually to analyze them and access the important patterns in which they are hidden. To use the data, you have to analyze it. Data mining can be considered as one of the most important methodologies and methods of data analysis. Data mining is now taught in the world's leading universities as data science. Those who will study in this field must be proficient in programming skills, mathematics, statistics, intelligence, etc, in order to be able to analyze the data and to reach meaningful and valuable patterns from incomprehensible data. According to human tools, technology and expertise, the sport industry is also facing a lot of data and methods of data mining used in the sport in order to achieve various goals such as predicting the outcome of competitions, evaluating the performance of players, marketing, talent identification and so on. The aim of this study is to evaluate the performance of team sports players using network analysis methods, graph concepts and its criteria. After reviewing the articles and identifying the research gap, we used the data that provided by Wyscout, which offers researchers with accurate information about the characteristics of football players and their coordinates on the football pitch. By implementing the network's closeness and betweenness criteria as well as the generalized linear model, we analyzed the France-Croatia competition at the 2018 FIFA World Cup. It is hoped that this study will pave the way for researchers.
