Who are the prominent players in the UEFA champions league? : an approach based on network analysis

This study aimed to analyze the centrality levels of elite football players. Tactical positions and tactical line-ups were considered factors to be used in analyzing the variance in the prominence of players, measured by social network measures. The best 16 teams from the UEFA Champions league were analyzed during the entire competition. A total of 109 matches were analyzed for this study. Significant statistical differences between positions were found in % indegree (p = 0.001; ES = 0.268, moderate effect), % outdegree (p = 0.001; ES = 0.301, moderate effect) and % betweenness (p = 0.001; ES = 0.114, minimum effect). No statistical differences between tactical line-ups in % outdegree (p = 1.000; ES = 0.001, no effect) or % indegree (p = 1.000; ES = 0.001, no effect) were found. Central midfielders had the greatest values of centrality, thus confirming their importance in the linkage process of the team. Position had great influence on the centrality levels of players.info:eu-repo/semantics/publishedVersio

Towards a new method to analyze the soccer teams tactical behaviour : measuring the effective area of play

Recently, new tactical metrics have been developed to increase the match analysis’ potential. Naturally, innovate metrics need some updates in order to improve the utility to the soccer coaches. Thus, this paper aims to update the surface area metric, proposing the effective area of play given some efficacy information’s about team players’ positioning. Furthermore, aim analyzes the effective area of play of each team depending on the state of ball possession and a full match of 7-a-side soccer game in the district final was also analysed. Results showed an inverse correlation between teams’ opposite effective areas of play(rp = -0.681), suggesting the expansion-contraction relationship. Furthermore, was analyzed statistical differences with large effect between the moments with and without ball possession for the team A (F(1; 1506) = 1343.893; p-value ≤ 0.001; η2 = 0.472; Power = 1.000) and B (F(1; 1506) = 968.500; p-value ≤ 0.001; η2 = 0.391; Power = 1.000).info:eu-repo/semantics/publishedVersio

Outdoor play and interaction skills in early childhood education : approaching for measuring using social network analysis

Over the last decade, researches show evidences that the free play and the outdoor activities have positive impact on children, namely on social skills’ development as cooperation. However, the study of children interaction, especially when we want to identify and study a particular type of interaction, like cooperative interaction, requires a reflexion about the most efficient tolls and methods that can be used. With this article we intend to present a first reflective review of mathematical techniques and methods that can be potentially useful in the study of cooperative interactions with children in outdoor free play. Considering the characteristics of outdoor learning environments, which are based on freedom and free play exploitation of nature, the methods used in studies of this nature in indoor environment, tend to be more controlled by adults and more spatially limited, may not be the most effective. We intend to demonstrate that it is necessary to create a combination of mathematical techniques and methods based on the social network analysis, given the specificity of the educational outdoor environment or the space level and the dynamics of activities that there are usually generated.info:eu-repo/semantics/publishedVersio

Identifying the centrality levels of futsal players : a network approach

The aim of this study it was verify the differences of prominence levels between tactical positions in futsal (indoor football). For that reason, it was performed an analysis of variance between competitive levels and tactical positions for the centrality metrics computed by using network analysis. Forty-six futsal players from different competitive levels (U12, U14, U16 and Amateurs) it were analysed during three official futsal matches. Results revealed no differences in centrality metrics between competitive levels (p = 1.00; = 0.001; very small effect size) had no significant statistical differences in the centrality metrics. Nevertheless, tactical position (p = 0.001; = 0.593; moderate effect size) had significant main effects on the centrality metrics. Centrality metrics revealed that defenders are the most prominent players in to receive the ball. By the other hand, defenders and wings are the positions with greater centralities in to pass the ball for the teammates.info:eu-repo/semantics/publishedVersio

Development of sports network analysis : methodological considerations

The understanding of dynamic and complex systems requires a multi-directional approach toward the whole system. New mathematical approaches have been proposing new tools and techniques to understand the collective dynamics in team sports. Nevertheless, to ensure the quality of the techniques it should be considered the data collecting procedures. For that reason, the aim of this article is to suggest a set of methodological considerations to optimize the match analysis based on network.info:eu-repo/semantics/publishedVersio

Assessment of interactions at children playgrounds using network measures : an exploratory study based on graph theory

This study has used network measures to classify the children interactions in playground. The variance of network processes between genders was tested. Five girls and boys (n = 10; 4.6 0.6 years old) were observed. Statistical procedures has revealed significant differences between genders in IDC (p = 0.027; ES = 0.476; moderate effect) and BC (p = 0.011; ES = 0.576; moderate effect). Results revealed a greater cooperation process between boys.info:eu-repo/semantics/publishedVersio

Soccer teams behaviors : analysis if the team distribution in function to ball possession

Innovative tools to soccer analysis are the main concern of the sport performance analysts. Considering team players occupation as one of the fundamental characteristics of soccer success, is too important generate new systems to interpret the post-match reality. These systems must be easy for the coach's applications. Thus, this study proposes an easy tool to understand the team’s collective behaviour in function to their ball possession status. Through the histograms of all team players will be possible understand the most occupied areas by each team, trying understand possible tendencies in the moments with and without ball possession. Were analyzed two teams during an official soccer match, collecting their positions at each instant. Using this tracking was possible perform two heat maps by each team, representing the moments with and without ball possession. Through this method was possible analyze different collective behaviors and explain how could coaches interpret outcomes.info:eu-repo/semantics/publishedVersio

How team sports behave as a team? : general network metrics applied to sports analysis

The aim of this study was to analyse the general properties of networks in different team sports. Therefore, the analysis of variance to the general network properties between different team sports and different competitive levels was carried out. Sixty-six official matches (from Handball, Basketball, Football, Futsal, Rink-Hockey and Volleyball) were observed in five possible competitive levels (U12, U14, U16, U18 and Amateurs with more than 20 years). Analysis of variance revealed that the type of sport (p = 0.001; ��=0.647; moderate effect size) and competitive level(p = 0.001; �� = 0.355; small effect size)had significant statistical differences in the general network metrics. It was also found that football generates more connections between teammates but basketball and volleyball promote better results of density and clustering coefficient.info:eu-repo/semantics/publishedVersio

Who is the prominent tactical position in rink-hockey? : a network approach based on centrality metrics

The aim of this study was to verify the prominence levels of rink-hockey players in different competitive levels. For that reason, it was analysed the variance of network centrality metrics between competitive levels and tactical positions. Fifty-four rink-hockey players from five different levels (U12, U14, U16, U18 and Elite) were analysed during three official matches. The results did not found statistical differences in centrality levels of players between competitive levels (p-value = 1.00; partial eta square = 0.001; very small effect size). Nevertheless, tactical position (p-value = 0.001; partial eta square = 0.534; moderate effect size) had significant main effects on the centrality metrics. In this study it was found that defender and forward are the positions that most receive balls from the teammates. In other hand, the forward is the position that most passes performed until the U16 and in older levels the defender assumes the centrality in passes performed.info:eu-repo/semantics/publishedVersio

Are the prominent players the most accurate and efficient? : study in football players from different competitive levels

The aim of this study was to study the association between technical performance (volume of play, efficiency index and performance score) and tactical behaviour (indegree and outdegree centrality) in different competitive levels of football. Sixty-six male soccer players (U12 – 11.23 ± 0.3 years old and 2.11 ± 0.9 years of practice; U14 – 13.43 ± 0.8 years old and 3.76 ± 2.4 years of practice; U16 – 15.67 ± 0.7 years old and 5.32 ± 1.3 years of practice; U18 – 17.84 ± 1.1 years old and 9.21 ± 2.2 years of practice; Amateurs with more than 20 years old – 23.45 ± 4.2 years old and 11.12 ± 2.7 years of practice) were observed in three official matches. The indegree centrality showed a large positive correlation with performance score (r = 0.630; p = 0.001) and very large positive correlation with volume of play (r = 0.735; p = 0.001). The outdegree centrality showed large positive correlation with volume of play (r = 0.535; p = 0.001), efficiency index (r = 0.590; p = 0.001) and performance score (r = 0.669; p = 0.001). In conclusion, this study revealed that the prominence in network activity might be associated with the technical performance in the match.info:eu-repo/semantics/publishedVersio