Characterizing different team sports using network analysis

Team sports are complex dynamic systems based on the frequent interaction of various players. Recently, social network analysis has been introduced to the study of sports dynamics in order to quantify the involvement of individual players in the interplay and to characterize the organizational proce...

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Bibliographic Details
Main Authors: Florian Korte (Author), Martin Lames (Author)
Format: Book
Published: Bern Open Publishing, 2018-04-01T00:00:00Z.
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042 |a dc 
100 1 0 |a Florian Korte  |e author 
700 1 0 |a  Martin Lames  |e author 
245 0 0 |a Characterizing different team sports using network analysis 
260 |b Bern Open Publishing,   |c 2018-04-01T00:00:00Z. 
500 |a 10.15203/CISS_2018.005 
500 |a 2414-6641 
520 |a Team sports are complex dynamic systems based on the frequent interaction of various players. Recently, social network analysis has been introduced to the study of sports dynamics in order to quantify the involvement of individual players in the interplay and to characterize the organizational processes used by teams. Nonetheless, only a limited set of team sports has been assessed to date, and the focus of most studies has been on the application of small sets of network metrics to a single sport. Our study aims at comparing the network patterns of different team sports in order to contribute to the understanding of their underlying nature. It considers three invasion games, namely professional matches from basketball, football and handball. By applying relevant centrality measures and minimum spanning trees a first comparison between the nature of interplay in various team sports is offered as well as a deeper understanding of the role of different tactical positions in each sport. The point guard in basketball, defensive midfielder in football and center in handball are identified as the most central tactical positions. Direct interplay is most balanced in football followed by basketball and handball. A visualization of the basic structure of interplay for each sport is achieved through minimum spanning trees. 
546 |a EN 
690 |a social network analysis 
690 |a team sports 
690 |a interaction matrices 
690 |a minimum spanning trees 
690 |a Sports 
690 |a GV557-1198.995 
690 |a Sports medicine 
690 |a RC1200-1245 
655 7 |a article  |2 local 
786 0 |n Current Issues in Sport Science, Vol 3 (2018) 
787 0 |n https://ciss-journal.org/article/view/7563 
787 0 |n https://doaj.org/toc/2414-6641 
856 4 1 |u https://doaj.org/article/8bc8057142ee4dbd8bde9a0e52b1e210  |z Connect to this object online.