Computational Metrics for Soccer Analysis: Connecting the dots: SpringerBriefs in Applied Sciences and Technology
Autor Filipe Manuel Clemente, João Bernardo Sequeiros, Acácio F.P.P. Correia, Frutuoso G. M Silva, Fernando Manuel Lourenço Martinsen Limba Engleză Paperback – 30 oct 2017
These metrics only require the bio-dimensional geo-referencing information from GPS or video-tracking systems to provide qualitative and quantitative information about the tactical behaviour of players and the inter-relationships between teammates and their opponents. Exercises, experimental cases and algorithms enable readers to fully comprehend how to compute these metrics, as well as introducing them to the ultimate performance analysis tool, which is the basis to run them on. The script to compute the metrics is presented in Python. The book is a valuable resource for professional analysts as well students and researchers in the field of sports analysis wanting to optimise the use of GPS trackers in soccer.
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Specificații
ISBN-13: 9783319590288
ISBN-10: 3319590286
Pagini: 79
Ilustrații: XIII, 79 p. 27 illus., 21 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.15 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seria SpringerBriefs in Applied Sciences and Technology
Locul publicării:Cham, Switzerland
ISBN-10: 3319590286
Pagini: 79
Ilustrații: XIII, 79 p. 27 illus., 21 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.15 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seria SpringerBriefs in Applied Sciences and Technology
Locul publicării:Cham, Switzerland
Cuprins
Brief review about computational metrics used in team sports.- How to use the dots to analyze the behavior and the collective organization.- Individual metrics to characterize the player’s behavior: variability in the trajectories and external load.- Metrics to measure the center of the team.- Metrics to measure the dispersion of the players.- Metrics to tactical analysis.- The use of computational metrics in case studies: from small-sided games to official 11 vs. 11.
Recenzii
“I truly believe that the information generated in this way should be highly valuable for soccer coaches, in particular to improve the tactical behavior of a team.” (Christian Meyer, SIAM Review, Vol. 61 (4), December, 2019)
Textul de pe ultima copertă
This book provides an account of the use of computational tactical metrics in improving sports analysis, in particular the use of Global Positioning System (GPS) data in soccer. As well as offering a practical perspective on collective behavioural analysis, it introduces the computational metrics available in the literature that allow readers to identify collective behaviour and patterns of play in team sports.
These metrics only require the bio-dimensional geo-referencing information from GPS or video-tracking systems to provide qualitative and quantitative information about the tactical behaviour of players and the inter-relationships between teammates and their opponents. Exercises, experimental cases and algorithms enable readers to fully comprehend how to compute these metrics, as well as introducing them to the ultimate performance analysis tool, which is the basis to run them on. The script to compute the metrics is presented in Python. The book is a valuable resource for professional analysts as well students and researchers in the field of sports analysis wanting to optimise the use of GPS trackers in soccer.
These metrics only require the bio-dimensional geo-referencing information from GPS or video-tracking systems to provide qualitative and quantitative information about the tactical behaviour of players and the inter-relationships between teammates and their opponents. Exercises, experimental cases and algorithms enable readers to fully comprehend how to compute these metrics, as well as introducing them to the ultimate performance analysis tool, which is the basis to run them on. The script to compute the metrics is presented in Python. The book is a valuable resource for professional analysts as well students and researchers in the field of sports analysis wanting to optimise the use of GPS trackers in soccer.
Caracteristici
Describes the best algorithms to measure collective behavior in soccer and other team sports Explains how computational metrics based on geo-referencing can be used to identify collective properties of the teams Clarifies the importance of using the dots to identify patterns Explores the metrics in experimental cases of soccer Includes supplementary material: sn.pub/extras