توضیحات
ABSTRACT
Until recently tactical analysis in elite soccer were based on observational data using variables which discard most
contextual information. Analyses of team tactics require however detailed data from various sources including technical skill, individual physiological performance, and team formations among others to represent the complex processes underlying team tactical behavior. Accordingly, little is known about how these different factors influence team tactical behavior in elite soccer. In parts, this has also been due to the lack of available data. Increasingly however, detailed game logs obtained through next-generation tracking technologies in addition to physiological training data collected through novel miniature sensor technologies have become available for research. This leads however to the opposite problem where the shear amount of data becomes an obstacle in itself as methodological guidelines as well as theoretical modelling of tactical decision making in team sports is lacking. The present paper discusses how big data and modern machine learning technologies may help to address these issues and aid in developing a theoretical model for tactical decision making in team sports. As experience from medical applications show, significant organizational obstacles regarding data governance and access to technologies must be overcome first. The present work discusses these issues with respect to tactical analyses in elite soccer and propose a technological stack which aims to introduce big data technologies into elite soccer research. The proposed approach could also serve as a guideline for other sports science domains as increasing data size is becoming a wide-spread phenomenon
Tactics are a central component for success in modern elite soccer. Yet until recently, there have been few
detailed scientific investigations of team tactics. One reason in this regard has been the lack of available, relevant
data. With the development of advanced tracking technologies this situation has changed recently. Instead, now the amount of available data is becoming increasingly difficult to manage. In the present article we discuss how recent developments of big data technologies from industrial data analytics domains address these problems
Further, the present work provide an overview how big data technologies may provide new opportunities to
study tactical behavior in elite soccer and what future challengers lie ahead
Year : 2016
Publisher : Springer
By : Robert Rein and Daniel Memmert
File Information : English Language /13 Page/ Size : 819 K
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سال : 2016
ناشر :Springer
کاری از : Robert Rein and Daniel Memmert
اطلاعات فایل : زبان انگلیسی / 13 صفحه /حجم : 819 K
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