Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2016 Aug 24;5(1):1410.
doi: 10.1186/s40064-016-3108-2. eCollection 2016.

Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science

Robert Rein  1 , Daniel Memmert  1
Affiliations
Review

Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science

Robert Rein et al. Springerplus. .

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.

Keywords: Big data; Deep learning; Machine learning; Neural networks; Quantified self; Simulation; Spatiotemporal data; Sports analytics; Sports performance.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Overview of factors influencing tactics in soccer
Fig. 2
Fig. 2
Big data technological stack for tactical analysis in elite soccer
See this image and copyright information in PMC

References

    1. Aguiar M, Goncalves B, Botelho G, Lemmink K, Sampaio J. Footballers’ movement behaviour during 2-, 3-, 4- and 5-a-side small-sided games. J Sports Sci. 2015;33(12):1259–1266. doi: 10.1080/02640414.2015.1022571. - DOI - PubMed
    1. Almeida CH, Duarte R, Volossovitch A, Ferreira AP. Scoring mode and age-related effects on youth soccer teams’ defensive performance during small-sided games. J Sports Sci. 2016;34(14):1355–1362. doi: 10.1080/02640414.2016.1150602. - DOI - PubMed
    1. Appelboom G, LoPresti M, Reginster JY, Sander Connolly E, Dumont EP. The quantified patient: a patient participatory culture. Curr Med Res Opin. 2014;30(12):2585–2587. doi: 10.1185/03007995.2014.954032. - DOI - PubMed
    1. Araújo D, Davids K, Hristovski R. The ecological dynamics of decision making in sport. Psychol Sport Exerc. 2006;7(6):653–676. doi: 10.1016/j.psychsport.2006.07.002. - DOI
    1. Baca A (2008) Tracking motion in sport—trends and limitations. Paper presented at the 9th Australasian conference on mathematics and computers in sport, Math Sport (ANZIAM)