For example, a team could decide to have a slow buildup during attack initiate in the defense third where individual players hold the ball for longer times whereas in the attacking third only fast on-touch pass sequences are preferred. Time in contrast describes variables like frequency of events and durations (ball possession) or how quick actions are being initiated. In this context, space specifies for example were on the pitch a certain actions takes places or which area a team wants to occupy during the attack and the defense. Following a classical practitioner’s approaches the tactic specifies how a team manages space, time, and individual actions to win a game (Fradua et al. Furthermore, it is not clear how these two concepts can be clearly delineated from each other as the real-time interactions between the players will be conditioned by the a priori strategy. This approach seems somewhat counter to the basic definition of the term tactics provide above. Here, the team strategy describes the decisions made before the game with respect to how the team wants to play whereas the tactic is the result of the ongoing interactions between the two opposing teams. Regarding the definition of tactics Gréhaigne and Godbout ( 1995) introduced a distinction between the strategy and tactics. 2011 Sampaio and Macas 2012 Yiannakos and Armatas 2006). Choosing an appropriate tactic is therefore crucial for every pre-game preparation (Carling et al. Regarding competitive soccer, naturally the aim the end of the activity is to win the game. The proposed approach could also serve as a guideline for other sports science domains as increasing data size is becoming a wide-spread phenomenon.Īccording to the Oxford dictionary, tactics describe “an action or strategy carefully planned to achieve a specific end”. 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. As experience from medical applications show, significant organizational obstacles regarding data governance and access to technologies must be overcome first. 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. 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.

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. In parts, this has also been due to the lack of available data. Accordingly, little is known about how these different factors influence team tactical behavior in elite soccer.

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. Until recently tactical analysis in elite soccer were based on observational data using variables which discard most contextual information.

Soccer big tactical board