Current Approaches to Tactical Performance Analyses in Soccer Using Position Data

Abstract

Tactical match performance depends on the quality of actions of individual players or teams in space and time during match-play in order to be successful. Technological innovations have led to new possibilities to capture accurate spatio-temporal information of all players and unravel the dynamics and complexity of soccer matches. The main aim of this article is to give an overview of the current state of development of the analysis of position data in soccer. Based on the same single set of position data of a high-level 11 versus 11 match (Bayern Munich against FC Barcelona) three different promising approaches from the perspective of dynamic systems and neural networks will be presented: Tactical performance analysis revealed inter-player coordination, inter-team and inter-line coordination before critical events, as well as team-team interaction and compactness coefficients. This could lead to a multi-disciplinary discussion on match analyses in sport science and new avenues for theoretical and practical implications in soccer.

Influencia de la longitud del campo en los comportamientos inter e intra -equipo en jóvenes jugadores de fútbol

“…Advances in technology and access to global positioning system (GPS) tracking devices have enabled the collection of large volumes of training data (Castellano, Silva, Usabiaga & Barreira, 2016;Travassos, Vilar, Araújo, & McGarry, 2014b) and expanded the possibilities for interpreting team performance in 11-a-side soccer (Memmert, Lemmink, & Sampaio, 2016). Moreover, numerous studies have shown how the use of task constraints in training situations leads to changes in indicators of collective performance.…”

A Gaussian mixture clustering model for characterizing football players using the EA Sports' FIFA video game system. [Modelo basado en agrupamiento de mixturas Gaussianas para caracterizar futbolistas utilizando el sistema de videojuegos FIFA de EA Sports].

“…The proposed methodology on this paper can be extended in order to describe football teams and also for performing more general tactical analyses (Memmert, Lemmink, & Sampaio, 2017). Teams could be clustered according to previously well-defined indicators of performance (Cintia, Giannotti, Pappalardo, Pedreschi, & Malvaldi, 2015), which allow to find characteristics associated to similar football teams, for example, with they play style (Gyarmati, Kwak, & Rodriguez, 2014) or even to predict and explain match outcomes (Spencer, Morgan, Zeleznikow, & Robertson, 2016).…”

A Pilot Study on Offensive Success in Soccer Based on Space and Ball Control – Key Performance Indicators and Key to Understand Game Dynamics

“…There is a large number of such quite simple indicators (for a recent overview: Memmert, Lemmink, & Sampaio, 2017) They all have in common that they do not say much about the playing process, the tactical quality, or the success of actions in defence or offence (Memmert et al, 2017). Different from these indicators, the presented approach tries to adapt "success" directly to the process dynamics, measuring it in terms of economics.…”