Frutuoso G. M. Silva

The effects of large-sided soccer training games and pitch size manipulation on time–motion profile, spatial exploration and surface area: Tactical opportunities:

Analysis of the physical, technical and physiological variations induced through the use of different soccer game formats have been widely discussed. However, the coaching justification for the specific use of certain game formats based on individual and collective spatial awareness is unclear. As a result, the purpose of this study was to analyze 11 versus 11 game formats conducted across two pitch sizes (half-size: 54u2009mu2009×u200968u2009m vs full-size: 108u2009mu2009×u200968u2009m) to identify effects of time–motion profiles, individual exploration behavior and collective organization. A total of 10 amateur soccer players from the same team (23.39u2009±u20093.91u2009years old) participated in this study. Data position of the players was used to calculate the spatial exploration index and the surface area. Distances covered in different speeds were used to observe the time–motion profile. The full-size pitch dimensions significantly contributed to greater distances covered via running (3.86–5.52u2009mu2009s−1) and sprinting (>5.52u2009mu2009s−1). Total distance and number of sprints were also significantly greater in the full-size pitch as compared to the half-size pitch. The surface area covered by the team (half-size pitch: 431.83u2009m2 vs full-size pitch: 589.14u2009m2) was significantly larger in the full-size pitch condition. However, the reduced half-size pitch significantly contributed to a greater individual spatial exploration. Results of this study suggest that running and sprinting activities increase when large, full-size pitch dimensions are utilized. Smaller surface area half-size pitch contributes to a better exploration of the pitch measured by spatial exploration index while maintaining adequate surface area coverage by the team. In conclusion, the authors suggest that the small half-size pitch is more appropriate for low-intensity training sessions and field exploration for players in different positions. Alternatively, the large full-size pitch is more appropriate for greater physically demanding training sessions with players focused on positional tactical behavior.

Individual Metrics to Characterize the Players

The purpose of this chapter is to present the individual measures that can be computed in the uPATO software. Each measure will be presented with a definition and case-studies to discuss the data and how results can be interpreted. Time-motion profile (including distances at different speeds), Shannon Entropy, Longitudinal and Lateral Displacements to the goal and variability, Kolmogorov Entropy and Spatial Exploration Index will be presented and discussed in this chapter. The case studies presented involve two five-player teams in an SSG considering only the space of half pitch (68 m goal-to-goal and 52 m side-to-side) and another eleven-player team in a match considering the space of the entire field (106.744 m goal-to-goal and 66.611 m side-to-side) even though only playing in half pitch.

uPATO—Overview of the Application

This chapter presents an overview of uPATO application, which was designed mainly for network analysis applied to team sports. However, this tool can be used for any network that can be represented by an adjacency matrix (e.g., a computer network, a telecommunication network, or even a social network, etc.). Thus, a first module was developed to allow codify the network, which, in the case of team sports, is given by a matrix with the sequences of interactions between teammates (i.e., a digraph). But this tool was designed to support graphs and digraphs, weighted and unweighted. Team sports are a good example of the necessity for calculating metrics on weighted networks. uPATO was developed with the main objective of analyzing team sports, where weights represent the frequency of the interactions between players, providing fundamental information on the analysis of the team factor. It calculates metrics in both weighted and unweighted networks and separates metrics into three major categories: individual metrics, subgroup metrics, and team metrics. Beyond metrics that uPATO allows calculating, a representation module that allows visualizing the network was also developed (i.e., the digraph or graph, weighted or not) and some charts for the data were calculated. Besides, the uPATO tool has an additional module for processing geolocation data. Currently, some teams use GPS devices to have the position of the players during the match (e.g., FieldWiz and TraXports formats are supported). Thus, uPATO has a set of metrics based on geolocation data of the players. This new functionality extends the uPATO capacities for team sports analysis but also for other activities where GPS data is available. But, it does not consider yet the possibility of the ball with a GPS device. However, this additional module is out of the scope of this book but the metrics implemented are described in a previous publication [3].

Network Analysis Tools

This chapter introduces the concept of network analysis and presents a series of software tools developed with the objective of analyzing networks. This analysis is usually performed on a set of metrics calculated on the network or on a representation of the network. Most of the existing tools are not free or are limited to unweighted networks, ignoring, in the case of weighted networks, the weight of the edges that connect the nodes. For example, in team sports, we have weighted networks, where edges represent the interactions between team players and their weight its frequency.

uPATO—A Case Study

This chapter introduces an analysis performed on real data. All described metrics were calculated for both teams and representations were created, using the Web application uPATO. The conclusions obtained from the analysis are then presented.

uPATO—Individual Measures

This chapter contains a set of individual metrics that can be used to analyze the importance of each player in a team sport. The metrics were divided into two main categories: Centrality (Sect. 3.1) and Prestige (Sect. 3.2). Each metric includes a description of a possible interpretation of the metric, and the pseudocode to implement it. Each pseudocode describes the cases (unweighted graphs, unweighted digraphs, weighted graphs, or weighted digraphs) for which it can be used. When the description simply contains graph (or graphs), without any other specifier, it means that the pseudocode is valid for any of the four types of graphs. The included interpretation considers that the connections between the players are the passes performed between them.

uPATO—Collective Measures

A collection of metrics is presented in this chapter. This collection is categorized as meso-level (subgroup) or team metrics, depending on the scope. Each metric includes a description of a possible interpretation of the metric, and the pseudocode to implement it. Each pseudocode describes the cases for which it can be used (unweighted graphs, unweighted digraphs, weighted graphs, or weighted digraphs). When the description simply contains graph (or graphs), without any other specifier, it means that the pseudocode is valid for any of the four types of graphs. The included interpretation considers that the connections between the players are the passes performed between them.

Metrics to Measure the Center of the Team

This chapter discusses a set of metrics involving the Geometrical Center (or centroid) of one or both teams. Each section describes a different metric, including the associated formulae and definitions, representations if valid, and an interpretation on what the metric can convey the user. The following measures will be presented: Geometrical Center; Longitudinal and Lateral Inter-team Distances; Time Delay between teams’ movements and Coupling Strength. The case studies presented involve two five-player teams in an SSG considering only the space of half pitch (68 m goal-to-goal and 52 m side-to-side) and another eleven-player team in a match considering the space of the entire field (106.744 m goal-to-goal and 66.611 m side-to-side) even though only playing in half pitch.

Measuring the Dispersion of the Players

The purpose of this chapter is to introduce the concepts of dispersion in the aim of soccer analysis. A set of different measures have been proposed to identify the level of dispersion between teammates and between opponents. Based on that, a summary of the dispersion measures, definitions, interpretation and graphical visualization will be presented on this chapter. The measures of Stretch Index, Surface Area, Team Length and Team Width and lpwratio will be introduced throughout the chapter. The case studies presented involve two five-player teams in an SSG considering only the space of half pitch (68 m goal-to-goal and 52 m side-to-side) and another eleven-player team in a match considering the space of the entire field (106.744 m goal-to-goal and 66.611 m side-to-side) even though only playing in half pitch.

Brief Review About Computational Metrics Used in Team Sports

The purpose of this chapter is to analyze how position data have been used in the aim of match analysis. A brief related work will present the main measures and results that come from soccer analysis based on georeferencing. Individual measures that characterize the time-motion profile, tactical behavior, predictability, stability and spatial exploration of players will be discussed. Collective measures that represent the Geometrical Center and team’s dispersion will be also presented during this chapter. The main evidences that resulted from these measures will be briefly discussed.