Hypernetworks Reveal Compound Variables That Capture Cooperative and Competitive Interactions in a Soccer Match (original) (raw)
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Sports Medicine
Despite its importance in many academic fields, traditional scientific methodologies struggle to cope with analysis of interactions in many complex adaptive systems, including sports teams. Inherent features of such systems (e.g., emergent behaviours) require a more holistic approach to measurement and analysis for understanding system properties. Complexity sciences encompass a holistic approach to research on collective adaptive systems, which integrates concepts and tools from other theories and methods (e.g., ecological dynamics and social network analysis) to explain functioning of such systems in natural performance environments. Multilevel networks, such as hypernetworks, comprise novel and potent methodological tools for assessing team dynamics at more sophisticated levels of analysis, increasing their potential to impact on understanding of competitive performance. Here, we discuss the potential of concepts and tools derived from studies of multilevel networks for revealing key properties of sports teams as complex, adaptive social systems. This type of analysis can provide valuable information on team performance, which can be used by coaches, sport scientists and performance analysts for enhancing practice and training. We examine the relevance of network sciences, as a sub-discipline of complexity sciences, for studying dynamics of relational structures in sports teams during practice and competition. We explore benefits of implementing multilevel networks, in contrast to traditional network techniques, highlighting future research opportunities. We conclude by recommending methods for enhancing applicability of hypernetworks in analysing collective dynamics at multiple levels.
2020
BACKGROUND: This paper presents an introduction and brief appraisal of the use of hyper networks metrics and its potential practical application in examining team dynamics' coordination patterns collective sports. AIM: Throughout their critique piece, we highlighted that game analysis, including the hyper network concept, may help overcome the limitations of previous tools such as social network measures. FINDINGS AND CONCLUSIONS: While the social network analysis generally considers only dyadic interactions (e.g., between two players), the hyper networks also take into account a multidimensional perspective, including both player level and team level communication and coordination. We also evidenced that new studies using hyper network metrics are required in a range of team sports, mainly using data gathered from official competition matches.
A multilevel hypernetworks approach to capture meso-level synchronisation processes in football
Journal of Sports Sciences
Understanding team behaviours in competitive sports performance requires a robust understanding of the interdependencies established between their levels of complexity in organisation (micro-meso-macro). Previously, most studies have tended to examine interactions emerging at micro-and macro-levels, thus neglecting those emerging at a meso-level (a level which reveals connections between the micro and macro levels, depicted by the emergence of coordination in specific subgroups of players during performance). We addressed this issue using the multilevel hypernetworks approach, adopting a cluster phase method, to record player-simplice local synchronies in two performance conditions where the number, size and location of goal scoring targets were manipulated (1st-condition: 6x6+4 mini-goals; 2nd-condition: Gk+6x6+Gk). We investigated meso-level coordination tendenciesas a function of ball-possession (attacking/defending), field-direction (longitudinal/lateral) and teams (Team A/Team B). Univariate Anova was used to assess the cluster amplitude mean values that emerged between game conditions, as a function of ball-possession, field-direction and team composition. Generally, large synergistic relations and more stable patterns of coordination were observed in the longitudinal direction of the field than the lateral direction for both teams, and for both game phases in the first condition. The second condition displayed higher synchronies and more stable patterns in the lateral direction than the longitudinal plane for both teams, and for both game phases. Results suggest: (i) usefulness of hypernetworks in assessing synchronisation of teams at a meso-level; (ii) coaches may consider manipulating the number, location and 3 size of goals to develop levels of local tendencies for emergent synchronies within teams.
Towards Quantifying Interaction Networks in a Football Match
We present several novel methods quantifying dynamic interactions in simulated football games. These interactions are captured in directed networks that represent significant coupled dynamics, detected information-theoretically. The model-free approach measures information dynamics of both pair-wise players' interactions as well as local tactical contests produced during RoboCup 2D Simulation League games. This analysis involves computation of information transfer and storage, relating the information transfer to responsiveness of the players and the team, and the information storage within the team to the team's rigidity and lack of tactical flexibility. The resultant directed networks (interaction diagrams) and the measures of responsiveness and rigidity reveal implicit interactions, across teams, that may be delayed and/or long-ranged. The analysis was verified with a number of experiments, identifying the zones of the most intense competition and the extent of interactions.
Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology
Social network analysis (SNA) has been increasingly applied to performance analytics in team sports, seeking to better understand the dynamic properties of competitive interactions. Despite considerable potential to analyze individual (micro) and team (macro) behavioral patterns of play, there are important limitations that can undermine the potential applicability of SNA. One important limitation in existing research is the lack of network analyses of defensive interactions, curtailing understanding of the functionality and adaptability of teams during competitive performance. This study developed an innovative network method for assessing interactions between players in defensive phases of play in football. The networking method was evaluated using a small-sided and conditioned game (SSCG; GK+7v7+GK) of 20 min duration (two halves of 10 min each, interspersed by 5 min intervals of active recovery). The method traced interactions between groups of three players (effective defensive...
Common and Unique Network Dynamics in Football Games
PLoS ONE, 2011
The sport of football is played between two teams of eleven players each using a spherical ball. Each team strives to score by driving the ball into the opposing goal as the result of skillful interactions among players. Football can be regarded from the network perspective as a competitive relationship between two cooperative networks with a dynamic network topology and dynamic network node. Many complex large-scale networks have been shown to have topological properties in common, based on a small-world network and scale-free network models. However, the human dynamic movement pattern of this network has never been investigated in a real-world setting. Here, we show that the power law in degree distribution emerged in the passing behavior in the 2006 FIFA World Cup Final and an international ''A'' match in Japan, by describing players as vertices connected by links representing passes. The exponent values c*3:1 are similar to the typical values that occur in many real-world networks, which are in the range of 2vcv3, and are larger than that of a gene transcription network, c*1. Furthermore, we reveal the stochastically switched dynamics of the hub player throughout the game as a unique feature in football games. It suggests that this feature could result not only in securing vulnerability against intentional attack, but also in a power law for self-organization. Our results suggest common and unique network dynamics of two competitive networks, compared with the large-scale networks that have previously been investigated in numerous works. Our findings may lead to improved resilience and survivability not only in biological networks, but also in communication networks.
Interpersonal dynamics in sport: The role of artificial neural networks and 3-D analysis
Behavior research …, 2006
In previous attempts to identify dynamical systems properties in patterns of play in team sports, only 2-D analysis methods have been used, implying that the plane of motion must be preselected and that movements out of the chosen plane are ignored. In the present study, we examined the usefulness of 3-D methods of analysis for establishing the presence of dynamical systems properties, such as phase transitions and symmetry-breaking processes in the team sport of rugby. artificial neural networks (anns) were employed to reconstruct the 3-D performance space in a typical one-versus-one subphase of rugby. results confirm that anns are reliable tools for reconstructing a 3-D performance space and may be instrumental in identifying pattern formation in team sports generally.
Using network metrics to inspect the teammates' interactions on football: A meso-analysis
The notational analysis (i.e., based on discrete actions) cannot identify the main reasons and processes that justify the outcomes (Passos et al., 2006). Therefore, some alternatives have been suggested and discussed seeking for a whole understanding about the product and mainly the process (Clemente et al., 2013). One of them is the network performed by the team players (Bourbousson et al., 2010). Despite their importance as a graphical viewpoint (Figure 1) for coaches and their staff, there are important metrics that can identify in an easier way some properties of the network. Therefore, the aim of this study was to propose two network metrics that measures the connectivity between teammates throughout one single case study match.