The Adaptive Behavior of a Soccer Team: An Entropy-Based Analysis

Neuman, Yair; Israeli, Navot; Vilenchik, Dan; Cohen, Yochai

doi:10.3390/e20100758

Cited by 28 publications

(18 citation statements)

References 13 publications

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“…Here, we provide a simple and general way for modeling verbal interactions along the lines described above. While the proposed approach might be portrayed as too simple for analysing the complexity of social interactions, it can be trivially extended to social interactions involving more than two people and to many other small systems that are of interest to the social sciences 22 . It is not clear though, how well the proposed approach scales up to the level of populations.…”

Section: Discussionmentioning

confidence: 99%

From physical to social interactions: The relative entropy model

Neuman

Vilenchik

Cohen

2020

Sci Rep

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complex social systems at various scales of analysis (e.g. dyads, families, tribes, etc.) are formed and maintained through verbal interactions. Therefore, the ability to (1) model these interactions and (2) to use models of interaction for identifying significant relations may be of interest to the social sciences. Adopting the perspective of social physics, we present a general approach for modeling interactions through relative entropy. For illustrating the benefits of the approach, we derive measures of "perspective-taking" and use them for identifying significant-romantic relations in a data set composed of the verbal interactions taken place at the famous tV series "Sex and the city". Using these measures, we show that significant-romantic relations can be identified with success. These results provide preliminary support for the benefits of using the proposed approach.Interactions are the hallmark of complex systems 1 but in the social context, interactions are totally different from those evident among the particles of non-living matter 2 . This difference calls into question the general meaning of the term "interaction" and points to the need to better understand social interactions while still using physics as "point de repère". In trying to address this challenge, one must be careful in bridging the gap between physics and the social sciences, as these two fields are not theoretically aligned and there is always the danger of using misleading analogies. In this context, one should also recall that in the social sciences the concept of interaction has been mostly used in a very general sense, covering a huge variety of instances from hugging to arguing. While there are some good reasons for this variety, it seems that it may be useful to use a general and physically motivated concept of interaction together with measures that are clearly derived from it. Adopting such an approach, may be relevant for the task of identifying significant relations/interactions. Not all social interactions are significant to the same extent and being able to identify significant interactions may be of interest both from a purely theoretical perspective and from a practical perspective (e.g. the development of interactive technologies such as Chatbots).The aim of the current paper is to introduce a general conceptualization of interaction which is accompanied by operationalization through the use of relative entropy.

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Section: Discussionmentioning

confidence: 99%

From physical to social interactions: The relative entropy model

Neuman

Vilenchik

Cohen

2020

Sci Rep

Self Cite

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“…In recent research, entropy as a partial measure of performance has been extensively used either in individual or in collective sports. Researchers have investigated a large set of variables in many sports disciplines (e.g., Hristovski et al, 2006 ; Passos et al, 2009 ; Fewell et al, 2012 ; Vilar et al, 2013 ; Couceiro et al, 2014 ; Silva et al, 2016 ; Castellano et al, 2017 ; Gonçalves et al, 2017 ; Neuman et al, 2018 ; Lopes and Tenreiro Machado, 2019 , 2020 ), showing that behavioral entropy, as measured by different entropy measures, has considerable effect on a number of teams and individual performance indexes. These immensely important results seem to point to a possible unifying explanation of how entropy, as a property of these systems, enters and becomes relevant for performance.…”

Section: Entropy As a Common Conceptual Currencymentioning

confidence: 99%

Theory of Cooperative-Competitive Intelligence: Principles, Research Directions, and Applications

Hristovski

Balagué

2020

Front. Psychol.

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We present a theory of cooperative-competitive intelligence (CCI), its measures, research program, and applications that stem from it. Within the framework of this theory, satisficing sub-optimal behavior is any behavior that does not promote a decrease in the prospective control of the functional action diversity/unpredictability (D/U) potential of the agent or team. This potential is defined as the entropy measure in multiple, context-dependent dimensions. We define the satisficing interval of behaviors as CCI. In order to manifest itself at individual or team level, this capacity harnesses properties such as degeneracy, pleiotropy (pluri-potentiality), synergies, and metastability. Intelligence is embodied because intelligent behavior is deeply dependent on body functionalities, defined as entropy measures. We base our theory on three principles: (a) relativity of functional entropy/information in agent (team)-environment systems, (b) tendency toward the satisficing level of D/U potential, and (c) tendency toward the non-decreasing D/U potential. The conjunction of these three principles provides existence of sub-optimal behaviors associated with CCI. First, we deal with the problem of how to reduce multidimensional behavior to a concept that accounts for the vast set of scenarios in which CCI is manifested. Secondly, we define and discuss the three interacting principles that underpin CCI behavior as well as providing an outline for a future CCI research program supported by agent-based modeling and empirical research. Finally, we provide some preliminary practical issues that stem from the theory.

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“…Statistical modelling in team sports has been an important analytical tool to predict match outcome and the evolution of team's performance [1][2][3][4]. In particular, examination of match-related performance indicators in basketball has enabled modelling to identify winning and losing teams [5], best and worst teams [6], the influence of contextual-related variables [7], the effect of consecutive games [8] or the impact of rule changes [9][10][11] on team's performances.…”

Section: Introductionmentioning

confidence: 99%

The Performance Evolution of Match Play Styles in the Spanish Professional Basketball League

et al. 2020

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The aim of this study is to analyse the performance evolution of all, and the dominant, team’s performances throughout an eight-season period within the Spanish professional basketball league. Match-related statistics were gathered from all regular season matches (n = 2426) played during the period 2009–2010 to 2016–2017. The non-metric multidimensional scaling model was used to examine the team’s profiles across seasons and for the most successful (playoff) teams. The main results showed that: 3-point field goals made (effect size, d = 0.61; 90% confidence interval, CI = 0.23; 1.37) and missed (d = 0.72; 90% CI = 0.35; 1.46), and assists (d = 1.27; 90% CI = 0.82; 1.86) presented a positive trend with an increased number of actions across the seasons; 2-point field goals made (d = 0.21; 90% CI = −1.25; 2.02) and missed (d = 0.27; 90% CI = −0.52; 0.92) were decreased; free throws made and missed, rebounds, fouls, blocks, steals and turnovers showed a relatively stable performance. The matrix solution (stress = 0.22, rmse (root mean squared error) = 7.9 × 104, maximum residual = 5.8 × 103) indicated minimal season-to-season evolution with the ordination plot and convex hulls overlapping. The two most dominant teams exhibited unique match patterns with the most successful team’s pattern, a potential benchmark for others who exhibited more dynamic evolutions (and less success). The current findings identified the different performances of teams within the Spanish professional basketball league over eight seasons with further statistical modelling of match play performances useful to identify temporal trends and support coaches with training and competition preparations.

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The Adaptive Behavior of a Soccer Team: An Entropy-Based Analysis

Cited by 28 publications

References 13 publications

From physical to social interactions: The relative entropy model

From physical to social interactions: The relative entropy model

Theory of Cooperative-Competitive Intelligence: Principles, Research Directions, and Applications

The Performance Evolution of Match Play Styles in the Spanish Professional Basketball League

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