Network centrality based team formation: A case study on T-20 cricket

Dey, Paramita; Ganguly, Maitreyee; Roy, Sarbani

doi:10.1016/j.aci.2016.11.001

Cited by 16 publications

(6 citation statements)

References 11 publications

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“…• Centrality-based CTS (C-CTS) [49] selects top ζ number of candidates based on players' betweenness to identify the influential members for the team.…”

Section: Comparing Schemesmentioning

confidence: 99%

PRADA-TF: Privacy-Diversity-Aware Online Team Formation

Mahajan

Cho

2021

2021 IEEE International Conference on Web Services (ICWS)

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“…• Centrality-based CTS (C-CTS) [49] selects top ζ number of candidates based on players' betweenness to identify the influential members for the team.…”

Section: Comparing Schemesmentioning

confidence: 99%

PRADA-TF: Privacy-Diversity-Aware Online Team Formation

Mahajan

Cho

2021

2021 IEEE International Conference on Web Services (ICWS)

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“…Applications of novel and sophisticated techniques, using non-linear statistical tools have supported capture of collective behaviours, identified by variables such as team centroids (geometric centre of a group o f p layers) a nd team dispersion (how far players are apart), as well as team communication (e.g., networks underpinnin g ball-passing sequences) and sequential patterns (predicting future passing sequences) [16]. Lately , t h ere has been increasing interest in research on team communication networks [e.g., [17][18][19].…”

Section: Complexity Sciences: a Multidisciplinary Approach For Studying Social Interactions In Team Sportsmentioning

confidence: 99%

A multilevel hypernetworks approach to capture properties of team synergies at higher complexity levels

Ribeiro

Davids

Araújo

et al. 2020

European Journal of Sport Science

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Figure captionsfeatures of such systems (e.g., emergent behaviours) require a more holistic approach to measurement and analysis for understanding system properties. Complexity sciences encompass a h olist ic a pp roach t o research on collective adaptive systems, which integrates concepts and tools f ro m o th er t h eories a nd methods (e.g., ecological dynamics and social network analysis) to explain functioning of such systems in natural performance environments. Multilevel networks, such as hypernetwork s, comp rise n ovel a nd 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 d iscu ss t h e 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 informatio n on team performance, which can be used by coaches, sport scien tist s a nd p erfo rmance analy st s f or enhancing practice and training. We examine the relevance of network sciences, as a su b-discip lin e of complexity sciences, for studying dynamics of relational structures in sports teams durin g p ra ct ice a nd 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.

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“…Several interesting studies have applied SNA in different sports ( Clemente et al, 2015 ; Dey et al, 2017 ; Praça et al, 2019 ) and to answer different problems ( Fewell et al, 2012 ; Ribeiro et al, 2019 ; Sasaki et al, 2017 ). In recent years, our research team has applied SNA to high-level volleyball ( Hurst et al, 2016 ; Laporta et al, 2018a , 2018b , 2019 ; Loureiro et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Coexistence of Distinct Performance Models in High-Level Women’s Volleyball

Laporta

Medeiros

Vargas

et al. 2021

Journal of Human Kinetics

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In performance analysis, and most notably in match analysis, generalizing game patterns in a sport or competition may result in formulating generic models and neglecting relevant variability in benefit of average or central values. Here, we aimed to understand how different game models can coexist at the same competitive level using social network analysis with degree centrality to obtain systemic mappings for six volleyball matches, one for each of the six national teams playing in the 2014 World Grand Prix Finals, guaranteeing a homogeneous game level and balanced matches. Although the sample was not recent, this was not relevant for our purposes, since we aimed to merely expose a proof of concept. A total of 56 sets and 7,176 ball possessions were analysed through Gephi Software, considering game actions as nodes and the interaction between them as edges. Results supported the coexistence of different performance models at the highest levels of practice, with each of the six teams presenting a very distinct game model. For example, important differences in eigenvector centrality in attack zones (ranging from 0 to 34) and tempos (20 to 38) were found between the six teams, as well as in defensive lines (20 to 39) and block opposition (22 to 37). This further suggests that there may be multiple pathways towards expert performance within any given sport, inviting a re-conceptualization of monolithic talent identification, detection and selection models. Future studies could benefit from standardizing the metrics in function of the number of ball possessions.

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Network centrality based team formation: A case study on T-20 cricket

Cited by 16 publications

References 11 publications

PRADA-TF: Privacy-Diversity-Aware Online Team Formation

PRADA-TF: Privacy-Diversity-Aware Online Team Formation

A multilevel hypernetworks approach to capture properties of team synergies at higher complexity levels

Coexistence of Distinct Performance Models in High-Level Women’s Volleyball

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