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uPATO—Individual Measures

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Ultimate Performance Analysis Tool (uPATO)

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

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Abstract

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.

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References

  1. M. Bolaños, E.M. Bernat, B. He, S. Aviyente, A weighted small world network measure for assessing functional connectivity. J. Neurosci. Methods 212(1), 133–142 (2013)

    Article  Google Scholar 

  2. P. Bonacich, Power and centrality: a family of measures. Am. J. Sociol. 92(5), 1170–1182 (1987)

    Article  Google Scholar 

  3. U. Brandes, T. Erlebach, Network Analysis: Methodological Foundations, Lecture Notes in Computer Science (Springer, New York Inc., Secaucus, USA, 2005)

    Book  Google Scholar 

  4. Y. Cho, J. Yoon, S. Lee, Using social network analysis and gradient boosting to develop a soccer win-lose prediction model. Eng. Appl. Artif. Intell. 72, 228–240 (2018)

    Article  Google Scholar 

  5. F.M. Clemente, F.M.L. Martins, R.S. Mendes, Social Network Analysis Applied to Team Sports Analysis, 1st edn. (Springer International Publishing, 2016)

    Google Scholar 

  6. E. Estrada, J.A. Rodríguez-Velázquez, Subgraph centrality in complex networks. Phys. Rev. E 71, 056103 (2005)

    Article  MathSciNet  Google Scholar 

  7. L. Freeman, A set of measures of centrality based on betweenness. 40, 35–41 (1977)

    Google Scholar 

  8. A. Gaggioli, G. Riva, L. Milani, E. Mazzoni, Networked Flow: Towards an Understanding of Creative Networks, SpringerBriefs in Education (Springer, Netherlands, 2012)

    Google Scholar 

  9. J. Gudmundsson, M. Horton, Spatio-temporal analysis of team sports. ACM Comput. Surv. 50(2), 22:1–22:34 (2017)

    Google Scholar 

  10. P. Hage, F. Harary, Eccentricity and centrality in networks. Soc. Netw. 17(1), 57–63 (1995)

    Article  Google Scholar 

  11. M.E.J. Newman, Mixing patterns in networks. Phys. Rev. E 67, 026126 (2003)

    Article  MathSciNet  Google Scholar 

  12. G.A. Pavlopoulos, M. Secrier, C.N. Moschopoulos, T.G. Soldatos, S. Kossida, J. Aerts, R. Schneider, P.G. Bagos, Using graph theory to analyze biological networks. BioData Min. 4(1), 10 (2011)

    Article  Google Scholar 

  13. J.L. Peña, H. Touchette, A network theory analysis of football strategies (2012)

    Google Scholar 

  14. X. Qi, E. Fuller, Q. Wu, Y. Wu, C.-Q. Zhang, Laplacian centrality: a new centrality measure for weighted networks. Inf. Sci. 194, 240–253 (2012). Intelligent Knowledge-Based Models and Methodologies for Complex Information Systems

    Google Scholar 

  15. M. Rubinov, O. Sporns, Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3), 1059–1069 (2010). Computational Models of the Brain

    Google Scholar 

  16. G. Scardoni, C. Laudanna, Centralities based analysis of complex networks, in New Frontiers in Graph Theory, ed. by Y. Zhang (2012), pp. 323–348

    Google Scholar 

  17. A. Shimbel, Structural parameters of communication networks. Bull. Math. Biophys. 15(4), 501–507 (1953)

    Article  MathSciNet  Google Scholar 

  18. S. Wasserman, K. Faust, in Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences (Cambridge University Press, 1994)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Universidade da Beira Interior, Covilhã, Portugal

    Frutuoso G. M. Silva

  2. Instituto de Telecomunicações, Delegação da Covilhã, Covilhã, Portugal

    Frutuoso G. M. Silva, Quoc Trong Nguyen, Acácio F. P. P. Correia, Filipe Manuel Clemente & Fernando Manuel Lourenço Martins

  3. Instituto Politécnico de Viana do Castelo, Escola Superior de Desporto e Lazer, Melgaço, Portugal

    Filipe Manuel Clemente

  4. Instituto Politécnico de Coimbra, Escola Superior de Educação, Coimbra, Portugal

    Fernando Manuel Lourenço Martins

Authors
  1. Frutuoso G. M. Silva
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  2. Quoc Trong Nguyen
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  3. Acácio F. P. P. Correia
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  4. Filipe Manuel Clemente
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  5. Fernando Manuel Lourenço Martins
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Corresponding author

Correspondence to Frutuoso G. M. Silva .

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© 2019 The Author(s), under exclusive license to Springer Nature Switzerland AG, part of Springer Nature

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Silva, F.G.M., Nguyen, Q.T., Correia, A.F.P.P., Clemente, F.M., Martins, F.M.L. (2019). uPATO—Individual Measures. In: Ultimate Performance Analysis Tool (uPATO). SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-99753-7_3

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