Small World Network Formation and Characterization of Sports Network

The motivation of this paper is formation of sports network and characterization of the small world network phenomenon by analyzing the data of individual players of a team. Analysis of the network suggests that sports network can be considered as small world and inherits all characteristics of small world network. Making a quantitative measure for an individual performance in the team sports is important in respect to the fact that for team selection of International football matches, from a pool of best players, only 11 players can be selected for the team. The statistical record of each player is considered as a traditional way of quantifying the performance of a player. But other criteria like performing against a strong opponent or executing a brilliant performance against a strong team deserves more credit. In this paper, a method based on social networking is presented to quantify the quality of player’s efficiency and is defined as the total matches played between each team members of individual teams and the members of different teams. The application of Social Network Analysis (SNA) is explored to measure performances and rank of the players. A bidirectional weighted network of players is generated using the information collected from English Premier League (2014–2015) and used for network formation.

[1]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[2]  Shiu-Wan Hung,et al.  A small world in the patent citation network , 2008, 2008 IEEE International Conference on Industrial Engineering and Engineering Management.

[3]  E. Ben-Naim,et al.  Scaling in tournaments , 2007 .

[4]  B. Bollobás.  The evolution of random graphs , 1984 .

[5]  Satyam Mukherjee,et al.  Quantifying individual performance in Cricket — A network analysis of batsmen and bowlers , 2012, 1208.5184.

[6]  Andreas Heuer,et al.  Soccer: Is scoring goals a predictable Poissonian process? , 2010, 1002.0797.

[7]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[8]  Brian Skinner,et al.  The Price of Anarchy in Basketball , 2009, 0908.1801.

[9]  W. Janke,et al.  Football fever: goal distributions and non-Gaussian statistics , 2006, physics/0606016.

[10]  Ramesh Sharda,et al.  Prediction of athletes performance using neural networks: An application in cricket team selection , 2009, Expert Syst. Appl..

[11]  Keiko Yokoyama,et al.  Common and Unique Network Dynamics in Football Games , 2011, PloS one.

[12]  Paul J. Laurienti,et al.  The Ubiquity of Small-World Networks , 2011, Brain Connect..

[13]  G. Robins,et al.  The Application of Social Network Analysis to Team Sports , 2010 .

[14]  S. Redner,et al.  Understanding baseball team standings and streaks , 2008 .

[15]  Matthieu Latapy,et al.  Main-memory triangle computations for very large (sparse (power-law)) graphs , 2008, Theor. Comput. Sci..