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PLOS ONE  2012 

Basketball Teams as Strategic Networks

DOI: 10.1371/journal.pone.0047445

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We asked how team dynamics can be captured in relation to function by considering games in the first round of the NBA 2010 play-offs as networks. Defining players as nodes and ball movements as links, we analyzed the network properties of degree centrality, clustering, entropy and flow centrality across teams and positions, to characterize the game from a network perspective and to determine whether we can assess differences in team offensive strategy by their network properties. The compiled network structure across teams reflected a fundamental attribute of basketball strategy. They primarily showed a centralized ball distribution pattern with the point guard in a leadership role. However, individual play-off teams showed variation in their relative involvement of other players/positions in ball distribution, reflected quantitatively by differences in clustering and degree centrality. We also characterized two potential alternate offensive strategies by associated variation in network structure: (1) whether teams consistently moved the ball towards their shooting specialists, measured as “uphill/downhill” flux, and (2) whether they distributed the ball in a way that reduced predictability, measured as team entropy. These network metrics quantified different aspects of team strategy, with no single metric wholly predictive of success. However, in the context of the 2010 play-offs, the values of clustering (connectedness across players) and network entropy (unpredictability of ball movement) had the most consistent association with team advancement. Our analyses demonstrate the utility of network approaches in quantifying team strategy and show that testable hypotheses can be evaluated using this approach. These analyses also highlight the richness of basketball networks as a dataset for exploring the relationships between network structure and dynamics with team organization and effectiveness.


[1]  Stander P (1992) Cooperative hunting in lions: the role of the individual. Behavioral Ecology and Sociobiology. 29(6): 445–454.
[2]  Boesch C (2002) Cooperative hunting roles among Tai chimpanzees. Human Nature 13(1): 27–46.
[3]  Gazda SK, Connor RC, Edgar RK, Cox F (2005) A division of labour with role specialization in group–hunting bottlenose dolphins (Tursiops truncatus) off Cedar Key, Florida. Proceedings of the Royal Society B: Biological Sciences 272(1559): 135–140.
[4]  Anderson C, Franks NR (2001) Teams in animal societies. Behavioral Ecology 12(5): 534–540.
[5]  Zaccaro SJ, Rittman AL, Marks MA (2001) Team leadership. The Leadership Quarterly 12(4): 451–483.
[6]  Mehra A, Smith BR, Dixon AL, Robertson B (2006) Distributed leadership in teams: The network of leadership perceptions and team performance. The Leadership Quarterly 17(3): 232–245.
[7]  Eccles DW, Tenenbaum G (2004) Why an expert team is more than a team of experts: A socialcognitive conceptualization of team coordination and communication in sport. Journal of Sport Exercise Psychology 26(4): 542–560.
[8]  Jackson P, Winter T (2009) Triangle Offense. In: Gandolfi G, editor. NBA coaches playbook. Human Kinetics Publishers p. 89–111.
[9]  Skinner B (2010) The price of anarchy in basketball. Journal of Quantitative Analysis in Sports 6(1).
[10]  Wilson E, H?lldobler B (2009) The Superorganism. New York, NY: WW Norton & Co.
[11]  Kniffin KM (2009) Evolutionary perspectives on salary dispersion within firms. Journal of Bioeconomics 11(1): 23–42.
[12]  Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40: 35–41.
[13]  Oliver D (2004) Basketball on Paper. Potomac Books Inc.
[14]  Newman MEJ (2003) The structure and function of complex networks. SIAM review 45: 167–256.
[15]  Shannon C (1948) A mathematical theory of communication. Bell System Technical Journal 27: 379–423.
[16]  Duch J, Waitzman JS, Amaral LAN (2010) Quantifying the performance of individual players in a team activity. PloS one 5(6): e10937.
[17]  Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, et al. (2002) Network motifs: simple building blocks of complex networks. Science 298(5594): 824–827.
[18]  McCallum J (2006) Seven Seconds or Less. New York City: Touchstone.
[19]  Chen F, De Vleeschouwer C (2010) Automatic production of personalized basketball video summaries from multi-sensored data. Proceedings of 2010 IEEE 17th International Conference on Image Processing September 26–29, 2010, Hong Kong.


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