Basketball is a network

Screen shot 2012-12-10 at 10.47.22Ever thought of basketball players as nodes in a small network, connected by passes? A recently published study did just that, revealing that the aggregated decisions on a basketball court reflect strengths and weaknesses in a team’s strategy. A team led by Jennifer Fewell and Dieter Armbruster published their findings in the journal PLoS One. They tracked all passes between basketball team members during the 2010 NBA play-offs. The networks reveal differences between teams’ strategies, and centrality and entropy measures are used to capture these differences.

Wired magazine wrote a very readable overview of this and similar sports statistics work.

Since the article is published in an open access journal it is freely available to all, isn’t that great. Here is the article abstract:

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.

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