Basketball Teams as Strategic Networks

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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“…Briefly, it is possible to identify that soccer have been well studied by using network approach. Nevertheless, such situation it is not similar in other team sports such as basketball (Bourbousson, Poizat, Saury, & Seve, 2010;Fewell, Armbruster, Ingraham, Petersen, & Waters, 2012). In the case of under-18 French basketball players it was found small mutual forms of interactions and suggested that the coordination networks of the team were built on local coordination's chaining together in such a way as to not necessarily form a single unit (Bourbousson et al, 2010).…”

“…In the case of under-18 French basketball players it was found small mutual forms of interactions and suggested that the coordination networks of the team were built on local coordination's chaining together in such a way as to not necessarily form a single unit (Bourbousson et al, 2010). By other hand, in a study that used data from NBA first round play-offs, it was showed a star pattern in the Bulls team who inbound only to the Point Guard at 60% and for which most passes were between the Point Guard and other players (Fewell et al, 2012).…”

Association Between Physician Teamwork and Health System Outcomes After Coronary Artery Bypass Grafting

“…[15][16][17] Social network theory suggests that highly clustered networks are more effective as information conduits because they improve the ability of collaborating physicians to share information and develop innovative protocols for treatment. 18,19 To the extent that clustering promotes these different forms of teamwork, we hypothesized that it may also lead to improved surgical outcomes.…”

Complex Network Analysis in Cricket: Community Structure, Player's Role and Performance Index

“…One way to evaluate this is by quantifying the frequency of triangles [54] within the BPN. Such an approach has been adopted in identifying relative involvement of players in Basketball [55]. This can also be extended to study player involvement in Soccer as well.…”