School of Life Sciences, Arizona State University, Tempe, Arizona, USA.
PLoS One. 2012;7(11):e47445. doi: 10.1371/journal.pone.0047445. Epub 2012 Nov 6.
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.
我们通过将 2010 年 NBA 季后赛首轮比赛视为网络,研究了团队动态与功能的关系。将球员定义为节点,球的运动定义为链接,我们分析了团队和位置的度中心性、聚类、熵和流中心性等网络属性,从网络角度描述了比赛,并确定是否可以通过网络属性评估团队进攻策略的差异。跨团队编制的网络结构反映了篮球策略的一个基本属性。它们主要表现出一种集中的球分布模式,控球后卫发挥领导作用。然而,个别季后赛球队在球分配方面涉及其他球员/位置的相对程度上存在差异,这反映在聚类和度中心性的差异上。我们还通过网络结构的变化来描述两种潜在的替代进攻策略:(1)球队是否始终将球传给他们的投篮专家,这可以通过“上山/下山”通量来衡量;(2)他们是否以降低球运动可预测性的方式分配球,这可以通过球队熵来衡量。这些网络指标量化了团队策略的不同方面,没有单一指标可以完全预测成功。然而,在 2010 年季后赛的背景下,聚类(球员之间的连接性)和网络熵(球运动的不可预测性)的值与团队晋级最密切相关。我们的分析证明了网络方法在量化团队策略方面的实用性,并表明可以使用这种方法评估可测试的假设。这些分析还突出了篮球网络作为探索网络结构和动态与团队组织和效率之间关系的数据集的丰富性。