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The game held by National Basketball Association (NBA) is the most popular basketball event on earth. Each year, tons of statistical data are generated from this industry. Meanwhile, managing teams, sports media, and scientists are digging deep into the data

The game held by National Basketball Association (NBA) is the most popular basketball event on earth. Each year, tons of statistical data are generated from this industry. Meanwhile, managing teams, sports media, and scientists are digging deep into the data ocean. Recent research literature is reviewed with respect to whether NBA teams could be analyzed as connected networks. However, it becomes very time-consuming, if not impossible, for human labor to capture every detail of game events on court of large amount. In this study, an alternative method is proposed to parse public resources from NBA related websites to build degenerated game-wise flow graphs. Then, three different statistical techniques are tested to observe the network properties of such offensive strategy in terms of Home-Away team manner. In addition, a new algorithm is developed to infer real game ball distribution networks at the player level under low-rank constraints. The ball-passing degree matrix of one game is recovered to the optimal solution of low-rank ball transition network by constructing a convex operator. The experimental results on real NBA data demonstrate the effectiveness of the proposed algorithm.
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    Title
    • Network Effects in NBA Teams: Observations and Algorithms
    Contributors
    Date Created
    2017
    Subjects
    Resource Type
  • Text
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    • Masters Thesis Computer Science 2017

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