For professional basketball, finding valuable and suitable players is the key
to building a winning team. To deal with such challenges, basketball managers,
scouts and coaches are increasingly turning to analytics. Objective evaluation
of players and teams has always been the top goal of basketball analytics.
Typical statistical analytics mainly focuses on the box score and has developed
various metrics. In spite of the more and more advanced methods, metrics built
upon box score statistics provide limited information about how players
interact with each other. Two players with similar box scores may deliver
distinct team plays. Thus professional basketball scouts have to watch real
games to evaluate players. Live scouting is effective, but suffers from
inefficiency and subjectivity. In this paper, we go beyond the static box score
and model basketball games as dynamic networks. The proposed Continuous-time
Stochastic Block Model clusters the players according to their playing style
and performance. The model provides cluster-specific estimates of the
effectiveness of players at scoring, rebounding, stealing, etc, and also
captures player interaction patterns within and between clusters. By clustering
similar players together, the model can help basketball scouts to narrow down
the search space. Moreover, the model is able to reveal the subtle differences
in the offensive strategies of different teams. An application to NBA
basketball games illustrates the performance of the model.

本研究针对职业篮球中选手的筛选和評估进行了深入的探讨，提出了一种基于动态网络模型的 Continuous-time 随机块模型，可以实现针对不同的数据维度进行切割，以减少应试者范围。此模型与传统的统计分析方法不同，可以捕捉到选手之间的相互作用，并揭示出不同球队的进攻策略之间的微妙差异，为篮球管理层提供了很好的支持。

篮球网络的连续时间随机块模型

A Continuous-time Stochastic Block Model for Basketball Networks

We have developed a probabilistic forecasting methodology through a synthesis
of belief network models and classical time-series analysis. We present the
dynamic network model (DNM) and describe methods for constructing, refining,
and performing inference with this representation of temporal probabilistic
knowledge. The DNM representation extends static belief-network models to more
general dynamic forecasting models by integrating and iteratively refining
contemporaneous and time-lagged dependencies. We discuss key concepts in terms
of a model for forecasting U.S. car sales in Japan.

本文提出了一种概率预测方法，通过信念网模型和经典时间序列分析的结合来构建、完善和进行推断，并扩展了静态信念网络模型以建立更普遍的动态预测模型，通过集成和反复优化同时和时滞的依赖关系，我们讨论了用于预测日本汽车销售的模型中的关键概念。