A doubly self-exciting Poisson model for describing scoring levels in NBA basketball
本文提出一个双重自激泊松模型，用于描述NBA球队或球员在赛季和比赛分钟级别的投篮得分，并通过贝叶斯方法估计参数，再根据参数相似性对球员进行聚类。
Abstract In this article, Poisson time series models are considered to describe the number of field goals made by a basketball team or player at both the game (within-season) and the minute (within-game) level. The model is endowed with a doubly self-exciting structure, following the INGARCH(1,1) specification. To estimate the model at the within-game level, a divide-and-conquer procedure is carried out under a Bayesian framework. Then, we perform a clustering of the players in terms of their similarity according to the corresponding posterior distributions of key model parameters. The model is tested with National Basketball Association (NBA) teams and players from the 2018–2019 season.