Abstract

The scoring processes of home and away team in basketball games are modeled by two dependent inverse Gaussian processes with a team-specific parameter that measures the team strength. A common latent variable that measures the game pace is designed to characterize the dependence. A moment estimation method combined with maximum likelihood estimation is proposed to fit the parameters and a Bayesian method is applied to update the estimation and make in-game predictions. It is shown that the proposed model can obtain the same performance as the benchmark model, Gamma process model, in outcome prediction, point spread betting and model gambling.

中文翻译：

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通过逆高斯过程对篮球比赛进行建模

摘要

篮球比赛中主客队的得分过程由两个依赖的逆高斯过程建模，并具有衡量球队实力的球队特定参数。衡量比赛节奏的常见潜在变量旨在表征依赖性。提出了一种结合最大似然估计的矩估计方法来拟合参数，并应用贝叶斯方法来更新估计并进行游戏内预测。结果表明，所提出的模型在结果预测、点差投注和模型赌博方面可以获得与基准模型、Gamma 过程模型相同的性能。