Real-time forecasting within soccer matches through a Bayesian lens
本文用贝叶斯方法实时预测足球比赛结果，利用比赛中的事件序列数据，通过多项概率回归估计协变量的时变影响，在英超数据上表现优于其他统计和机器学习模型。
Abstract This article employs a Bayesian methodology to predict the results of soccer matches in real-time. Using sequential data of various events throughout the match, we utilise a multinomial probit regression in a novel framework to estimate the time-varying impact of covariates and to forecast the outcome. English Premier League data from eight seasons are used to evaluate the efficacy of our method. Different evaluation metrics establish that the proposed model outperforms potential competitors inspired by existing statistical or machine learning algorithms. Additionally, we apply robustness checks to demonstrate the model’s accuracy across various scenarios.