This paper employs a Bayesian methodology to predict the results of soccer matches in real-time. Using sequential data of various events throughout the match, we utilize 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, which are inspired from existing statistical or machine learning algorithms. Additionally, we apply robustness checks to demonstrate the model's accuracy across various scenarios.

翻译：本文采用贝叶斯方法对足球比赛结果进行实时预测。通过利用比赛过程中各类事件的序列数据，我们在一个新颖的框架下运用多项概率比回归来估计协变量的时变影响并预测比赛结果。基于八个赛季的英格兰足球超级联赛数据，我们评估了所提方法的有效性。多项评估指标表明，该模型优于受现有统计或机器学习算法启发的潜在竞争模型。此外，我们通过稳健性检验证明了该模型在不同场景下的预测准确性。