  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 inspired by existing
statistical or machine learning algorithms. Additionally, we apply robustness
checks to demonstrate the model's accuracy across various scenarios.
