<p>Prediction and modeling of association football (soccer) outcomes has gained increasing interest in the scientific community in recent years, both due to betting concerns and the need for a deeper understanding of the factors influencing soccer events. We introduce and examine the validity of a Bayesian model, which belongs to the class of accelerated failure time (survival) models and is characterized by its straightforward structure. We implement MCMC methodology to estimate the posterior summaries of the model parameters and suggest a novel algorithm that can be used to transform simulated goal arrival times into predicted goals. The proposed model achieves exceptional in-sample and out-of-sample performance by replicating the entire league in a remarkably precise manner and by making accurate predictions on the second half of the league using the first half as a training dataset. The structure of the proposed model is extendable, allowing for the inclusion of in-play covariates that can be used to further map the complex dynamics of soccer matches.</p>