Passing is one of the key actions for building tactics in football and one of the most common events on the pitch, which allows us to use a limited amount of spatiotemporal data for analysis. Our study aims to understand who is the most likely receiver of a pass initiated at a given moment, as well as what the chances are of the pass being successful based on player movement dynamics on the pitch. We incorporate game state and contextual information, such as possession and phase of play, into the modeling process. We use spatiotemporal data to construct custom features and compare the performance of both classical machine learning methods and latent state-space probabilistic models with Bayesian inference. Predictions and evaluation produced by this method outperform the proposed baseline and provide reasonable assumptions and explainability for a given game context using empirical data from 7 professional football matches. The results demonstrate the predictive power of the proposed features and can be used as a foundation for future modeling.