Understanding player contributions is an important component of lineup construction, advance scouting, and performance evaluation. However, traditional methods utilize oversimplified percentages that fail to acknowledge latent variables and situational nuance. These shortcomings are addressed in this work via a Bayesian approach that incorporates player roles, lineup matchups, and additional context-specific information into its estimates. A Markov chain with inputs derived from multi-year spatiotemporal event data is used to provide continuously updating point scoring probabilities during a rally. Changes in this probability after each ball contact are used to divide credit between players. The results demonstrate an increased ability to differentiate between players and measure contribution in a stable manner over time. The resultant model outputs are illustrated in multiple case studies, with direct application to volleyball coaches, players, and the community at large.