Abstract: The sports industry has seen a rapid adoption of dynamic pricing practices in recent years. However, there is still limited understanding on the effect of dynamic pricing on revenue in sport event settings and how to execute effective dynamic pricing strategies. In this paper, we address these issues by developing a comprehensive demand model for single-game ticket sales that can be used to predict the revenue associated with a particular pricing strategy over the course of a sport season. We apply the model to actual ticket sales and pricing data from an anonymous Major League Baseball franchise during a recent MLB season, and evaluate the effectiveness of the dynamic pricing policy applied by our partner franchise during that period. We find that the actual dynamic pricing strategy used by this franchise resulted in revenue decrease of 0.78% compared to a pricing policy where prices are fixed over time. We propose alternative pricing policies to help improve revenue and find that an optimized dynamic pricing policy can improve revenue by 2.36% compared to a pricing policy where prices are fixed over time.