The P300 Speller brain-computer interface (BCI) is a virtual keyboard that allows users to type without requiring neuromuscular control. P300 Speller research commonly aims to improve the system accuracy, which is typically estimated by spelling a small number of characters and calculating the percent spelled correctly. In this paper we introduce a new method for estimating the long-term ("projected") accuracy, which utilizes all available flash data and a probabilistic model of the Speller system to produce an estimate with lower variance and lower granularity than the standard measure. We apply the new method to 110 previously-collected P300 Speller runs to confirm its consistency, and simulate spelling runs from real subject data to demonstrate lower variance on the accuracy estimate for any given amount of data.