We describe and demonstrate a telescope performance model based on Monte Carlo simulations. As a specific example, we apply this method to our delivered image quality error budgets for the Advanced Technology Solar Telescope (ATST). The ATST site survey database provides us with probability distributions for parameters that affect image quality, like wind velocity and Fried’s seeing parameter. The histograms characterizing these parameters can be sampled many times randomly to yield fact-based predictions of system performance. From this we are able to estimate the fraction of the time that a given site will meet or exceed the performance goals of the telescope. The calculations are performed using Crystal Ball, an after-market add-in for Microsoft Excel marketed by Decisioneering, Inc. of Denver Colorado.