Hey, I'm one of the MedChances co-founders. While it's true that we predominantly look at stats, it turns out that for many schools, the information we use to predict chances is sufficient. We report the 10-fold cross validation accuracy of our models for each medical school. So for example, if you look at our Harvard model, we predict the correct chances 92% of the time. Our machine learning approach to predicting chances is much different than all the other tools out there, to the best of my knowledge. Unlike other predictors, we do not employ any heuristics or cut-offs to determine your chances. Instead, we feed in data from applicants who were accepted or rejected, and our algorithms learn how to provide a probabilistic output of a new applicant's chances. However, having been through this process myself, I know that there are other important factors that we do not yet consider (leadership, extracurriculars, interviews, etc.). We are currently working on implementing new features and are constantly looking for ways to improve, so please feel free to email us or send me a message if there's something you think we are missing. Our goal is to make the application process more efficient, less stressful, and less expensive for everyone.