March Data-Ness II – The Method Behind the Madness

You asked for it and here it is! We’ve received a ton of buzz since introducing our March Data-Ness Machine Learning Data Model and we are bringing you all the details! There can be learning curves to just about anything and that includes machine learning. Our data model wasn’t exactly perfect, and we expected that because that is the nature of sports forecasting. But that’s okay because this was a learning moment both for the AI as well as for us sports fans. We have already had several upsets and can expect several more as the bracket proceeds. The ML model is still an outstanding piece of data engineering and, as the brackets continue, we are learning what types of tweaks can be used to perfect the process for next year. While we are working on our tweaks, though, we wanted to share the model creation process with you so […]