Stat Mills

In my last post I covered how you can fit Penalized Splines using the glum library in Python. Notionally glum was built to fit Generalized Linear Models. However it was designed to give the user the option to pass in a custom penalty matrix. We took advantage of this capability... [Read More]

I recently discovered a new python library for fitting GLMs called glum. This package is developed not only to be fast but also allows custom penalties for different terms in the model, as well as including your own penalty matrix in your model fitting. This could allow us to include... [Read More]

Recently I encountered an interesting paper from the facebook research team that outlines a method for using decision trees (specifically boosted trees) to create transformed data to be used as input to a final logistic regression. I thought this was really cool and wanted to try and recreate the method... [Read More]

In order to build a prediction model for next season I need to know how good each team was in the previous season(s). A decent and simple way to measure team quality is to use the Massey rankings, which basically just finds the points above average that each team contributes... [Read More]

I have seen some great visualizations lately that show the play-by-play point differential for the NBA and the NFL and I wanted to recreate that for College Football. You can view all the source code on github. I also need to give a huge thanks to the r/CFBAnalysis subreddit for... [Read More]