The previous post in this series covered how LRMC works and its mathematical implementation. Now it's time to take things a step further and apply this concept to ranking college football teams. »
You might wonder, why is it so difficult to calculate some of these metrics? We have games and data, right? While that may be true, there are several factors that serve to complicate things this season. »
As always with machine learning models, a common question that comes up is “Why?”. Why did the model choose this team to win vs. the other? What variables are the most and least influential in a given predictive model? Time to take a deep dive. »
Predictive models for college football are a great application of machine learning techniques. Today, we'll look at one technique called gradient boosted decision trees using the LightGBM and NGBoost libraries. »
It's been awhile since I've done one of these. If you're familiar with my Talking Tech series, this entry will be much shorter. If you follow »
College football can be confusing. It feels like a 13 game regular season can have so many twists and turns that the team you are watching »
Welcome to By The Numbers, a series in which I plan to preview upcoming games using numerical analysis. I hope to make this a regular feature »
In the last edition of Talking Tech, we created our own rating system using an SRS algorithm. We're going to build off of that work to »