CFBD Blog

Talking Tech: Applying LRMC Rankings to College Football, Part One

While SRS can go a long way towards calculating a difficult problem like schedule strength, it has its pitfalls. Logistic-Regression/Markov Chain (LRMC) ratings provide an alternate approach.

Talking Tech: Applying LRMC Rankings to College Football, Part Two

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.

Making charts with Plotly and the CFBD Python library

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 me on Twitter, you may have seen that the official CFBD Python client library dropped this past weekend. Introducing the official CFBD Python client

Talking Tech: Predicting Play Calls Using a Random Forest Classifier

Welcome back to Talking Tech! It's been awhile since our last post. To catch everyone up with where we've been thus far, we first went through setting up an environment for data science using Docker and Project Jupyter. When then went over creating a Simple Rating System for college football

Talking Tech: Charting Data with Plotly

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 demonstrate how to plot charts in Python using Jupyter notebooks. Before we get started, we need to find a charting library that will suit

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