In this edition of Talking Tech, we're going to be diving into some machine learning. Specifically, we're going to be looking at my preferred method for building predictive models for just about anything: the artificial neural networks, or ANN for short. The name may sound intimidating, but ANNs aren't really

Back in the early days of this blog, we walked through the process of building a Simple Rating System to rank teams [https://blog.collegefootballdata.com/talking-tech-bu/], also known as SRS. SRS has a lot of benefits, such as providing a good relativistic rating of teams since it is based

In this guide, we'll walkthrough how to create geo charts using the Cartopy and Matplotlib libraries in conjunction with the CFBD Python library, taking advantage of CFBD's recruit and player location data.

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.

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 [https://blog.collegefootballdata.com/talking-tech-building-an-environment-for-predictive-analysis/] using Docker and Project Jupyter. When then went over creating a Simple

Welcome back! We didn't get to have much fun last time, as we were mostly concerned with setting up an environment for data analytics in Python [https://blog.collegefootballdata.com/talking-tech-building-an-environment-for-predictive-analysis/] . A few people even offered feedback on different tools and setups they use, which is fantastic. Just as there's

Welcome to the first edition of Talking Tech! This series is going to be a deeper dive into the weeds of predictive analysis. It will be geared more towards coders and less towards data scientists. In other words, expect to see more code in this series rather than mathematical jargon.