Talking Tech: Calculating Elo Ratings for College Football
Back in the early days of this blog, we walked through the process of building a Simple Rating System to rank teams, also known as SRS. »
Talking Tech: Creating Geo Charts
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. »
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. »
Talking Tech: Calculating SRS (Pandemic Edition)
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. »
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 »
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 »
Talking Tech: Creating a Simple Rating System
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. »
Talking Tech: Building an environment for data analysis
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 »