Programming - CFBD Blog

Talking Tech: Creating Charts with matplotlib

In one of my earlier blog posts, I wrote a guide on creating charts using the (at the time) nascent CFBD Python library and a charting library/platform called Plotly. I was still relatively new to Python myself and was trying to sort out the ecosystem of Python charting libraries.

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Talking Tech: Navigating the CFBD API with Insomnia

There are a lot of good tools for working with APIs. Historically, Postman has been ubiquitous in this area. While Postman is still a great tool, I ditched it a few years back for a competing tool called Insomnia. If you've never used either of these tools, you may be

Analysis Featured

Rockstars or Flop-stars? Examining Recruit Ratings and NFL Draft Success

College football recruiting is quickly becoming a billion-dollar business. Since the NCAA's ruling in the summer of 2021, there's been an explosion of valuations and incentives for young men and women across the country to trade their name, image and likeness (NIL) for brand advertising. Local businesses, from car dealerships

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Talking Tech: Analyzing Feature Importance in Neural Networks

Probably the biggest issue I come across working with artificial neural networks is that they are a bit of a black box.

Programming Featured

Getting Started with CFB Analytics

The 2022 season is about to be upon us and you are looking to get into CFB analytics of your own, like creating your own poll or picks simulator. Or maybe you've largely used spreadsheets and are looking to graduate to something that gives more capabilities and flexibility. Perhaps you've

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Talking Tech: Building an Artifical Neural Network to Predict Games

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

Programming

Opponent Adjusted Stats Using Ridge Regression

"The biggest variable in football is the fact that each team plays a different schedule against teams of disparate quality." - Football Outsiders "They ain't played nobody Pawllllllll." - SEC Proverb, author unknown Motivation One of the interesting quirks of football is that some statistics are more reflective of the

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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. SRS has a lot of benefits, such as providing a good relativistic rating of teams since it is based purely on scoring margins. It

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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.

Programming Featured

Using API Keys with the CFBD API

Key authentication is now being enforced for all requests make to the CFBD API. This guide will walk through how to authenticate across several different programming languages.

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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

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.

Programming Featured

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

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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

Analysis Featured

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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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. A few people even offered feedback on different tools and setups they use, which is fantastic. Just as there's more than one way to

Programming Featured

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 geared more towards coders and less towards data scientists. In other words, expect to see more code in this series rather than mathematical jargon.