Enhancements Featured

Subscribing to Data Events with the CFBD GraphQL API

Over the weekend, I announced the new and experimental CFBD GraphQL API. I already broke down most of the benefits of using GraphQL, which includese more dynamic querying and granular control over the data. One benefit is so big that it merits its own post, GraphQL Subscriptions. Subscriptions do exactly

Programming Featured

Building Dynamic Queries with the CFBD GraphQL API

Have you ever wanted more granular control over how you query data from CFBD? By more granular control, I mean dynamic filtering and sorting, querying related pieces of data in one query, and even the ability to specify which specific fields you want to be queried. What about better real-time

Talking Tech Featured

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.

Analysis Featured

Measuring Field Goal Kicker Efficiency

I was recently in a conundrum about the best way to go about measuring field goal kicker efficiency. This has been a topic of discussion in much of the college football content I follow, which is largely centered around the Michigan football team. For the past three years, Michigan has

Talking Tech Featured

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

Talking Tech Featured

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

Analysis Featured

Lessons from picking the 2022 CFB Season

As we reach the end of the 2021-22 college football season (the national championship game is unfolding on my TV as I type this), I wanted to take a look back at my performance in the College Football Data predictions contest. Minimum 400 games picked (not counting the NCG, but

Talking Tech Featured

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

Talking Tech Featured

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

Talking Tech Featured

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.

Announcement Featured

API keys are now available!

The API will start requiring API key authorization starting April 1st. Here's what you need to know.

Talking Tech Featured

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.

Talking Tech Featured

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.

Analysis Featured

Analyzing Variable Importance in CFB Machine Learning Models

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.

Analysis Featured

CFBD: Using machine learning to predict game outcomes and spreads

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.

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

Analysis Featured

The Four Quadrants of College Football

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 at the end of the season hardly resembles the one in September. So how can we properly group teams to assess their performance? The

Talking Tech Featured

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

By The Numbers

By The Numbers: Previewing the National Championship Game

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 starting next year. Since there are innumerable games to cover in a given year and only one of me, I am looking for people

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