If you have followed this publication for a while, you know it as the CFBD Blog. It grew alongside CollegeFootballData.com and the community around it: people using data to answer football questions, build models, make charts, learn new tools, and occasionally find the edge cases that I missed.
That name made sense when nearly everything here began and ended with college football. It makes less sense now as the platform and user base grow.
Rad Sports Analytics is becoming the new home of the CFBD Blog because the work has expanded. College basketball is now part of the picture, and many of the most useful subjects I want to cover do not belong to one sport. Data engineering, reproducible analysis, API design, modeling decisions, and the process of learning with real data matter whether the rows describe a football drive or a basketball possession.
The goal is not to replace CFBD or blur the product brands. It is to give the writing a home that fits what the community is building now.
Where this started
CollegeFootballData.com began with a fairly simple frustration: useful college football data was fragmented, difficult to access, or packaged in ways that made it hard for independent builders to do much with it. I wanted a better foundation for my own work and it quickly became clear that other people wanted one too.
The college football analytics community shaped CFBD from there. Questions, corrections, feature requests, open-source contributions, and projects built on top of the API all helped clarify what the platform needed to become. The underlying idea has remained consistent: serious sports data should be accessible to the people who want to build with it.
That principle still guides CollegeFootballData. It also guides CollegeBasketballData, which creates room for a new set of tutorials, analyses, models, and visualizations. A football-branded blog is simply not the natural shared home for all of that work.
What is changing and what is not
The publication name and canonical home are changing. The editorial scope is expanding to include college football, college basketball, and subjects that cross both sports. I also intend to publish more regularly, with a goal of roughly one useful post each week. That is a direction, not an inflexible promise; quality and usefulness matter more than filling a slot on a calendar.
The product platforms are not moving into the blog. CFBD and CBBD will remain the dedicated places for their respective data, tools, documentation, and account workflows. API keys, product information, and authenticated features will continue to live on those product sites. Rad Sports Analytics connects the learning and editorial resources around them; it does not replace them.
The same is true of the existing CFBD archive. Useful articles will remain available at their new canonical locations, now alongside basketball and cross-sport work in the Rad Sports Analytics blog.
Most importantly, the standard for the work is not changing. I want the examples to be practical and inspectable. When a method has limitations, those limitations should be visible. When the data cannot support a strong conclusion, the writing should say so. A model result is more useful when readers can understand the choices behind it.
What you can expect
The publication will generally follow four recurring lanes:
Build something. API walkthroughs, notebooks, dashboards, charts, and small models that move from a question to a working result.Understand something. Approachable explanations of metrics, methods, assumptions, and analytical tradeoffs.Explore something. Football and basketball questions tied to the sports calendar and answered with data.Behind the data. Engineering decisions, product updates, data limitations, and lessons from operating the platforms.
In practice, that might mean building a first CFBD team profile, comparing basketball teams with tempo-free metrics, or working through what opponent adjustment does and does not tell us. It might also mean explaining why an API behaves a certain way, how to make a notebook reproducible, or where a dataset requires more caution than its clean schema suggests.
The common thread is usefulness. I want each post to help you build something, understand a method, investigate a question, or make a better decision about your own workflow.
Choose the path that fits your work
If your focus is football, start with the college football learning hub or go directly to CollegeFootballData. If you are working with basketball, the college basketball learning hub and CollegeBasketballData are the best starting points.
If you want a more guided local workflow, the data packs bring together structured datasets, notebooks, and supporting material you can inspect and adapt. If you would rather browse first, the full blog will include work across both sports.
Help shape what comes next
This publication should reflect the practical problems people are actually trying to solve. What would you like to learn, build, or better understand? I welcome tutorial ideas, analytical questions, corrections, and potential contributions. You can reach me through the Rad Sports Analytics contact page.
To everyone who has read, shared, questioned, corrected, or built on CFBD over the years: thank you. You helped turn a personal solution into a much larger community resource. And to basketball readers and newer builders, welcome.
The name is broader, but the promise is familiar: useful, understandable work that helps more people do more with college sports data.
