Analytics Guides: Volume 1 - Intro and History

"Roads? Where we're going, we don't need roads." - Back to the Future

Photo by Robert Beck/Getty Images

One of the key reasons I started digginbasketball was to make it different. To create series like this. To connect with Mavs fans and basketball lovers who want more than just watching or reading about the game. To dive deeper. Together. To learn.

So much of the narrative around analytics and data in sports is binary. Black or white. You're either a true hooper or a nerd. You're an X's and O's guy or a data guy. And I hate that.

Data and analytics can be an awesome way to add context. Not to replace the game itself, but as a great resource to understand it better. If you're good at it, or if you get lucky, you might even predict some things before they happen.

As a digginbasketball subscriber, you know my articles and analysis are packed with different data points and stats. I get that not all of you know or understand every part of it. The goal of this series is to break down some of those numbers and concepts.

So, what will this series look like?

We’ll start with a quick look at the history, showing how basketball analytics evolved from its early days to today’s advanced data science. Then, we’ll dive into the key concepts and explain them using Mavericks examples. In each article, we’ll break down one stat or concept that matters. So later, during the season, when you see some of the Mavs trend or other charts, you’ll think, "OK, I know what he’s talking about."

I’ll keep a Mavericks perspective, but all of this applies to any team—even other basketball leagues.

Everything I share is my view, my understanding. I’m a self-learner, so some stuff might not be explained 100% perfectly or academically enough. I've learned most of this from various books about basketball and sports analytics, and from books on statistics and data science. I also do advanced analytics in other fields like marketing and e-commerce, and one of my biggest takeaways was how similar the concepts and models are—only the context and data in basketball are way more fun.

The other part of my learning is more practical. From watching coaching clinics that break down X's and O's (younger coaches often refer to metrics and data points), talking to different coaches here in Europe and some with NBA experience, and engaging with NBA analytics pros who do the real work. I’ve also run analytics sessions for coaches looking to bring data to their teams, and I've created draft profiles and other reports for NBA scouts and front office people in Europe.

If you like any of this and want to learn more, reach out to me through messages here on Substack or any other way (my Twitter DMs are open). Once the season starts, I’ll try to form a smaller group from my Founding Members and more engaged subscribers, and maybe we can even dig into some real Mavs or other NBA data together.

The unexpected pioneer and father of possession-based analysis