Most of what you think you know about basketball analytics is wrong.
It’s not your fault.
Many people in the media and even in the industry misrepresent what basketball analytics is really about.
Why do they do it?
Because they don’t understand it either.
In short, they don’t know any better.
They don’t know what basketball analytics really means and what it is all about.
So don’t sweat it.
Let me tell you the real story about about basketball analytics.
Basketball analytics is not just fancy statistics.
Traditional and advanced statistics are a part of it but they are only a piece of the puzzle.
Box score stats like points and rebounds and advanced metrics like PER are just another type of data within a collection of basketball analytics tools.
You know what else falls under the subject of “basketball analytics?”
- Basketball scouting reports
- Psychological profiles
- Film study
- Coaching staff reports
- Market evaluations
- Medical reports
Basketball analytics data can include “numbers,” but there’s so much more to it.
It can be shocking to learn that analytics includes these things because 99% of the media and online conversation about analytics boils down to, “Who created the most exciting, complicated, new statistics?”
While that practice is meaningful, there is so much that can be learned from the other forms of data too.
In Sports Analytics: A Guide For Coaches, Managers, and Other Decision Makers, basketball analytics consultant Ben Alamar defines sports analytics as:
- the management of structured historical data,
- the application of predictive models that utilize that data, and
- the use of information systems to inform decision makers and enable them to help their organizations in gaining a competitive advantage on the field of play.
Phrased differently, basketball analytics is about using different types of data to solve a problem you have.
It is about helping people make better decisions.
When viewed through that lens, it’s easier to see how scouting reports and medical profiles fall under the category of basketball data as much as points and rebounds do.
So it’s not just about numbers and more numbers.
The people and organizations who use a variety of data in combination will make the best decisions.
Basketball analytics is about asking the right questions.
A quotation often (falsely) attributed to Albert Einstein says, “Not everything that can be counted counts, and not everything that counts can be counted.”
This line tells us two things:
- Every meaningful quotation will eventually be attributed to either Albert Einstein or Michael Jordan. (Let’s call it Kerti’s Rule of Quotations.)
- Just because something looks like a number that should matter, doesn’t mean it matters.
The eye test in the form of scouting reports is a completely legitimate form of data.
Anyone who says differently is selling something — probably an expensive new analytical tool.
Scouting reports, advanced metrics, and psychological profiles are all data that can be used to different extents to answer different questions.
It reminds me of something I read from Ian Levy.
When it comes to deciding on the value of a statistic everything is tied to the questions they are being used to answer . . . There are no inherently good or bad stats, no perfect ones or useless ones either. Statistics can be used badly and carelessly, but the fault is in the application, not the measures themselves. The value of a statistic is always derived from how well it matches the question you are asking, and what other statistics you can put around it to add as much context as possible.
Ian is spot on.
Asking the right questions is a more important basketball analytics skill than manipulating numbers.
Being able to answer the question you ask is obviously important too, but if you’re asking the wrong question to begin with, you’ll end up in a worse place than where you started.
Instead of being in a beginner’s place of uncertainty, you’ll think you have an answer worth acting on. It will waste your time and get you in trouble.
I’ve seen analytics “experts” get alienated and shunned because their certainty led them to be abrasive to people who didn’t share their conclusion. Communication skills are at play, but so is agreeing on what question to ask.
The key to understanding the fundamental principles of basketball analytics is to know the importance of asking the right questions.
You can’t draw any helpful conclusions to the question you’re asking if the question you are asking is the wrong one.
What basketball analytics really means
So that’s it for today.
I’ll leave you with these two points:
- Numbers aren’t the only type of important data in basketball analytics.
- Always take the time at the beginning to ask the best question possible.
The next time someone tells you that basketball analytics is all about numbers, share this article with them.