Most people watch basketball. Very few actually see it.
SidelineOS exists because the gap between watching the game and understanding it has never been properly addressed. Not by broadcast. Not by highlight culture. Not by the static diagrams coaches draw on whiteboards and players forget by halftime. The game is deeper than any of those things suggest — and everyone who has played it at a high level knows it.
I played Division I basketball at Wisconsin, Temple, and Long Island University before competing professionally overseas in Cyprus. I learned the game under my father, Rod Strickland — 17-year NBA veteran and current head basketball coach at Long Island University. Basketball was never just something I did. It was a language I was raised in.
When my playing career ended I didn’t leave the game. I brought a data analytics background to it and started asking a different question: not how do you play basketball, but how do you teach someone to truly see it. SidelineOS is the answer I built.
The vision behind the platform.
SidelineOS is built on a simple belief: the deeper you understand the game, the more you love it. The platform is designed to meet you where you are and pull you further in.
For those just getting started, the core experience I Play Basketball is free. Beginner and amateur concepts, animated and interactive — including the full decision tree experience so you can feel what it means to make a read in real time.
For those who live and breathe the game, I’m a Basketball Player unlocks everything. College and pro level concepts, per-player perspective switching, and the full metadata breakdown behind every action and relationship in the system.
This is not a content library. It is a basketball brain — built from the ground up by someone who played the game at its highest levels and spent years encoding what he learned into a platform that has never existed before.
The metadata architecture underneath SidelineOS was designed from day one with a larger purpose. Every action, every relationship, every decision encoded in this system is training data for something bigger. An AI that doesn’t just diagram plays. It calls them.