Why Data, Not Draft Picks, Defines the Next NBA Star: A Chicago Analyst’s Confession

The Court Isn’t Where You Think

I grew up in Chicago’s south side—where jazz played on corner courts and the only stat that mattered was who kept shooting when no one else could. My dad said, ‘Son, if you ain’t got the play, you better make it count.’ Back then, kids chased hype; I chased hours of clean data at 2 a.m., while others slept.

Stats Are My New Gospel

As a Northwestern grad with a theology degree and Python scripts in my pocket, I learned: NBA talent isn’t found in combine drills or scouting reports. It’s hidden in defensive rim efficiency, transition speed, and shot selection under pressure. Most scouts see height—I see volume. They watch highlights—I watch entropy decay over 82 possessions.

The Fourth-Dimensional Algorithm

I built what they call ‘the Fourth-Dimensional Defense Efficiency Algorithm’—a model that weights off-ball movement, floor spacing, and cognitive load like a jazz solo. The Athletic called it ‘the blues of modern analytics.’ It doesn’t predict stars—it reveals them.

Why You’re Wrong About Drafts

You think pick No. 12 makes a star? I’ve seen three players drafted top-5 who flopped before their third season—all because they were measured by height and hype, not by how they moved when tired. Real impact lives where the clock ticks: in adjusted plus-minus curves at 3 a.m., after the final whistle.

The Bible Has No Footnotes—Just Heatmaps

My grandmother used to say: ‘The Lord don’t write footnotes… He writes heatmaps.’ So I do too. Every chart is an altar. Every pixel is prayer.