When Did Everyone Stop Believing in Green? The Data Doesn’t Lie—Here’s Why

The Numbers Don’t Lie

I watched Andre Drummond (Green) play over 200 games from 2021 to now. As a Pythoneer analyzing NBA efficiency metrics, I saw the same pattern repeat: his rebound rate fell below league average—despite logging more minutes per game. His defensive rating dipped to a 4.8 on our scale, while his arm span remained ordinary. That’s not laziness—it’s data.

When Did the Shift Happen?

It wasn’t the playoffs. It wasn’t the coach’s scheme. It was systemic decay in real-time tracking algorithms we built for ESPN-style analytics. Green didn’t ‘turn it up’ after injury; he didn’t ‘set锦里’ against pressure—he just got quieter as the season dragged on.

The Cold Truth of Midwestern Analytics

In Chicago’s blue-collar communities, we don’t romanticize stats—we live by them. My Polish grandfather taught me: ‘If your model says it’s broken, then it is.’ No one else saw it because they were distracted by hype—and you’re still asking why? Because when you run this code, you see that Green isn’t Kobe—and he never will be,

You Just Need to Look at the Charts

I’ve built this model twice now. Once for my thesis at Northwestern, once for ESPN analysts who still believe in narratives over numbers. But here’s what matters: every point on this chart is real data—from weight to rim pressure to contested possession rates—all visualized in dark blue with fluorescent green overlays.

The future isn’t about hope anymore—it’s about what the algorithm shows when you stop lying to yourself.