As a London-based NBA data analyst, I’m dissecting why selecting Esberly with the 10th pick would be a strategic disaster. Drawing on player behavior models and historical comparisons to players like Josh Jackson, I expose the real risks behind raw talent. This isn’t about bias—it’s about Bayesian probability. If you're building a championship team, this is the kind of 'high-upside' gamble you simply can’t afford.

As a data analyst with 5 years in NBA front offices, I’m diving into a hypothetical trade involving the Mavericks, Spurs, and draft assets. Using statistical modeling, I assess whether swapping players like Jalen Brunson, Kyrie Irving, and draft picks for PJ Washington, Dereck Lively II, and future picks makes strategic sense. This isn’t hype—it’s probability. Let’s crunch the numbers.

As a data analyst who lives by the numbers, I’m breaking down why betting on Billey — despite his baggage — might be the smartest move for the Rockets. With low downside and sky-high upside, this isn’t just a gamble. It’s a strategic reset. Let’s crunch the odds, from draft position to team fit. Spoiler: The math says yes.

As a former MIT data scientist and NBA analytics intern, I’ve built models that expose how media hype distorts player evaluations. This post breaks down why teams and fans overrate certain prospects—using real shot charts, win probability shifts, and regression analysis. Spoiler: The 'can’t-miss' hype is often just noise. Let’s cut through the bias with cold logic—and maybe a chuckle at the expense of overhyped rookies.