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Why Do the Top Picks Lose? The Grizzlies’ Statistical Blind Spot in Draft Prep

Why Do the Top Picks Lose? The Grizzlies’ Statistical Blind Spot in Draft Prep

As a data scientist raised in Brooklyn with a foot in both stats and sports, I’ve seen this pattern before: elite prospects like Zhai Qi, Trey Lewis, and Jalen Jones are being tested by the Grizzlies—despite models showing clear regression. This isn’t about hype. It’s about misaligned priors. The team holds the 17th and 57th picks, yet ignores quantifiable signals. Here’s why intuition still beats analysis—and what that costs.
NBA Draft—NCAA
nba draft
statistical bias
•1 month ago
Why Do 76% of Playoff Favorites Lose? The Statistical Truth Behind the Bulls’ Collapse

Why Do 76% of Playoff Favorites Lose? The Statistical Truth Behind the Bulls’ Collapse

As a data scientist who built predictive models for NBA playoff outcomes, I’ve seen the math lie. History shows that favorites don’t win because of noise, not talent. In this piece, I unravel why public perception is wrong — and how Bayesian logic exposes the hidden bias in betting markets. This isn’t luck. It’s a system failure.
Thunder Zone
nba playoffs
bayesian modeling
•1 month ago
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