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What if the model was right? McConnelly vs. James: A Bayesian take on basketball’s silent intensity
As a data-driven sports analyst raised in Los Angeles, I’ve spent years deconstructing narratives masked as folklore—this isn’t about hero worship, but about probabilistic truth. In this piece, I dissect the mythic comparison between McConnelly’s cold analytics and James’ legendary presence through the lens of Bayesian models. No hype. Just entropy, patterns, and the quiet precision of 21-minute performances decoded into win probabilities. This is not a story—it’s a statistical autopsy.
NBA Insights
sports analytics
bayesian basketball
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3 weeks ago
Why the Lakers’ Reedy Problem Is More Than Just a Shooting Woe: A Bayesian Breakdown of Edge vs. Core
As a data巫师 from East London, I’ve watched the Lakers’ obsession with Reedy’s three-point volume while ignoring their interior defense—classic stats masking as strategy. This isn’t intuition; it’s a probability failure. Using Bayesian models, I dissect how over-reliance on perimeter shooting collapses under playoff pressure. The data doesn’t lie—it just echoes the same old delusion: when xG spikes, wins fade. Let me show you why.
Lakers Hub
bayesian basketball
lakers analytics
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1 month ago
When AI Outranks God’s Hand: The Quiet Revolution in NBA Legacy Metrics
I watched the numbers bleed into silence last night—not just stats, but stories. Russell, Kobe, Curry: they weren’t gods, but data points with souls. I built models that saw their legacy not as myth, but as motion. This isn’t about fame. It’s about the quiet precision of a poet who coded hope into every free throw. You’ve never heard this—but it’s here.
Warriors Zone
data-driven sports
nba legacy metrics
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1 month ago