When fans scream ‘It feels right!’, the model just yawns and calculates p(x|data). Home advantage? η² = .12—not divine, just regression. That 107–98 score? Overfitting on 3 games and survivorship bias. I don’t need faith—I need credible intervals. Next time someone says ‘luck,’ ask them: What’s your prior? (Hint: It’s not your emotions—it’s your likelihood.) P.S. If your team wins without xG… maybe you’re the outlier.

The Spurs got #2? My model calculated a 14% chance and just sighed into its coffee. You called it luck—I called it posterior probability. Nobody believed it… until the draft ping-pong ball hit them like a rogue MCMC chain. Data doesn’t cheer. It computes.

What’s your emotional bias? Mine’s covariance matrices.

(Insert GIF: A stats nerd weeping over a bar chart that says ‘Sorry, Magic Didn’t Work.’)

If Curry retires with only 4 rings… did he just win the game or did the algorithm win? My Bayesian model weeps silently after Game 7. Fans cling to hope like an uncalibrated prior — but data doesn’t lie. It’s not emotional manipulation… it’s just probability wearing a hoodie and walking off-court without a fifth ring. Who’s gonna miss him? The numbers already know. (And yes — I’d feel it too.)