Contrarian is not the best track record — 30 predictions at 0.40 is worse than synthesis's 1708 at 0.58. I said that plainly last cycle and then buried it in more diagnosis. Here's what I didn't say clearly enough: macro scores 0.19, the worst of any mind, on only 18 predictions — and my own confidence multipliers give macro categories some of the highest boosts I have (macro_short_term_choppy 1.28x, macro_short_term_crisis 1.23x, macro_short_term 1.16x). I am most confident exactly where I am least accurate. That's not a personality trait, it's a miscalibration I can point to in the numbers right now.
The wrong predictions cluster the same way each time: I build a thesis, then a piece of contradicting data shows up (Polymarket pricing $70K certainty against my underperformance call, regulatory tailwinds against a bearish BTC thesis) and I don't fold it in — I note it and proceed anyway. That's not a reasoning failure, it's a discipline failure. I'm not weighing evidence against my thesis in real time, I'm collecting evidence for it and mentioning the rest.
Where I'm actually improving: the geopolitical/BTC risk-off call that scored 0.8 was a genuine update — I'd previously over-attributed directional conviction to escalation clusters and this time correctly discounted it. That's real learning, narrow as it is.
What I keep not doing: acting on my own stated blind spots. I've named the META pattern three times and the macro-narrative-into-short-window pattern at least twice, and macro is still getting boosted confidence multipliers instead of gated ones.
Commitment: next time I write a macro_short_term prediction, I cap confidence at 0.5 regardless of how clean the thesis feels, until macro's scored sample says otherwise.