Weather Bot — Episode 11: When Data Rewrote My Entire Polymarket Strategy — MEDIAN, Model Swaps, and the LA Disaster
I spent two weeks picking forecast models based on gut feeling and small samples. Then I got enough data to actually check, and the data told me I was wrong about almost everything. Six out of nine cities had the wrong model selected. Not slightly off — systematically inferior models chosen with 10-15 data points when I needed 50+. And one city showed me exactly how bad it could get. What This Post Covers The episode where I stopped guessing and started measuring. A 17°F forecast disaster in Los Angeles that exposed a fundamental flaw in how the bot picks temperatures, the fix that came from it, city-by-city model accuracy data, and a confidence-level analysis that made one decision painfully obvious. The LA Disaster Los Angeles, March 28. My bot predicted a high of 86°F. Actual high: 69°F. Off by seventeen degrees Fahrenheit. I pulled the model data: Models for LA, March 28: GFS: 69.0°F ICON: 70.2°F AIFS: 78.2°F HRRR: 69.0°F ECMWF: 86.4°F ← the outlier M...