isotherm

calibrated decision model · Kalshi temperature ladders · 7 cities

An open, Jev-style decision model: typed questions in, calibrated probabilities out, all read off one distribution over tomorrow's high. Every chart here is scored out of sample against the market's own price at the same timestamp. Code and raw results: physicalreasoning/isotherm.

Edge over the market, by half-year

Log score gain in nats per ladder. The simple pools went to zero in 2025; isotherm stays positive but decays.

results/benchmark.json, benchmark_g2.json

Leaderboard on identical rows

Every model against the market with a 95% date-block CI. The control is trained on labels sampled from the market, so it can only win if the pipeline leaks.

results/benchmark_g2.json

Calibration

Every bucket as a yes/no question: predicted probability against how often it happened.

results/benchmark_g2.json · reliability

Which forecast the market already prices

Last 12 months. Adding NBM, the forecast behind weather.gov, no longer helps. GFS MOS still does.

results/benchmark_g2.json · recent slice

By city, last 12 months

isotherm minus market. Blue is better than the market, red worse; a dot marks a CI that excludes zero.

results/benchmark_g2.json