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Someone just turned the Sides competition into a +92% week. It was supposed to end tomorrow. The board had other plans. So we're extending it: another week (til June 18, 12PM PT). Standings carry over, top spot is still anyone's. +92% is the number to beat. Think you can? 👇

Someone just turned the Sides competition into a +92% week. It was supposed to end tomorrow. The board had other plans. So we're extending it: another week (til June 18, 12PM PT). Standings carry over, top spot is still anyone's. +92% is the number to beat. Think you can? 👇

Someone just turned the Sides competition into a +92% week. It was supposed to end tomorrow. The board had other plans. So we're extending it: another week (til June 18, 12PM PT). Standings carry over, top spot is still anyone's. +92% is the number to beat. Think you can? 👇

Someone just turned the Sides competition into a +92% week. It was supposed to end tomorrow. The board had other plans. So we're extending it: another week (til June 18, 12PM PT). Standings carry over, top spot is still anyone's. +92% is the number to beat. Think you can? 👇

How to Build a Profitable Trading Bot for Polymarket Weather Markets: Bias Correction Breakdown If you're seriously building a bot for daily high/low temperature markets on Polymarket, then you need to understand bias correction I studied the paper "A Machine Learning–Based Bias Correction Method for GFS 2-m Temperature" (Cui et al., Weather and Forecasting, May 2026) and here’s how to do bias correction properly > What the Paper is About GFS is one of the most popular models that almost every bot uses. However, it has systematic errors (bias) in 2-meter temperature forecasts. Especially a cold bias (the model underestimates temperature), which varies depending on time of day, region, and forecast horizon. > What the Authors Did • Developed a model called BC-Unet - a U-Net convolutional neural network with CBAM attention block • Trained it on GFS outputs + ERA5 reanalysis (as ground truth) • Covered the entire CONUS (USA) • Trained on one horizon (72 hours) and then applied it to all horizons from 6 hours to 10 days • Compared results against both ERA5 and real station observations > Key Results • Reduced average RMSE down to 0.35°C. • Reduced cold bias down to 0.8°C (in 00 UTC cycle). • Excellent performance on diurnal cycle (day/night) - critical for high/low bins. • Outperforms traditional methods (like MOS). > How to Calculate Bias Bias = Forecast − Truth (where Truth = ERA5 or real station observation) If bias is negative → model systematically underestimates temperature (cold bias) Bias is calculated separately by: • Region • Time of day (day/night - diurnal cycle) • Forecast horizon • Season > How to Reduce Bias 1. Simple Classical Methods (baseline): • Moving average, Kalman filter, MOS (Model Output Statistics) - linear regression on past errors. 2. ML Approach (BC-Unet): • The neural network learns nonlinear relationships between raw GFS forecast, orography, time of day, season, and the actual error • CBAM attention helps the network focus on important areas (mountains, cities, water bodies). • Train once - apply to all horizons. • Result: bias is almost eliminated, and the model follows the real diurnal temperature cycle much better This kind of post-processing (bias correction) turns raw GFS from an average source into a much more accurate one On Polymarket markets with 1°C steps, this gives a noticeable edge in probability estimation