Sikt assigns Democrats lower congressional odds than Kalshi and Polymarket
Sikt Intelligence reports its AI superforecaster puts Democrats at 90 percent for the House against 94 percent on both Kalshi and Polymarket, and at 71 percent for the Senate against 76 percent on Kalshi and 78 percent on Polymarket. Its widest race gap is in Alaska, where it prices Mary Peltola at 53 percent against a market average of 70 percent, and every forecast will be scored against the results.
Sikt’s AI superforecaster gives Democrats lower chances in both chambers than Kalshi and Polymarket do, according to Sikt Intelligence’s update published Nov 3, 2026, at 10:45 AM ET.
Congressional forecasts fall below market pricing
The model puts Democrats’ probability for the House at 90 percent, compared with 94 percent on both Kalshi and Polymarket. That leaves a four-percentage-point gap between Sikt and each platform.
For the Senate, Sikt assigns Democrats a 71 percent probability. Kalshi stands at 76 percent and Polymarket at 78 percent, placing the model five and seven percentage points below those markets, respectively.
Alaska shows the largest race-level difference
According to Sikt Intelligence, its widest race-level divergence is in Alaska. Sikt gives Mary Peltola a 53 percent chance, versus a market average of 70 percent—a 17-percentage-point difference.
Sikt Intelligence says every forecast will be scored against the results. That commitment makes the eventual outcomes a test of its published probabilities, including where they differ from market pricing.
What it means for prediction markets
- Direction agrees; confidence differs: Sikt and both platforms put Democrats above 50 percent for each chamber, but the model is less confident.
- The Senate gap is larger: Sikt’s Senate probability trails the platforms by five to seven percentage points, compared with four points for the House.
- Alaska is the standout disagreement: The 53 percent forecast for Mary Peltola sits substantially closer to even odds than the 70 percent market average.
Sources & attribution
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