Nov 5, 2026 · 13 min
Election models captured the range, not the certainty
Predicting elections is getting harder. What did the midterms teach us?
The episode shows why forecasting is useful precisely when it makes uncertainty visible rather than pretending to eliminate it.
- 1Jack Rametta’s model correctly called most competitive Senate races and placed their outcomes within its predicted ranges.
- 2Forecasts differ because researchers make reasonable but consequential choices about fundamentals, polling, and race-specific assumptions.
- 3Prediction markets can aggregate information while also influencing campaigns when flawed polls or inputs shape expectations.
Don't miss
Jack Rametta explains why a forecast can perform well even when it does not identify every winner: the outcomes mostly fell within its predicted ranges.
The brief
Before the results, Jack Rametta’s final Senate forecast carried personal and professional stakes. Afterward, he found that most competitive races landed within the model’s predicted range.
Rametta explains that election models combine historical fundamentals with polling, while making assumptions about incumbency, open contests, and how national conditions affect individual states.
Polling improved compared with 2016, but low response rates and non-response bias remain central problems. Pollsters adjusted for those weaknesses without making uncertainty disappear.
Different models can produce different probabilities without one being irrational or definitive. The disagreement often reflects reasonable choices about assumptions, not simple analytical failure.
Prediction markets can aggregate information, but they may also influence candidate behavior when fraudulent polls or flawed inputs distort market expectations.
The broader lesson is political: this election appeared unusually nationalized, with the national environment outweighing state-specific and candidate-specific factors.
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