The final pre-election forecast gives Donald Trump a >99% chance of beating Kamala Harris in Utah.
Each day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races. Each candidate’s probability of winning is the proportion of simulations that they’ve won.
The model first constructs a polling average, pooling data across similar states when polls are sparse. It then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.
The model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another. Similar states are more likely to share polling biases and see similar shifts in polling trendlines.
Sources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com
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