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Volume 24: The 2018 Election, Who Projected It Best?

It's a lot of blue.

A log-loss comparative analysis of quantitative and qualitative 2018 U.S. House of Representatives election projections

Well, how did your projections do?” – Dale Cohodes.

It will come as a shock to nobody that I maintained a personal set of projections for the recently completed elections to the House of Representatives. It may surprise you more to know that reviewing my projections alongside the so-called “professionals” gives us an excellent opportunity to think through one of our favorite topics—probability. Elections are an interesting class of random event: probabilistic with a single trial and a discrete outcome. The tools we have to predict their outcome—polls, demographics, past voting patterns—result in distributions that include deviations from a mean. But no matter how much we’d like to, we cannot re-run the recent election in Georgia’s 7th or North Carolina’s 9th Congressional district, even though each was decided by fewer than 1,000 votes. And no matter how small the margin, the candidate with a plurality of the votes wins; a margin of 10,000 votes or 1,000,000 votes results in the same practical outcome. Elections are fundamentally different from random processes like flipping a coin or tomorrow’s high temperature.

Because of this, a simple question about forecast quality can be extended to provide insight into the general nature of probabilistic forecasts.

• What’s a good probabilistic forecast? • Whose House projections were the best?

What’s a good probabilistic forecast?