Elections Forecasting: Big Data, Algorithms–and Baseball

There are less than 50 days before the Nov. 8 elections and the stage is set with local, state, and national media churning out a blizzard of stretch-run campaign coverage.

In print media, there is a uniformity in how elections articles are structured. Most routinely note how the race fits into the “big picture;” how much money candidates raised; and what polls say.

This century, another element has become standard with the convergence of ‘Big Data’ and algorithmic modeling—electorate ratings and forecasts from analysts mining historical statistics, crunching voter data, and aggregating polls to make predictive judgments based on an ever-expanding realm of factors and relationships.

Until the late 1970s, predicting the outcome of an election was a parlor game for academics, political scientists, and pundits on par with Ouija boards, tea leaves, and the ‘Washington Rule,’ which said if the city’s NFL team won its last home game before Election Day, the president’s party would win. The “rule” picked every presidential winner between 1940 and 2000, but since then has been right only once. 

The templates for the “modern era of elections forecasting” were set in 1978 by a Yale University professor’s model based on the economy and incumbency, and in 1979 by a University of Kentucky professor’s model charting relationships between presidential approval ratings and subsequent votes.

By the late 1990s, the American Political Science Association’s PS: Political Science & Politics magazine and International Journal of Forecasting were annually introducing new elections forecasting models developed by universities, analysts, consultants, nonprofits, media outlets, and the gaming industry, based on “econometrics,” public opinion, incumbency, party unity, scandals, poll aggregation, and historical voting patterns, to provide “fluid intelligence” for voters, media, pundits, candidates, and campaigns.

But it wasn’t until a sports writer used the same tools developed to project baseball players’ performances

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