In July of 2018, I raised eyebrows by predicting some four months before the midterm election that Democrats would pick up 42 seats in the House of Representatives. In hindsight, that may not seem such a bold prediction, but when my forecast was released, election Twitter was still having a robust debate as to whether the Blue Wave would be large enough for Democrats to pick up the 23 seats they needed to take control of the House of Representatives and return the Speaker’s gavel to Nancy Pelosi.
Based on its 2018 performance, my model, , seem well poised to tackle the 2020 presidential election – 16 months out. I’ll serve up that result below, but first let’s set the table by reviewing my model’s 2018 forecasting success.
Not only did I predict that they would gain nearly double the seats they needed, but I also identified a specific list of Republican seats Democrats would flip, including some, such as , that were listed as “Lean Republican” by the majority of race raters at the time. At a time when other analysts coded even the most competitive House races for Democrats as Lean or Tilt Democrat, I identified 13 Republican-held districts as “Will Flips,” 12 as “Likely to Flip,” and 6 as “Lean Democrat.” I also identified a large list of “Toss Ups,” from which I would later identify the remaining “flippers.” In addition, I identified some “long-shot toss-up” districts that could be viable flips under some turnout scenarios. Of the original 25 districts I identified as definitely or highly likely to flip, all but one, Colorado CD3, did so, possibly because the party failed to invest in their nominee there.
The post-election diagnostics of my forecasting model, which departs significantly from the approaches used in conventional election forecasting models, such as those used by , reveal just how powerful my model was at identifying the House districts and Senate races capable of producing Blue Wave effects powered by Trump backlash in the electorate. Indeed, the places I went astray in my final, “handicapped” predictions are races where I ignored the clear signals of my model, such as Georgia’s 6th congressional district, which my model was quite clear about flipping, and Kentucky’s 6th, which my model was quite clear couldn’t. Still, in other races, my manual handicapping was necessary, and correct, because despite its overall accuracy, my model underpredicts the Democrats’ two-party vote share in Utah’s 4th district.
Looking ahead to the 2020 Electoral College map, my model delivers on two of the most critical elements of election forecasting: , that is, simplicity. It’s probably not lost on you, dear reader, that I am offering a forecast not for the presidential primary election, itself still in its infancy, but for the November 2020 general election that is some 16 months away. And I am offering a forecast free from all the trappings you are used to. There are no poll aggregators, no daily or weekly updates, no simple versus deluxe versions. Right now, there is not even a nominee! By and large, I don’t expect that the specific nominee the Democratic electorate chooses will matter all that much unless it ends up being a disruptor like Bernie Sanders.
Barring a shock to the system, Democrats recapture the presidency. The leaking of the Trump campaign’s internal polling has somewhat softened the blow of this forecast, as that polling reaffirms what my model already knew: Trump’s 2016 path to the White House, which was the political equivalent of getting dealt a Royal Flush in poker, is probably not replicable in 2020 with an agitated Democratic electorate. And that is really bad news for Donald Trump because the Blue Wall of the Midwest was then, and is now, the ONLY viable path for Trump to win the White House.
I can't copy and paste the graphics but her electoral vote prediction is Dems: 278 Reps: 197
Her graphic has the breakdown of electoral votes by state.
http://cnu.edu/wasoncenter/2019/07/01-2020-election-forecast/