On July 15, Election Lab, The Post’s election model, gave Republicans an 86 percent chance of winning the six seats they needed to take over the Senate majority. Today — 50 days later — it gives Republicans only a 52 percent chance of winning the majority. So, how did the model go from predicting a sure-thing Republican majority to now calling the fight for the majority a statistical toss up?
I put that question to John Sides, a political science professor at George Washington University, contributor to The Post’s Monkey Cage blog and one of the three-headed political science monster who built Election Lab. (Eric McGhee and Ben Highton are the two other heads.)
Here’s what he told me:
[It’s] not that races have narrowed, but that the model has begun weighting information differently — mainly by (a) incorporating polling data (where possible) after the relevant primaries, and by (b) increasing the weight that polls have in the forecast. What this suggests is that in several states, Democrats are arguably ‘out-performing’ the fundamentals. This doesn’t always translate into a high chance of the Democrat actually winning (see: Kentucky) but it does help the Democrats’ overall chances of retaining a majority.
To understand Sides’s point, it’s important to understand how models work. At the start of an election cycle, the model is based heavily on underlying fundamentals of past elections. It’s almost an entirely generic calculation that takes little account of candidates, polling etc. There’s a reason for that, of course. Early in election cycles there often aren’t candidates in races yet and, therefore, polling is limited. As the cycle gets into its latter stages — like now — the model tilts to rely more heavily on candidates and polling and less on fundamentals. (That doesn’t mean the underlying fundamentals don’t matter at all in the model. They just matter less and less as the election gets closer.)
And, as Sides notes, Democratic candidates are currently overperforming how past history suggests they should be doing in a number of races.