Explore The Ways Republicans Or Democrats Could Win The Midterms

Explore The Ways Republicans Or Democrats Could Win The Midterms

UPDATED Aug. 31, 2022, at 2:44 PM

Explore The Ways Republicans Or Democrats Could Win The MidtermsPick the winner of each race to see how FiveThirtyEight’s forecast would change.

2022 election forecastHow our forecast works

By Ryan Best, Jay Boice, Aaron Bycoffe and Nate Silver

FiveThirtyEight’s Senate and House forecasts are based on myriad factors, with changes in one race often influencing odds in another. To see just how much individual races can change the forecast, first try picking different winners in key Senate races (or feel free to skip ahead to key races in the House!).1 But beware, the choices you make in the Senate affect the House, and vice versa.

iIncumbent candidate Scroll to see more racesDemocrats are slightly favored to win the Senate

Based on FiveThirtyEight’s current forecast

Republicans win33 in 100MAJORITY56REPSEATS5452525456DEMSEATSDemocrats win67 in 100Average: 50.656565454525250505050525254545656REPSEATSDEMSEATSMorelikelyMorelikelyRepublicans are favored to win the House

Based on FiveThirtyEight’s current forecast

Republicans win76 in 100MAJORITY270REPSEATS255240225225240255270DEMSEATSDemocrats win24 in 100Average: 228.7270270255255240240225225225225240240255255270270REPSEATSDEMSEATSMorelikelyMorelikelyWho will control Congress?

Based on FiveThirtyEight’s current forecast

Republicans win both chambersRepublicans win the Senate
Democrats win the HouseDemocrats win the Senate
Republicans win the HouseDemocrats win both chambersOdds displayed in the graphics may not match numeric odds due to rounding.

How this works: We start with the 40,000 simulations that our election forecast runs every time it updates. When you choose the winner of a race, we throw out any simulations where the outcome you picked didn’t happen and recalculate each party’s chances of winning each chamber using just the remaining simulations. If you choose enough unlikely outcomes, we’ll eventually wind up with so few simulations that we can’t produce accurate results. When that happens, we go back to our full set of simulations and run a series of regressions to see how your scenario might look if it turned up more often.

In simplified terms, the regressions start off by looking at the vote share for each candidate in every simulation and seeing how the rest of the map changed in response to big or small wins. Let’s say you picked Democratic Sen. Raphael Warnock to win Georgia. In some of our simulations, Warnock may have won Georgia very narrowly, and in others, he may have lost it very narrowly. But in simulations where he won Georgia by a big margin, Democrats may have also won big in toss-up races and pulled some Democratic-leaning races into their column, while the reverse may be true in simulations where Warnock lost Georgia by a wide margin. We figure out how every other race tended to look in that full range of scenarios, tracking not just whether other Democratic candidates usually won other races but also how much they generally won or lost each one by.

After all of that, we take some representative examples of scenarios that include the picks you made and use what we learned from our regression analysis to adjust all 40,000 simulations, and then recalculate win probabilities for each race and chamber of Congress. Finally, we blend those adjusted simulations with any of the original simulations that still apply and produce a final forecast.

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