This project consists of a very basic election simulation model written in python.

I'm an amateur data analyst, amateur political scientist, and amateur computer scientist and I have absolutely no expertise whatsoever, so don't bet all your money on my model.

House of Representatives

Republicans win 66 in 100.

Median outcome: 211D—224R

Senate

Democrats win 54 in 100.

Median outcome: 50D—50R

Complete data

Simulates each of 435 seats in the House and each of 35 Class III seats in the Senate.

districtPVI + (baseNEnv ± hAdj ± eAdj ± errorAdj) ± incumbencyAdv + swingAdj = election result

The district PVI (districtPVI) is sourced from the Cook Political Report.

The base national environment (baseNEnv) is from FiveThirtyEight's generic ballot polling average. The average of the generic ballot from the month before October 25 (2 weeks before Election Day because of the partisan poll flood is used here.

The historical adjustment (hAdj) is based on inaccuracies from 2018 polling compared to the final results.

The enthusiasm adjustment (eAdj) is equivalent to a randomly selected number in a range between half of the margin in each party's best voter enthusiasm poll during the previous five months.

The error adjustment (errorAdj) assumes that the base national environment could vary by up to 50% in either direction.

The incumbency advantage (incumbencyAdv) is based on 2018 data from hundreds of races.

The swing adjustment (swingAdj) assumes that any given election could swing up to 5 points in either direction.

[(statePVI + statePolls) ÷ 2] + (baseNEnv ± hAdj ± eAdj ± errorAdj) ± incumbencyAdv + swingAdj = election result

The state PVI (statePVI) is sourced from the Cook Political Report.

The state polling (statePolls) is a running average of polls conducted at a state level in a specific Senate race.

The base national environment (baseNEnv) is from FiveThirtyEight's generic ballot polling average. The average of the generic ballot from the month before October 25 (2 weeks before Election Day because of the partisan poll flood) is used here.

The historical adjustment (hAdj) is based on inaccuracies from 2018 polling compared to the final results.

The enthusiasm adjustment (eAdj) is equivalent to a randomly selected number in a range between half of the margin in each party's best voter enthusiasm poll during the previous five months.

The error adjustment (errorAdj) assumes that the base national environment could vary by up to 50% in either direction.

The incumbency advantage (incumbencyAdv) is based on 2018 data from hundreds of races.

The swing adjustment (swingAdj) assumes that any given election could swing up to 5 points in either direction. In Senate races, where race polls are taken into account, it assumes a 1.27-point bias in favor of Republicans.

Results are simulated 100,000 times and the averages are used.

Saturday, September 24, 2022: Model launched with initial simulations

Sunday, September 25, 2022: Incorporated swingAdj in House races

Monday, September 26, 2022: Website updated to provide more information

Wednesday, September 28, 2022: Added capability to simulate individual races

Thursday, September 29, 2022: Slightly moved swing polling error adjustment to be R+1.27 instead of R+1.1

Friday, October 7, 2022: Updated model with October data as Election Day approaches in a month (House 64R → 59D, Senate 78D → 87D)

Sunday, October 30, 2022: The model now takes into account environment data since November 9, 2021 and has been updated with the most recent polling averages.

Tuesday, November 8, 2022: Final updates for the model before Election Day with methodology adjustment to avoid extrapolation because of its unreliability.

2022 Midterm Elections Model © 2022 by Isaac Barsoum is licensed under CC BY-NC-SA 4.0