Methodology - 2020 U.S. Gubernatorial Election Model

Below you can read about how the winner for each data category was established, and how they were all combined to get the final race ratings. Unfortuantely, I do not still have access to the full details, so some of the specifics on picking winners will not be known.

View Full Model (Google Sheets)

Specific Categories

Simply put, the winners for each category are all about which party is ahead in said category. With the Recent History category, this was whichever party had won more gubernatorial elections in the past 12 or so elections. This data was from 270toWin.com. For the last election category, it was whichever party won the election, that's it. This was retrieved from Wikipedia. For the expert forecast category, it was whichever party was favored by the 270toWin consesus forecast/map. For the polls category, this was based on either FiveThirtyEight's polling average or whatever the latest poll result was if no average was available.

Race Ratings

The race ratings took into account the winner of each category and assigned a rating depending on who won more categories. Unfortuantely, this was a slightly opinion-based process as I didn't have a specific formula I was sticking with. If a party won all four categories, then the race would be assigned a "Safe" rating for that party. If a party had three categories, the race was "Leaning". If it was a two-two split, the race was a "Toss-up". You can view the ratings and see the correspondence to the categories won above by clicking the spreadsheet link. (green button)