Methodology

Overview

My 2020 Presidential Forecast is my first presidential race that I am forecasting. However, it is not my first election forecasting. I created a forecast for the 2018 Senate Midterms , and did reasonably well. I correctly called all of the races except Florida and Indiana. I ended up with a brier score of.0484. Now lets get onto this forecast for the 2020 Presidential Election. The forecast is probabilstic, not definitive. So when you see a race that has a candidate winning 56.3% of the simulations, it is not saying they are going to win, rather that they are favored. A probabilstic model is calibrated correctly if some underdogs win races they aren't favored to. For example if there are 10 states between 20% & 30% win rate for the underdog in that state, then, if correctly calibrated, you should expect 2 or 3 wins for the underdogs in those states.

Polling

My 2020 Presidential Forecast is my first presidential race that I am forecasting. However, it is not my first election forecasting. I created a forecast for the 2018 Senate Midterms , and did reasonably well. I correctly called all of the races except Florida and Indiana. I ended up with a brier score of.0484. Now lets get onto this forecast for the 2020 Presidential Election. The forecast is probabilstic, not definitive. So when you see a race that has a candidate winning 56.3% of the simulations, it is not saying they are going to win, rather that they are favored. A probabilstic model is calibrated correctly if some underdogs win races they aren't favored to. For example if there are 10 states between 20% & 30% win rate for the underdog in that state, then, if correctly calibrated, you should expect 2 or 3 wins for the underdogs in those states.

Priors

My 2020 Presidential Forecast is my first presidential race that I am forecasting. However, it is not my first election forecasting. I created a forecast for the 2018 Senate Midterms , and did reasonably well. I correctly called all of the races except Florida and Indiana. I ended up with a brier score of.0484. Now lets get onto this forecast for the 2020 Presidential Election. The forecast is probabilstic, not definitive. So when you see a race that has a candidate winning 56.3% of the simulations, it is not saying they are going to win, rather that they are favored. A probabilstic model is calibrated correctly if some underdogs win races they aren't favored to. For example if there are 10 states between 20% & 30% win rate for the underdog in that state, then, if correctly calibrated, you should expect 2 or 3 wins for the underdogs in those states.

Randomness

My 2020 Presidential Forecast is my first presidential race that I am forecasting. However, it is not my first election forecasting. I created a forecast for the 2018 Senate Midterms , and did reasonably well. I correctly called all of the races except Florida and Indiana. I ended up with a brier score of.0484. Now lets get onto this forecast for the 2020 Presidential Election. The forecast is probabilstic, not definitive. So when you see a race that has a candidate winning 56.3% of the simulations, it is not saying they are going to win, rather that they are favored. A probabilstic model is calibrated correctly if some underdogs win races they aren't favored to. For example if there are 10 states between 20% & 30% win rate for the underdog in that state, then, if correctly calibrated, you should expect 2 or 3 wins for the underdogs in those states.

Output

My 2020 Presidential Forecast is my first presidential race that I am forecasting. However, it is not my first election forecasting. I created a forecast for the 2018 Senate Midterms , and did reasonably well. I correctly called all of the races except Florida and Indiana. I ended up with a brier score of.0484. Now lets get onto this forecast for the 2020 Presidential Election. The forecast is probabilstic, not definitive. So when you see a race that has a candidate winning 56.3% of the simulations, it is not saying they are going to win, rather that they are favored. A probabilstic model is calibrated correctly if some underdogs win races they aren't favored to. For example if there are 10 states between 20% & 30% win rate for the underdog in that state, then, if correctly calibrated, you should expect 2 or 3 wins for the underdogs in those states.