Prediction for 2024 Election

65% Trump

I’ve taken the polling of most states at face value, and have diverged from them if they’ve historically been inaccurate in that state or if the state has been trending towards one direction for a reasonable amount of time. Given Trump’s rhetoric with regard to deportations, I think it’s likely that more illegal immigrants will try to vote this election, particularly in states that don’t have voter ID laws. Historically, only about 2-5% of illegals admit to voting in elections, and I could see that number getting to 10% or maybe even 15% this election. That could lead to gains for Democrats of about 0.1 to 1% in some states, depending on how many illegals there are and how willing they are to vote.

On specific states:

• Nevada: large illegal population, no voter ID requirement, and polls have historically underestimated the performance of Democrats in presidential elections that include Trump. (80% Harris)

• Arizona: has voter ID laws, and polls look good for Trump now. (70% Trump)

• New Mexico: no voter ID requirement, has not underestimated Trump in presidential elections, and polls have a large lead for Harris. (~95% Harris).

• Texas: polls predict Trump will win, and they’ve accurately estimated Trump’s performance there. (~95% Trump).

• Wisconsin: has voter IDs and polls persistently underestimate his performance in presidential elections that involve Trump. Polls right now are even. (75% Trump).

• Michigan: same reasoning as Wisconsin (65% Trump).

• Pennsylvania: the polls are even. There are no voter ID laws, which may enable illegals to vote, though the state does not have many to begin with. (55% Trump).

• Georgia: Trump’s polling is good and Georgia’s polling has historically been accurate. The state has been trending Democrat ever since the aughts, which reduces Trump’s chances. (55% Trump).

• North Carolina: polling looks good for Trump, and both the RCP and 538 polls have historically underestimated his performance. (85% Trump).

• Florida: polling looks very good for Trump and they have historically underestimated his performance there. Illegals voting should not be much of an issue due to the strict voter ID laws. (~95% Trump).

If a simple probabilistic model is made, then Trump wins 55% of the time and Kamala wins 45% of the time. The odds for Trump here are lower than what I expected, but some of it is driven by the fact that Kamala was given a 5% chance of winning Texas and Florida. If she wins either of those two states, she almost certainly wins the whole thing.

My take? Watch the three rust belt states. The thinking is that Trump winning Georgia and Wisconsin don’t have that much to do with each other, but that Trump winning Wisconsin is also dependent on him doing well with the broader rust belt demographic.

Notes

Note: these numbers will have increased since.

from here

2020 poliing error (538):

from here

2016 polling error (modeled 538):

from here

2020 polling error (modeled 538):

from here

Polling error in swing states (RCP):

from here

#The reasoning for not taking the polling error that seriously is that this is not the same election. More celebrities and high status people are publicly endorsing Trump, and social media websites are much less hostile to him now. 

n <- 10000

nv <- sample(c(0, 1), size = n, replace = TRUE, prob = c(0.2, 0.8))
az <- sample(c(0, 1), size = n, replace = TRUE, prob = c(0.7, 0.3))
nm <- sample(c(0, 1), size = n, replace = TRUE, prob = c(0.05, 0.95))
tx <- sample(c(0, 1), size = n, replace = TRUE, prob = c(0.95, 0.05))
wi <- sample(c(0, 1), size = n, replace = TRUE, prob = c(0.75, 0.25))
mi <- sample(c(0, 1), size = n, replace = TRUE, prob = c(0.65, 0.35))
pa <- sample(c(0, 1), size = n, replace = TRUE, prob = c(0.55, 0.45))
ga <- sample(c(0, 1), size = n, replace = TRUE, prob = c(0.55, 0.45))
nc <- sample(c(0, 1), size = n, replace = TRUE, prob = c(0.85, 0.15))
fl <- sample(c(0, 1), size = n, replace = TRUE, prob = c(0.95, 0.05))

nv <- nv*6
az <- az*11
nm <- nm*5
tx <- tx*40
wi <- wi*10
mi <- mi*15
pa <- pa*19
ga <- ga*16
nc <- nc*16
fl <- fl*30
el <- rep(227, 10000)

kamalavote <- el + nv + az + nm + tx + wi + mi + pa + ga + nc + fl
kdf <- data.frame(vote=kamalavote,nv, az, nm, tx, wi, mi, pa, ga, nc, fl)
kdf$kamalawin <- 0
kdf$kamalawin[kdf$vote>270] <- 1
kdf$kamalawin[kdf$vote==270] <- NA
kdf %>% group_by(kamalawin)%>% summarise(n = n())
# A tibble: 3 × 2
  kamalawin     n
      <dbl> <int>
1         0  5521
2         1  4401
3        NA    78