Created by TruthIsAll
Final Projection
Last update: Nov.1, 2004 7:00 pm
Kerry 337 EV / 51.8%
Bush 201 EV / 48.2%
The model projects Kerry the winner in 27
states:
AR, CA, CO, CT, DE, DC, FL, HI,
IL, IA, ME, MD, MA, MI, MN, MO, NH, NJ, NM, NY, OH, OR, PA, RI, VT, WA, WI
Election
Model Projections
If the election
were held today, then based on recent state
polling, the Electoral Vote Simulation
model calculates that John Kerry has a 99.8%
probability of winning an electoral vote majority by a 337201 margin and 51.80%
of the popular vote. Kerry won 4990
of 5000 Monte Carlo simulated election trials.
Based on the
average of eighteen national polls, the National
Vote Projection model calculates that Kerry has a 99.99% probability of winning a popular vote majority
with 51.63% of the vote.
For the final
projection, the base case undecided/other
allocation assumption to Kerry has been changed from 60% to 75%.
This is consistent with the opinion of professional political pollsters.
To gauge the sensitivity of the expected
electoral vote and win probability to the allocation, the model calculated five
scenarios: 60%, 67%, 75%, 80% and 87%.








Current (%) 
Simulation Model State Polling Weighted Average 
Projection Model National Polling Combined Average 

Kerry 
47.88 
47.17 

Bush 
46.89 
46.89 





Projected (%) 
EV/Popular Vote 
Popular Vote 

Kerry 
337 / 51.80 
51.63 

Bush 
201 / 48.20 
48.38 





Win Prob (%) 
Electoral Vote (5000 trials) 
Majority Vote (MOE: 0.73%) 

Kerry 
99.80 
99.99 

Bush 
0.20 
0.01 

Bush Job
Approval: 48.50% (11Poll average)
Click
for detailed polling and analytic reports
Click a graph to view:
1. Kerry/Bush National Trend derived from Weighted State Polls
2. Kerry Electoral Vote and Win
Probability Projection Trend
3. Kerry Electoral and National
Vote Projection Trend
4. Undecided Voter Allocation
Impact on Kerry EV and Win Probability
5. Final Battleground State
Polls
6. Battleground States:
Probabilities of a Kerry Win
7. Independent National Polls
Monthly Trend
8. Final Independent and
Corporate Media National Polls
9. Bush Monthly Job Approval
Ratings from Feb. 2001
10. Win Probability Sensitivity
to Number of Polls and Group Average
11. 5000 Monte Carlo Simulation
Trials
12. 5000 Monte Carlo Simulation
Trials: Kerry Electoral Vote Frequency
The Gospel according to the Polling Gurus:
1 If an incumbent is polling below 50%, he's in trouble.
Bush is barely averaging 47%.
2 If an incumbent's approval rating is below 50%, he's in trouble.
Bush is at 48.50%.
3 If an incumbent has less than a 3%4% lead in the final polls, he’s in
trouble.
Bush is tied with Kerry.
4 Undecided voters break for the challenger.
Poll
Updates:
Zogby: Kerry 47 Bush 48 (Kerry 1)
TIPP: Kerry 44 Bush 45 (Kerry +4)
Rasmussen: Kerry 47.4 Bush 48.8 (Kerry .4)
FOX: Kerry 48 Bush 45 (Kerry +1)
WaPo: Kerry 48 Bush 48 (Kerry 1)
Florida
and Ohio scenarios:
If
Kerry
1) wins FL and loses OH, he has a 99.3% win probability
with 307 EV.
2) loses FL and wins OH, he has a 98% win probability
with 300 EV.
3) loses FL and loses OH, he has a 75% win probability
with 280 EV.
4) wins FL and wins OH, he has a 99.8% win probability
with 327EV.
Election
Model Methodology (see below for a complete description):
The Election Model actually
consists of three individual models:
1) National Polling Model I – based on national polls from 9
independent polling firms.
2) National Polling Model II – based on national polls from 18
independent and corporate media firms.
3) Monte Carlo Simulation model  based on state polls.
In each National Polling
model, the average vote percentage split is calculated. All three models PROJECT a vote percentage
by ALLOCATING the undecided and others to Kerry and Bush. The base case
assumption is that 60% will split for Kerry and 40% to Bush. The rationale for
the assumption: historically, the undecided vote breaks for the challenger.
National and state win
probabilities are calculated based on the adjusted poll projections. The Normal
Distribution is used to compute the probability of winning a majority of the
national votes in the National models, and the probability of winning a
majority in each of the states in the Monte Carlo simulation model.
The Monte Carlo simulation
method (consisting of 5000 election trials) is executed to calculate Kerry’s
EXPECTED Electoral Vote and win probability, based on his individual state win
probabilities. The national election win probability is equal to the total
number of electoral vote wins, divided by 5000 (election trials).
Sensitivity
Analysis
A powerful feature of the Election Model is the builtin
sensitivity analysis. We analyze how various undecided voter allocation
assumptions effect Kerry’s projected popular vote, electoral vote and win
probability. The base case assumption
is that Kerry will win 60% of the undecided vote. But what if he does better
than that? What if he does worse? To get a feel for the probabilities, we
calculate Kerry’s prospects for the following undecided allocations: 50%, 55%, 60%, 67% and 75%.
In the EV Simulation model, Kerry’s electoral vote win
probabilities increase as his undecided allocation increases from 50% to 75%.
His projected vote, electoral vote margin and number of winning states increase
accordingly.
Both National models calculate the probability of a
popular vote majority, given the same undecided allocation scenarios. The win
probabilities are calculated using national polling data, unlike the EV
simulation which uses state polling.
Election
Model Methodology
There are three primary methods for tracking and predict
elections. Each utilizes different data sources.
The first analyzes economic factors: growth, jobs, inflation, etc. Economic and
political forecasters have had some success using this approach (after all,
this is what they do for a living) by employing an econometric models based on
multiple regression and/or factor analysis. The derived formula weights the
variables in order to predict those which most affect the popular vote. How
some can forecast a 58% popular vote for Bush, considering the economic and
political events of the last four years, is a mystery to me.
The second method tracks the national polls and projects undecided or third
party voters in order to predict the winner of the popular vote. There are
about 1520 national pollsters. A
majority of the popular vote does not mean the winner will gain 270 electoral
votes. For all practical purposes the
winner of the popular vote will most certainly win the electoral vote. The
possibility that he won’t can only occur in extremely close elections where the
winning margin is less than 0.5%. In fact, in a 5149 popular vote split, there
is virtually zero probability that the popular vote winner would lose in the
Electoral College. In 2000, Gore won the national vote by 0.5% and would have
won Florida if all the votes were counted.
Unfortunately, the Supreme Court stopped the recount and voted 54 for
Bush.
A third method tracks the individual state polls. The focus of this method is
to predict the electoral vote spread.
Ten to twenty tight battleground states usually hold the key to the
election.
In the Election Model, methods two and three are used. Polls have been pretty
good indicators, provided they are current and unbiased.
The Model uses national and state polls as the basis for the projections. The
only projection assumption is in the
allocation of undecided/other voters. Historically, undecided voters have split
at least 21 for the challenger. The Model projects 60% will vote for Kerry as
a base case assumption. So if a poll
has the race tied at 4545, then Kerry’s is considered to be leading by 5149,
since he will receive 60% of the remaining 10%.
One advantage of national
polling is its relative simplicity and point “spread” focus. If the spread
exceeds the polling margin of error (MoE), typically +/3% for polls of 1000
sample size, then based on statistical theory, the leader has a 95% chance of
winning the election  assuming a) the
election was held that day and b) poll is an unbiased sample of the actual
voting population.
But that is just the
probability for a single poll. If we consider three polls, or equivalently, a
single poll of 3000 samples, the MoE tightens to +/1.80%. Assuming that the average split is 5248%,
there is a 95% probability that the leader will receive between 50.2% and
53.8%. If we add the 2.5% probability
that he will exceed 53.8%, then he has a 97.5% probability of winning at least 50.2% of the vote.
Now let’s consider fifteen polls. Here the MOE is a very tight +/0.80%
confidence interval. For the same 5248
% average spread, the probability is 95% that the leader will receive between
51.2% and 52.8% of the popular vote. The probability that the leader will
exceed 50% of the national vote is 99.99+%. If the leader has an average
52%48% lead in 15 national polls the day before the election, then an election
defeat will be extremely unlikely. In
fact the odds would be less than one in a thousand.
The 95% confidence interval around the mean is derived from the MOE. The MOE is
1.96 times the standard deviation, which is a statistical measure of the
variability of polling observations. The standard deviation, along with the
2party poll ratio, is input to the normal distribution (the bellshaped curve)
in order to determine the probability of winning a majority of the vote in the
national (2) and state models.
But an electoral vote majority (270), not the popular vote, is the magic
number. To calculate the expected EV from state polling data, we calculate the
probability of winning each state and then apply the popular Monte Carlo simulation
method. State polls typically sample 500600, so the MOE is wider (+/4%) than
the 3% MOE in National polls. Just like in the National model, the probability
of winning each state is calculated based on the state polling spread, adjusted
for the same allocation of undecided/other voters.
In the case of a 5050 poll
split, assuming undecided voters are allocated equally, each candidate has a
50% probability of winning the state. If the split is 6040, the probability
that the leader will win the state is 99.99%. If the polling split is 5149,
the leader has a 69% chance of winning the election. For 5248, the probability
is 83%. It’s 97% for a 5347 split
(outside the MOE).
So this is how we determine the probability of winning the election: In a Monte
Carlo simulation, we run 5000 simulated election trials to determine the
probability of winning 270 Electoral Votes. The probability is the number of
election trial wins divided by 5000.
In each state trial run, the model generates a random number (RND) between 0
and 1. The RND determines who wins the state. For example, if the RND generated
for FL is .55 and Kerry has a .60 probability of winning the state, then he
wins the state in this trial since the RND fell in the interval from 0 to 0.60. If the RND is greater than .60, then FL
would go to Bush in this trial run. In
this fashion, the model proceeds to generate an RND for each state, assigning
its electoral votes (EV) to the winner. The total number of electoral votes
calculated for each of the 5000 election trials. If Kerry wins 4900, then he has a 98% probability (4900/5000) of
winning the election. The model also calculates Kerry’s expected (mean)
electoral vote by averaging his EV totals in the 5000 trials.
An advantage of the simulation approach is that it minimizes poll “whiplash”
(slight changes in state polling which causes the leader to change. This will
not affect the total expected EV as much it would if we assigned ALL of the
electoral votes to the leader, even if he was ahead by just 0.5%.
Using national and state models has another advantage: it provides a
mathematical confirmation between the two methods. If the results differ, it
could mean that the state polls are more current than the nationals, or that
the accuracy of the state or national polling data (or both) is questionable.
That is why the model uses 18 national and 51 state polls. This reduces the
margin of error, so that we have more confidence in the results.
A final word, one that cannot be overemphasized: The Election Model calculates
the PROBABILITY of a Kerry win. It does not PREDICT a Kerry win.
The Election Model AVERAGES the
latest national and state polls, then ADJUSTS the averages by ADDING an ASSUMED
undecided voter allocation, and APPLIES statistical theory, based on the number
of polls and the average MOE, to determine the PROBABILITY of winning the
election.