2004-2006 Election Fraud Analytics
Further Confirmation of a Kerry Landslide
1988-2008 Election Calculator Model (Excel)
Interactive Election Simulation Model (Excel)
Monte Carlo Polling Simulation Model (Excel)
Latest Threads (Progressive Independent)
Polling Analysis Threads (2004-2006)
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 337-201 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 |
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|
|
|
|
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% (11 Poll
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. Recent
Battleground State Polls
6. Battleground
States: Probabilities of a Kerry Win
7. Independent National Polls: Kerry Vs. Bush
Monthly Trend
8. Recent
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 built-in 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 15-20 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 51-49 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 5-4 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 2-1 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 45-45, then Kerry’s is considered to be leading by 51-49,
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 52-48%,
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 52-48
% 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
2-party poll ratio, is input to the normal distribution (the bell-shaped 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 500-600, 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 50-50 poll
split, assuming undecided voters are allocated equally, each candidate has a
50% probability of winning the state. If the split is 60-40, the probability
that the leader will win the state is 99.99%. If the polling split is 51-49,
the leader has a 69% chance of winning the election. For 52-48, the probability
is 83%. It’s 97% for a 53-47 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 over-emphasized: 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.
Favorite Links
An
incumbent who can’t break 50 percent is in trouble, even if he’s ahead.
http://www.prospect.org/web/page.ww?section=root&name=ViewWeb&articleId=8694
Undecided voters
break for the challenger.
http://unfutz.blogspot.com/2004/09/how-undecideds-break.html
Polling Data
http://online.wsj.com/public/resources/documents/info-battleground04.html
http://www.americanresearchgroup.com/
http://www.economist.com/media/pdf/YouGovN.pdf
http://www.electoral-vote.com/
Commentary and
Analysis
http://www.emergingdemocraticmajorityweblog.com/donkeyrising/
http://members1.chello.nl/~a.horlings/doc-polls.html
http://synapse.princeton.edu/~sam/pollcalc.html
2000 Election
Exit Poll Statistics
http://www.cnn.com/ELECTION/2000/epolls/US/P000.html