Tag Archives: election models

Exposing the 2016 Popular Vote Myth

Exposing the 2016 Popular Vote Myth

Richard Charnin
April 5, 2018

The myth that Clinton won the popular vote by nearly 3 million is parroted daily by pundits, even Trump supporters. Clinton won a fraudulent recorded popular vote, but Trump won the True Vote. It’s 2018 and the pundits still fail to recognize the historical fact that the recorded vote is never the same as the True Vote.  It’s past time for a great awakening.

Trump won the estimated True Vote by 50.5-43.4%, a 9.7 million vote margin. We estimate the True Vote based on the following simple models:

  • 1 Adjustments to the recorded vote: illegal votes , disenfranchised voters, voting machine flips 
  • 2 Race: Census breakdown and shares of white and non-white voters
  • 3 Returning 2012 voters and 2016 vote shares
  • 4 Party-ID: Gallup voter survey and vote shares
  • 5 Decided: Vote shares before and after Sept. 1

Given Model 1 adjustments to the recorded vote, we calculate an estimated True Vote. In models 2,3,4,5 we estimate vote shares required to match the True Vote.

 1 Adjustment Estimate Clinton Trump Other
Illegal 3.0 mil 85% 10% 5%
Disenfran 4.0 mil 85% 10% 5%
Net Voting Machine Flip 7.0 mil 0% 90% 10%
Total Clinton Trump Other Margin
Recorded  136.22 65.72 62.89 7.61 2.83
48.25% 46.17% 5.59% 2.08%
Illegal -3.0 -2.55 -0.30 -0.15 -2.25
Disenfran 4.0 3.40 0.40 0.20 3.00
Vote Flip 0.0 -7.00 6.30 0.70 -13.30
Total Vote 137.22 59.57 69.29 8.36 9.72
 True Vote 43.41% 50.50% 6.09% 7.08%
2 Census Pct Clinton Trump Other
white (adj.) 73.30% 32.4% 61.14% 6.5%
Black 12.45% 84% 13% 3%
Latino 9.22% 66% 28% 6%
Asian 3.67% 65% 27% 8%
Other 1.36% 56% 36% 8%
True Vote 100.00% 43.41% 50.50% 6.09%
Recorded 100% 48.25% 46.17% 5.59%
3 Party-ID Gallup Pct Clinton Trump Other
Dem 31.0% 88.0% 10.0% 2.0%
Rep 28.0% 5.0% 92.0% 3.0%
Ind 41.0% 36.0% 53.0% 11.0%
True Vote 100.0% 43.44% 50.59% 5.97%
Votes 137.22 59.61 69.42 8.19
4 Returning 2012 voters Mix Clinton Trump Other
Obama 41.33% 85% 10% 5%
Romney 40.80% 5% 92% 3%
Other 1.54% 35% 40% 25%
DNV (new) 16.32% 35% 51% 14%
True Vote 100.0% 43.43% 50.61% 5.96%
Votes 137.22 59.59 69.45 8.18
5 When Decided Pct Clinton Trump Other
Before Sept 1 60.0% 48% 48% 4.0%
After Sept 1 40.0% 37% 54% 9.2%
True Vote   43.41% 50.50% 6.09%

Sensitivity Analysis

7 million vote flip  Trump Flip    
 to Clinton 80%= 5.6mm 90%= 6.3mm 100%=7mm
Trump True Vote
6.0 68.79 69.49 70.19
4.0 68.59 69.29 69.99
2.0 68.39 69.09 69.79
6.0 50.1% 50.6% 51.2%
4.0 50.0% 50.50% 51.0%
2.0 49.8% 50.4% 50.9%
6.0 43.8% 43.3% 42.8%
4.0 43.9% 43.41% 42.9%
2.0 44.1% 43.6% 43.0%
6.0 8.72 10.12 11.52
4.0 8.32 9.72 11.12
2.0 7.92 9.32 10.72

My Books
Trump Won the True Vote
77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Poll
Reclaiming Science: The JFK Conspiracy


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Election Fraud Slides for the “Real Deal”

Richard Charnin
Feb. 10, 2016

Reclaiming Science: The JFK Conspiracy
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud


I created this slide presentation for an interview with Jim Fetzer (on the Real Deal). It includes links to the 1988-2008 State and National Presidential True Vote Model  and articles by other mathematicians confirming the Cumulative Vote Share (CVS) analysis.

Mathematical models

Prove election fraud and confirm unadjusted exit polls.

True Vote (TVM) – plausible vote shares of estimated returning voter mix.
Cumulative Vote Shares (CVS) – sorted county precinct votes.
Voter Turnout (VTM) –  registered voter turnout  vs. exit poll Party-ID (forced to match)

In the 2014 Governor elections,   the models indicated that the Democrats very  likely won the True Vote in at least 6-8 elections officially won by the GOP.  The following model turnout assumptions favored the Republicans, therefore the Democrats must have done better than indicated.
TVM: 2012 presidential recorded vote understated Obama’s true vote.
VTM: Registered Republican percentage voter turnout was  higher than the Democrat.

Myth of 50/50 electorate

The Democrats would win every national election if votes were accurately counted.
They get an estimated 83% of the minority vote (30% of the electorate).
Therefore they need just 36% of white voters (70% of the electorate) to reach 50%.
1968- 2012: Census indicates 80 million more votes cast than recorded (uncounted).

Adjusted Polls

Pre-election polls are biased due to the Likely Voter Cutoff Model.
The LVCM eliminates newly registered and others (mostly Democratic) deemed unlikely to vote in adjusting the Registered Voter (RV) to Likely Voter (LV) polls.

Unadjusted exit polls are always fixed to match the recorded vote.
Corporate media-funded pollsters always assume ZERO fraud.

Unadjusted exit polls are not for public viewing.
In 2012, just 31 states were exit polled. Why?

2002 – HAVA (Help America Vote Act)
Installed unverifiable touchscreens; central tabulators.
Only a few states have a strong auditing process.







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Posted by on February 5, 2016 in Uncategorized


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9/19/ 2012 Presidential True Vote and Election Fraud Simulation Model: Obama 320 EV; 100% Win Probability

9/19/ 2012 Presidential True Vote/Election Fraud Simulation Model:Obama 320 EV; 100% Win Probability

Richard Charnin
Sept.19, 2012

The analysis assumes the election is held on the latest poll date:
2012 Presidential True Vote and Monte Carlo Simulation Forecast Model

UPDATE: FINAL 11/5 FORECAST. Go here for the latest version.

Forecast Summary
Obama has jumped out to a commanding 49-44% lead in the battleground state polls. He has 320 expected electoral votes based on his state win probabilities. The 500 trial Monte Carlo simulation indicates that if the election were held today, he would have a 100% win probability (he won all 500 election simulation trials). But it’s still too early to project him a winner.

The 7% of voters who are still undecided hold the key to the election. The model currently assumes an equal split of the undecided vote. I suspect they are mostly Democrats disillusioned with Obama but scared by Romney and Ryan. If the undecided voters break for Obama, he will be in a commanding position to win re-election. But look for an October surprise.

Obama needs at least a 55% True Vote to overcome the Fraud factor. He has held a steady 4% lead in the state polls since April. The polls are anticipating the inevitable 5% reduction in Obama’s True Vote. Immediately after the Democratic Convention, Obama moved into a 5% lead in the Gallup (RV) and Rasmussen (LV) national tracking polls, but the polls are tied once again.

The forecast model is a combination of a) a pre-election Monte Carlo Simulation Model, which is based on the latest state polls, and b) the True Vote Model, based on a feasible estimate of new and returning 2008 voters and corresponding estimated vote shares. The model will be updated periodically for the latest state and national polls.

The source of the polling data is the Real Clear Politics (RCP) website. The simulation uses the latest state polls. Recorded 2008 vote shares are used for states which have not yet been polled.

Model.......... Obama Romney
True Vote...... 55.25% 44.75%
Expected EV.... 379.64 158.36
Snapshot EV.... 380 158
EV Win Prob.... 99.97% 0.03%

State Polls
Average........ 49.3% 44.4%
Projection..... 52.5% 47.5%
Pop. Win Prob.. 94.8% 5.2%
Expected EV.... 320.2 217.8
Snapshot EV.... 322 216

National Polls
Average....... 48.20% 45.30%
Projection.... 51.45% 48.55%
Pop. Win Prob.. 92.2% 7.8%
Gallup......... 47.0% 46.0%
Rasmussen...... 46.0% 47.0%

Projection..... 52.5% 47.5%
Mean EV........ 320.4 217.6
Max EV......... 351 187
Min EV......... 278 260
EV Win Prob.... 100.0% 0.0%

The 2008 True Vote Model (TVM) determined that Obama won in a landslide by 58-40.3%. Based on the historical red-shift, he needs at least a 55% True Vote share to overcome the systemic 5% fraud factor. The TVM was confirmed by the unadjusted state exit poll aggregate: Obama had an identical 58-40.5% margin (76,000 respondents). He won unadjusted National Exit Poll (17,836 respondents) by an even bigger 61-37% margin.

The National Exit Poll displayed on mainstream media websites (Fox, CNN, ABC, CBS, NYT, etc.) indicate that Obama had 52.9%. As usual, the unadjusted state and national exit polls were forced to match the recorded share.

The True Vote Model

In projecting the national vote, the required input to the TVM are returning 2008 voter turnout rates in 2012 and estimated 2012 vote shares. The rates are applied to each state in order to derive the national aggregate turnout . A 1.25% annual voter mortality rate is assumed. There are two options for estimating returning voters. The default option assumes that 2008 voters return in proportion to the unadjusted 2008 exit poll aggregate (Obama won by 58-40.5%). In this scenario, Obama wins by 55-45% with 380 EV and has a 100% EV win probability.

It is important to note that the True Vote is never the same as the recorded vote. The 1988-2008 True Vote Model utilizes estimates of previous election returning and new voters and and adjusted state and national exit poll vote shares.

Sensitivity analysis

The TVM displays the effects of effects of incremental changes in turnout rates and shares of returning voters. Three tables are generated consisting of nine scenario combinations of a) Obama and McCain turnout rates and b) the Obama/Romney shares of returning Obama and McCain voters. The output tables display resulting vote shares, vote margins and popular vote win probabilities.

Monte Carlo Simulation: 500 election trials
There are two options for the simulation model. Both should be used and the results compared. The default option uses the TVM projected state vote shares. The second option uses projections based on the latest pre-election state polls.

The projected vote share is the sum of the poll share and the undecided voter allocation (UVA). The model uses state vote share projections as input to the Normal Distribution function to determine the state win probability.

The simulation consists of 500 election trials. The electoral vote win probability is the number of winning election trials divided by 500.

In each election trial, a random number (RND) between 0 and 1 is generated for each state and compared to Obama’s state win probability. If RND is greater than the win probability, the Republican wins the state. If RND is less than the win probability, Obama wins the state. The winner of the election trial is the candidate who has at least 270 electoral votes. The process is repeated in 500 election trials.

2008 State Exit Poll and recorded vote data is displayed in the ‘2008‘ worksheet. The latest state polls are listed in the ‘Polls” worksheet which will be used for trend analysis. The data is displayed graphically in the ‘PollChart’ worksheet. A histogram of the Monte Carlo Simulation (500 trials) is displayed in the ‘ObamaEVChart’ worksheet.

Electoral Votes and Win Probabilities

The Electoral Vote is calculated in three ways.

1. The snapshot EV is a simple summation of the state electoral votes. It could be misleading since there may be several very close elections which go one way.
2. The Theoretical EV is the product sum of the state electoral votes and win probabilities. A simulation or meta-analysis is not required to calculate the expected EV.
3. The Mean EV is the average electoral vote in the 500 simulated elections.

The Mean EV will be close to the Theoretical EV, illustrating the Law of Large Numbers. The snapshot EV will likely differ slightly from the Theoretical EV, depending on the number of state election projections that fall within the margin of error.

Obama’s electoral vote win probability is the percentage of 500 simulated election trials that he won.

The national popular vote win probability is calculated using the normal distribution using the national aggregate of the the projected vote shares. The national aggregate margin of error is 1-2% lower than the average MoE of the individual states. That is, if you believe the Law of Large Numbers and convergence to the mean.

The Fraud Factor

Election fraud reduced the 1988-2008 Democratic presidential unadjusted exit poll margin from 52-42% to 48-46% recorded. View the 1988-2008 Unadjusted State and National Exit Poll Database

The combination of True Vote Model and state poll-based Monte Carlo Simulation enables the analyst to determine if the electoral and popular vote share estimates are plausible. The aggregate state poll shares can be compared to the default TVM.

The TVM can be forced to match the aggregate poll projection by…
– Adjusting the vote shares by entering an incremental adjustment in the designated input cell. A red flag would be raised if the match required that Obama capture 85% of returning Obama voters while Romney gets 95% of returning McCain voters (a 10% net defection).

– Adjusting 2008 voter turnout in 2012 in order to force a match to the aggregate projected poll shares. For example, if McCain voter turnout is required to be 10-15% higher than Obama’s, that would also raise a red flag.

– Setting the returning voter option to assume the 2008 recorded vote. This is an implicit assumption that the 2008 recorded vote was the True Vote. Of course, the 2008 election was highly fraudulent, but this is what the election forecasters effectively do: they ignore the fraud factor. The resulting model vote shares would then closely match the LV polls and would suggest that Romney has a good chance of winning a rigged election.

In any case, check the simulated, theoretical and snapshot electoral vote projections and the corresponding win probabilities.

Election Model Projections: 2004-2010

In 2004, I created the Election Model , and posted weekly forecasts using the latest state and national polls. The model was the first to use Monte Carlo simulation and sensitivity analysis to calculate the probability of winning the electoral vote. The final Nov.1 forecast had Kerry winning 337 electoral votes with 51.8% of the two-party vote. The forecast closely matched the unadjusted exit polls.

In 2006, the adjusted National Exit Poll indicated that the Democrats won the House by a 52-46% vote share. My 120 Generic Poll Forecasting Regression Model indicated that the Democrats would capture 56.43% of the vote. It was within 0.06% of the unadjusted exit poll.

The 2008 Election Model projection exactly matched Obama’s 365 electoral votes and was within 0.2% of his 52.9% recorded share. He won by 9.5 million votes. But the model understated his True Vote. The forecast was based on final likely voter (LV) polls that had Obama leading by 7%. Registered voter (RV) polls had him up by 13% – before undecided voter allocation. The landslide was denied. The post-election True Vote Model determined that Obama won by 23 million votes with 420 EV. His 58% share matched the unadjusted state exit poll aggregate (83,000 respondents).

The exit pollsters have never explained the massive 11% state exit poll margin discrepancy, much less the impossible National Exit Poll 17% discrepancy. If they do, they will surely claim that the discrepancies were due to flawed polling samples. Nor can they explain the rationale of using impossible returning voter weight adjustments to force the exit polls to match the recorded votes in the 1988, 1992, 2004 and 2008 presidential elections.

Pre-election RV and LV Polls

Virtually all early pre-election polls are of Registered Voters (RV). An exception is the Rasmussen poll. It uses the Likely Voter (LV) subset of the full RV sample. Rasmussen is an admitted GOP pollster.

One month prior to the election, the pollsters replace the full RV sample polls with LV subsamples. The RV polls are transformed to LVs to promote an artificial “horse race” – and the poll shares invariably tighten. The Likely Voter Cutoff Model (LVCM) effectively understates the turnout of millions of new Democratic voters – and therefore increases the projected Republican share. Democrats always do better in RV polls than in the LVs.

Media pundits and pollsters are paid to project the recorded vote – not the True Vote. And they are usually right. The closer they are, the better they look. They expect there will be fraud, so they prepare the public for it by switching to LV polls which are usually excellent predictors of the recorded vote. But they never mention the fraud factor which gets them there.

Historically, RV polls have closely matched the unadjusted exit polls – after undecided voters are allocated. They have also been confirmed by the True Vote Model. The loop is closed when implausible/impossible exit polls are forced to match bogus recorded votes that were predicted by biased LV pre-election polls.

Election Model Forecast; Post-election True Vote Model

2004 (2-party vote shares)
Model: Kerry 51.8%, 337 EV (snapshot)
State exit poll aggregate: 51.7%, 337 EV
Recorded Vote: 48.3%, 255 EV
True Vote Model: 53.6%, 364 EV

Model: Obama 53.1%, 365.3 EV (simulation mean);
Recorded: 52.9%, 365 EV
State exit poll aggregate: 58.0%, 420 EV
True Vote Model: 58.0%, 420 EV

2012 (2-party state exit poll aggregate shares)
Model: Obama 51.6%, 332 EV (Snapshot)
Recorded : 51.6%, 332 EV
True Vote 55.2%, 380 EV


Posted by on April 26, 2012 in 2012 Election, True Vote Models


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Richard Charnin's Blog

JFK Conspiracy and Systemic Election Fraud Analysis