## US Presidential Elections: Forecasting the Vote, Simulating the “Fraud Factor”

### The True Vote Model indicates that Obama would have 54.5% and 358 expected EV in a fraud-free election. Will he be able to overcome the systemic fraud factor?

**By Richard Charnin**

*The 2012 Presidential True Vote and Monte Carlo Simulation Forecast Model is updated on a daily basis. The election is assumed to be held on the latest poll date. Link to this post for the daily update summary. This worksheet contains the weekly polling trend analysis.*

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.

10/20/2012

Obama: 299 expected electoral votes; 95% win probability (477 of 500 election trials).

He leads the state poll weighted average by 48.3-45.8%.

He leads in 13 of 18 Battleground states by 49.9-47.7% with 134 of 205 EV.

The RCP National Poll average is tied 47.1-47.1%.

The True Vote Model indicates that Obama would have 54.5% and 358 expected EV in a fraud-free election. Will he be able to overcome the systemic fraud factor?

**The Ultimate Smoking Gun that proves Systemic Election Fraud:**

**Model Overview**

Two forecasting methods are used.

– The Monte Carlo Electoral Vote Simulation is based on the latest state polls and currently assumes an equal split of undecided voters. The expected electoral vote is the sum of the products of the state win probabilities and corresponding electoral votes.

– The True Vote Model is based on plausible turnout estimates of new and returning 2008 voters and corresponding vote shares.

The model calculates an estimated True Vote forecast for the National aggregate or any state. The calculation is displayed below the input data section. State poll-based national vote shares, electoral vote and probabilities are displayed on the right side of the screen.

**No exit polls in 19 states**

The *National Election Pool (NEP)* is a consortium of six corporate media giants which funds the pollster *Edison Research* to do exit polling in the U.S and abroad. Last week, the NEP announced that they would not exit poll in 19 states, 16 of which are universally thought of as being solid RED states. Or are they?

In 2008, Obama won exit polls in AK, AL, AZ, GA, NE, SD. He came close to winning in TX, KY, SC, TN, MS. These former RED states may have turned PURPLE. View this worksheet in the model.

The bad news is that the NEP decision to eliminate the polls makes it easier for vote margins to be padded and electoral votes flipped. Without the polls, it is much more difficult to calculate the statistical probabilities of fraud based on exit poll discrepancies. In the 1988-2008 elections, the Democrats led the unadjusted state exit polls by 52-42%, but by just 48-46% in the official recorded vote. This is a mathematically impossible result which proves systemic election fraud.

The good news is that the post-election *True Vote Model* should find implausible discrepancies in the recorded state and national votes. After all, that is what it was designed to do.

**Sensitivity Analysis**

The pre-election TVM built in the 2012 Election Model uses alternative scenarios of 2008 voter turnout and defection rates to derive a plausible estimate of the total final share. The returning voter assumptions are based on Obama’s 58% True Vote (a plausible estimate) and his 53% recorded share. The latter scenario results in vote shares that are close to the LV polls. The sensitivity analysis of alternative turnout and vote share scenarios is an important feature in the model. The model displays the effects of effects of incremental changes in turnout rates and shares of returning voters. The tables display nine scenario combinations of a) Obama and McCain turnout rates and b) Obama/Romney shares of returning Obama and McCain voters. Obama’s vote share, winning margin and popular vote win probability are displayed for each scenario.

**Registered and Likely Voters**

Historically, RV polls have closely matched the unadjusted exit polls after undecided voters are allocated and have been confirmed by the True Vote Model.

Likely Voter (LV) polls are a subset of Registered Voter polls and are excellent predictors of the recorded vote – which always understate the Democratic True Vote. One month prior to the election, the RV polls are replaced by LVs. An artificial “horse race” develops as the polls invariably tighten.

The Likely Voter Cutoff Model (LVCM) understates the voter turnout of millions of new Democrats, thereby increasing the projected Republican share. Democrats always do better in RV polls than in the LVs. Based on the historical record, the Democratic True Vote share is 4-5% higher than the LV polls indicate. The LVs anticipate the inevitable election fraud reduction in Obama’s estimated 55% True Vote share.

Media pundits and pollsters are paid to project the recorded vote – not the True Vote. The closer they are, the better they look. They never mention the *fraud factor* which gets them there, but they prepare for it by switching to LV polls.

The disinformation loop is closed when the unadjusted, pristine state and national exit polls are adjusted to match the LV recorded vote prediction.

**2004 and 2008 Election Models**

The 2004 model matched the unadjusted exit polls. Kerry had 51.7% and 337 electoral votes. But the election was stolen. Kerry had 48.3% recorded. View the 2004 Electoral and popular vote trend

The 2008 model exactly matched Obama’s 365 EV. The National model exactly matched his official recorded 52.9% share; the State model projected 53.1%. His official margin was 9.5 million votes.

Obama had 58.0% in the unadjusted, weighted state exit poll aggregate (83,000 respondents) which exactly matched the post-election True Vote Model. Obama’s 23 million true vote vote margin was too big to steal.

The National Exit Poll displayed on mainstream media websites (Fox, CNN, ABC, CBS, NYT, etc.) indicates that Obama had 52.9% – his recorded vote. Unadjusted state and national exit polls are always forced to match the recorded share.

But the media never discussed the fact that *Obama had 61% in the unadjusted National Exit Poll (17,836 respondents)*. View the 2008 Electoral and popular vote trend

**1988-2008: 274 Exit state polls. An 8% Discrepancy**

In the six presidential elections from 1988-2008, the Democrats won the average recorded vote by 48-46%. But they led both state and national exit polls by 52-42%. There were approximately 375,000 respondents in the 274 state polls and 90,000 respondents in the six national polls. Overall, an extremely low margin of error.

View the 1988-2008 Unadjusted State and National Exit Poll Database

This graph summarizes the discrepancies between the 1988-2008 State Exit Polls and the corresponding Recorded Votes.

**The True Vote Model **

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 (83,000 respondents). He won unadjusted National Exit Poll (17,836 respondents) by an even bigger 61-37% margin.

In projecting the national and state vote, a 1.25% annual voter mortality rate is assumed. The TVM uses estimated 2008 voter turnout in 2012 and corresponding 2012 vote shares. The rates are applied to each state in order to derive the national aggregate result.

There are two basic options for estimating returning voters. The default option assumes the unadjusted 2008 exit poll as a basis. The second assumes the recorded vote. 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.

**Monte Carlo Simulation **

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

There are two forecast options in the model. The default option uses projections based on the latest pre-election state polls. The second is based on the state True Vote. The fraud factor is the difference between the two.

The projected vote share is the sum of the poll 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.

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.

**Electoral Votes and Win Probabilities**

The Electoral Vote is calculated in three ways.

1. The Snapshot EV is a simple summation of the electoral votes. It could be misleading if close state elections favor one candidate.

2. The Mean EV is the average of the 500 simulated election trials.

3. The Theoretical EV is the product sum of the state electoral votes and corresponding win probabilities. A simulation or meta-analysis is not required to calculate the expected EV.

The Mean EV approaches the Theoretical EV as the number of election trials increase. This is an illustration of the Law of Large Numbers.

Obama’s *electoral vote win probability *is his winning percentage of 500 simulated election trials.

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

**The Fraud Factor**

The combination of True Vote Model and state poll-based Monte Carlo Simulation enables an analyst to determine if the forecast 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 vote shares by an incremental change. A red flag would be raised if the match required, if for example Obama captured 85% of returning Obama voters and Romney had 95% of returning McCain voters (a 10% net defection).

– Adjusting 2008 voter turnout in 2012. For example, if McCain voter turnout is required to be 10-15% higher than Obama’s, that would raise a red flag.

– Setting the returning voter option to the 2008 recorded vote. The implicit assumption is that the 2008 recorded vote was the True Vote. But the 2008 election was highly fraudulent. Therefore, model vote shares will closely match the likely voter polls.

Check the simulated, theoretical and snapshot electoral vote projections and corresponding win probabilities.

In 2004, ** Election Model ** forecasts were posted weekly 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, closely matching the unadjusted exit polls. *

**2004 Election Model Graphs**

State aggregate poll trend

Electoral vote and win probability

Electoral and popular vote

Undecided voter allocation impact on electoral vote and win probability

National poll trend

Monte Carlo Simulation

Monte Carlo Electoral Vote Histogram

In the 2006 midterms, the adjusted National Exit Poll was forced to match the House 52-46% Democratic margin. * But the 120 Generic Poll Trend Model forecast that the Democrats would have a 56.4% share – exactly matching 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% – even before undecided voters were allocated. *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).

Exit pollsters and media pundits have never explained the massive 11% state exit poll margin discrepancy or the impossible 17% National Exit Poll discrepancy. If they did, they would surely claim that the discrepancies were due to reluctant Republican responders. But they will not even try to explain the impossible returning voter adjustments required to force the polls to match the recorded vote in the 1988, 1992, 2004 and 2008 elections.

**2008 Election Model Graphs**

Aggregate state polls and projections (2-party vote shares)

Undecided vote allocation effects on projected vote share and win probability

Obama’s projected electoral vote and win probability

Monte Carlo Simulation Electoral Vote Histogram

1) The Likely Voter Cutoff Model eliminates newly registered Democrats from the LV sub-sample. Kerry had 57-61% of new voters; Obama had 72%.

2) Exit poll precincts are partially selected based on the previous election recorded vote.

3) In the 1988-2008 presidential elections, 226 of 274 exit polls red-shifted to the Republicans. Only about 137 would normally be expected to red-shift. The probability is zero.

4) 126 of the 274 exit polls exceeded the margin of error. Only 14 (5%) would normally be expected. The probability is ZERO.

5) 123 of the 126 exit polls that exceeded the margin of error red-shifted to the Republicans. The probability is ZERO.

**Richard Charnin**is Mathematician with an MS in Applied Mathematics from Adelphi University and an MS in Operations Research from the Polytechnic Institute of NY. He started out as a numerical control engineer/programmer for a major defense/aerospace manufacturer and then moved to Wall Street as a manager/developer of corporate finance quantitative applications for several major investment banks.