2016 Election: illegal voters, uncounted votes, machine vote flipping

Richard Charnin
Updated Sept. 19, 2017

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
LINKS TO  POSTS

Clinton won the recorded vote by 2.8 million. But the recorded vote is never equal to the True Vote due to election fraud.

There is evidence that millions of illegals probably voted in 2016. View this 1988-2016 trend analysis of Hispanic voter registration and turnout.

According to Greg Palast, least one million Democratic minority voters were disenfranchised via Crosscheckwhich eliminated voters with duplicate names from voter rolls. He claims that 7 million minority voters were disenfranchised.

There is evidence that  George Soros , a Clinton backer,  controls voting machines in 16 states.  Election analyst Bev Harris has posted Fraction Magic , an algorithm used to flip votes on Central tabulators.

Sensitivity analysis shows the effects of a range of assumptions on the vote count.

Let TV = True Vote; RV = Recorded vote
RV = TV + Fraud

Given the Recorded vote in millions:
Clinton 65.7, Trump 62.9, Other 7.6

Election fraud components:
-Vote flipping on maliciously coded, proprietary voting machines and central tabulators
-Illegal voters (non-citizens)
-Uncounted votes (spoiled ballots, disenfranchised voters)

Base Case Assumptions
Uncounted- 7 million: 85% for Clinton
Vote Flip- 5 million (net): 8% of Trump’s votes flipped to Clinton on voting machines and central tabulators. 
Illegals- 2 million: 85% for Clinton
Trump wins by 3.7 million: 68.7-64.9 (48.6-46.0%)

Assume 12 million uncounted: 85% to Clinton 
(2 million illegal, 5 million flip)
Trump still wins: 69.4-69.2 million (47.48-47.32%)

………..Total………Clinton….Trump……Other
Vote…..136.2……..65.7………62.9………7.6
Pct……,,100%..,….48.3%…..46.2%……5.6%

Illegal… 2.0…….  -1.70…..  -0.30…………0 non-citizens
Unctd…..7.0………5.95……..1.05…………0 disenfranchised 
Flip……..5.0…….  -5.0……….5.0………….0 voting machine

Net……141.2……64.9…….68.7………7.6
Adjusted………..46.0%….48.6%……5.4%

Sensitivity Analysis (assume 7 million uncounted, 85% for Clinton)
Worst case (7% flip, 80% of illegals to Clinton):  Trump wins by 2.3 million
Base case: (8%  flip, 85% of illegals to Clinton): Trump wins by 3.7 million
Best case: (9% flip, 90% of illegals to Clinton): Trump wins by 5.2 million

View the spreadsheet:  https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1672204415

http://www.pewhispanic.org/2016/01/19/millennials-make-up-almost-half-of-latino-eligible-voters-in-2016/ph_election-2016_chap1-chart-08/

	Total	Clinton	Trump	Other
Recorded vote	136.2 million	65.7	62.9	7.6
		48.25%	46.17%	5.59%
Illegal	2.0	-1.7	-0.3	0
Uncounted	7.0	5.95	1.05	0
Vote Flip	5.0	-5.0	5.0	0
Adjusted	141.22	64.9	68.7	7.6
		46.0%	48.6%	5.4%
	7.0 million uncounted	85% to Clinton
		Illegals to	Clinton
		80%	85%	90%
	Flip to Clinton		Trump Vote
	9%	69.20	69.30	69.40
	8%	68.57	68.67	68.77
	7%	67.94	68.04	68.14
	Vote Flip		Trump Vote
	9%	49.00%	49.07%	49.14%
	8%	48.56%	48.63%	48.70%
	7%	48.11%	48.18%	48.25%
	Vote Flip		Clinton vote
	9%	45.61%	45.54%	45.47%
	8%	46.06%	45.98%	45.91%
	7%	46.50%	46.43%	46.36%
	Vote Flip		Trump margin
	9%	4.79	4.99	5.19
	8%	3.53	3.73	3.93
	7%	2.27	2.47	2.67

Advertisements

 

University of Virginia Study: 20% of Trump Voters were former Obama Voters

Richard Charnin
April 29, 2017

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
LINKS TO  POSTS

Larry Sabato is the founder and director of the Center for Politics at the University of Virginia. 

http://www.thegatewaypundit.com/2017/04/larry-sabato-20-trump-voters-former-obama-voters-video/

Sabato said: “This is the largest study of just Trump voters… The first thing that is perfectly clear is that Trump has not lost almost none of his backers, which includes the soft Trump voters. He’s still got 92-93% of them supporting him. It’s also true he hasn’t gained many people from the other side. We live in a very polarized era… What I found fascinating, nobody else has identified this, 20% of Trump voters actually voted for Obama either in 2008 or in 2012 or in both years. In other words a fifth of his vote came from Obama voters”.

I calculated Trump’s vote share based on the above: If 20% of Trump voters were former Obama voters, then the vote share calculation indicates that Trump won by an estimated 48.3-42.9% (7 million votes), confirming the True Vote Model: Trump by 48.5-44.3% (351-187 EV).

2012….. Pct……Trump Clinton Other……..Trump share
Obama…. 51.1%…. 19%….75%….. 6%……….9.7% 20% <<<<
Romney… 47.2%…. 80%…..8%…..12%……..37.8% 78%
Other…….. 1.7%….. 48%….46%……6%……….0.8% 2%

Total….. 100%…..48.3%. 42.9%…8.8%…..48.3% 100%

TRUE VOTE MODEL

Includes estimated 2012 voter turnout in 2016 and new voters.

Assumption: 18.3% of Trump voters were returning Obama voters

True Vote share: Clinton 42.8%, Trump 47.8%, Other 9.4%

True Vote: Clinton 58.3 million, Trump 65.1, Other 12.8

Recorded share: Clinton 48.3%, Trump 46.2%, Other 5.5%

Recorded Vote: Clinton 65.7 million, Trump 62.9, Other 7.6

(9.7 million flip in margin (7.1%) from the Recorded to True vote)

https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1768941212

Returning and new voters
	Clinton	Trump	Other	Trump%
Obama	44.6	11.9	3.0	18.3%
Romney	4.6	44.1	8.6	67.8%
Other	0.9	0.9	0.2	1.4%
DNV (new)	8.1	8.1	1.0	12.5%
Total	58.3	65.1	12.8	100.0%

2012	Mix	Clinton	Trump	Other	Turnout
Obama	43.66%	75%	20%	5%	94%
Romney	42.09%	8%	77%	15%	98%
Other	1.54%	45%	45%	10%	95%
DNV (new)	12.70%	47%	47%	6%
True Share	100%	42.8%	47.8%	9.4%
True Vote	136.2	58.3	65.1	12.8
Recorded	136.2	65.7	62.9	7.6
Change	–	-7.4	2.2	5.2
Trump% Obama	18%	19%	20%	21%	22%
Trump% Romney	Trump share
79%	47.8%	48.2%	48.7%	49.1%	49.5%
78%	47.4%	47.8%	48.2%	48.7%	49.1%
77%	46.9%	47.4%	47.8%	48.2%	48.7%
76%	46.5%	47.0%	47.4%	47.8%	48.3%
75%	46.1%	46.5%	47.0%	47.4%	47.8%
Clinton share
79%	42.8%	42.4%	41.9%	41.5%	41.1%
78%	43.2%	42.8%	42.4%	41.9%	41.5%
77%	43.7%	43.2%	42.8%	42.3%	41.9%
76%	44.1%	43.6%	43.2%	42.8%	42.3%
75%	44.5%	44.1%	43.6%	43.2%	42.7%
Trump % margin
79%	5.0%	5.8%	6.7%	7.6%	8.5%
78%	4.1%	5.0%	5.9%	6.7%	7.6%
77%	3.3%	4.2%	5.0%	5.9%	6.8%
76%	2.4%	3.3%	4.2%	5.1%	5.9%
75%	1.6%	2.5%	3.3%	4.2%	5.1%
Trump vote margin
79%	6.77	7.96	9.15	10.33	11.52
78%	5.62	6.81	8.00	9.19	10.38
77%	4.47	5.66	6.85	8.04	9.23
76%	3.33	4.52	5.71	6.89	8.08
75%	2.18	3.37	4.56	5.75	6.94

https://richardcharnin.wordpress.com/2016/12/30/why-the-recorded-vote-and-unadjusted-exit-polls-are-wrong/

https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=0

 

MY COMMENTS TO THE MSM ON THE RIGGING OF THE 2016 PRE-ELECTION POLLS

Richard Charnin
April 24, 2017

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
LINKS TO  POSTS

The MSM just interviewed the authors of  Shattered: Inside Hillary Clinton’s Doomed Campaign on the reasons for Clinton’s loss.  I commented to Chris Mathews and Brian Williams of MSNBC as well as FOX and CBS on how MSM pollsters rigged the pre-election polls for Clinton.

FYI: Your guests may not have looked at my 2016 Election model. It was based adjustments to final pre-election polls which were biased for Clinton. The Democratic Party-ID share was overstated at the expense of Independents who went solidly for Trump. In addition, there is strong evidence that votes were stolen from Jill Stein – by Clinton.

The 2016 Model projected Trump’s 306 RECORDED EV. But he actually had approximately 351 TRUE EV after adjusting for late undecided voters. https://richardcharnin.wordpress.com/2016/11/07/2016-election-model-forecast/

Recorded Vote: Clinton 48.3-46.2%, Trump 306-232 EV
Recorded Vote Forecast: Trump 44.4-42.9% with 306-232 EV
True Vote Model: Trump 48.5-44.3% with 351-187 EV

Note: I exactly forecast the RECORDED EV in the last three elections: 365, 332, 306. In each case the winner did better in the True Vote than the Recorded vote.

Here is the proof: https://richardcharnin.wordpress.com/2014/09/14/summary-2004-2012-election-forecast-1968-2012-true-vote-model/

 

 

 

 

2016 Voter Turnout and Vote share Sensitivity Analysis: Trump won the Popular Vote

Richard Charnin
Mar. 15, 2017

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
LINKS TO  POSTS

Trump wins all 25 scenarios over various combinations of voter turnout

Assumption
Party ID (registration) 38I-31D-27R
(Gallup voter affiliation survey average Nov.1-13,  2016)

1. Base Case Voter Turnout: Dem 65%, Rep 70%, Ind 70%
Trump 48.3-45.2% (4.2 million vote margin)

2. Worst Case Turnout: Dem 67%, Rep 68%, Ind 70%
Trump 47.6-45.9% (2.3 million vote margin)

3. Best Case Turnout: Dem 63%, Rep 72%, Ind 70%
Trump 49.1-44.5% (6.2 million vote margin)

https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=610568510

Reg Voter	Gallup	Base Case
Turnout	Voter Affil	Clinton	Trump	Johnson	Stein
70% Ind	38%	40%	50%	5%	5%
65% Dem	31%	88%	8%	1%	3%
70% Rep	27%	7%	89%	3%	1%
Vote share	100.0%	45.2%	48.3%	3.2%	3.2%
Votes	136.2	61.6	65.8	4.4	4.4
		Trump %
Dem		Rep Turnout
Turnout	68%	69%	70%	71%	72%
63%	48.3%	48.5%	48.7%	48.9%	49.1%
64%	48.2%	48.3%	48.5%	48.7%	48.9%
65%	48.0%	48.2%	48.3%	48.5%	48.7%
66%	47.8%	48.0%	48.2%	48.3%	48.5%
67%	47.6%	47.8%	48.0%	48.2%	48.3%
		Trump Vote
Dem		Rep Turnout
Turnout	68%	69%	70%	71%	72%
63%	65.9	66.1	66.3	66.6	66.8
64%	65.6	65.8	66.1	66.3	66.6
65%	65.4	65.6	65.8	66.1	66.3
66%	65.1	65.3	65.6	65.8	66.1
67%	64.9	65.1	65.3	65.6	65.8
		Clinton %
Dem		Rep Turnout
Turnout	68%	69%	70%	71%	72%
63%	45.2%	45.0%	44.9%	44.7%	44.5%
64%	45.4%	45.2%	45.1%	44.9%	44.7%
65%	45.6%	45.4%	45.2%	45.1%	44.9%
66%	45.8%	45.6%	45.4%	45.2%	45.1%
67%	45.9%	45.8%	45.6%	45.4%	45.2%
		Trump %	Margin
Dem		Rep Turnout
Turnout	68%	69%	70%	71%	72%
63%	3.1%	3.5%	3.8%	4.2%	4.5%
64%	2.8%	3.1%	3.5%	3.8%	4.2%
65%	2.4%	2.8%	3.1%	3.5%	3.8%
66%	2.0%	2.4%	2.7%	3.1%	3.4%
67%	1.7%	2.0%	2.4%	2.7%	3.1%
		Trump  Vote	Margin
Dem		Rep Turnout
Turnout	68%	69%	70%	71%	72%
63%	4.3	4.7	5.2	5.7	6.2
64%	3.8	4.2	4.7	5.2	5.7
65%	3.3	3.7	4.2	4.7	5.2
66%	2.8	3.3	3.7	4.2	4.7
67%	2.3	2.8	3.2	3.7	4.2

 

2016 True Vote Sensitivity analysis: illegal voters, uncounted votes, machine vote flipping

Richard Charnin
Updated Sept. 19, 2017

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
LINKS TO  POSTS

Clinton won the recorded vote by 2.8 million. But the recorded vote is never equal to the True Vote due to election fraud.

There is evidence that millions of illegals probably voted in 2016. View this 1988-2016 trend analysis of Hispanic voter registration and turnout.

According to Greg Palast, least one million Democratic minority voters were disenfranchised via Crosscheck which eliminated voters with duplicate names from voter rolls. He claims that 7 million minority voters were disenfranchised.

There is evidence that  George Soros , a Clinton backer,  controls voting machines in 16 states.  Election analyst Bev Harris has posted Fraction Magic , an algorithm used to flip votes on Central tabulators.

Sensitivity analysis shows the effects of a range of assumptions on the vote count.

Let TV = True Vote; RV = Recorded vote
RV = TV + Fraud

Given the Recorded vote in millions:
Clinton 65.7, Trump 62.9, Other 7.6

Election fraud components:
-Vote flipping on maliciously coded, proprietary voting machines and central tabulators
-Illegal voters (non-citizens)
-Uncounted votes (spoiled ballots, disenfranchised voters)

Base Case Assumptions
Uncounted- 7 million: 85% for Clinton
Vote Flip- 5 million (net): 8% of Trump’s votes flipped to Clinton on voting machines and central tabulators. 
Illegals- 2 million: 85% for Clinton
Trump wins by 3.7 million: 68.7-64.9 (48.6-46.0%)

Sensitivity Analysis (assume 7 million uncounted, 85% for Clinton)
Worst case (7% flip, 80% of illegals to Clinton):  Trump wins by 2.3 million
Base case: (8%  flip, 85% of illegals to Clinton): Trump wins by 3.7 million
Best case: (9% flip, 90% of illegals to Clinton): Trump wins by 5.2 million

View the spreadsheet: https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1672204415

http://www.pewhispanic.org/2016/01/19/millennials-make-up-almost-half-of-latino-eligible-voters-in-2016/ph_election-2016_chap1-chart-08/

	Total	Clinton	Trump	Other
Recorded vote	136.22	65.72	62.89	7.61
		48.25%	46.17%	5.59%
Illegal	2.0	-1.7	-0.3	0
Uncounted	7.0	5.95	1.05	0
Vote Flip	5.0	-5.0	5.0	0
Adjusted	141.22	64.9	68.7	7.6
		46.0%	48.6%	5.4%
	7.0 uncounted	85% to Clinton
		Illegals to	Clinton
		80%	85%	90%
	Flip to Clinton		Trump Vote
	9%	69.20	69.30	69.40
	8%	68.57	68.67	68.77
	7%	67.94	68.04	68.14
	Vote Flip		Trump Vote
	9%	49.00%	49.07%	49.14%
	8%	48.56%	48.63%	48.70%
	7%	48.11%	48.18%	48.25%
	Vote Flip		Clinton vote
	9%	45.61%	45.54%	45.47%
	8%	46.06%	45.98%	45.91%
	7%	46.50%	46.43%	46.36%
	Vote Flip		Trump margin
	9%	4.79	4.99	5.19
	8%	3.53	3.73	3.93
	7%	2.27	2.47	2.67

 

Probability of exactly forecasting the electoral vote in the last three elections

Richard Charnin
Feb. 11, 2017

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
LINKS TO  POSTS

I was asked to calculate the probability of my exact forecast of the Electoral Vote in the last three elections (365,332,306). It was a combination of experience and luck. I do not expect to exactly forecast the EV in 2020.

My Track Record
https://richardcharnin.wordpress.com/2014/09/14/summary-2004-2012-election-forecast-1968-2012-true-vote-model/

Note that the following calculation is just an approximation.

Assume the following:
1) the probability of Obama winning in 2008 was 0.95; it was also 0.95 in 2012. The probability of Trump winning in 2016 was 0.05.
Therefore the probability of forecasting all three winners correctly is
P1 = 0.045 =.95*.95*.05

2) the winning EV is in the 270-370 range.
The probability of exactly forecasting the EV in a given election is 0.01. The probability of exactly forecasting the EV in all 3 elections is 1 in a million:
P2 =.000001 = 0.01*0.01*0.01

Therefore, the probability of forecasting the winner and the EV in the three elections is
P3 = P1*P2 = .045* 0.000001 or 1 in 22 million.

To put it another way, forecasting the electoral vote exactly in three successive elections would be expected to occur just once in 22 million elections (88 million years).

 

 

More clues on Election Fraud from Humboldt Cty, CA

Richard Charnin
Jan.1, 2017

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
LINKS TO  POSTS

Humboldt is the gift that keeps on giving. It is the only county in the U.S. which uses an Open Source System (TEVS) to count and audit votes. The system was installed in 2006.

In the CA primary, Bernie Sanders had his highest margin (71%) in Humboldt. https://richardcharnin.wordpress.com/2016/07/02/bernie-landslide-in-ca-humboldt-cty-open-source-system/

In 2008-2012, Obama did 2.58% better in Humboldt than he did in the full state. This is to be expected. But in 2016, Clinton did 1.75% worse in Humboldt. Her 4.26% increase over Obama in CA represents a 1.2 million increase in margin. Was she really that popular? Or was her vote padded?

In the 2016 presidential election, Jill Stein’s 6.1% share in Humboldt was her highest in the state – just like it was for Bernie. Clinton’s 56% share in Humboldt ranked #20 of 58 California counties. Stein’s average in the 19 counties was 2.3%. Clinton averaged 68.0%. So how come Stein did so much better in Humboldt than she did in the other 19 liberal counties?

Could it be Humboldt’s nearly foolproof Open Source voting system? Could it be that fraud was prevented in Humboldt? Could it be that nearly 2/3 of Stein’s votes were blue-shifted to Clinton? Could it be that Clinton’s 61% CA share was inflated by at least 4%? Note that 4% of 14 million votes is 560,000. That is a 1.2 million difference in vote margin.

Keep in mind that the recorded vote is never equal to the True Vote. There is always election fraud. But in Humboldt, we can assume that the recorded vote is the True Vote due to its near foolproof Open Source system. There is no reason to believe Clinton’s recorded CA vote is legitimate.

Humboldt Democratic 2-party share
1988-2004 Before TEVS: 57.2%
2008-2016 After TEVS: 64.6%

California Presidential share
……Dem… Rep…Other
2008 60.21% 36.46% 3.33%
2012 60.24% 37.12% 2.64%
2016 61.73% 31.62% 6.66% HRC margin 7% over Obama?

Humboldt Presidential share
……Dem… Rep…Other
2008 62.05% 33.95%.4.00%
2012 59.68% 32.61% 7.72%
2016 56.04% 31.01% 12.95% HRC loses 3.64% vs Trump 1.60%

Democratic 2-party Presidential share
……CA….Humboldt..Diff
2008 62.28% 64.64% 2.36%
2012 61.87% 64.67% 2.80%
2016 66.13% 64.37% -1.75% HRC gains 4.26% over Obama?

…………………. Stein Clinton
1 San Francisco.. 2.4% 85.0%
2 Alameda……… 2.7  78.7
3 Marin…………..2.2  78.1
4 San Mateo……..1.6  75.7
5 Santa Cruz……..3.5  73.9
6 Santa Clara…….1.8  72.7
7 Los Angeles……2.2  71.8
8 Sonoma……….. 3.2  69.4
9 Contra Costa…..1.9  68.5
10 Imperial……….1.6  67.9
11 Monterey………2.1  66.8
12 Yolo…………….2.2 66.7
13 Napa……………2.1  63.9
14 Solano………….1.7  61.6
15 Santa Barbara ..2.1  60.6
16 Mendocino…….5.6  58.9
17 Sacramento….. 1.8  58.3
18 San Benito……. 1.7 57.1
19 San Diego………1.8 56.3
20 Humboldt……..6.2 56.0

View this spreadsheet of 58 county votes. https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1462588532

https://docs.google.com/spreadsheets/d/1R9Y3ae2uyW8SUxVUnnOt9ZyvheAxa0fAhesAw_nhciM/edit#gid=1010903783