Update 11/19/2020: Dr. Shiva’s analysis was called into question by Matt Parker of Stand-Up Maths, where he pointed out that Biden’s data showed the same pattern as Trump’s and explained that you can’t subtract percentages of two different sized groups from each other, which is why the patterns between the two candidates data are similar. Essentially this is the same as saying you can subtract two fractions from each other with different denominators.
Dr. Shiva responded in this video to Parker’s criticisms. Dr. Shiva argues that, when doing pattern analysis for voting, the goal isn’t to necessarily create a correct mathematical function, but rather to look for differences in the patterns themselves. He argues that Biden’s data shows a much higher plot, and that the missing votes Parker said weren’t there, are actually there, they are just at the opposite end of the function where Biden is gaining a much higher percentage of votes in strongly republican districts than Trump is losing in strongly Republican districts.
It’s an interesting argument, so I’m restoring this article back to how it was. I’ll leave it up to you to decide who you believe on the pattern analysis – that said, there’s a ton of other issues with this software and the election that go well beyond suspicious patterns in the voting data.
Dr. Shiva Ayyadurai, who is a MIT trained data scientist and Fulbright Scholar, along with Bennie Smith a Democrat, who is a software engineer, data analyst and an election commissioner and Phil Evans, who is also a trained engineer and data analyst, discuss some very troubling findings after reviewing the election data coming out of Michigan. Dr. Ayyadurai is a US Senate candidate in Massachusetts, which is a contributing factor as to why he began looking into the integrity of our voting systems.
Dr. Ayyadurai’s discovered through a series of FOIA requests related to his own election that ballot counting machines don’t directly count ballots. Instead, they take an image of the ballot that is submitted to them, and then they tabulate the votes based on the ballot images collected.
Federal election statues dictate that all election records must be held for 22 months. However, Dr. Ayyadurai discovered that many states do not retain the ballot images produced by the machines. The default settings of the machines automatically store the ballot images, so this means election officials are manually disabling the image storage feature.
Because ballot images are not being retained in states like MA and MI, the election results cannot be meaningfully audited, leaving ambiguity as to the actual election outcome. The only way to audit the results of an election without ballot images would be to manually count the ballots, which is very difficult to do.
Dr. Ayyadurai learned that in 2001, a weighted race feature was added to Global Election Systems (GEMS 1.18.1) voting systems. This feature allows elections to be skewed to one candidate or another based on the settings of the software. This is not a hack, but a built-in FEATURE of the software. Why this feature exists in US ballot tabulation machines has never been explained.
Not only does the software allow for the weighting of election outcomes, but the way votes are stored and counted is also problematic. The systems store the votes as fractional decimals instead of whole numbers. This means the machines can be set to tabulate half a vote, which makes no sense at all, yet that’s how they were designed.
After voting “glitches” were reported in Michigan, Dr. Ayyadurai’s team decided to analyze the top four counties in MI for irregularities. They looked at data from Oakland, Macomb, Kent and Wayne counties. What they found was absolutely shocking. Data analytics show, at a minimum, 69,000 votes were TRANSFERRED from Trump to Biden in these four counties, in a pattern that is consistent with algorithmic weighting of a race.
In Michigan, there are two different kinds of ballots a person can chose to vote on. A person can chose to vote a straight party ticket or they can chose to vote for each individual candidate separately. They cannot do both. This means straight party ballots and individual selection ballots are tabulated in separate piles which produces two sets of data.
The fact that party ballots and individual ballots are separate in MI allowed the data scientists to compare the differences between how often people in a given precinct voted for Trump vs how often people voted for a straight Republican ticket.
For example, in this image, the bottom X axis percentage indicates how often people in a given precinct voted a straight Republican ticket. The Y axis indicates how often people voted specifically for Trump on an individual ballot minus the percentage who voted a straight Republican ticket.
So if a given precinct had 50% of people vote a straight Republican ticket and 50% of them vote for Trump on an individual ballot, we would see a dot at 50% on X axis and at 0% Y axis. Intuitively, we would expect most dots to fall along the 0% Y axis line.
In other words, if a given precinct tends to vote 60% straight Republican tickets, we would most likely expect to see about 60% of people in that precinct who vote on the individual ballots to chose Trump as well.
Instead, what the data shows is a clear linear relationship that starts at about the 20% X axis mark, where the more Republican a precinct is, based on straight party tickets, the less likely people who vote on individual ballots in that precinct are to vote for Trump.
Not only is this relationship inverted from what we would expect to see, the manner in which it displays itself is what really gives the fraud away. Notice the linearity of the slope. This linearity can only be the product of an algorithm. The perfection of the slope and distribution is what gives the game away.
This same slope is observed in three of the counties that were analysed. It shows up in early voting data as well as election day data. The pattern is consistent spatially as well as temporally. There is absolutely no way this is the product of anything other than an algorithm.
Consider what this data is telling us. This data is telling us that the more likely a precinct is to be Republican, the more likely it is the Republicans in that district voted for Biden over Trump, and they did so at a mathematically perfect rate.
This is an impossibility. People are sent to jail in fraud cases with far less evidence than this.
If this was a legitimate election, we would expect to see a scatter distribution along the 0% Y axis where the red arrow is, with perhaps either a positive or negative bias along axis depending on how much people liked or hated Trump.
Here’s what Wayne county’s results look like where no algorithm was detected. Wayne county is strongly Democratic, so its likely they didn’t bother rigging the votes here. There could still be cheating going on here, but this what we would expect all the graphs to look like. We can see voters here liked Trump a lot more than the Republican party.
When Dr. Ayyadurai began complaining on Twitter about the lack of transparency and the fact that ballot images were being deleted, he was attacked by an organization called The National Association of State Election Directors. This organization is funded by corporate interests with ties to the Rockefellers and other international business conglomerates. The NASED actually contacted Twitter and tried to get him banned. Keep in mind he’s a Senate candidate. This organization has been working with the election software companies involved in this fraud.
This software is in use far beyond the four counties that were analysed by Dr. Ayyadurai’s team. It explains how Joe Biden, a man who couldn’t get more than a handful of people to turn up at one of his “rallies” managed to get more votes than Donald Trump, a man who routinely held rallies that drew over 50,000 screaming fans who were collectively chanting “WE LOVE YOU!”
There are plenty of other mathematical anomalies that demonstrate fraud is afoot.
CCTV captures ballot fraud at State Farm Arena in GA.
Election Fraud Analysis So Powerful Twitter Banned Bobby Piton’s Account In The Middle Of His Testimony!
MASSIVE fraud was detected in a crowd funded study done by Matt Braynard:
**A) Out of infrequent Republican registered voters who the State has recorded an Absentee vote in 2020, when called the voter said that they did NOT vote.
IN OTHER WORDS, (POSSIBLY) SOMEONE VOTING WITH YOUR NAME AND FORGING YOUR SIGNATURE.**
- MI: 2.8% out of all voters called in the sample
- NV: 2.22%
- AZ: 0.94%
- GA: 0.85%
- PA: 0.70%
- WI: 0.66%
**B) Out of Republican registered voters who requested, received, and stated that they returned the absentee ballot – the State records they did NOT VOTE AT ALL.
IN OTHER WORDS, YOU VOTE AND IT “DISAPPEARED”.**
- AZ: 50.01% out of all in the sample size
- PA: 41.86%
- GA: 44.08%
- MI: 32.61%
- WI: 20.00%
His team also found over a thousand voters in GA who declared a post office box as their address and tried to hide it by saying it was an apartment number, and that’s just from a partial audit – many more suspected.
His team also found that in Wisconsin, over 200,000 people requested an absentee ballot using “indefinite confinement status” to avoid having to show an ID. Normally only 1000 people request ballots this way. Further, the absentee ballot system itself in WI does not conform to WI state law.
Matt Braynard goes over all of his teams findings in this conclusive video:
Revolver News presents extensive statistical analysis proving 90,000 votes were dumped into Montgomery County, PA two days after election day, while only increasing the vote total by 9,000 votes. These were almost entirely Biden votes, with the 81,000 vote remainder being subtracted from Trumps in-person vote totals.
We find considerable evidence consistent with the possibility of electoral fraud in vote counts in Montgomery County, PA.
In particular, we examine a highly anomalous update to mail vote totals in the NYT/Edison data which enormously benefited Biden, and which looks suspicious on a number of dimensions.
At a high level, our results are suggestive of a new and highly suspicious batch of mail ballots being added to the count sometime between Wednesday early morning and Thursday morning. These ballots are drawn from an implausible distribution that enormously favored Biden and simultaneously harmed Trump (the latter being done in addition by allocating more votes to Jorgensen). Said mail ballots end up being extremely different both from the mail ballots that came before (as measured in NYT data), and the mail ballots that came afterwards (as measured in the county’s own data).
“A thorough and damning new analysis just published calls the legitimacy of this critical period into question and shows just how completely ridiculous and far-fetched the core of Joe Biden’s comeback really was in Michigan, Wisconsin, and Georgia. It flags four individual vote dumps critical to Joe Biden’s “victory” in these states and shows, convincingly, that their ratios of Biden votes to Trump votes were profoundly anomalous when compared to other dumps in those states and virtually every other vote dump across the country.”
Based on current population data from the Census Bureau and voting data from previous elections, Just Facts has conducted a study to estimate the number of votes illegally cast by non-citizens in the battleground states of the 2020 election. The results—documented in this spreadsheet—show that such fraudulent activities netted Joe Biden the following extra votes in these tightly contested states:
Arizona: 51,081 ± 17,689
Georgia: 54,950 ± 19,025
Michigan: 22,585 ± 7,842
Nevada: 22,021 ± 7,717
North Carolina: 46,218 ± 16,001
Pennsylvania: 32,706 ± 11,332
Wisconsin: 5,010 ± 1,774
BIDEN takes MINIMUM 98% of a 23,487 vote batch at 12:18AM – Impossible!
Russ Ramsland, of the Allied Security Operations Group, was on Lou Dobbs 11/17/2020 and he confirmed proof of massive fraud in MI.
We have been out looking mostly at Michigan. We are beginning on turning our sights on Pennsylvania and Georgia. The things you find in Michigan are amazing. There are over 3,000 precincts where the presidential votes cast compared to the estimated voters from the SOS (Secretary of State) is 99% all the way up to 350%. Those kind of numbers don’t exist in the real world. So where did all those votes come from?
And looking at that, we’ve gone back and looked at some of these huge vote dumps that were mostly Biden’s. We call them spikes. We’ve gone back and traced the spikes. We’ve seen where they were cast, primarily in four counties. We looked at how long it took to cast those votes. And we looked at the equipment that exists at all of those locations by serial number. And the fact of the matter is we can’t see any physical way possible for some of those votes to have been in those kind of numbers because they just don’t have the equipment that can produce it in that timing.
Here’s some extreme oddities in WI:
Terry Dittrich, chairman of the Republican Party of Waukesha County, points to Biden votes in Brookfield, which borders Milwaukee County.
“It’s very, very big, and it’s like, Boy, this just doesn’t seem right,” he said about absentee ballots in particular, which showed close to 80 percent going for Biden. Trump won Brookfield overall, getting 53.5 percent of the vote. But Biden got 12,434 votes in the city, a stunning 36 percent increase over Clinton’s vote total four years ago.
“The answer is I don’t know,” Dittrich says when asked why he thought the numbers were so high.
“No one knows who these added voters are and won’t know for a few weeks as county clerks have 45 days to get the voter data for this election entered into the system.
The bottom line is that it’s just incredible that this guy could get this big of a bump, in New Berlin, Brookfield, Elm Grove, Menomonee Falls, and even some of our strongholds in the county like Hartman and Sussex,” said Dittrich.
- In New Berlin, Biden got 28 percent more votes than Clinton in 2016 and Obama in 2012.
- In the Village of Elm Grove, Biden got 28 percent more votes than Clinton and 54.6 percent more than Obama.
- In the Village of Menomonee Falls, Biden got 34 percent more votes than Clinton and 41 percent more than Obama.
- In the Village of Hartland, Biden got almost 39.7 percent more votes than Clinton and 40 percent more than Obama.
- In the Village of Sussex, Biden got 39.69 percent more votes than Clinton and 43 percent more than Obama.
Dittrich also points to Biden’s 5,576 votes in the city of Muskego, a 26 percent increase over Clinton’s vote total in 2016.
“Which is really incredible,” he says, “because we canvassed the hell out of the place and it’s very, very red.”
The National Pulse reports:
“We seek to estimate the fraction of registered Democratic voters that voted among the outlier counties. We want an unbiased estimate, so we removed Allegheny and Philadelphia counties as they are rather unique. Ten counties were used for simple linear regression.
“The data are fit well with a simple line.
Biden2020 = -21215.45 + 1.1943149*Democratic
“This means that the number of Biden votes in ten of the outlier counties was 101%± of registered Democratic voters (vs the majority of other PA counties where it was 70%± — an extraordinary statistical difference). That is not logical or reasonably explainable legally. The most likely explanation is that excess votes were added to the Biden total that did not come from voters.“
On page 18, an executive summary of a “Testable Hypothesis of Fraud using a Predictive Model in the Pennsylvania 2020 Presidential Vote for Montgomery County,” states:
“These facts suggest a mathematically extraordinary event occurring in multiple counties simultaneously at a magnitude well above what is needed to change which candidate won Pennsylvania’s electoral votes.”
Predictive models in the report reveal tens of thousands of potentially fraudulent votes across Pennsylvania, leading the statisticians collaborating on the report to conclude that a “singular or small set of actors in a position to intercept and modify all precincts data” may have participated in a “vote fraud scheme to increase Biden’s votes in Democrat heavy districts that would be undetected by the workers at the precincts but exceedingly trivial to detect.”
Oh, well would you look at what Gateway Pundit just reported!
What happened with the mail in votes is what is statistically impossible (See the orange line below). In almost every county throughout the state, the President was awarded a percent of votes 40% less than the percent the President won on election day (see the grey line below). If Trump won a county by 80% of the vote on election day, he won 40% of the mail in vote for a county. If the President won 60% of the vote on election day he won 20% of the mail in vote in another county. This pattern occurred in almost every county with the only noticeable exception of Philadelphia, where the President only earned 30% of the vote on Election Day.
These numbers are so consistent that they are almost certainly fraudulent. This is statistically impossible. This NEVER happens in data sets.