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Election Analysis for PA
As you may have seen my testimony in AZ at the end of November, I have been busy doing analysis for additional states and for individual counties. https://givesendgo.com/bobbypiton
As you may have seen my testimony in AZ at the end of November, I have been busy doing analysis for additional states and for individual counties. https://givesendgo.com/bobbypiton
I have information about one particular race in PA that is extremely meaningful. I did the work with the understanding that my results would be made public in a timely manner and I would be paid $10,000 plus a $50,000 bonus if I helped reverse the outcome of this race.
I had this conversation on speaker phone (I always need a witness when discussing payments) and was shocked to receive an email from this candidate that indicated that they had no recollection of the bonus figure. They said they mailed mailed me a check for $5,000 a week ago.
I have yet to receive it, but if I do, I will VOID and return to sender. I will NOT accept any payment from this candidate nor any other candidate going forward.
Click link if you want to read more.
Thank you,
Bobby
Click link if you want to read more.
Thank you,
Bobby
The following that I am presenting is redacted, but I signed an affidavit and turned it over team members @LLinWood @SidneyPowell1
From: Bobby Piton
To:Candidate A
Date:December 13, 2020
From: Bobby Piton
To:Candidate A
Date:December 13, 2020
Re:Congressional Election A
Everything I am about to say is what I would say under oath, under penalty of law. I believe it to be the Truth, nothing but the Truth, so help me God.
A quick recap. Candidate A contacted me just over a week ago to review the M F U voter data files
Everything I am about to say is what I would say under oath, under penalty of law. I believe it to be the Truth, nothing but the Truth, so help me God.
A quick recap. Candidate A contacted me just over a week ago to review the M F U voter data files
for the Congressional District in which she ran PA-A. According to Ballotpedia Candidate B is the suggested winner of this race receiving xxxxxx votes vs xxxx or a difference of xxx. Based on the Data that I will break down, I believe this race was stolen.
I’ve attached my write up outlining how this sophisticated fraud was perpetrated on the Great People of PAX and Candidate A. Let’s take it from the top using Registered Voter Files:
There are XXX,776 Voters in the file that I was provided with. Of these files, xxx,485
There are XXX,776 Voters in the file that I was provided with. Of these files, xxx,485
Male vs xxx,797 Female. Under normal circumstances, 51% of the US population is biologically a Female or in this case xxx,246 and the Remaining 49% is a biological Male or xxx,530. The Data suggests that there are 52,449 biological women that classify themselves as U and x8,045
biological men that do so.
With a total number of ballots cast being xxx,645 out of xxx,776, the voter turnout for this race was just over 62%.
Key Takeaways:
•Bobby Piton estimates that as many as 120,494 of the voter files are potentially suspect
•Over 200,000 Voter
With a total number of ballots cast being xxx,645 out of xxx,776, the voter turnout for this race was just over 62%.
Key Takeaways:
•Bobby Piton estimates that as many as 120,494 of the voter files are potentially suspect
•Over 200,000 Voter
Files were modified on November 4, 2020. Examining the files before and after throughout the past year will help confirm our assertions.
•Bobby Piton created samples of 1582 Names that were identified by various algorithms and bundled into 317 batches. (batches of names had
•Bobby Piton created samples of 1582 Names that were identified by various algorithms and bundled into 317 batches. (batches of names had
affiliations that were questionable)
•49.58% of the number of batches that were met with in person had some sort of voting anomaly (or so was reported – I will not attempt to confirm this).
•29.412% of the homes that we had contact with had at least 1 person that was
•49.58% of the number of batches that were met with in person had some sort of voting anomaly (or so was reported – I will not attempt to confirm this).
•29.412% of the homes that we had contact with had at least 1 person that was
classified as a Phantom Sleeper Voter (or so was reported – I will not attempt to confirm this).
•36 different people were associated with 1 phone number. Not surprisingly this person refused to answer any questions. The break down of the voters in the files associated with
•36 different people were associated with 1 phone number. Not surprisingly this person refused to answer any questions. The break down of the voters in the files associated with
this number 6 U, 10 nothing, 11 M and 9 F. All using the same phone number. Very suspect indeed.
•Similar problems were identified with nursing homes. Keep in mind I did not know any details about the locations when conducting his analysis and allowed the math to lead him
•Similar problems were identified with nursing homes. Keep in mind I did not know any details about the locations when conducting his analysis and allowed the math to lead him
to the locations that were expected to have issues.
•31.85% of 1582 or 504 people were in facilities that we couldn’t reach to confirm any votes had taken place (or so was reported – I will not attempt to confirm this).
•31.85% of 1582 or 504 people were in facilities that we couldn’t reach to confirm any votes had taken place (or so was reported – I will not attempt to confirm this).
•9 locations that we visited, simply did not exist. This is about 3% of our sample size just isn’t there (or so was reported – I will not attempt to confirm this).
Some Mathematical Disturbing Observations:
•Over time, men and women age relatively similarly until late in
Some Mathematical Disturbing Observations:
•Over time, men and women age relatively similarly until late in
life. The number of men and women in a population set moves up or down in tandem. Said another way, high correlation. Upon examining the data here, we have discovered that F and M have not a positive correlation between the ages of 18 and 100, but a negative one. This runs
so contrary to logic and reality that is outright disturbing. The correlation was -.4955.
An Example. 18 men were 42% of the number of 18 years in total, then that number went a steady decline bottoming at under 31% for 26 years…. Then it started to climb back to 40% for
An Example. 18 men were 42% of the number of 18 years in total, then that number went a steady decline bottoming at under 31% for 26 years…. Then it started to climb back to 40% for
40 years. It’s amazing, but the percentage of M, F, and U’s are behaving as randomly as stock prices. This reality is why Bobby was able to make the connection to VWAP and call it Average Weighted Vote Percentage target. Simply put the math of algorithms is showing up in the
swings of the number of voters in the 3 different classifications. The U voter is meant to cover the tracks of this elaborate fraud.
Let’s talk Correlation:
The Correlation of a M1 (Republican Male) and a U1 (Undefined M) is 85.32%. This on the surface is extremely high. What
Let’s talk Correlation:
The Correlation of a M1 (Republican Male) and a U1 (Undefined M) is 85.32%. This on the surface is extremely high. What
does this mean in layman’s terms. 86% of 18 year M from the number of males that were in M and U were M1. The other 14% was U1. Then the roller coaster as show below:
% U1 is the Number of Republican Undefined at any given age divided by the total number of Republican Men and
% U1 is the Number of Republican Undefined at any given age divided by the total number of Republican Men and
Republican Undefined.
As you compare % M1 and % U1, you might notice that specific ages have the % of total population of that particular age for these two classes Fall for %M1 and Rise for % U1. Please look at the differentials between 18, 26, 45, 53 and see for yourself. In a more wide scale audit
, I would start by focusing on those age groups to identify how the votes were annihilated by U voters, fake ballots were processed, or the records were outright deleted.
Listed below is the correlation matrix of 9 different classes of voters
M = Male, F = Female, U = Undefined
1 = Republican; 3 = Swing, 5 = Democrat
M = Male, F = Female, U = Undefined
1 = Republican; 3 = Swing, 5 = Democrat
M1 and F1
Notice the high correlation of 94.8% Makes sense Republican Men and Women have similar numbers of people through age through time.
F5 and M5
Notice the high correlation of 92.7% Makes sense Democrat Men and Women have similar numbers of people through age through time.
Notice the high correlation of 94.8% Makes sense Republican Men and Women have similar numbers of people through age through time.
F5 and M5
Notice the high correlation of 92.7% Makes sense Democrat Men and Women have similar numbers of people through age through time.
M3 and F3
Notice the nearly perfect correlation of Male and Female voters between Swing voters across all ages for Male and Female voters. This is clearly next to impossible that the numbers would line up with similar movement over an 82 year period of time.
Notice the nearly perfect correlation of Male and Female voters between Swing voters across all ages for Male and Female voters. This is clearly next to impossible that the numbers would line up with similar movement over an 82 year period of time.
Total M F U
Sheer Number of voter files by classified by age. Columns on left raw number of individuals by type by age. Table on right: total of 534,962 on top, the total per age beneath that number starting with 18.
Column M is total of 2441 from left at 18 and divided by
Sheer Number of voter files by classified by age. Columns on left raw number of individuals by type by age. Table on right: total of 534,962 on top, the total per age beneath that number starting with 18.
Column M is total of 2441 from left at 18 and divided by
5,814. Repeat this process by age and classification all of the way down.
As you scroll down, you can see the wild gyrations that occur between M and U in particular; the Male Vote in this Nation appears to be the one most likely to be annihilated by Phantom Sleeper Voters
As you scroll down, you can see the wild gyrations that occur between M and U in particular; the Male Vote in this Nation appears to be the one most likely to be annihilated by Phantom Sleeper Voters
The evidence in the raw data provided the guideposts for which voters would most likely be #PhantomSleeperVoters.
The evidence from our sample size thus far has been overwhelming. When almost 50% of all individuals contacts attest that there is some sort of anomaly; it’s a
The evidence from our sample size thus far has been overwhelming. When almost 50% of all individuals contacts attest that there is some sort of anomaly; it’s a
HUGE Problem
A full audit of each vote as it was processed and examining any relations between M, F and U voters throughout the processes process will help determine the extent of this fraud.
A full audit of each vote as it was processed and examining any relations between M, F and U voters throughout the processes process will help determine the extent of this fraud.
We the People of Pennsylvania and America deserve a Fair, Legal and Fully Auditable Election.
@SenMastriano Here is the file I texted you I would be posting. I prefer not to list the candidate. They know who they are. I have signed an affidavit to this evidence 2 weeks+ ago.
@SenMastriano Here is the file I texted you I would be posting. I prefer not to list the candidate. They know who they are. I have signed an affidavit to this evidence 2 weeks+ ago.
