edthecynic
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He dishonestly does not disclose that fact on the video, but when confronted with that fact by a fact checker he admitted he "interpolated" the Census Bureau data.
How The 2020 Election Was Stolen
- Thread starter asaratis
- Start date
- Thread starter
- #62
I edited my post after reading the first time you claimed this. You're still wrong. Judicial Watch did not mention Lowndes County, GA.
Faun
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He was also refuted here...
No, an algorithm didn't manipulate Michigan election results
Antrim County, Michigan, the site of a human error that briefly produced inaccurate unofficial results on election night
www.politifact.com
... which echoes what I pointed out earlier that he's ignoring artificially inflated voter roles.
What he also ignores is that virtually every ballot comes from a registered voter and that when you add up the ballots, whether by machine or by hand, Biden won the election. And in order for ballots to have been submitted by folks other than registered voters, which is what he implies despite having no proof, there would have to be fraud. And there's been only a handful of cases of fraud detected.
He, and Lindell, are playing the shell game with the election because they have no actual proof.
No one does because there was no widespread fraud.
- Thread starter
- #64
He admits the some of them are, but none that count. What is made up is the algorithms that were used to adjust the tallies to match the expected outcome.
- Thread starter
- #65
Show a link.
The Census Bureau predicted their own numbers for the years between the 2010 census and the 2020 census.
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- #66
BS. Just listen to the first 60 seconds of the video. He mentions inflated voter registration data bases three times....approximately at (0.28, 0.42, and 0.52) These manipulations happened before the election.He was also refuted here...
No, an algorithm didn't manipulate Michigan election results
Antrim County, Michigan, the site of a human error that briefly produced inaccurate unofficial results on election night
... which echoes what I pointed out earlier that he's ignoring artificially inflated voter roles.
What he also ignores is that virtually every ballot comes from a registered voter and that when you add up the ballots, whether by machine or by hand, Biden won the election. And in order for ballots to have been submitted by folks other than registered voters, which is what he implies despite having no proof, there would have to be fraud. And there's been only a handful of cases of fraud detected.
He, and Lindell, are playing the shell game with the election because they have no actual proof.
No one does because there was no widespread fraud.
Every absentee ballot, has an absentee ballot envelope, with signature and checked signature mark from the precinct it is received by....
I've only watched the first few minutes of the video, and already have a question on what he is saying...
How do you hack voter registration offices, all across the country to increase their voter registrants, to increase their voter rolls with phantom voters?
How do you print ballots for these phantom voters to be inserted in to their counts, without also having an absentee ballot ENVELOPE with matching signature, to go with the phantom ballots?
You can't have more absentee ballots than absentee envelopes.
I'm totally lost....?
Elections are not done at county level, people vote in their district.... And absentee ballots are mailed to the district.... Are hundreds or thousands of voting districts involved in this, for it to work, within the checks and balances done in an election at the district level, then what is forwarded to the county??? Ballot stuffing as he claimed can't be done just when scanning votes....it would have to be done in the district itself....
Which would involve so so so so so many people!, that all would be willing to commit a FELONY....???
I've only watched the first few minutes of the video, and already have a question on what he is saying...
How do you hack voter registration offices, all across the country to increase their voter registrants, to increase their voter rolls with phantom voters?
How do you print ballots for these phantom voters to be inserted in to their counts, without also having an absentee ballot ENVELOPE with matching signature, to go with the phantom ballots?
You can't have more absentee ballots than absentee envelopes.
I'm totally lost....?
Elections are not done at county level, people vote in their district.... And absentee ballots are mailed to the district.... Are hundreds or thousands of voting districts involved in this, for it to work, within the checks and balances done in an election at the district level, then what is forwarded to the county??? Ballot stuffing as he claimed can't be done just when scanning votes....it would have to be done in the district itself....
Which would involve so so so so so many people!, that all would be willing to commit a FELONY....???
Faun
Diamond Member
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Fair enough. I searched on Lowndes County and it came up with Georgia, not Alabama. So I refined my search and found this was already a known issue and was attributed to out-of-date voter roles...
Ten Alabama counties have more voters than eligible people
The Public Interest Legal Foundation notified 10 counties in Alabama and 141 nationwide of the discrepancy.
www.al.com
Faun
Diamond Member
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No, he LIES about about voter registration. It's well known voter rolls are not up-to-date. Dead people and people who moved away are regularly floating on voter rolls for some time. He doesn't refer to that. He claims, with zero evidence, someone who wants to alter the outcome of the election nefariously inflates the voter registration count. He has no evidence of this. He offers no evidence of this. He just says it. From that, he extrapolates it's those extra voter registrations used to cast additional votes for candidates. Again, he has no evidence of this. He offers no evidence of this. Again, he just claims it. He then claims, again with no evidence, that additional ballots are then printed up to alter the election.
Every county in every state maintains a database of registered voters. For his claim to even begin to hold some level of credibility requires that every such database in every county in every state was hacked.
Show the proof of that...
Censoring a tweet doesn't solve any of the real problems with social media...its a bandaid they are using to make people think they are doing something imo.
The problem is not the tweet or post , or a big fat lie in the post.... The real problem is with their algorithms....and how they are set up to spread the tweet or fake news, (in this example, the lie). And once you've read one of the lies, they feed you all similar lies...reinforcing the lie, cementing the lie as truth in the reader's mind through all the similar articles, a confirmation bias..... Or simply, a means of brainwashing.
If a person were just telling a lie, which anyone can legally do, and only those who know the person lying heard it...then really, no harm done...the locals that heard the lie may or may not believe it, but it is limited.
Whereas the social networks push the lies out, to people all across the country who don't know the person lying at all... People are self rewarded with how many views or likes they get and whether they can make their lie go viral and get their 15 minutes of fame.... Etc etc etc. The more outrageous and untruthful, the better!
It's an awful business model developed to follow you day and night, for advertising purposes....that people pushing fake news are able to use, against us....imho.
The problem is not the tweet or post , or a big fat lie in the post.... The real problem is with their algorithms....and how they are set up to spread the tweet or fake news, (in this example, the lie). And once you've read one of the lies, they feed you all similar lies...reinforcing the lie, cementing the lie as truth in the reader's mind through all the similar articles, a confirmation bias..... Or simply, a means of brainwashing.
If a person were just telling a lie, which anyone can legally do, and only those who know the person lying heard it...then really, no harm done...the locals that heard the lie may or may not believe it, but it is limited.
Whereas the social networks push the lies out, to people all across the country who don't know the person lying at all... People are self rewarded with how many views or likes they get and whether they can make their lie go viral and get their 15 minutes of fame.... Etc etc etc. The more outrageous and untruthful, the better!
It's an awful business model developed to follow you day and night, for advertising purposes....that people pushing fake news are able to use, against us....imho.
Faun
Diamond Member
- Nov 14, 2011
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How people who should know better abuse math to bolster the ‘election fraud’ lie
Imagine that you run a small deli. Over the years, you’ve noticed that there’s a rhythm to when you sell sandwiches during the week, with a peak generally landing about 1:30 p.m. or so and then fading over the next few hours.
Curious about whether you can precisely predict daily sales, allowing you to manage supplies and staffing, you decide you’re going to track sandwich sales for a week. The result looks like this.
You’re not a math whiz, so you pass the data to your cousin Fritz, who has a PhD. Your question to Fritz is simple: How many sandwiches should you expect to sell each hour of a weekday?
In short order, he tells you something unexpected. The data you gave him isn’t sandwich sales at all. Instead, it’s phony data, derived from an algorithm aimed at masking deli fraud. And he can prove it.
See, if you take the sales from two days — say, Monday and Tuesday — and average the values, you can then create a sixth-order polynomial that describes the hourly pattern. Fritz’s PhD allows him to do the math himself, he assures you, but he passes along a formula that he derived from Excel. There it is: the precise formula for determining how many sandwiches (the y value) you will sell each hour (the x value). That’s math, hard at work, way beyond your ken — but precise.
But wait! If you take that same formula and compare it to the sales each day of the week, something alarming happens. The formula predicts the number of sandwiches being sold very well. Suspiciously well. If you look at the R-value of the correlation between the sales each hour and compare it to the formula, you get numbers that are very close to 1, meaning it’s a perfect correlation. And in a human-based system like sandwich sales, that shouldn’t happen!
Below, we used the average to do the R-value calculation, but you get the point. For each day, the predicated sales — here, the average — is extremely close to perfectly correlated to the actual sales. Ergo: This could be a function only of a computer-based effort to forge sales data.
You find this surprising for quite a few reasons. The first is that you tallied the sales yourself, so you know they’re correct. The second is that, even if Fritz were right that the numbers were artificial, why does he assume there’s some deli-fraud algorithm out there that’s responsible? The third is that, even without a PhD, you see a problem with Fritz’s analysis. He’s comparing an average derived from two of the values with all five of the values. Doesn’t it seem obvious that the result would be a strong correlation?
The answer, of course, is yes. Being surprised that sandwich sales over the course of the day is correlated to an average of the number of sandwiches sold over the course of two days is like being surprised that a coin comes up heads about half the time you flip it.
Or, more to the point, like being surprised that an estimate of voter turnout based on four counties in Michigan correlates strongly to voter turnout in nine counties in Michigan — including the four used to generate the “sixth-degree polynomial” (that complicated formula) in the first place.
This, however, is what the analysis of Douglas Frank, PhD, offers. Frank’s analysis of voter data in Michigan has led him to determine with seeming authority that the election results in that state were rigged, tailored to match the precise formula he himself derived from the state’s results. Claims like Frank’s analysis of Michigan have earned him the attention of MyPillow chief executive Mike Lindell, whose efforts to prove that voter fraud occurred in 2020 has led him to elevate all sorts of unfounded allegations about last year’s presidential election. Frank’s analysis has convinced others, too, with the conservative polling firm Rasmussen Reports elevating a write-up of his allegations over the weekend.
Rasmussen highlighted a different part of Frank’s assessment, the idea that about 66,000 Michigan voters cast ballots in last year’s election who weren’t in voter rolls in October. As The Washington Post’s Lenny Bronner quickly pointed out, Michigan has same-day voter registration, so those 66,000 voters are almost certainly just people who actually weren’t registered in October but who voted anyway.
The firm, which consistently showed more favorable approval data for Donald Trump over the course of his presidency than other pollsters, has repeatedly elevated dubious and unfounded fraud claims over the past few months. That’s aligned with a broader shift in its public-facing presence to be more aggressive toward critics from the mainstream media. (Last year, it accused me of “republishing a defamatory falsehood [and] committing fraud” for pointing out that its 2018 general-election polling showed Republicans with a one-point lead over the Democrats in an election where Democrats won more votes in House races national by a nearly 10-point margin.) Responding to Bronner’s tweet, Rasmussen offered the equivalent of a “just asking questions” shrug.
It should know better than to take Frank’s analysis at face value. This is a polling firm, after all, a company whose business is statistical analysis. Yet, there it was, sharing Frank’s claims uncritically.
Frank has been working with an attorney named Matthew DePerno, who has been sharing graphs from Frank’s presentation on Twitter with a bit of colorful commentary.
So what do those graphs show? What our third sandwich chart shows: that a prediction of how many votes would be cast in a Michigan county by age derived from the number of votes cast in a Michigan county by age correlates with the number of votes cast in a Michigan county by age. Frank does a lot of hand-waving on the side, like that discrepancy between the October voter roll and votes cast and by including comparisons of Census Bureau population estimates — which appear to be five-year averages of the population from 2015 to 2019 — are lower than the number of registered voters in some places. (Frank does point out that this could be a function of outdated voter rolls, but he doesn’t dwell on it.)
The heart of his analysis, though, is that R-value correlation between his predicted turnout and the actual turnout. How did he generate his prediction?
“What I actually did is I averaged four counties, the four largest counties, and used that key to predict all nine,” he explains. A few seconds later, he marvels that “the accuracy of my prediction is just ridiculously good. It shouldn’t be that good.”
Well, it should, because you are predicting data based on the data itself. If it weren’t a really close correlation, that’s when things would get funky.
Incidentally, that Frank is using a “sixth-order polynomial” doesn’t mean he’s doing some incredibly complicated calculation. It just means that he’s trying to fit his prediction as closely as possible to the existing data, thereby increasing the correlations.[/I]
Imagine that you run a small deli. Over the years, you’ve noticed that there’s a rhythm to when you sell sandwiches during the week, with a peak generally landing about 1:30 p.m. or so and then fading over the next few hours.
Curious about whether you can precisely predict daily sales, allowing you to manage supplies and staffing, you decide you’re going to track sandwich sales for a week. The result looks like this.
You’re not a math whiz, so you pass the data to your cousin Fritz, who has a PhD. Your question to Fritz is simple: How many sandwiches should you expect to sell each hour of a weekday?
In short order, he tells you something unexpected. The data you gave him isn’t sandwich sales at all. Instead, it’s phony data, derived from an algorithm aimed at masking deli fraud. And he can prove it.
See, if you take the sales from two days — say, Monday and Tuesday — and average the values, you can then create a sixth-order polynomial that describes the hourly pattern. Fritz’s PhD allows him to do the math himself, he assures you, but he passes along a formula that he derived from Excel. There it is: the precise formula for determining how many sandwiches (the y value) you will sell each hour (the x value). That’s math, hard at work, way beyond your ken — but precise.
But wait! If you take that same formula and compare it to the sales each day of the week, something alarming happens. The formula predicts the number of sandwiches being sold very well. Suspiciously well. If you look at the R-value of the correlation between the sales each hour and compare it to the formula, you get numbers that are very close to 1, meaning it’s a perfect correlation. And in a human-based system like sandwich sales, that shouldn’t happen!
Below, we used the average to do the R-value calculation, but you get the point. For each day, the predicated sales — here, the average — is extremely close to perfectly correlated to the actual sales. Ergo: This could be a function only of a computer-based effort to forge sales data.
You find this surprising for quite a few reasons. The first is that you tallied the sales yourself, so you know they’re correct. The second is that, even if Fritz were right that the numbers were artificial, why does he assume there’s some deli-fraud algorithm out there that’s responsible? The third is that, even without a PhD, you see a problem with Fritz’s analysis. He’s comparing an average derived from two of the values with all five of the values. Doesn’t it seem obvious that the result would be a strong correlation?
The answer, of course, is yes. Being surprised that sandwich sales over the course of the day is correlated to an average of the number of sandwiches sold over the course of two days is like being surprised that a coin comes up heads about half the time you flip it.
Or, more to the point, like being surprised that an estimate of voter turnout based on four counties in Michigan correlates strongly to voter turnout in nine counties in Michigan — including the four used to generate the “sixth-degree polynomial” (that complicated formula) in the first place.
This, however, is what the analysis of Douglas Frank, PhD, offers. Frank’s analysis of voter data in Michigan has led him to determine with seeming authority that the election results in that state were rigged, tailored to match the precise formula he himself derived from the state’s results. Claims like Frank’s analysis of Michigan have earned him the attention of MyPillow chief executive Mike Lindell, whose efforts to prove that voter fraud occurred in 2020 has led him to elevate all sorts of unfounded allegations about last year’s presidential election. Frank’s analysis has convinced others, too, with the conservative polling firm Rasmussen Reports elevating a write-up of his allegations over the weekend.
Rasmussen highlighted a different part of Frank’s assessment, the idea that about 66,000 Michigan voters cast ballots in last year’s election who weren’t in voter rolls in October. As The Washington Post’s Lenny Bronner quickly pointed out, Michigan has same-day voter registration, so those 66,000 voters are almost certainly just people who actually weren’t registered in October but who voted anyway.
The firm, which consistently showed more favorable approval data for Donald Trump over the course of his presidency than other pollsters, has repeatedly elevated dubious and unfounded fraud claims over the past few months. That’s aligned with a broader shift in its public-facing presence to be more aggressive toward critics from the mainstream media. (Last year, it accused me of “republishing a defamatory falsehood [and] committing fraud” for pointing out that its 2018 general-election polling showed Republicans with a one-point lead over the Democrats in an election where Democrats won more votes in House races national by a nearly 10-point margin.) Responding to Bronner’s tweet, Rasmussen offered the equivalent of a “just asking questions” shrug.
It should know better than to take Frank’s analysis at face value. This is a polling firm, after all, a company whose business is statistical analysis. Yet, there it was, sharing Frank’s claims uncritically.
Frank has been working with an attorney named Matthew DePerno, who has been sharing graphs from Frank’s presentation on Twitter with a bit of colorful commentary.
So what do those graphs show? What our third sandwich chart shows: that a prediction of how many votes would be cast in a Michigan county by age derived from the number of votes cast in a Michigan county by age correlates with the number of votes cast in a Michigan county by age. Frank does a lot of hand-waving on the side, like that discrepancy between the October voter roll and votes cast and by including comparisons of Census Bureau population estimates — which appear to be five-year averages of the population from 2015 to 2019 — are lower than the number of registered voters in some places. (Frank does point out that this could be a function of outdated voter rolls, but he doesn’t dwell on it.)
The heart of his analysis, though, is that R-value correlation between his predicted turnout and the actual turnout. How did he generate his prediction?
“What I actually did is I averaged four counties, the four largest counties, and used that key to predict all nine,” he explains. A few seconds later, he marvels that “the accuracy of my prediction is just ridiculously good. It shouldn’t be that good.”
Well, it should, because you are predicting data based on the data itself. If it weren’t a really close correlation, that’s when things would get funky.
Incidentally, that Frank is using a “sixth-order polynomial” doesn’t mean he’s doing some incredibly complicated calculation. It just means that he’s trying to fit his prediction as closely as possible to the existing data, thereby increasing the correlations.[/I]
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candycorn
Diamond Member
Ahh...you know nothing will ever come from all of this "proof"....
That must be frustrating.
- Thread starter
- #73
Alabama: was printed in bold letters in the JW watch article, right next to the first listing, Lowndes County (130%), just as I printed it under the link.
You had no need of a search.
Regardless of the reason for the registration roll exceeding the eligible voter population, the opportunity for fraud existed.
Your link is to a site that denies reading without first disabling ad-blockers or signing up, both of which I refuse to do.
Your link also says Ten Alabama counties...
Judicial Watch lists 25:
Judicial Watch said:
edthecynic
Censored for Cynicism
- Oct 20, 2008
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The only things inflated were his fake numbers.
- Thread starter
- #75
Not at all, troll. I'm just not getting into a stupid discussion with you about when something will happen.
Like I said, fuck off, troll.
candycorn
Diamond Member
You sound frustrated that nobody believes you....
Again...just wondering why (don't worry about a time frame) none of this "proof" you keep yammering about ever gets presented in court.
"Fuck off troll" is a response worthy of your intellect but I was hoping you could rise to the occasion and be truthful for once.
- Thread starter
- #77
Of course the voter rolls were not up to date. Dead people and voters that moved did have ballots cast for them in the election.
He didn't claim that voter rolls were changed by the county precinct officials. He claimed that the voter databases were hacked and manipulated (before the election) to add fabricated phantom voters that did not exist so that during the election, if the results were not going the way the hackers wanted, they could add the phantom ballots to the tally (without actually scanning a real ballot) to maintain the lead in favor of their desired winner. They just had to be careful not to add so many as to exceed the number of age-eligible voters in the precinct they were hacking. That may explain their use of census data. If extra votes were not needed, they were not used.
Not so. In the 2020 election, this only had to occur in the swing states. You know, the ones that all stopped counting in the middle of the night because Trump was getting so many votes he was overwhelming their fraud system. That's when vote counts were actually taken from Trump and given to Biden. That's when a massive dump of votes occurred in one precinct where there were not enough scanning machines to have actually processed as many votes as were dumped in for Biden.
Anomalies in Vote Counts and Their Effects on Election 2020
A Quantitative Analysis of Decisive Vote Updates in Michigan, Wisconsin, and Georgia on and after Election Night
votepatternanalysis.substack.com
Executive Summary
In the early hours of November 4th, 2020, Democratic candidate Joe Biden received several major “vote spikes” that substantially — and decisively — improved his electoral position in Michigan, Wisconsin, and Georgia. Much skepticism and uncertainty surrounds these “vote spikes.” Critics point to suspicious vote counting practices, extreme differences between the two major candidates’ vote counts, and the timing of the vote updates, among other factors, to cast doubt on the legitimacy of some of these spikes. While data analysis cannot on its own demonstrate fraud or systemic issues, it can point us to statistically anomalous cases that invite further scrutiny.This is one such case: Our analysis finds that a few key vote updates in competitive states were unusually large in size and had an unusually high Biden-to-Trump ratio. We demonstrate the results differ enough from expected results to be cause for concern.
With this report, we rely only on publicly available data from the New York Times to identify and analyze statistical anomalies in key states. Looking at 8,954 individual vote updates (differences in vote totals for each candidate between successive changes to the running vote totals, colloquially also referred to as “dumps” or “batches”), we discover a remarkably consistent mathematical property: there is a clear inverse relationship between difference in candidates’ vote counts and and the ratio of the vote counts. (In other words, it's not surprising to see vote updates with large margins, and it's not surprising to see vote updates with very large ratios of support between the candidates, but it is surprising to see vote updates which are both).The significance of this property will be further explained in later sections of this report. Nearly every vote update, across states of all sizes and political leanings follow this statistical pattern. A very small number, however, are especially aberrant. Of the seven vote updates which follow the pattern the least, four individual vote updates — two in Michigan, one in Wisconsin, and one in Georgia — were particularly anomalous and influential with respect to this property and all occurred within the same five hour window.
In particular, we are able to quantify the extent of compliance with this property and discover that, of the 8,954 vote updates used in the analysis, these four decisive updates were the 1st, 2nd, 4th, and 7th most anomalous updates in the entire data set. Not only does each of these vote updates not follow the generally observed pattern, but the anomalous behavior of these updates is particularly extreme. That is, these vote updates are outliers of the outliers.
The four vote updates in question are:
- An update in Michigan listed as of 6:31AM Eastern Time on November 4th, 2020, which shows 141,258 votes for Joe Biden and 5,968 votes for Donald Trump
- An update in Wisconsin listed as 3:42AM Central Time on November 4th, 2020, which shows 143,379 votes for Joe Biden and 25,163 votes for Donald Trump
- A vote update in Georgia listed at 1:34AM Eastern Time on November 4th, 2020, which shows 136,155 votes for Joe Biden and 29,115 votes for Donald Trump
- An update in Michigan listed as of 3:50AM Eastern Time on November 4th, 2020, which shows 54,497 votes for Joe Biden and 4,718 votes for Donald Trump
Extensive mathematical detail is provided and the data and the code (for the data-curation, data transformation, plotting, and modeling) are all attached in the appendix to this document[1].
Background
Late on Election Night 2020, President Donald J. Trump had a lead of around 100,000 votes in Wisconsin, a lead of around 300,000 votes in Michigan, and a lead of around 700,000 votes in Pennsylvania. Back-of-the-envelope calculations showed that in order to overtake President Trump, Joe Biden would have to substantially improve his performance in the remaining precincts — many of which were in heavily blue areas like Detroit, Milwaukee, and Philadelphia.On Election Night, conflicting news reports came in that various precincts were stopping their count for the evening, sending election officials home, or re-starting their counts. There remains a large amount of confusion to this day about the extent to which various precincts stopped counting, as well as the extent to which any state election laws or rules were broken by sending election officials home prematurely. Whatever the case is, various precincts in Wisconsin, Michigan, and Pennsylvania continued to report numbers throughout the night.
By the early hours of the following morning, Wisconsin had flipped blue, as did Michigan soon after. A few days later, Georgia and Pennsylvania followed suit. Given the uncertain context, many American observers and commentators were immediately uncomfortable or skeptical of these trends.
For context, using publicly available data from the New York Times, here is a visualization of the number of votes by candidate in Michigan from the beginning of election night to 7pm Eastern Standard Time (EST) on November 4th, 2020:
Fig. 1. X-axis is the Month-Year Hour of the time, Y-axis is the number of votes as of that time, expressed in millions of votes. The red series is the running number of votes for Donald Trump, and the blue series the running number of votes for Joe Biden.
As this graph shows, Joe Biden overtook President Trump’s lead through a small number of vote updates which broke overwhelmingly for Biden in Michigan in the early hours of the morning of November 4th.The situation in Wisconsin is even more stark: a single update to the vote count brought Biden from trailing by over 100,000 votes into the lead. Here is the comparable graph, over the same time range, for Wisconsin, with the x-axis (time) expressed in Central Standard Time (CST):
Fig. 2. X-axis is the Month-Year Hour of the time, Y-axis is the number of votes as of that time, expressed in millions of votes. The red series is the running number of votes for Donald Trump, and the blue series the running number of votes for Joe Biden.
Various versions of these graphs spurred online discourse. While some commentators provided relatively partisan analysis, others merely expressed surprise at the near-vertical leaps in some of these vote updates. Is it likely this phenomenon would arise organically? In an attempt to address this question, this report assesses how extreme and unusual these spikes are with respect to both other vote updates in the states of Michigan, Wisconsin, and Georgia, as well as those around the nation.Through several investigative mechanisms, we find these four vote updates to be extraordinarily anomalous. While these alone do not prove the existence of fraud or systemic issue, it invites further scrutiny.
(clip)
Quantifying the Extremity
Having demonstrated visually how anomalous the four key vote updates are, we can now proceed to attempt to quantify how unusual it is that these three points exist at once and that two of them are from the same state.The below graph has two particularly interesting visual properties:
- The graph is presented two-dimensionally, but it’s really three-dimensional. It’s visibly much denser in the center, has what appear to be something like two normal distributions, and as you move farther from the origin along a positive-sloping line which runs through the origin, the lower the density you can expect.
- The outer “edges” of the graph, in the top-right and bottom-left quadrants, closely resemble the shape of the line y = 1 / x.
We thus, for each (again, both standardized by state) coordinate pair of Biden-Trump margin and the log-ratio of Biden to Trump votes, can multiply these values and examine the distribution of the resulting products. Here, the larger a value is in magnitude, the less it follows the non-co-extremity. Plotting these products gives us:
Fig. 11. Histogram of products of x and y values for each coordinate pair in Fig. 10
As we can see, the values are overwhelmingly concentrated near the median, and the graph is profoundly right-skewed — otherwise, the x-axis would not need to stretch all the way to 80. All but 60 out of 8,954 unique updates have values less than 10, and all but 10 have values less than 20. In other words, an overwhelming share of updates seem to track this rule pretty closely, but a small number of updates are truly extreme outliers.A quick dive into these ten points reveals data which, by this point in the report, will be very familiar to the reader:
As we can see, four of the seven most anomalous vote updates — which is to say, updates in which the margin and ratio are co-extreme — are in election-critical states and occurred during the same five hour period where the circumstances on the ground were (and remain) contested and highly suspicious.
It is worth noting here that roughly 15% of the vote updates in the data set of 8,954 were from these three states. If we assumed it equally likely that any particular state should end up at any of these extreme points, there would be about a 1.2% chance that three states are represented in three out of the top four or four out of the top seven spots, and about a 0.99% chance that these three states would occupy five out of the top seven spots. It is thus very surprising to see the states in question be so disproportionately represented in the top 0.11% of the distribution of co-extremity[17].
(clip)
Conclusion
This report studies 8,954 individual updates to the vote totals in all 50 states and finds that four individual updates — two of which were widely noticed on the internet, including by the President — are profoundly anomalous; they deviate from a pattern which is otherwise found in the vast majority of the remaining 8,950 vote updates. The findings presented by this report [28]suggest that four vote count updates — which collectively were decisive in Michigan, Wisconsin, and Georgia, and thus decisive of a critical forty-two electoral votes — are especially anomalous and merit further investigation.In particular, the finding that the broader data follows general patterns and our ability to measure just how much any individual vote update does — or doesn’t — follow this pattern allows us to make concrete claims about both how extreme any given vote update is and about what any particular vote update might have looked like, had it been less extreme one one axis or another.
We further find that if these updates were only more extreme than 99% of all updates nationally in terms of their deviation from this generally-observed pattern, that, holding all else equal, Joe Biden may very well have lost the states of Michigan, Wisconsin, and Georgia, and that he would have 42 fewer Electoral votes — putting Biden below the number required to win the Presidency. Either way, it is indisputable that his margin of victory in these three states relies on four most anomalous vote updates identified by the metric developed in this report.
We once again note that this analysis is largely restricted to four individual vote updates out of a sample of nearly 9,000. This report by no means suggests stopping investigations in Michigan, Wisconsin, Pennsylvania, Georgia, or elsewhere; it is merely that these four key ballot updates are both profoundly anomalous with respect to a metric which removes any component of different states having different partisan leanings or a different number of voters. Furthermore, this analysis does not require that we regard the final vote totals in any of these states (or counties thereof) as suspicious, nor, critically, does it require that we accept that the observed data should follow any particular distribution a priori. We merely show that the data, adjusted appropriately to remove differences in size and political leaning between states, does follow a certain pattern, and that four key vote updates deviate profoundly from that pattern.
It is our belief that the extraordinarily anomalous nature of the studied vote updates here, combined with the staggering political implications, demands immediate and thorough investigation.
Correction: a previous version of this post calculated the probability of vote updates Georgia, Wisconsin, and Michigan constituting five of the ten most co-extreme vote updates as 0.0037%. The actual value is closer to 0.99%. The authors apologize for the error and the post has been corrected to reflect this.
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Last edited:
edthecynic
Censored for Cynicism
- Oct 20, 2008
- 43,044
- 6,886
- 1,830
For Tramp!
edthecynic
Censored for Cynicism
- Oct 20, 2008
- 43,044
- 6,886
- 1,830
Nothing but pure BULLSHIT!Of course the voter rolls were not up to date. Dead people and voters that moved did have ballots cast for them in the election.
He didn't claim that voter rolls were changed by the county precinct officials. He claimed that the voter databases were hacked and manipulated (before the election) to add fabricated phantom voters that did not exist so that during the election, if the results were not going the way the hackers wanted, they could add the phantom ballots to the tally (without actually scanning a real ballot) to maintain the lead in favor of their desired winner. They just had to be careful not to add so many as to exceed the number of age-eligible voters in the precinct they were hacking. That may explain their use of census data. If extra votes were not needed, they were not used.
Not so. In the 2020 election, this only had to occur in the swing states. You know, the ones that all stopped counting in the middle of the night because Trump was getting so many votes he was overwhelming their fraud system. That's when vote counts were actually taken from Trump and given to Biden. That's when a massive dump of votes occurred in one precinct where there were not enough scanning machines to have actually processed as many votes as were dumped in for Biden.
Anomalies in Vote Counts and Their Effects on Election 2020
A Quantitative Analysis of Decisive Vote Updates in Michigan, Wisconsin, and Georgia on and after Election Night
Executive Summary
In the early hours of November 4th, 2020, Democratic candidate Joe Biden received several major “vote spikes” that substantially — and decisively — improved his electoral position in Michigan, Wisconsin, and Georgia. Much skepticism and uncertainty surrounds these “vote spikes.” Critics point to suspicious vote counting practices, extreme differences between the two major candidates’ vote counts, and the timing of the vote updates, among other factors, to cast doubt on the legitimacy of some of these spikes. While data analysis cannot on its own demonstrate fraud or systemic issues, it can point us to statistically anomalous cases that invite further scrutiny.
This is one such case: Our analysis finds that a few key vote updates in competitive states were unusually large in size and had an unusually high Biden-to-Trump ratio. We demonstrate the results differ enough from expected results to be cause for concern.
With this report, we rely only on publicly available data from the New York Times to identify and analyze statistical anomalies in key states. Looking at 8,954 individual vote updates (differences in vote totals for each candidate between successive changes to the running vote totals, colloquially also referred to as “dumps” or “batches”), we discover a remarkably consistent mathematical property: there is a clear inverse relationship between difference in candidates’ vote counts and and the ratio of the vote counts. (In other words, it's not surprising to see vote updates with large margins, and it's not surprising to see vote updates with very large ratios of support between the candidates, but it is surprising to see vote updates which are both).
The significance of this property will be further explained in later sections of this report. Nearly every vote update, across states of all sizes and political leanings follow this statistical pattern. A very small number, however, are especially aberrant. Of the seven vote updates which follow the pattern the least, four individual vote updates — two in Michigan, one in Wisconsin, and one in Georgia — were particularly anomalous and influential with respect to this property and all occurred within the same five hour window.
In particular, we are able to quantify the extent of compliance with this property and discover that, of the 8,954 vote updates used in the analysis, these four decisive updates were the 1st, 2nd, 4th, and 7th most anomalous updates in the entire data set. Not only does each of these vote updates not follow the generally observed pattern, but the anomalous behavior of these updates is particularly extreme. That is, these vote updates are outliers of the outliers.
The four vote updates in question are:
This report predicts what these vote updates would have looked like, had they followed the same pattern as the vast majority of the 8,950 others. We find that the extents of the respective anomalies here are more than the margin of victory in all three states — Michigan, Wisconsin, and Georgia — which collectively represent forty-two electoral votes.
- An update in Michigan listed as of 6:31AM Eastern Time on November 4th, 2020, which shows 141,258 votes for Joe Biden and 5,968 votes for Donald Trump
- An update in Wisconsin listed as 3:42AM Central Time on November 4th, 2020, which shows 143,379 votes for Joe Biden and 25,163 votes for Donald Trump
- A vote update in Georgia listed at 1:34AM Eastern Time on November 4th, 2020, which shows 136,155 votes for Joe Biden and 29,115 votes for Donald Trump
- An update in Michigan listed as of 3:50AM Eastern Time on November 4th, 2020, which shows 54,497 votes for Joe Biden and 4,718 votes for Donald Trump
Extensive mathematical detail is provided and the data and the code (for the data-curation, data transformation, plotting, and modeling) are all attached in the appendix to this document[1].
Background
Late on Election Night 2020, President Donald J. Trump had a lead of around 100,000 votes in Wisconsin, a lead of around 300,000 votes in Michigan, and a lead of around 700,000 votes in Pennsylvania. Back-of-the-envelope calculations showed that in order to overtake President Trump, Joe Biden would have to substantially improve his performance in the remaining precincts — many of which were in heavily blue areas like Detroit, Milwaukee, and Philadelphia.
On Election Night, conflicting news reports came in that various precincts were stopping their count for the evening, sending election officials home, or re-starting their counts. There remains a large amount of confusion to this day about the extent to which various precincts stopped counting, as well as the extent to which any state election laws or rules were broken by sending election officials home prematurely. Whatever the case is, various precincts in Wisconsin, Michigan, and Pennsylvania continued to report numbers throughout the night.
By the early hours of the following morning, Wisconsin had flipped blue, as did Michigan soon after. A few days later, Georgia and Pennsylvania followed suit. Given the uncertain context, many American observers and commentators were immediately uncomfortable or skeptical of these trends.
For context, using publicly available data from the New York Times, here is a visualization of the number of votes by candidate in Michigan from the beginning of election night to 7pm Eastern Standard Time (EST) on November 4th, 2020:
Fig. 1. X-axis is the Month-Year Hour of the time, Y-axis is the number of votes as of that time, expressed in millions of votes. The red series is the running number of votes for Donald Trump, and the blue series the running number of votes for Joe Biden.
As this graph shows, Joe Biden overtook President Trump’s lead through a small number of vote updates which broke overwhelmingly for Biden in Michigan in the early hours of the morning of November 4th.
The situation in Wisconsin is even more stark: a single update to the vote count brought Biden from trailing by over 100,000 votes into the lead. Here is the comparable graph, over the same time range, for Wisconsin, with the x-axis (time) expressed in Central Standard Time (CST):
Fig. 2. X-axis is the Month-Year Hour of the time, Y-axis is the number of votes as of that time, expressed in millions of votes. The red series is the running number of votes for Donald Trump, and the blue series the running number of votes for Joe Biden.
Various versions of these graphs spurred online discourse. While some commentators provided relatively partisan analysis, others merely expressed surprise at the near-vertical leaps in some of these vote updates. Is it likely this phenomenon would arise organically? In an attempt to address this question, this report assesses how extreme and unusual these spikes are with respect to both other vote updates in the states of Michigan, Wisconsin, and Georgia, as well as those around the nation.
Through several investigative mechanisms, we find these four vote updates to be extraordinarily anomalous. While these alone do not prove the existence of fraud or systemic issue, it invites further scrutiny.
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Quantifying the Extremity
Having demonstrated visually how anomalous the four key vote updates are, we can now proceed to attempt to quantify how unusual it is that these three points exist at once and that two of them are from the same state.
The below graph has two particularly interesting visual properties:
We similarly expect points to be in both the top-right and bottom-left quadrants, and between an outer line which has the shape of y = 1 / x and the origin. Since these values will thus mostly be either both negative or both positive, we can see that multiplying each point’s x-coordinate with its y-coordinate is a useful way of assessing the extent to which it follows this sort of distribution. Since there are more points near the origin than there are on the visible “boundary lines” (i.e. the sequences of points on the outer edges in the first and third quadrants which visibly form these lines which look like a graph, if perhaps scaled, of y = 1/x).
- The graph is presented two-dimensionally, but it’s really three-dimensional. It’s visibly much denser in the center, has what appear to be something like two normal distributions, and as you move farther from the origin along a positive-sloping line which runs through the origin, the lower the density you can expect.
- The outer “edges” of the graph, in the top-right and bottom-left quadrants, closely resemble the shape of the line y = 1 / x.
We thus, for each (again, both standardized by state) coordinate pair of Biden-Trump margin and the log-ratio of Biden to Trump votes, can multiply these values and examine the distribution of the resulting products. Here, the larger a value is in magnitude, the less it follows the non-co-extremity. Plotting these products gives us:
Fig. 11. Histogram of products of x and y values for each coordinate pair in Fig. 10
As we can see, the values are overwhelmingly concentrated near the median, and the graph is profoundly right-skewed — otherwise, the x-axis would not need to stretch all the way to 80. All but 60 out of 8,954 unique updates have values less than 10, and all but 10 have values less than 20. In other words, an overwhelming share of updates seem to track this rule pretty closely, but a small number of updates are truly extreme outliers.
A quick dive into these ten points reveals data which, by this point in the report, will be very familiar to the reader:
As we can see, four of the seven most anomalous vote updates — which is to say, updates in which the margin and ratio are co-extreme — are in election-critical states and occurred during the same five hour period where the circumstances on the ground were (and remain) contested and highly suspicious.
It is worth noting here that roughly 15% of the vote updates in the data set of 8,954 were from these three states. If we assumed it equally likely that any particular state should end up at any of these extreme points, there would be about a 1.2% chance that three states are represented in three out of the top four or four out of the top seven spots, and about a 0.99% chance that these three states would occupy five out of the top seven spots. It is thus very surprising to see the states in question be so disproportionately represented in the top 0.11% of the distribution of co-extremity[17].
(clip)
Conclusion
This report studies 8,954 individual updates to the vote totals in all 50 states and finds that four individual updates — two of which were widely noticed on the internet, including by the President — are profoundly anomalous; they deviate from a pattern which is otherwise found in the vast majority of the remaining 8,950 vote updates. The findings presented by this report [28]suggest that four vote count updates — which collectively were decisive in Michigan, Wisconsin, and Georgia, and thus decisive of a critical forty-two electoral votes — are especially anomalous and merit further investigation.
In particular, the finding that the broader data follows general patterns and our ability to measure just how much any individual vote update does — or doesn’t — follow this pattern allows us to make concrete claims about both how extreme any given vote update is and about what any particular vote update might have looked like, had it been less extreme one one axis or another.
We further find that if these updates were only more extreme than 99% of all updates nationally in terms of their deviation from this generally-observed pattern, that, holding all else equal, Joe Biden may very well have lost the states of Michigan, Wisconsin, and Georgia, and that he would have 42 fewer Electoral votes — putting Biden below the number required to win the Presidency. Either way, it is indisputable that his margin of victory in these three states relies on four most anomalous vote updates identified by the metric developed in this report.
We once again note that this analysis is largely restricted to four individual vote updates out of a sample of nearly 9,000. This report by no means suggests stopping investigations in Michigan, Wisconsin, Pennsylvania, Georgia, or elsewhere; it is merely that these four key ballot updates are both profoundly anomalous with respect to a metric which removes any component of different states having different partisan leanings or a different number of voters. Furthermore, this analysis does not require that we regard the final vote totals in any of these states (or counties thereof) as suspicious, nor, critically, does it require that we accept that the observed data should follow any particular distribution a priori. We merely show that the data, adjusted appropriately to remove differences in size and political leaning between states, does follow a certain pattern, and that four key vote updates deviate profoundly from that pattern.
It is our belief that the extraordinarily anomalous nature of the studied vote updates here, combined with the staggering political implications, demands immediate and thorough investigation.
Correction: a previous version of this post calculated the probability of vote updates Georgia, Wisconsin, and Michigan constituting five of the ten most co-extreme vote updates as 0.0037%. The actual value is closer to 0.99%. The authors apologize for the error and the post has been corrected to reflect this.
(clip)
candycorn
Diamond Member
Total bullshit.Of course the voter rolls were not up to date. Dead people and voters that moved did have ballots cast for them in the election.
He didn't claim that voter rolls were changed by the county precinct officials. He claimed that the voter databases were hacked and manipulated (before the election) to add fabricated phantom voters that did not exist so that during the election, if the results were not going the way the hackers wanted, they could add the phantom ballots to the tally (without actually scanning a real ballot) to maintain the lead in favor of their desired winner. They just had to be careful not to add so many as to exceed the number of age-eligible voters in the precinct they were hacking. That may explain their use of census data. If extra votes were not needed, they were not used.
Not so. In the 2020 election, this only had to occur in the swing states. You know, the ones that all stopped counting in the middle of the night because Trump was getting so many votes he was overwhelming their fraud system. That's when vote counts were actually taken from Trump and given to Biden. That's when a massive dump of votes occurred in one precinct where there were not enough scanning machines to have actually processed as many votes as were dumped in for Biden.
Anomalies in Vote Counts and Their Effects on Election 2020
A Quantitative Analysis of Decisive Vote Updates in Michigan, Wisconsin, and Georgia on and after Election Night
Executive Summary
In the early hours of November 4th, 2020, Democratic candidate Joe Biden received several major “vote spikes” that substantially — and decisively — improved his electoral position in Michigan, Wisconsin, and Georgia. Much skepticism and uncertainty surrounds these “vote spikes.” Critics point to suspicious vote counting practices, extreme differences between the two major candidates’ vote counts, and the timing of the vote updates, among other factors, to cast doubt on the legitimacy of some of these spikes. While data analysis cannot on its own demonstrate fraud or systemic issues, it can point us to statistically anomalous cases that invite further scrutiny.
This is one such case: Our analysis finds that a few key vote updates in competitive states were unusually large in size and had an unusually high Biden-to-Trump ratio. We demonstrate the results differ enough from expected results to be cause for concern.
With this report, we rely only on publicly available data from the New York Times to identify and analyze statistical anomalies in key states. Looking at 8,954 individual vote updates (differences in vote totals for each candidate between successive changes to the running vote totals, colloquially also referred to as “dumps” or “batches”), we discover a remarkably consistent mathematical property: there is a clear inverse relationship between difference in candidates’ vote counts and and the ratio of the vote counts. (In other words, it's not surprising to see vote updates with large margins, and it's not surprising to see vote updates with very large ratios of support between the candidates, but it is surprising to see vote updates which are both).
The significance of this property will be further explained in later sections of this report. Nearly every vote update, across states of all sizes and political leanings follow this statistical pattern. A very small number, however, are especially aberrant. Of the seven vote updates which follow the pattern the least, four individual vote updates — two in Michigan, one in Wisconsin, and one in Georgia — were particularly anomalous and influential with respect to this property and all occurred within the same five hour window.
In particular, we are able to quantify the extent of compliance with this property and discover that, of the 8,954 vote updates used in the analysis, these four decisive updates were the 1st, 2nd, 4th, and 7th most anomalous updates in the entire data set. Not only does each of these vote updates not follow the generally observed pattern, but the anomalous behavior of these updates is particularly extreme. That is, these vote updates are outliers of the outliers.
The four vote updates in question are:
This report predicts what these vote updates would have looked like, had they followed the same pattern as the vast majority of the 8,950 others. We find that the extents of the respective anomalies here are more than the margin of victory in all three states — Michigan, Wisconsin, and Georgia — which collectively represent forty-two electoral votes.
- An update in Michigan listed as of 6:31AM Eastern Time on November 4th, 2020, which shows 141,258 votes for Joe Biden and 5,968 votes for Donald Trump
- An update in Wisconsin listed as 3:42AM Central Time on November 4th, 2020, which shows 143,379 votes for Joe Biden and 25,163 votes for Donald Trump
- A vote update in Georgia listed at 1:34AM Eastern Time on November 4th, 2020, which shows 136,155 votes for Joe Biden and 29,115 votes for Donald Trump
- An update in Michigan listed as of 3:50AM Eastern Time on November 4th, 2020, which shows 54,497 votes for Joe Biden and 4,718 votes for Donald Trump
Extensive mathematical detail is provided and the data and the code (for the data-curation, data transformation, plotting, and modeling) are all attached in the appendix to this document[1].
Background
Late on Election Night 2020, President Donald J. Trump had a lead of around 100,000 votes in Wisconsin, a lead of around 300,000 votes in Michigan, and a lead of around 700,000 votes in Pennsylvania. Back-of-the-envelope calculations showed that in order to overtake President Trump, Joe Biden would have to substantially improve his performance in the remaining precincts — many of which were in heavily blue areas like Detroit, Milwaukee, and Philadelphia.
On Election Night, conflicting news reports came in that various precincts were stopping their count for the evening, sending election officials home, or re-starting their counts. There remains a large amount of confusion to this day about the extent to which various precincts stopped counting, as well as the extent to which any state election laws or rules were broken by sending election officials home prematurely. Whatever the case is, various precincts in Wisconsin, Michigan, and Pennsylvania continued to report numbers throughout the night.
By the early hours of the following morning, Wisconsin had flipped blue, as did Michigan soon after. A few days later, Georgia and Pennsylvania followed suit. Given the uncertain context, many American observers and commentators were immediately uncomfortable or skeptical of these trends.
For context, using publicly available data from the New York Times, here is a visualization of the number of votes by candidate in Michigan from the beginning of election night to 7pm Eastern Standard Time (EST) on November 4th, 2020:
Fig. 1. X-axis is the Month-Year Hour of the time, Y-axis is the number of votes as of that time, expressed in millions of votes. The red series is the running number of votes for Donald Trump, and the blue series the running number of votes for Joe Biden.
As this graph shows, Joe Biden overtook President Trump’s lead through a small number of vote updates which broke overwhelmingly for Biden in Michigan in the early hours of the morning of November 4th.
The situation in Wisconsin is even more stark: a single update to the vote count brought Biden from trailing by over 100,000 votes into the lead. Here is the comparable graph, over the same time range, for Wisconsin, with the x-axis (time) expressed in Central Standard Time (CST):
Fig. 2. X-axis is the Month-Year Hour of the time, Y-axis is the number of votes as of that time, expressed in millions of votes. The red series is the running number of votes for Donald Trump, and the blue series the running number of votes for Joe Biden.
Various versions of these graphs spurred online discourse. While some commentators provided relatively partisan analysis, others merely expressed surprise at the near-vertical leaps in some of these vote updates. Is it likely this phenomenon would arise organically? In an attempt to address this question, this report assesses how extreme and unusual these spikes are with respect to both other vote updates in the states of Michigan, Wisconsin, and Georgia, as well as those around the nation.
Through several investigative mechanisms, we find these four vote updates to be extraordinarily anomalous. While these alone do not prove the existence of fraud or systemic issue, it invites further scrutiny.
(clip)
Quantifying the Extremity
Having demonstrated visually how anomalous the four key vote updates are, we can now proceed to attempt to quantify how unusual it is that these three points exist at once and that two of them are from the same state.
The below graph has two particularly interesting visual properties:
We similarly expect points to be in both the top-right and bottom-left quadrants, and between an outer line which has the shape of y = 1 / x and the origin. Since these values will thus mostly be either both negative or both positive, we can see that multiplying each point’s x-coordinate with its y-coordinate is a useful way of assessing the extent to which it follows this sort of distribution. Since there are more points near the origin than there are on the visible “boundary lines” (i.e. the sequences of points on the outer edges in the first and third quadrants which visibly form these lines which look like a graph, if perhaps scaled, of y = 1/x).
- The graph is presented two-dimensionally, but it’s really three-dimensional. It’s visibly much denser in the center, has what appear to be something like two normal distributions, and as you move farther from the origin along a positive-sloping line which runs through the origin, the lower the density you can expect.
- The outer “edges” of the graph, in the top-right and bottom-left quadrants, closely resemble the shape of the line y = 1 / x.
We thus, for each (again, both standardized by state) coordinate pair of Biden-Trump margin and the log-ratio of Biden to Trump votes, can multiply these values and examine the distribution of the resulting products. Here, the larger a value is in magnitude, the less it follows the non-co-extremity. Plotting these products gives us:
Fig. 11. Histogram of products of x and y values for each coordinate pair in Fig. 10
As we can see, the values are overwhelmingly concentrated near the median, and the graph is profoundly right-skewed — otherwise, the x-axis would not need to stretch all the way to 80. All but 60 out of 8,954 unique updates have values less than 10, and all but 10 have values less than 20. In other words, an overwhelming share of updates seem to track this rule pretty closely, but a small number of updates are truly extreme outliers.
A quick dive into these ten points reveals data which, by this point in the report, will be very familiar to the reader:
As we can see, four of the seven most anomalous vote updates — which is to say, updates in which the margin and ratio are co-extreme — are in election-critical states and occurred during the same five hour period where the circumstances on the ground were (and remain) contested and highly suspicious.
It is worth noting here that roughly 15% of the vote updates in the data set of 8,954 were from these three states. If we assumed it equally likely that any particular state should end up at any of these extreme points, there would be about a 1.2% chance that three states are represented in three out of the top four or four out of the top seven spots, and about a 0.99% chance that these three states would occupy five out of the top seven spots. It is thus very surprising to see the states in question be so disproportionately represented in the top 0.11% of the distribution of co-extremity[17].
(clip)
Conclusion
This report studies 8,954 individual updates to the vote totals in all 50 states and finds that four individual updates — two of which were widely noticed on the internet, including by the President — are profoundly anomalous; they deviate from a pattern which is otherwise found in the vast majority of the remaining 8,950 vote updates. The findings presented by this report [28]suggest that four vote count updates — which collectively were decisive in Michigan, Wisconsin, and Georgia, and thus decisive of a critical forty-two electoral votes — are especially anomalous and merit further investigation.
In particular, the finding that the broader data follows general patterns and our ability to measure just how much any individual vote update does — or doesn’t — follow this pattern allows us to make concrete claims about both how extreme any given vote update is and about what any particular vote update might have looked like, had it been less extreme one one axis or another.
We further find that if these updates were only more extreme than 99% of all updates nationally in terms of their deviation from this generally-observed pattern, that, holding all else equal, Joe Biden may very well have lost the states of Michigan, Wisconsin, and Georgia, and that he would have 42 fewer Electoral votes — putting Biden below the number required to win the Presidency. Either way, it is indisputable that his margin of victory in these three states relies on four most anomalous vote updates identified by the metric developed in this report.
We once again note that this analysis is largely restricted to four individual vote updates out of a sample of nearly 9,000. This report by no means suggests stopping investigations in Michigan, Wisconsin, Pennsylvania, Georgia, or elsewhere; it is merely that these four key ballot updates are both profoundly anomalous with respect to a metric which removes any component of different states having different partisan leanings or a different number of voters. Furthermore, this analysis does not require that we regard the final vote totals in any of these states (or counties thereof) as suspicious, nor, critically, does it require that we accept that the observed data should follow any particular distribution a priori. We merely show that the data, adjusted appropriately to remove differences in size and political leaning between states, does follow a certain pattern, and that four key vote updates deviate profoundly from that pattern.
It is our belief that the extraordinarily anomalous nature of the studied vote updates here, combined with the staggering political implications, demands immediate and thorough investigation.
Correction: a previous version of this post calculated the probability of vote updates Georgia, Wisconsin, and Michigan constituting five of the ten most co-extreme vote updates as 0.0037%. The actual value is closer to 0.99%. The authors apologize for the error and the post has been corrected to reflect this.
(clip)
Take it to court so we can all get a good laugh at it.
PS: Any word on why the supposedly rigged Wisconsin election sent more R's to the House than D's? No? Carry on.
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