Tens of millions

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41341204)

So, fire away. Thank.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41340954)

He's complaining about the structural setup of mail-in voting which was approved by the Pennsylvania Legislature.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41336412)

The statistical anomalies have enough impact but they have to be teased out of the data.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41336426)

However, the ballots received after the deadline are apparently being counted separately. It is unclear how much effect, if any, this issue will have on the outcome.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41340929)

None.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41340965)

Does this upset you?

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41341074)

https://www.usatoday.com/story/news/politics/elections/2020/11/05/pennsylvania-mail-ballots-arriving-3-day-window-not-make-break/6179946002/

It's literally moot.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41341197)

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41340730)

He’s retarded and likely poor

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41340957)

I was wrong. Kansas does not have straight ticket voting.

As a result, it might be suspicious, unless there's another easy explanation for such a difference (e.g., how much larger is the PA population than the KS population? what's the HS graduation rate difference, i.e., does PA have a higher percentage of uneducated voters?)

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41341676)

Author: Insanely creepy trip trailer park

Sidney Powell -- a very smart woman -- seems to be concerned about this issue. Most everyone else is chasing nickel and dime stuff. Frustrating.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41420260)

https://twitter.com/CortesSteve/status/1326300100742828033

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41342007)

Right cons?

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41341059)

2. Election software switching votes, as in Antrim County. Software is deterministic, it doesn't make mistakes but does exactly what it's told. Until the cause of the vote switching is isolated and replicated we should assume that similar behavior occurred elsewhere.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41342129)

1. Fact checkers at USA Today and elsewhere are claiming that vote counts were never suspended. This is demonstrably false in the case of Georgia, which stopped counts in Fulton County at 10:30 due to a broken water pipe. However I would like to see hard evidence in the case of WI, MI, PA. Google searches are not helpful.

2. Multiple sources are now claiming that the Antrim error was due to a clerk and not software.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41343137)

https://youtu.be/XQba647SNmM

All I am saying is there needs to be evidence to prevent memory holing. Alleging memory holing is not sufficient to convince skeptics.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41343504)

If in fact counting stopped at roughly the same time, in just a few highly contested states, then it indicates collusion.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41343460)

If X is not necessary for Y, then why would X suggest Y?

Let X = microwaving and Y = reheated leftovers.

If microwaving is not necessary for reheated leftovers, then why would microwaving suggest reheated leftovers?

Because it is a plausible cause where the probability, while less than 1, is still greater than 0.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41343679)

But if there *was* collusion, it is likely that an order went down to multiple operatives at the same time, which would explain the approximate simultaneity of count stoppages.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41343912)

but the truth is, progressivism is a religion for shitlibs. The blue checks are their gods and Instagram is their chapel. This isn't a "debate" for them in the sense they want to have an honest examination of this unprecedented event. There's tremendous statistical evidence out there already that has yet to be disputed - only censored. Thats why this feels so hollow to them.

on the positive side, enough damage has been already been done where the onus is now on shitlibs to prove accuracy. No one gives a shit about the courts when 45% of the country is seriously doubting the election results (and because of hard stats, not facebook memes, nonetheless)

its also worth noting that the average shitlib is a hyper emotional creature with no quantitative background, so they really can't interpret the numbers to begin with

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41343556)

http://www.xoxohth.com/thread.php?thread_id=4685222&mc=27&forum_id=2

i eagerly await your reply

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41343808)

What's your throwaway I'll send the csv

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41343878)

http://directorblue.blogspot.com/2020/11/dna-level-statistical-proof-smartmatic.html

The charts below are derived from The New York Times' real-time election feeds (e.g., here). They show "DNA-level" evidence of vote fraud that was systematically used to overcome massive Trump leads with "vote flips" to Biden.

The twin charts below depict the shifts in votes starting on election day. The X-axis is the date/time and the Y-axis represents the change in votes (positive values denote shifts for Trump, negative values represent shifts for Biden, in hundreds).

Notice the similarities in PA and GA? How the right sides of the graph show virtually no movement for Trump; and very predictable vote movements to Biden. How predictable?

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41384802)

The most interesting evidence to me:

"Patrick Basham, a pollster with an accurate track record and the director of the Democracy Institute in D.C., highlighted two observations made by fellow colleagues, polling guru Richard Baris of Big Data Poll and Washington Post election analyst Robert Barnes. Baris noted a statistical oddity from 2020’s election returns: “Biden underperformed Hillary Clinton in every major metro area around the country, save for Milwaukee, Detroit, Atlanta and Philadelphia.”"

=======

5 More Ways Joe Biden Magically Outperformed Election Norms

Surely the journalist class should be intrigued by the historic implausibility of Joe Biden’s victory. That they are not is curious, to say the least.

J.B. Shurk

By J.B. Shurk

NOVEMBER 23, 2020

In all the excitement among objective journalists for Joe Biden’s declared victory, reporters are missing how extraordinary the Democrat’s performance was in the 2020 election. It’s not just that the former vice president is on track to become the oldest president in American history, it’s what he managed to accomplish at the polls this year.

Candidate Joe Biden was so effective at animating voters in 2020 that he received a record number of votes, more than 15 million more than Barack Obama received in his re-election of 2012. Amazingly, he managed to secure victory while also losing in almost every bellwether county across the country. No presidential candidate has been capable of such electoral jujitsu until now.

While Biden underperformed Hillary Clinton’s 2016 totals in every urban county in the United States, he outperformed her in the metropolitan areas of Georgia, Michigan, Wisconsin, and Pennsylvania. Even more surprising, the former VP put up a record haul of votes, despite Democrats’ general failures in local House and state legislative seats across the nation.

He accomplished all this after receiving a record low share of the primary vote compared to his Republican opponent heading into the general election. Clearly, these are tremendous and unexpected achievements that would normally receive sophisticated analysis from the journalist class but have somehow gone mostly unmentioned during the celebrations at news studios in New York City and Washington, D.C.

The massive national political realignment now taking place may be one source of these surprising upsets. Yet still, to have pulled so many rabbits out of his hat like this, nobody can deny that Biden is a first-rate campaigner and politician, the likes of which America has never before seen. Let’s break down just how unique his political voodoo has been in 2020.

1. 80 Million Votes

Holy moly! A lot of Americans turned out for a Washington politician who’s been in office for nearly 50 years. Consider this: no incumbent president in nearly a century and a half has gained votes in a re-election campaign and still lost.

President Trump gained more than ten million votes since his 2016 victory, but Biden’s appeal was so substantial that it overcame President Trump’s record support among minority voters. Biden also shattered Barack Obama’s own popular vote totals, really calling into question whether it was not perhaps Biden who pulled Obama across the finish lines in 2008 and 2012.

Proving how sharp his political instincts are, the former VP managed to gather a record number of votes while consistently trailing President Trump in measures of voter enthusiasm. Biden was so savvy that he motivated voters unenthusiastic about his campaign to vote for him in record numbers.

2. Winning Despite Losing Most Bellwether Counties

Biden is set to become the first president in 60 years to lose the states of Ohio and Florida on his way to election. For a century, these states have consistently predicted the national outcome, and they have been considered roughly representative of the American melting pot as a whole. Despite national polling giving Biden a lead in both states, he lost Ohio by eight points and Florida by more than three.

For Biden to lose these key bellwethers by notable margins and still win the national election is newsworthy. Not since the Mafia allegedly aided John F. Kennedy in winning Illinois over Richard Nixon in 1960 has an American president pulled off this neat trick.

Even more unbelievably, Biden is on his way to winning the White House after having lost almost every historic bellwether county across the country. The Wall Street Journal and The Epoch Times independently analyzed the results of 19 counties around the United States that have nearly perfect presidential voting records over the last 40 years. President Trump won every single bellwether county, except Clallam County in Washington.

Whereas the former VP picked up Clallam by about three points, President Trump’s margin of victory in the other 18 counties averaged over 16 points. In a larger list of 58 bellwether counties that have correctly picked the president since 2000, Trump won 51 of them by an average of 15 points, while the other seven went to Biden by around four points. Bellwether counties overwhelmingly chose President Trump, but Biden found a path to victory anyway.

3. Biden Trailed Clinton Except in a Select Few Cities

Patrick Basham, a pollster with an accurate track record and the director of the Democracy Institute in D.C., highlighted two observations made by fellow colleagues, polling guru Richard Baris of Big Data Poll and Washington Post election analyst Robert Barnes. Baris noted a statistical oddity from 2020’s election returns: “Biden underperformed Hillary Clinton in every major metro area around the country, save for Milwaukee, Detroit, Atlanta and Philadelphia.”

Barnes added that in those “big cities in swing states run by Democrats…the vote even exceeded the number of registered voters.” In the states that mattered most, so many mail-in ballots poured in for Biden from the cities that he put up record-breaking numbers and overturned state totals that looked like comfortable leads for President Trump.

If Democrats succeed in eliminating the Electoral College, Biden’s magic formula for churning out overwhelming vote totals in a handful of cities should make the Democrats unbeatable.

4. Biden Won Despite Democrat Losses Everywhere Else

Randy DeSoto noted in The Western Journal that “Donald Trump was pretty much the only incumbent president in U.S. history to lose his re-election while his own party gained seats in the House of Representatives.” Now that’s a Biden miracle!

In 2020, The Cook Political Report and The New York Times rated 27 House seats as toss-ups going into Election Day. Right now, Republicans appear to have won all 27. Democrats failed to flip a single state house chamber, while Republicans flipped both the House and Senate in New Hampshire and expanded their dominance of state legislatures across the country.

Christina Polizzi, a spokesperson for the Democratic Legislative Campaign Committee, went so far as to state: “It’s clear that Trump isn’t an anchor for the Republican legislative candidates. He’s a buoy.” Amazingly, Biden beat the guy who lifted all other Republicans to victory. Now that’s historic!

5. Biden Overcame Trump’s Commanding Primary Vote

In the past, primary vote totals have been remarkably accurate in predicting general election winners. Political analyst David Chapman highlighted three historical facts before the election.

First, no incumbent who has received 75 percent of the total primary vote has lost re-election. Second, President Trump received 94 percent of the primary vote, which is the fourth highest of all time (higher than Dwight Eisenhower, Nixon, Clinton, or Obama). In fact, Trump is only one of five incumbents since 1912 to receive more than 90 percent of the primary vote.

Third, Trump set a record for most primary votes received by an incumbent when more than 18 million people turned out for him in 2020 (the previous record, held by Bill Clinton, was half that number). For Biden to prevail in the general election, despite Trump’s historic support in the primaries, turns a century’s worth of prior election data on its head.

Joe Biden achieved the impossible. It’s interesting that many more journalists aren’t pointing that out.

https://thefederalist.com/2020/11/23/5-more-ways-joe-biden-magically-outperformed-election-norms/

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41420179)

If there was no fraud, it's the most amazing POTUS election in decades -- maybe ever.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41420259)

Let's apply trumpmo logic. Why don't we just declare Florida a fraud victory for trump? That kind of swing is ridiculous and obvious evidence of fraud.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41427844)

There must be a compelling reason for the anomalies that cut in Biden's favor, but the only explanation that remains plausible is massive voter fraud.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41454696)

https://spectator.us/reasons-why-the-2020-presidential-election-is-deeply-puzzling/

Reasons why the 2020 presidential election is deeply puzzling

If only cranks find the tabulations strange, put me down as a crank

Patrick Basham

2020

Ballots are recounted in Gwinnett County, Georgia (Getty)

Patrick Basham

November 27, 2020

3:44 PM

To say out-loud that you find the results of the 2020 presidential election odd is to invite derision. You must be a crank or a conspiracy theorist. Mark me down as a crank, then. I am a pollster and I find this election to be deeply puzzling. I also think that the Trump campaign is still well within its rights to contest the tabulations. Something very strange happened in America’s democracy in the early hours of Wednesday November 4 and the days that followed. It’s reasonable for a lot of Americans to want to find out exactly what.

First, consider some facts. President Trump received more votes than any previous incumbent seeking reelection. He got 11 million more votes than in 2016, the third largest rise in support ever for an incumbent. By way of comparison, President Obama was comfortably reelected in 2012 with 3.5 million fewer votes than he received in 2008.

Trump’s vote increased so much because, according to exit polls, he performed far better with many key demographic groups. Ninety-five percent of Republicans voted for him. He did extraordinarily well with rural male working-class whites.

He earned the highest share of all minority votes for a Republican since 1960. Trump grew his support among black voters by 50 percent over 2016. Nationally, Joe Biden’s black support fell well below 90 percent, the level below which Democratic presidential candidates usually lose.

Trump increased his share of the national Hispanic vote to 35 percent. With 60 percent or less of the national Hispanic vote, it is arithmetically impossible for a Democratic presidential candidate to win Florida, Arizona, Nevada, and New Mexico. Bellwether states swung further in Trump’s direction than in 2016. Florida, Ohio and Iowa each defied America’s media polls with huge wins for Trump. Since 1852, only Richard Nixon has lost the electoral college after winning this trio, and that 1960 defeat to John F. Kennedy is still the subject of great suspicion.

Midwestern states Michigan, Pennsylvania, and Wisconsin always swing in the same direction as Ohio and Iowa, their regional peers. Ohio likewise swings with Florida. Current tallies show that, outside of a few cities, the Rust Belt swung in Trump’s direction. Yet, Biden leads in Michigan, Pennsylvania, and Wisconsin because of an apparent avalanche of black votes in Detroit, Philadelphia, and Milwaukee. Biden’s ‘winning’ margin was derived almost entirely from such voters in these cities, as coincidentally his black vote spiked only in exactly the locations necessary to secure victory. He did not receive comparable levels of support among comparable demographic groups in comparable states, which is highly unusual for the presidential victor.

We are told that Biden won more votes nationally than any presidential candidate in history. But he won a record low of 17 percent of counties; he only won 524 counties, as opposed to the 873 counties Obama won in 2008. Yet, Biden somehow outdid Obama in total votes.

Victorious presidential candidates, especially challengers, usually have down-ballot coattails; Biden did not. The Republicans held the Senate and enjoyed a ‘red wave’ in the House, where they gained a large number of seats while winning all 27 toss-up contests. Trump’s party did not lose a single state legislature and actually made gains at the state level.

Another anomaly is found in the comparison between the polls and non-polling metrics. The latter include: party registrations trends; the candidates’ respective primary votes; candidate enthusiasm; social media followings; broadcast and digital media ratings; online searches; the number of (especially small) donors; and the number of individuals betting on each candidate.

Despite poor recent performances, media and academic polls have an impressive 80 percent record predicting the winner during the modern era. But, when the polls err, non-polling metrics do not; the latter have a 100 percent record. Every non-polling metric forecast Trump’s reelection. For Trump to lose this election, the mainstream polls needed to be correct, which they were not. Furthermore, for Trump to lose, not only did one or more of these metrics have to be wrong for the first time ever, but every single one had to be wrong, and at the very same time; not an impossible outcome, but extremely unlikely nonetheless.

Atypical voting patterns married with misses by polling and non-polling metrics should give observers pause for thought. Adding to the mystery is a cascade of information about the bizarre manner in which so many ballots were accumulated and counted.

The following peculiarities also lack compelling explanations:

1. Late on election night, with Trump comfortably ahead, many swing states stopped counting ballots. In most cases, observers were removed from the counting facilities. Counting generally continued without the observers

2. Statistically abnormal vote counts were the new normal when counting resumed. They were unusually large in size (hundreds of thousands) and had an unusually high (90 percent and above) Biden-to-Trump ratio

3. Late arriving ballots were counted. In Pennsylvania, 23,000 absentee ballots have impossible postal return dates and another 86,000 have such extraordinary return dates they raise serious questions

4. The failure to match signatures on mail-in ballots. The destruction of mail in ballot envelopes, which must contain signatures

5. Historically low absentee ballot rejection rates despite the massive expansion of mail voting. Such is Biden’s narrow margin that, as political analyst Robert Barnes observes, ‘If the states simply imposed the same absentee ballot rejection rate as recent cycles, then Trump wins the election’

6. Missing votes. In Delaware County, Pennsylvania, 50,000 votes held on 47 USB cards are missing

7. Non-resident voters. Matt Braynard’s Voter Integrity Project estimates that 20,312 people who no longer met residency requirements cast ballots in Georgia. Biden’s margin is 12,670 votes

8. Serious ‘chain of custody’ breakdowns. Invalid residential addresses. Record numbers of dead people voting. Ballots in pristine condition without creases, that is, they had not been mailed in envelopes as required by law

9. Statistical anomalies. In Georgia, Biden overtook Trump with 89 percent of the votes counted. For the next 53 batches of votes counted, Biden led Trump by the same exact 50.05 to 49.95 percent margin in every single batch. It is particularly perplexing that all statistical anomalies and tabulation abnormalities were in Biden’s favor. Whether the cause was simple human error or nefarious activity, or a combination, clearly something peculiar happened.

If you think that only weirdos have legitimate concerns about these findings and claims, maybe the weirdness lies in you.

Patrick Basham is director of The Democracy Institute

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41454739)

Despite the blandishments of the narrative, which seek to seduce you into acquiescence with rumors of inevitability, we really do not know how this story, which seems so familiar, will end.

By Roger Kimball

November 28, 2020

Oscar Wilde was such a card. Sitting for his viva voce examination in Greek, he was given a passage to translate from one of the Passion stories in the New Testament. He started in and was barreling along fluently. At some point, one of the examiners interrupted, noting that he was satisfied by Wilde’s performance and that he could stop. Wilde ignored him and kept at it. The examiner interrupted again. “Really, Mr. Wilde, you may stop now. It is clear that you know the Greek.” “Oh please let me continue,” Wilde is supposed to have responded. “I want to see how it ends.”

Yuck, yuck, yuck. Who knows whether the story is true? I like to think it is. It’s not that I believe Wilde was ignorant of the plot of a Gospel story. He knew how it ended all right. But I admire the insouciance of his response.

Many people think the world is in a position akin to Wilde’s with respect to the 2020 presidential election. We’re all assumed to know how it ended. Joe Biden won. Any demurral on that score is put down to feigned ignorance, attempted cleverness, or petulant perversity.

After all, the Associated Press called the election for Joe Biden a couple of weeks ago. Other news agencies, from the Wall Street Journal and Fox News to CNN, the New Woke Times, and the Washington Post were right there on cue, hailing him the winner. Time, the former news weekly, devoted its cover to Joe Biden, “46th President of the United States.” Twitter was on the case, adding little warning messages to tweets about the election it didn’t like, suspending the accounts of people whose opinions it disagreed with, throttling the ability of those who dissented to broadcast their dissent. Who knows what Google and Facebook are doing with their search results. Some secrets are too deep for the light of day.

And that is my point. The strongest argument for Biden’s victory is not the vote tally. It is the monolithic narrative, pumped up like one of those inflatable play castles at a child’s birthday party. With every passing day, that narrative becomes more boisterous, more assertive, more uncompromising. It is a collective primal scream, emitted with eyes shut and ears plugged.

There is a problem for the narrative, however. Or more to the point, there are 73 million problems. A major concession in the Biden-won-give-it-up-narrative is revealed by the hawkers of the “Unity Now” meme. Let us all come together as one nation, under Joe, and reassert the American normality that has been so sorely missing under the despotic reign of Donald Trump.

No. No, that’s not going to fly, and not only because of the snarling viciousness that attended Donald Trump and his entire administration from the moment he was elected until now. Granted, Democrats are masters of hypocrisy. I will give them that. Brazenness is part of the formula. They are utterly unembarrassed by double standards. Indeed, they glory in them.

On November 12, Kamala Harris was happy to emit this saccharine Tweet:

Hope.

Unity.

Decency.

Truth.

These are the ideals that will guide a [Biden–Harris] administration.

An alert commentator provided some illuminating historical context from the Left’s latest how-to manual, George Orwell’s Nineteen-Eighty-Four:

Winston turned a switch and the voice sank somewhat, though the words were still distinguishable. The instrument (the telescreen, it was called) could be dimmed, but there was no way of shutting it off completely …

HOPE

UNITY

DECENCY

EMPATHY

TRUTH

EXPERTS

SCIENCE

The inclusion of “Science” is especially nice.

In any event, Harris wouldn’t give Orwell a moment’s thought. Her sense of entitlement is unshakable, beyond embarrassment. “When we do it”—go without masks, eat out with friends after telling hoi polloi to stay home, run a private email server for government business, collude with Russians to upset an election, leak classified material, lie under oath, etc.—“it’s OK because—reasons.” “What difference, at this point, does it make?”

But glaring hypocrisy is not the only reason that the narrative’s call for unity is failing. There is also its essential fragility. It is loud. It is seamless. It is asserted by all the best and most beautiful people, the really smart ones with fancy degrees, the right attitudes, the impressive ZIP codes. But it is also like an elaborate barque in high winds and choppy seas on a leeward course off a rocky coast.

That coast is the anti-narrative, otherwise known as reality.

The really hard and jagged part of the impinging reality, the “impervious horrors of leeward shore,” is the actual vote tally in Arizona, Georgia, Michigan, Nevada, Pennsylvania, and Wisconsin.

Huge anomalies have been alleged in all of those key states. With an assist from various professionals, I’ve summarized some of them in various columns, here, for example, here and here.

Inquiring minds want to know, how is it possible that voter turnout in just those key cities in just those key states was so high: often 90 percent or more? How is it possible that Joe Biden, who barely campaigned, garnered more votes in just those spots than even Barack Obama had done? How is it possible that, as everyone was getting tucked into bed on the night of November 3, Donald Trump had notable leads in almost all of those states and then, suddenly, all at once, in the wee hours, floods of votes poured in and—wouldn’t you know it—they were overwhelmingly, sometimes exclusively, for Biden? And what about those voting machines from Dominion: are we confident that they are secure?

Aristotle tells us that “Probable impossibilities are to be preferred to improbable possibilities.” Do we have instruments capacious enough to measure the improbabilities that attend Joe Biden’s performance in these key states?

There seems to be a couple of different attitudes towards voter fraud. For some, a little voter fraud is just the cost of doing business. The best is the enemy of the good, don’t you know, and after all the FDA maintains a meticulous chart of just what proportion of rodent hair, insect heads, and rat feces, and other such “defects” are permitted in the food supply. It’s quite a lot, it saddens me to report, and perhaps voter fraud is like a bag of wheat: if we insist on purity, we won’t have any wheat with which to bake bread.

That, anyway, is one point of view. But even if one grants that in principle, it seems legitimate to ask, how much voter fraud is OK? I am not aware of any political FDA weighing in and telling us what percentage of the vote can be tainted before it is ruled inadmissible. In this election, hundreds of thousands of votes are alleged to be fraudulent. At the moment, Joe Biden is said to be ahead by some 150,000 votes in Michigan, 80,000 in Pennsylvania, 20,000 in Wisconsin, 10-12,000 in Georgia, Arizona, about 30,000 in Nevada. What if his standing in a majority of those states were shown to be the result of fraud?

Then there is that stretch of coastline known as election law. The particular rules of our elections are generally entrusted to legislatures of the various states. But in several instances, courts or various executive entities weighed in at the last moment to change the rules about how votes would be counted. Pennsylvania is an especially egregious case. As Julie Kelly showed, “Election officials clearly violated the law by inspecting mail-in ballots before November 3,” in clear defiance of the law, which requires such ballots to be safely kept in “sealed or locked containers” until 7 a.m. on Election Day.

Because of this and other irregularities, a state judge on Friday, finding that mail-in ballot procedures likely violated the Pennsylvania constitution, ordered that Pennsylvania halt the process of certifying the vote. “Petitioners,” Judge Patricia McCollough wrote, “appear to have a viable claim that the mail-in ballot procedures set forth in Act 77 contravene” the law. In a blow to Team Trump, the Pennsylvania Supreme Court vacated Judge McCollough’s order Saturday night, clearing the way for the state to certify the election. Next stop? The Supreme Court of the United States.

Something similar is happening in all the battleground states. Rudy Giuliani and Jenna Ellis, part of Trump’s official legal team, are pursuing alleged violations of the law in Pennsylvania, Arizona, and elsewhere. Sidney Powell, an activist lawyer who is not on Trump’s official legal team, has filed suit in Georgia and Michigan, alleging “massive” voter fraud significant enough to overturn the vote there. Whether this will be the “Kraken” she promised to unleash or merely a crayfish is something we will know soon.

But this brings me back to Oscar. Despite the blandishments of the narrative, which seek to seduce you into acquiescence with rumors of inevitability, we really do not know how this story, which seems so familiar, will end.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41454752)

=======

The Real Kraken: What Really Happened to Donald Trump in the 2020 Election

BY J. CHRISTIAN ADAMS DEC 02, 2020 12:41 PM ET

AP Photo/Andrew Harnik, File

Eric Holder was a big loser on election night. He was the guy raising tens of millions of dollars to make America safe for Democratic redistricting. A red wave turned Holder’s dreams into dust in state legislative races. State legislatures are where the redistricting action is, and the GOP flipped three chambers red, gaining 192 state house seats and 40 state senate seats nationwide.

Republicans now control both House and Senate chambers in 31 states. The country is a huge swath of red legislative control with Democrats largely confined to the cultural monoliths on the Pacific coast and urban Northeast.

The red wave extended to the United States House of Representatives, where for now, Republicans have gained nine seats.

But this wasn’t supposed to happen. The president isn’t supposed to lose when all the Republicans are winning.

Something’s fishy.

Indeed, something profoundly fishy happened in the 2020 election, but it wasn’t the Kraken or Venezuelan communists running remote software when they can’t even make the red lights work in their own country. Those shiny objects will play out with time and examination of evidence.

What happened in 2020 is something more fundamental and profound. What happened in 2020 is cultural and systemic, and sadly, generally legal. Until Republicans, and more importantly Trump supporters, understand what happened to them this year, it will happen again.

Two things happened in 2020. First, COVID led to a dismantling of state election integrity laws by everyone except the one body with the constitutional prerogative to change the rules of electing the president – the state legislatures.

Second, the Center for Technology and Civic Life happened.

If you are focused on goblins in the voting machines but don’t know anything about the CTCL and what they did to defeat Donald Trump, it’s time to up your game.

The Center for Technology and Civic Life and allied groups are responsible for building an urban get-out-the-vote-machine of the sort that Democrats could only dream up on a bender fueled by jugs of Merlot and all the legalized pot they could smoke.

The Capital Research Center has this deep dive into what the Center for Technology and Civil Life did in just Georgia. It starts with this:

This year, left-leaning donors Mark Zuckerberg and wife Priscilla Chan gave $350 million to an allegedly “nonpartisan” nonprofit, the Center for Tech and Civic Life (CTCL), which in turn re-granted the funds to thousands of governmental election officials around the country to “help” them conduct the 2020 election.

“Help.” That’s a good one.

What these grants did was build structural bias into the 2020 election where structural bias matters most – in densely populated urban cores. It converted election offices in key jurisdictions with deep reservoirs of Biden votes into Formula One turnout machines. The hundreds of millions of dollars built systems, hired employees from activist groups, bought equipment and radio advertisements. It did everything that street activists could ever dream up to turn out Biden votes if only they had unlimited funding.

In 2020, they had unlimited funding because billionaires made cash payments to 501(c)(3) charities that in turn made cash payments to government election offices.

Flush with hundreds of millions in new cash, government election offices turned those donations into manpower, new equipment, and street muscle to turn often sluggish and incompetent urban election offices into massive Biden turnout machines across the country – in Madison, Milwaukee, Detroit, Lansing, Philadelphia, and Atlanta among dozens of others.

Philadelphia’s election office budget was normally $9.8 million. The CLTC gave Philadelphia $10 million, more than doubling the city budget.

Those millions were used to hire local activists as city employees to drive around and collect ballots. The millions bought new printers and scanners to accommodate mail ballots. Philadelphia established brand new satellite election offices across the most Biden-friendly neighborhoods in the entire Commonwealth of Pennsylvania. The millions bought scores of convenient drop boxes across the same neighborhoods where mail ballots could be conveniently dropped. Even though laws limited third parties from collecting and dropping off multiple ballots, people were photographed dropping off bundles of ballots at the boxes.

If voters couldn’t muster the initiative to travel a few blocks to the drop-off boxes or new satellite offices, the city went to them to collect their ballot.

CLTC dollars flowed through Philadelphia election officials to the pricey public relations firm Aloysius Butler & Clark. They designed billboards, posters, bus advertisements, and print ads. Radio advertisements and street marketing all added to the blitz.

(Philadelphia City Commissioners)

(Philadelphia City Commissioners)

(Philadelphia City Commissioners)

In Philadelphia and the surrounding urban counties that received millions of dollars in CLTC grants, turnout exploded.

The plan worked.

In case you still don’t follow: Hundreds of millions of private charitable dollars flowed into key urban county election offices in battleground states. The same private philanthropic largess did not reach red counties. Urban counties were able to revolutionize government election offices into Joe Biden turnout machines.

Here’s the best part — All of this is legal. Do not allow your shock and confusion about what happened in 2020 lead you to mislabel all of this as “voter fraud” or “quasi-legal.” The Left excels at making the unprecedented real and the seemingly illegal, legal.

Yet none of this should be a surprise. Last April, I warned on these pages:

On one recent teleconference call, leaders of this effort even considered giving state election officials direct payments to do what the progressives want them to do. In other words, they are researching the possibility of privately funding state and local elections offices in such a way that it doesn’t appear to be a bribe. This could include paying for expansive vote-by-mail, providing ballot design, and manpower help to run the elections. It could include things that those of us in the real America probably couldn’t even make up.

The hundreds of millions poured into urban election offices by the CTLC and affiliated charities also explains how Trump dramatically increased his share of the black and Hispanic vote and still lost. Hadn’t we been told that if Trump could increase his share of the black vote by only a few percentage points that he would win? Well, he did, and he lost.

Even if Trump increased his share of the black and Hispanic vote, the opening of the urban turnout floodgates through private donations to government election offices easily swamped Trump statewide in Pennsylvania, Georgia, and Michigan.

It doesn’t matter if Trump has 15 percent of the black vote in Detroit if turnout there soared by 92,891 Detroit votes, which it did. It doesn’t matter if Trump has even 20 percent of the black vote in Atlanta if turnout in DeKalb soared by 54,550 votes, which it did.

This also explains how the GOP was so successful everywhere… except at the top of the ticket. A flood of blue votes gushing out of deep blue urban areas has a statewide effect only for statewide candidates. It doesn’t affect legislative races outside of the cities.

But what about fraud, you might wonder. Sure enough, fraud was a problem. There is a long list of things being reported as fraud that are not fraud that will need to wait for another day to address, but the singular fact is the rush to mail balloting created weaknesses all across the system.

Mail ballots went to dead people. Mail ballots went to abandoned mines in Nevada. Mail ballots went to vacant lots in Pittsburgh. Mail ballots went in the garbage. Mail ballots were voted by people other than the voter.

I successfully argued in court that Virginia election officials violated Virginia statute when they issued rules that ballots can arrive late and without a postmark. But sadly, that case was one of the few instances of success at blocking the Democrats’ frenzy to throw out election integrity laws. By and large, the Democrats succeeded in tossing out state laws related to absentee ballot verification, deadlines and a whole range of laws all in the name of COVID. By and large, GOP efforts in court failed. It was a courtroom bloodbath that created vulnerabilities across the system.

The important point to understand is that elections are messy, and in 2020 hundreds of millions of dollars thrown at lawsuits and at election officials made the 2020 elections the messiest ever. Elections are also complicated, and you don’t always need outright fraud or communist hackers to craft a scheme to defeat Donald Trump. Why take that risk when you can do it all mostly legally by simply fundamentally transforming the entire process?

There’s the word we need to think more about. Process. It’s what the Left has been doing better than us for decades.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41478339)

==

Democrats Had a Plan for Georgia, Republicans Didn't

Fri Dec 4, 2020 Daniel Greenfield 36

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In 2020, Republicans were out-organized and out-spent. Being out-spent isn't fatal. Republicans were out-spent in 2016. But being out-organized frequently is. There's a huge difference between spending money on ad buys and consultants, and spending it to change the structural environment of the election.

Just to start, I'm going to reference this J. Christian Adams piece at PJ Media which went viral.

The Capital Research Center has this deep dive into what the Center for Technology and Civil Life did in just Georgia. It starts with this:

This year, left-leaning donors Mark Zuckerberg and wife Priscilla Chan gave $350 million to an allegedly “nonpartisan” nonprofit, the Center for Tech and Civic Life (CTCL), which in turn re-granted the funds to thousands of governmental election officials around the country to “help” them conduct the 2020 election.

What these grants did was build structural bias into the 2020 election where structural bias matters most – in densely populated urban cores. It converted election offices in key jurisdictions with deep reservoirs of Biden votes into Formula One turnout machines. The hundreds of millions of dollars built systems, hired employees from activist groups, bought equipment and radio advertisements. It did everything that street activists could ever dream up to turn out Biden votes if only they had unlimited funding.

Michael Patrick Leahy at Breitbart was doing a lot of important work on the the Center for Technology and Civil Life that hasn't gotten much attention.

Most of Joe Biden’s 221,751 vote margin gain in Georgia, compared to Hillary Clinton’s performance in 2016, came from three metropolitan Atlanta counties that received more than $15 million from the Mark Zuckerberg-funded Center for Technology and Civic Life (CTCL) “safe elections” project.

Those three counties — Cobb, Fulton, and Gwinnett–accounted for 168,703 of Biden’s 221,751 vote margin gain, or 76 percent.

And CTCL is doubling down on Georgia.

The Mark Zuckerberg-funded Center for Technology and Civic Life (CTCL) announced on Tuesday that it will provide additional “safe elections” grants to county election departments in Georgia in advance of the two U.S. Senate runoff elections that will be held on January 5, 2021.

As Breitbart News reported last month, former Barack Obama campaign manager David Plouffe worked for the Chan-Zuckerberg Initiative from 2017 through 2020:

Meanwhile a ton of money also flowed into Stacey Abrams' operation which is currently under investigation, the operation, not the money.

The voting rights organization Stacey Abrams founded in 2018 after losing a close gubernatorial election raised $34.5 million in just 39 days from late October to the last week of November, funneling a chunk of the money into helping Democratic candidates in key races.

The $34.5 million is about what the group had raised the previous two years.

The common denominator with Fair Fight and CTCL is that both set out to alter the structural electoral environment, rather than just throwing money at ads.

All of that work was out in the open, yet Republicans, taking Georgia for granted, were caught by surprise. And Georgia, as I keep saying, is a testbed for the real deal, which is Texas. That's their big endgame. Take Texas, transform its electoral system, and secure every presidential election until doomsday.

Democrats went into 2020 with a lot of money but, more importantly, a plan. Republicans have a lot of infighting, but so far, no plan.

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41506195)

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Statistical Model Indicates Trump Actually Won Majorities in Five Disputed States and 49.68 Percent of the Vote in a Sixth

December 14, 2020 (2d ago)

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EXECUTIVE SUMMARY

We report a simple yet powerful statistical model of county-level voter behavior in the November 2020 presidential election using two main types of data:

County-specific voting data from the five previous presidential elections.

Selected demographic variables (race and education) plotting how different national voter groups voted differently in 2020 overall.

These two types of predictors allow us to explain over 95% of the variation in county-level votes, and therefore allow us identify which counties (and consequently, states) look substantially anomalous in the 2020 election.

The model provides substantial support for the allegation that the outcome of the election was affected by fraud in multiple states. Specifically, the model’s predictions match the reported results in all other states, i.e. states where no fraud has been alleged, but predicts Trump won majorities in five disputed states (AZ, GA, NV, PA and WI) and 49.68% of the vote in the sixth (MI).

In other words, the reported Biden margin of victory in at least five of the six contested states cannot be explained by any patterns in voter preference consistent with national demographic trends.

SUMMARY OF MAIN ARGUMENTS

1. Our model explains 96% of county-level variance in Trump’s two-party vote share with four demographic variables (non-college white, college-educated white, black and hispanic) and one historical variable (the average of county-level GOP two-party presidential vote share, 2004-2016). All five variables are highly significant. This reinforces the conclusion that the model is generally a very strong predictor of vote shares, and so deviations from it should be considered surprising.

2. Under conservative assumptions, regression analysis shows Trump ought to have won AZ, GA, NV, PA, WI.

[See the end of the article for the full table.]

3. Every one of the contested states shows a larger predicted vote share for Trump than what he actually received. This is surprising, because in any set of observations, random chance might expect some predictions to favor Biden, but none do. In Georgia and Arizona, the model does not predict a narrow race, but a decisive Trump victory; the size of the anomaly is (much) larger than the reported margin of victory.

4. The model also performs well in battleground states that have not been contested, and thus where the election was presumably clean. Every one of these is correctly predicted, including both battleground states that voted for Trump (e.g. Ohio, Florida) and those that voted for Biden (e.g. New Hampshire). Indeed, there are no states that Trump won which the model predicts should have been won by Biden. Meanwhile, the errors in the model are constructed to average to zero, so the model cannot favor one candidate over the other. Instead, it reveals the places where actual outcomes differ the most from our predictions.

5. The model is robust to alternative specifications of the regression formula and weighting.

6. The model places the burden of proof on fraud skeptics to explain why nearly all the states where fraud has been alleged, and only those states, have results inconsistent with statistical trends in the rest of the country.

7. Our model highlights the importance of a systematic comparison of all counties in the US when trying to understand whether the contested states are actually unusual. Simply picking isolated comparison cities, or one-off comparisons to past elections, is a very inferior way of doing the comparison. This model takes this base intuition (which is actually good), but greatly improves it by making the comparison systematic. The fact that the contested states are mostly predicted to have been won by Trump using simple but powerful demographic models further adds weight to the existing evidence that these outcomes may have been altered by fraud.

MAIN ANALYSIS

DATA

Our analysis used the following county-level datasets:

“total_results_CONDENSED.csv” [link]

“county_pres_2000_2016_source_MIT.csv” [MIT Election Lab]

“ACSST5Y2018.S1501_data_with_overlays_2020-11-16T170124.csv” (U.S. Census)

“cc-est2019-alldata.csv” (U.S. Census)

The demographic variables use US Census 2019 total population figures for non-hispanic white, black, and white hispanic to generate the white, black (“b”) and hispanic (“h”) categories, respectively. Working-class (“wwc”) and professional-class (“wpc”) whites were further distinguished using US Census educational attainment data (variables S1501_C01_031E, S1501_C01_033E).

County average historical GOP two-party vote share for presidential elections (“avg”) is an unweighted average of results for the 2004, 2008, 2012, and 2016 elections in the MIT dataset. Trump’s 2020 two-party vote share is derived from vote totals for 3106 counties in the lower 48 contiguous United States in “total_results_CONDENSED”.

THE MODEL

Our model is based on predicting county-level two-party vote share for Trump, using the five variables above. Essentially, we are combining two broad types of predictor, each of with helps augment the weaknesses in the other. To begin with, we take the outcomes from all five past presidential elections for that county. This gives us a measure of the overall relationship of past elections to current election. This is the first order predictor — how does this county specifically generally vote in past elections? This captures the simplest intuition that the best predictor of how a county will vote in general is the pattern that it displays in the past. This is crucial for avoiding the kinds of broad errors like assuming that working class whites in Vermont should be the same as working class whites in Arkansas. Rather than trying to explain why Cook County IL is the way it is, we start with the prediction that Cook County IL in 2020 should be a function of how it was in the past. Because we fit a coefficient, the prediction isn’t that the current election should be identical to the past, but rather that there will be an average change from past elections to the current one.

Then, on top of that, we add demographic variables. First, we need to choose groups that we think are at least somewhat comparable across the country. These will allow us to capture the insight that regional results are at least partly the result of a region’s demographic composition multiplied by the average political preferences of each component group: this rule doesn’t capture everything, but it captures a lot. The demographic categories universally assumed in all mainstream American political analysis, journalism, and polling are: white college-educated, white working-class, black and hispanic, and we use those conventional categories to put our model above any suspicion that any part of our model was selected to bias the data.

Because these are added in addition to the base historical performance variable, they represent the additional effect of each demographic group in the 2020 election over and above historical same-county numbers. For instance, suppose working class whites voted more heavily for Trump than they have in past elections. In that case, including this variable would also help predict 2020 outcomes. Deviations from the model predictions thus represent simultaneous deviations from (i) what you would broadly expect for that county, based on how it historically votes, and (ii) what you would expect to be the change in 2020 relative to past years, based on the demographics of the county.

Later, we consider more complicated variants of this model, and find that the results do not greatly change. We present the above as a simple but powerful predictor of how each county will vote.

First, we present the results of the county-level regressions.

Not only are all the results highly statistically significant, but more importantly, the model has an extremely high R-squared when using only five explanatory variables – over 95% of the variation in county outcomes is explained. This is important in the next step, as it shows that the model overall does a very good job of matching the data, and so deviations from the model are thus interesting. If the model did a poor job of fitting the data, large deviations would simply be expected.

2. Under conservative assumptions, regression analysis shows Trump ought to have won AZ, GA, NV, PA, WI.

Besides giving us an explanation of where (changes in) voter preference are coming from, the model makes predictions: it tells us how every county would have voted if every county followed the best average relation between these predictive variables and vote outcomes. All counties will differ from this prediction by a little due to random “noise” and we always expect a few to differ by quite a lot, but too many large deviations in one direction in a single region demonstrate a pattern of voting behavior that cannot be explained by any law that operates in the rest of the country. In other words, it is either a sudden outbreak of idiosyncrasy in one state, or the reported vote totals are not the result of voter behavior, but of fabrication. For the 2020 election, the first and most obvious question is whether the model highlights possible fraud on a scale that would change the winner of the election: aggregating the model’s predictions at the state level shows us that the answer is yes.

Needless to say, the assumption that Trump “ought to have won” assumes these large deviations (a) are not model errors and (b) are not real anomalies which nonetheless have innocent explanations. Nonetheless the statistical assumptions underlying this inference can be called conservative because they are only sensitive to new instances of fraud (any past history of fraud is already built into the model’s predictions), and because there are other reasonable model specifications that predict an outright Trump majority in Michigan as well (see Section 5).

3. Every one of the contested states shows a larger predicted vote share for Trump than what he actually received. This is surprising, because in any set of observations, random chance might expect some predictions to favor Biden, but none do. In Georgia and Arizona, the model does not predict a narrow race, but a decisive Trump victory; the size of the anomaly is (much) larger than the reported margin of victory.

Notably, none of the contested states gave Trump a larger share of their votes than the model predicts he should have received; combined with his net gain in votes in these areas overall, this fact suffices to rule out the possibility that the discrepancy between the model and the reported results is due to errors (which, being random, must hurt Trump as much as they help, overall). Either the inhabitants of Arizona, Georgia, Pennsylvania and (to a lesser extent) the three other contested swing states are totally unlike other Americans and exempt from the statistical regularities that bind them, or the outcome anomalies here represent voter fraud, consistent with the various evidence that has been introduced in the states in question.

In the most conservative linear model, the prediction for Michigan is Trump’s 2-party vote-share is 0.4968477; this doesn’t preclude the possibility that after a careful audit Trump’s share would be > 0.50, because the model includes Wayne County fraud in past elections in its assumptions. Further, the model is not precise to the extent of predicting 0.05-point swings in a state with a population in the millions. Just as it is open to fraud-skeptics to concede that the possibly-fraudulent anomalies in Nevada, Pennsylvania, or Wisconsin are “in the ballpark” of Biden’s margin of victory while arguing (on some other grounds) that the actual magnitude of fraud might slightly less than enough to overturn the result, it likewise remains open to Michigan Republicans with independent evidence of fraud to believe that the appropriate kind of recount or audit would give Trump the 0.315-pt gain over the model’s predictions he needs to win their state.

What is not open to discussion in any of these four states is whether the margin of Biden’s reported victory is on the same scale as fraud-like anomalies: it can no longer be claimed about any of these states that the evidence for and against fraud in these states is beside the point. The irregularities in question add up to a number that would change the result.

But conversely, just as narrow margins of model-predicted victory in certain states leave it open to concede the possibility of fraud while reserving judgment about whether this fraud definitely reversed the true results, in Arizona and Georgia the large margins of Trump’s predicted victories rule out this kind of measured doubt. If fraud explains Arizona or Georgia’s deviations from the national statistical regularities the model measures, Trump was robbed. Skeptics may propose alternative, more innocent explanations for these deviations, but the numbers involved are the difference between a narrow Biden win and solid Trump victory.

Indeed, given the huge magnitudes of the anomalies in these two states, if convincing evidence does emerge that widespread fraud (or incompetence by election officials) explains the results in either state, the appropriate courts or state legislatures would be justified in awarding that state’s electors to Trump immediately even if it was no longer possible to do an accurate recount, e.g. due to the destruction of ballots or other evidence-tampering. (We are not lawyers so we cannot opine whether past precedents for reversing election results without a new election require proof that the magnitude of fraud reversed the results, or only that one candidates’ representatives made a concerted effort to steal the election; however we can confirm that either Georgia and Arizona would meet the stricter standard, if fraud explains even a fraction of that state’s deviation from our model.)

4. The model also performs well in battleground states that have not been contested, and thus where the election was presumably clean. Every one of these is correctly predicted, including both battleground states that voted for Trump (e.g. Ohio, Florida) and those that voted for Biden (e.g. Minnesota, New Hampshire). Indeed, there are no states that Trump won which the model predicts he should have lost. Meanwhile, the errors in the model are constructed to average to zero, so the model cannot favor one candidate over the other. Instead, it reveals the places where actual outcomes differ the most from our predictions.

Next, we examine the performance of the model in six battleground states where fraud has not been widely alleged. These are Iowa, Minnesota, North Carolina, New Hampshire, Ohio, and Texas (all chosen to be those where Trump’s two party vote share is between 46% and 54%).

In these states, the model’s predictions are

The final two columns summarize whether the residuals (that is, the gap between the prediction and the actual outcome) favor Trump, and whether they favor the candidate who won or lost that state. These allow us to reject the hypotheses that our model is biased towards Trump in all swing states, and that it favors the underdog in all swing states.

5. The model is robust to alternative specifications of the regression formula and weighting.

In this section, we discuss alternative variations on the model that we have explored, using slightly different variables and different weighting of counties. A reader who is satisfied with our base model can skip this section. Broadly, changing the particular model doesn’t tend to alter any of the main conclusions. This is important, as it reinforces that the anomalies in the contested states do not rely on one particular choice of modeling assumption, but show up under a variety of benchmarks.

We report results for the (y~wwc+wpc+b+h+avg) regression model because it is the simplest model formula, the first we tried, and because it proved to be powerful, highly significant, and comparable to all more complex variations on the model. However we did vary the simple model along several parameters to see whether any of them radically changed the model. If they did, it would have implied that the simple model’s predictions were brittle, either relying heavily on one (perhaps contentious) assumption about how elections work, or even reflecting some modeling artifact that disappears in other models. However, alternative specifications of the model do not weaken, and in some cases strengthen, the model.

(a) Interaction effects.

We first considered whether the demographic and historical performance measures might interact with each other (rather than just the linear and independent effects modeled in the base regressions).

We examined a number of variants on the main variables in question:

y ~ wwc + wpc + b + h + avg

y ~ (wwc+wpc+b+h+avg)^2

y ~ (wwc+wpc+b+h)^2 + avg

The first formula is the primary, simple model: in it, the four demographic variables can be interpreted (loosely) as how likely an average member of that group is to vote for Trump. The second and third formulas include interaction terms like “b:h” (which would reflect the propensity of blacks or white hispanics to support Trump more when they are living together in a county). The second formula differs from the third in that it also includes the county’s historical average (which embeds county deviations from national demographic means) in the interaction terms: this can be interpreted as allowing some demographic groups to change more than others in the 2020 election.

All three model variants explain >95% of observed variance and predict almost the same state results. The (wwc+wpc+b+h+avg)^2 model predicts that Trump will win Michigan with 50.41% of the vote, flipping it into his column. The (wwc+wpc+b+h)^2+avg model predicts that Trump will not win Nevada.

The terms in variant models were for the most part highly significant. In the (wwc+wpc+b+h)^2+avg model (the one that awards NV to Biden) two of the six interaction effects were not significant (which does not necessarily make it a bad model). In the (wwc+wpc+b+h+avg)^2 model (the one that awards MI to Trump) the wwc:b and the b:avg interaction terms by themselves explained nearly all the variation connected to black vote — leaving all the other terms including “b” very close to zero, and thus insignificant.

(b) Regression weightings.

The main model uses simple ordinary least squares (OLS), and thus weights each county equally when trying to find the line of best fit. However, it is possible that one might care more about fitting larger counties, as these are more important to the overall outcome of a state. As a result, we consider alternative specifications that overweight larger counties in the estimation procedures. Taking the logarithm of a population strikes a balance between fitting our observations and fitting population means. We also looked at weighting directly by population, which will place emphasis on the biggest counties.

We examined:

Ordinary least squares

Least squares weighted by log county population

Least squares weighted by county population

Weighting by log total population gives the same state-level results as OLS except for the (wwc+wpc+b+h)^2+avg formula, where it awards Trump only 49.96% of the PA vote.

Weighting by total population without logarithm changes the results moderately. This weighting predicts flips in AZ, GA, WI _and FL_ (from Trump to Biden) for the simple formula and the (wwc+wpc+b+h)^2+avg formula; and in AZ, GA and FL only for the (wwc+wpc+b+h+avg)^2 formula. This is consistent with asking the regression to place the heaviest weight on explaining the outcomes in the largest urban counties. It is noticeable (and surprising to the authors) that even in the most extreme weighting of the data towards Biden’s urban strongholds, Wisconsin usually and AZ/GA always emerge as suspicious.

For reference the results of the nine combined model specifications (numbered as: model, weighting) are summarized in the following table, where “1” indicates that a model predicts a different result than observed.

6. The model places the burden of proof on fraud skeptics to explain why nearly all the states where fraud has been alleged, and only those states, have results inconsistent with statistical trends in the rest of the country.

If these allegations were simply sour grapes, we would expect to see more or less random errors in these states. No statistical model of the 2020 election would predict flips in 5 of 6 and near-flips in 6 of 6 randomly selected states unless it predicted flips for almost every state, or at least every close state.

Even if (in fact, particularly if) the fraud skeptic accepts the validity of the simple linear model of the election but still questions whether fraud is the most probable explanation for the gap between the model’s predictions for these states and the reported results, he must confront the burden of constructing five or six accounts of idiosyncratic voter behavior in particular states, and then explaining how it happens to be that these idiosyncrasies are synchronized. It is plausible to attribute one anomalous prediction to random error, and a second anomalous prediction to unique and irreproducible local events, but any rationalization that intends to introduce six coincidentally-aligned irreproducible local flukes should begin by apologizing for straining the credulity of its audience.

And in particular:

7. Our model highlights the importance of a systematic comparison of all counties in the US when trying to understand whether the contested states are actually unusual. Simply picking isolated comparison cities, or one-off comparisons to past elections, is a very inferior way of doing the comparison. This model takes this base intuition (which is actually good), but greatly improves it by making the comparison systematic. The fact that the contested states are mostly predicted to have been won by Trump using simple but powerful demographic models further adds weight to the existing evidence that these outcomes may have been altered by fraud.

One of the key advantages of this model is that it provides a systematic comparison of whether the contested states look unusual. This is far preferable to the general way commentary has proceeded, which has been generally to cherry pick individual cities or counties, assert that they are comparable control cases, and then do one-off comparisons with other years or locations. In some sense, this intuition is good, but the methodology is extremely poor – the chosen places may or may not be comparable in terms of demographics, and the choice to pick them may ignore other comparable controls. The regression setting avoids both problems — we consider all possible counties for comparison, and systematically examine the importance of the kinds of variables that people mostly think about in an ad hoc way.

Ross Douthat, for example, has opined on Twitter and in his New York Times column that two forms of direct evidence of fraud in Montgomery County, PA (both first published in Revolver) are irrelevant because Biden performed well in the Connecticut suburbs as well. But while Fairfax County, CT may be notable as a site to skinny-dip off Bill Buckley’s yacht — the event which marked Douthat’s initiation into the world of “insider intellectuals” — in the 2020 elections, events in the Connecticut suburbs were less memorable. Our model predicts a Trump two-party vote share of 39.865%, against reported 39.828% — not quite enough to flip the Nutmeg State. Our simple model finds Biden outperforming past Democratic performances with the college-educated white professional class not just in Connecticut or Pennsylvania but everywhere, and in all but five states the model is able to use those results to predict the winner. Douthat is free to reject any direct evidence of fraud in MontCo or elsewhere on its own merits, but the implicit argument that fraud is unlikely to have occurred in suburban PA (or AZ, GA, NV, or WI) because the results in these counties are similar to comparable counties elsewhere cannot be sustained, because the premise is false. These five states are not similar, they are idiosyncratic in some respect, and if Mr. Douthat wishes to remain a NY Times columnist in 2021 I suggest he get to work finding an innocent explanation for Biden’s statistically inexplicable strength in these five states.

The independent journalist Michael Tracey (and in Tracey’s defense it should be noted he has made heroic attempts to respond to a variety of theories about the 2020 election, some from quite obscure sources) has repeatedly made similar arguments against claims of fraud in metro Detroit, Milwaukee, and Philadelphia, on the grounds that Trump’s 2020 performance in these cities (like Trump’s urban performance elsewhere, notably in NYC) was actually an improvement on his 2016 results. Tracey takes for granted key aspects of our analysis here (that 2020 results should be consistent with other changes from past results in comparable counties in other states), but he has no numerical measure of “consistency” beyond pairwise comparisons of the cities in question: and when that measure is supplied, it becomes clear that while nationwide cities are predictably similar to other cities, suburbs predictably similar to other suburbs, in certain states the model’s predictions deviate from the reported outcome considerably: in these states Tracey is not free to argue that fraud is impossible because the county results are consistent with national patterns — in fact they are not consistent.

In aggregate, at the state level, anomalies larger than Biden’s margin of victory occurred somewhere in each of these five states: Douthat and Tracey are free to argue about what the nature of those anomalies was, in which counties they are most likely to have occurred, whether the best explanation is innocent or not, but they are not free to claim the anomalies occurred in every state, or that they are consistent with any general demographic pattern in changes in voter behavior in the 2020 election. By definition, they are not.

We do not mention Tracey and Douthat here to pick on them. Rather, they present in clear and intellectually honest form (honest, because it lays out its implicit empirical assumptions fairly unambiguously) a line of thinking that can be detected in nearly all skeptical responses to evidence of fraud.

CONCLUSIONS

This analysis has made formal an intuition that many people have had on an informal basis — namely, the contested states where Biden narrowly won showed strange voting patterns relative to what one might generally expect for those states, and relative to what one might expect on the basis of the final results in other key swing states (or plausibly even a sufficiently large number of “swing counties”). Our results show that this intuition can be made concrete — in the contested states of PA, WI, GA, AZ, and NV Biden’s vote share is implausible relative to both historical voting patterns in counties in those states, and with demographic trends in the 2020 election.

When a few simple rules suffice to explain almost all of the behavior of large numbers of people over enormous areas, when exceptions to the rules are too infrequent and small to leave any doubt about their operation, and various tweaks or additions to the rules don’t do much to improve, or even fundamentally change, the explanation (in other words: when a model is parsimonious, powerful, general, significant, and robust), then you can be confident in your results. The evidence presented here is very strong; not (by itself) overwhelming, but strong enough that with further corroboration of the statistical claims by evidence about particular counties and states, it must become overwhelming. Either the inhabitants of Arizona, Georgia, Pennsylvania, and (to a lesser extent) the three other contested swing states are totally unlike other Americans, and exempt from the statistical regularities that bind them, or rogue elements in the Democratic party have committed fraud on a scale that will permanently destroy America’s faith in elections unless their crime is quickly reversed and the guilty parties punished.

https://www.revolver.news/2020/12/statistical-model-indicates-trump-won-landslide/

(http://www.autoadmit.com/thread.php?thread_id=4685400&forum_id=2.#41557296)