Voter Birthday Explainer Thread
revolver.news/2020/12/pennsy…

This came out in Revolver recently. It’s a new twist on identifying voter fr**d: Instead of starting with weird vote patterns, find *other data* that look weird (here, voter birthdays), and then relate it to votes.
(1/N)
It’s surprisingly hard to generate fake birthdays without leaving some trace in the data. The piece considers two broad ways that pull in opposite directions. First, you’ll probably pick too many round numbers – 1st, 15th & 31st of the month, Jan and Dec etc.
(2/N)
So, you think, I’ll be clever. I’ll use a uniform distribution over months of the year. Bzzt! Months have different numbers of days. Okay, hmm. I’ll choose uniformly over days of the year. Bzzt! Wrong again. It turns out that actual birth data aren’t uniform here either.
(3/N)
To get truly convincing data, you’d actually have to go through administrative datasets and find out the distribution of months of the year among births in your county, then find a different dataset to also match the distribution of days of the month.
(4/N)
Who’s got time for that? Apparently, the author of the piece! This is why fake data is hard – you never know who might think up some odd way to test it down the line. The piece does a lot of ways of aggregating weird measures of birthdays.
(5/N)
And when you do that, there’s a bunch of counties in PA that look highly suspicious.
“(Northumberland, Delaware, Montgomery, Lawrence, Dauphin, LeHigh, and Luzerne) have numbers of suspicious birthdays above the 99.5th percentile of plausible distributions”
(6/N) ImageImage
On its own, this is an interesting list. The biggest outlier votes majority Republican! But also very high on the list is our old friend Montgomery PA, home of the most suspicious vote update in America
revolver.news/2020/11/explos…
(7/N) Image
The big question is, are these benchmarks right? The piece considers a number of them, and they’re pretty compelling to me, but it’s tough to say. I think it’s hard to think of obviously better ones you could get from actually available data, but it’s still an assumption.
(8/N)
Importantly, the fact that these counties look weird *doesn’t actually depend on their votes*. It just comes from birthdays, regardless of how they vote. But you can then use this to find out the relationship of suspicious birthdays to votes.
(9/N)
And second, even if you don’t think the benchmarks aren’t totally correct, this implies that the *levels* might be wrong. But this wouldn’t obviously explain why the *variation* that does exist should nonetheless predict vote outcomes.
(10/N)
And when you do that, Northumberland notwithstanding, having more suspicious birthdays is strongly related to more votes for Biden, with a p-value of 0.000008 (the probability of observing a relation this strong by chance).

(11/N) Image
This effect is also big! A one standard deviation increase in suspicious birthdays is associated with higher Biden vote share by 6.8 percentage points.
(12/N)
“Out of the 13 counties in Pennsylvania who voted majority Biden, 9 are above the 95th percentile of suspicious birthdays.”
(13/N)
Mores suspicious birthdays also positively predict more votes for Biden relative to Democrat performance in all elections since 2000, with a p-value of 0.003. So it’s not just that these counties always vote Democrat, but they do so at unusually high levels this election.
(14/N) Image
They’re also positively associated with more Jorgensen votes relative to Trump, a secondary indicator of suspicious vote patterns that has been looked at in a few places, such as the Montgomery analysis.
(15/N) Image
Finally, the piece tries to quantify how important this stuff might be. It does this by relating the magnitude of suspicious birthdays to Biden vote share, and asks what would happen if the birthday distribution were a little less suspicious
(16/N)
This is the most speculative part, and is probably best thought of as a back of the envelope estimate. But the numbers seem to be big enough to potentially swing the state to Trump. We might have guessed this from the large effect sizes, but it’s interesting to quantify.
(17/N)
I think that the strongest case for voter fr**d comes from the piling up of coincidences, across lots of datasets, and lots of ways of measuring suspicious outcomes. Individually, any one has weaknesses.
(18/N)
But when they keep identifying the same odd places in different ways, that adds to an increasing cause for concern. Pennsylvania vote outcomes are looking pretty damn strange, and Montgomery County is looking the strangest of the strange.
(/fin)

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More from @shylockh

30 Nov
I'm firmly of the opinion that nobody owes any duty to investigate anyone else's claims. But I note that he doesn't address any of the anomalies I found most suspicious:

1. In Milwaukee, why did later votes swing more towards Democrats in races they were previously losing?
What on earth was going on in Montgomery County with the most suspicious looking update in the entire NYT dataset? As in, what's the specific theory here? Or is it just "unspecified errors"?
revolver.news/2020/11/explos…
Read 13 tweets
27 Nov
If you want the absolute best coverage of the state of election fr**d coverage, check out everything posted by @PereGrimmer, who's putting on a masterpiece of informed coverage of all the goings-on, including serious legal analysis.
I'm trying to divide my time between original research, summarizing and popularizing others' findings, and keeping abreast of new developments. But he's working full time on the last two, and is the absolute best game in town right now.
The saddest indictment of 2020 is that the only place to get up to the minute coverage of the lawsuits and analysis that affect the presidential election is from samizdat twitter threads by internet anons.
Read 4 tweets
25 Nov
Vote Pattern Analysis Thread
votepatternanalysis.substack.com/p/voting-anoma…
This article does something very interesting – quantifying how weird the middle of the night updates in Michigan, Wisconsin and Georgia were. I want to explain in simple terms what it does, and why it’s so important.
(1/N)
Tl; dr - the entire presidential election swings on the plausibility of these updates. And they look extremely unusual.
(2/N)
Recall, these are the places where election night saw complete banana republic stuff like boarding up windows in Wayne County vote counting centers to stop people who’d been excluded from the room even looking in.
bizpacreview.com/2020/11/05/let…
(3/N)
Read 36 tweets
22 Nov
My thoughts on the Revolver news story on Montgomery County PA
(A thread)

The original story is here – read it first:
revolver.news/2020/11/explos…
(1/N)

Read that first, at least the summary, main facts, and discussion of alternative explanations.
First, I find the analysis very persuasive. The evidence that something very weird is going on in the data is almost irrefutable. The big question is whether these have innocent explanations, or malicious ones.
(2/N)
The case for malicious is, of course, circumstantial. But it seems a lot more coherent than the alternatives, and explains a lot of different facts with far fewer moving parts than specifying an arbitrary allowable form of “errors” across multiple datasets.
(3/N)
Read 20 tweets
9 Nov
Want to identify possible election fraud, but don’t know where to start? Here’s a clean CSV format dataset from the NYT, identifying county-level presidential votes at periodic snapshots since counting began. There’s a lot to possibly analyze here.
ufile.io/q3ysydfm
It can’t do the kinds of analysis I did, for which you need down-ballot races, and it would be nice to have ward or precinct-level data, but it makes up for it with fantastic repeated snapshots, and covering all of America.
The great thing about big data is that if there is something dubious going on, it has a tendency for some trace of it to show up somewhere. If you find something, spread the word!
Read 4 tweets
7 Nov
Evidence Suggesting Voter Fraud in Milwaukee – a thread.

I’ve been looking at the vote counts in Milwaukee, and there’s suspicious patterns in the data that need explaining. Proving fraud is difficult, but a lot of irregularities point in that direction. First, the tl;dr.
(1/N)
1. Democrat votes started increasing massively relative to Republicans after Tuesday night counts. This can’t be accounted for by explanations like heavily Democratic wards reporting later. When we look at the changes *within wards*, 96.6% of them favored the Democrats.
(2/N)
2. Democrats also improved massively against third party candidates, but Republicans and third party candidates are similar to each other. Since there’s little incentive to manipulate third party counts, the big change is in Democrat votes, not in Republican ones.
(3/N)
Read 52 tweets

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