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Do you mean me in particular?
I'm quite proud of that. The New York Times posted two data points from ongoing vote tallies, based on their direct access to the data. I said that those two couldn't be consistent with each other, based on nothing more than a priori mathematics. It turned out that I was right and the New York Times was wrong, because one of the updates in their data source was just a typo and a later update reverted it. The conspiracy theorists' explanation for the discrepancy was also wrong, but the final score in that particular round was still New York Times 0, Specific Numbers 0, Conspiracy Theorists 0, TheMotte Statistics 1.
I don't remember who it was, exactly, but what you posted wasn't the kind of thing I was referring to. I'm too lazy to research the specifics here, but there was some kind of law used in auditing that says certain numbers are evidence of fraud because of how the digits are distributed or something along those lines, and they were using that alone as evidence that vote totals from certain counties were fabricated.
Ah, Benford's Law. Great in other contexts, but here that one didn't pass the smell test for me; the "law" only applies if you're sampling from distributions spread over orders of magnitude, not voting districts drawn to be nearly equally sized multiplied by vote percentages centered around .5. I later learned there's a clever trick where you can look at later digits' distributions instead of the first digit's, but all the skeptics I saw in 2020 were just misapplying the basic version of the law.
I've seen final vote tallies that were obvious fakes from the numbers alone, but for elections like Saddam's or Putin's, not Trump's or Biden's.
I still heartily approve of trying to check, though. An election isn't just about getting the right result, it's also supposed to be about getting the right result in a transparently trustworthy way.
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You're talking about Benford's Law, which works by counting the leading digits in order to build a distribution, then comparing this distribution to the expected. Perhaps unintuitively, 1 is by far the most common leading number, and 9 the least.
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