That was not convincing at all and had some really glaring errors. Dr Shiva somehow confuses percent of overall voters with percent of Republicans to make it seem like there is a conspiracy going on. He says that margin for Trump should be a flat line so that if there 5% republican all-ballot voters it should be off by the same margin in Trump vote as a precinct with 80%. However if 20% of republicans flip that would mean 1% flipped vs 16% in those examples which is a line with a slope. Literally flipping those graphs would make them look the same for Biden as for Trump. All it shows is basically a kind of regression to the mean. Precincts with high republican or democratic all-ballot voting weren’t as republican or democratic as they seem. There could be many good reasons for that such as that voters that go against the way their communities vote tend to not be all-ballot voters.
Also, Dr. Shiva is not an independent voice. He ran in Massachusetts as a Republican.
He found that the more a republican a county is, the more republicans voted for Gop candidates, and against Trump.
But only after the first 20% or so of the vote was in. In the first 20% of the vote Trump was doing better than the Gop candidates on the ticket.
Comes to the conclusion that an algorithm is syphoning votes from Trump to Biden. In a predictable linear fashion after the first 20% of the vote is counted. And flips more votes in more republican counties in an almost perfect linear way.
It doesn't make sense because Trump was polling better than the Gop candidates before the election, and was popular with republican voters.
The tallying software has a weighted vote feature. Where votes can be weighted for example 2:1 for one candidate over the other. Thinks that this was kicked in after 20% of the vote was in. Which would produce a linear drop that the graphs show.
Here's an in-depth breakdown of the trickery he's employing: https://kabir-naim.medium.com/dr-shiva-ayyadurai-the-danger-...