Kind of quick, but I covered his flowchart which I thought was well done. Interesting project.

watching it now or listening to it while I generate a simulation of Dr. Shiva's data

@meglide oh that sounds interesting

am curious what you are doing but don't interrupt 😛

finish that simulation

it's a spreadsheet and here's a screenshot

essentially I assumed something like 5 percent of Dems vote for Trump and 30 percent of Reps don't vote for Trump, all Reps vote for Reps downline and no Dems vote for Reps downline

zoom on chart

I need to watch his video again

what is the x axis and y axis measuring

above/below what?

I'm trying to mimic his graph ... my number is a ratio of total trump votes/rep votes downline

the above/below is percent above or below 1.0 ratio

that's the y-axis

x-axis is percent Republican

got it

my point is that with just some simple assumptions about human voting patterns I can get a graph to mimic what he plotted

mmm

I could dynamically change the percent of Reps that defect from Trump based on how Republican the precinct is, etc. ... maybe adjust the Dems voting for Trump etc. ... probably get any kind of curve you with just a few tweaks here and there

Again just some simple assumptions about folks voting in that particular county could explain Dr. Shiva's data

@DrSammyD did a big data grab and looked for Dr. Shiva's pattern on a county-by-county basis for every state and didn't see that pattern BUT my point is that a local level, within a county say if you just make some simple assumptions about they way folks vote in that county then you can get some peculiarities that over a larger dataset you would not expect

@meglide https://bitcadia.github.io/DownBallot/index.html

You have to do some min max to figure out how many people didn't vote for the top level candidate

So the diff between the lowest number vote in that party, and the highest is the minimum possible total # of none down ballot votes.

But non-down ballot votes could (and almost certainly do) exceed this number, and they mix/match to come with the numbers they have.

The problem is that aggregating the data at the county level can never show what Shiva says. If the syphon happens at the precinct level, that simply lowers the one dot you have for a county.

You'd have to scrape every precinct.

If all of the counties have this syphon, then every dot just looks like it was shifted down equally.

https://youtu.be/XLJEiC2BPV4

Seriously, Michigan non-down ballot voters are suppressed by a minimum of 50% next to MN, IL, and WI https://bitcadia.github.io/DownBallot/Outputs/CountyDownBallotDiffsByStraightMI.html

Nessel is targeting conservative websites I hear

Suppression sounds boilerplate

well MI allows for voting straight party by filling in just one box, what about the other states?

None, but we're only comparing people who didn't use that

I have no idea, Trump really almost earned my first ever vote this year. /Regrets wouldnt have affected my state but still..I honest to god wanna see Amy Barret with ted cruz vp. Thats what im feelin rn. If shes as impartial as she comes off while still understanding being a human I like her alot.

Dr. Shiva talked about it in his video on just MI precincts so now I'm wondering how he broke the data out

They found the precinct data. probably from michigan

no, I mean what exactly was he plotting

Ask him ? https://vashiva.com/contact/

I doubt you'll see a response but I feel like anyone trying to prove or disprove something should be willing to share applicable data to make the results irrefutable.

He plotted exact numbers of ballots checked as Party Ticket, and exact numbers of non-party ticket per precinct. He then plotted percentage of party ticket republican per precinct on the x axis, and percentage of individual votes for trump on the y axis.