Anomalies in the D to R ratio prove election fraud. By David Evans, from the material collected by data scientists and written up by CulturalHusbandry.
The postal system shuffles together the mail-in ballots from the many districts around a state. So, the ratio of Democrat to Republican votes in a batch of mail-in votes should be nearly uniform in a state. This contrasts with the D to R ratio for precincts, which can differ quite a lot from each other depending on the neighborhood (rich, poor, black, white, etc.).
In nearly every state, the D to R ratio of batches of mail-in votes as they are counted is indeed the same. Here, for example, are the D to R ratios of the batches of votes as they were counted in the six hours following the close of voting in Florida:
Each batch is a dot on the graph. The batches that form the almost continuous line across the middle, at a D to R ratio of about 1.0, are the mail-in ballots. The other batches are from individual precincts, of votes made on polling day at polling places. Notice that the polling station votes were nearly all counted within two hours, and then batches of mail-in ballots were counted for the next few hours.
(Note that the overall D to R ratio of the mail-in ballots was higher than the overall D to R ratio of the ballots at polling places, because more Democrats cast their votes by mail. But that is not is what is being measured here, and it does not impact this analysis. Here, we are looking just at the evolution through time of the D to R ratio in mail-in ballots.)
Another example, Minnesota:
This pattern is typical of all the states — except the swing states with widespread accusations of voter fraud (and, for reasons unknown, Virginia).
Notice the jump in the D to R ratio at 4am. This should not happen. This anomaly suggests — but does not prove — that many extra Democrat votes were added after 4am to the batches of mail-in ballots.
Importantly, the size of the batches and the extent of the anomalous increases in the D to R ratio combine to allow the number of extra Democrat votes to be estimated fairly precisely. This hasn’t been done yet, but when it is we will know if these votes were enough to swing the election in Wisconsin.
Again, notice the increase in the D to R ratio starting in the early hours of Wednesday morning.
Same anomalous pattern of extra D votes in the mail-in batches, suggesting fraudulent D ballots were added to the mail-in batches after some point in time in the counting process.
Again, clear evidence of anomalous behavior that benefits Democrats.
The Democrat fraudsters played too loose and fast this time, perhaps caught off guard and having to scramble because they didn’t expect a red wave. They got careless and left their cheating footprints as clear as day in the data shown above.
Were the fraudulent votes enough to flip the results? Further analysis by the data scientists is required.
Will the courts act? The Republican lawyers need to know about this evidence, understand it, and explain it to the judge. Time will tell.
UPDATE: The data scientist quoted above has made an error, but it doesn’t change the conclusion.
The line of dots in each graph above are associated with batches of mail-in votes, but are not necessarily the D to R ratios of the individual batches, as claimed above. The basic idea above was sound, but the data from the election count only gave the cumulative D to R ratio of the count to date to one decimal point as a percentage, which complicates the interpretation of exactly what the line of dots represents. (Without doing the analysis myself and seeing precisely what is going on, I am not going to try and describe it.)
To cut through the complications, simply look here to see the graphs for each of the fifty states. The pattern is clear enough:
- In swing states with widespread cheating allegations, the cumulative D to R ratio rises as the count progresses, starting in the early morning hours of election night.
- In all the other states, the line is steady or falls a bit. Except Virginia, for reasons unknown.
As noted above, this analysis gives a way to roughly estimate the total number of D votes fraudulently added to the count.