Our analysis of the 2016 Presidential Race in Ohio shows that 34 of its 44 largest counties show a “red shift”, meaning the Republican share of the vote increases when votes are totaled from smallest to largest precinct. 21 of these counties, including all but one of the 10 largest counties, show a very strong red shift – greater than 5%. We estimate that the margin between the candidates was changed by at least 389,000 votes due to these red shifts.
As explained on our home page, an election should normally obey the law of large numbers fairly well, unless there is a strong reason for people in larger precincts to vote one way rather than another.
This type of analysis is known as a Cumulative Vote Analysis (CVA) or Cumulative Vote Total (CVT).
Very High deltaM Values
Here are the top “deltaM” values for Ohio counties, along with an estimate of the number of votes “flipped” (deltaMxV) in each county.
Turnout By Precinct
In Ohio we were able to obtain voter turnout statistics by precinct. When we added them to our graphs we noticed something very peculiar. The “cumulative voter turnout” almost exactly matches the cumulative Republican turnout in almost all counties. You can see how voter turnout is correlated with larger precincts even when you look at the state as a whole.
Exactly as with Republican votes, there should be no reason for turnout to be so consistently and pervasively higher in all the larger precincts in the state. This indicates that votes may have been moved from smaller, bluer precincts into larger more Republican-leaning precincts, artificially inflating the apparent size of these Republican-leaning precincts.
County by County
Here are the graphs for two of Ohio’s largest counties with the votes added in order of the number of ballots reported to be cast in each precinct. This shows clearly how much the Republican votes increase as apparent precinct size increases. These graphs also show how strongly the reported voter turnout parallels the number of reported Republican votes.
But look what happens when you count the votes starting from the precincts that have the smallest number of registered voters, rather than the smallest number of reported votes. There is no relationship at all between the precincts with the most voters and those with the largest number of Republican votes.
Other researchers have noticed similar strong correlations between turnout and votes cast for the winning candidate in elections in Russia and Uganda where vote tampering was strongly suspected. In their 2012 paper titled “Statistical detection of systematic election irregularities” and published in the Proceedings of the National Academy of Sciences, Klimek et. al. examine “fingerprints” for nine elections. These fingerprints are scatter plots of votes for the winning candidate vs. turnout for each precinct.
The researchers found that in the three suspect elections these scatter plots had a diagonal trendline, rather than a vertical one, indicating a correlation between turnout and winning-candidate votes. In the image below, all of the scatter plots trend nearly straight up and down, except for the two from Russian elections and the one from a Ugandan election. These three scatter plots appear to be tilted 45 degrees and “smeared” along the diagonal from lower left to upper right.
We plotted all the data from this election in a similar way:
Then we randomized the turnout numbers for each precinct. This shows what the scatter plot would look like if there was no correlation between percent turnout and percent who voted for Trump. No matter how many times we shuffled the data our plot looked largely vertical, rather than being smeared to the right.
We leave it up to you to decide whether our plot of the 2016 Ohio Presidential election more closely resembles that of a Russian or Ugandan election or of a Swiss or Canadian election.
It is hard to believe that voter turnout could have been so strongly and consistently correlated with the number of Republican votes in every precinct, in every county in the entire state of Ohio.