The EPEC staff monitors the Virginia Daily Absentee List for unexpected values. We essentially “audit” the electoral process in Virginia during an election cycle. We are currently monitoring the 2022 General Election.
One of the areas of interest is the DAL – Daily Absentee List. It shows the current status of absentee voting in Virginia – by mail in ballot and early voting (absentee in person).
In Virginia, Absentee In-Person Early Voting started on Friday, September 23. Our initial DAL file was saved on Saturday, September 24, at 9 PM.
The official Ballot Status in the DAL at 9 PM was:
Issued: 290,095
Federal Worker Absentee Ballot (FWAB) 1
Marked: 2,118
On Machine: 8,397
Not Issued: 5,766
Unmarked: 546
Pre-Processed: 1
Deleted: 13,015
Grand Total: 319,939
Nearly 19,327 ballots – 6 % of those requested, were in a state which would not be counted if the election vote counting period were over today – Not Issued, Unmarked, or Deleted. There was also 1 ballot in a Pre-Processed Ballot Status state. The magnitude of ballots in one of these “states” is surprising but not alarming.
It appears Not Issued means there is either a backlog in mailing out ballots or an issue with voter registration – legal name, address of record in the registration database, citizenship, etc. Unless the backlog or issue is resolved, the voter will be denied a ballot.
Unmarked is associated with mail-in Absentee Ballots. A Marked ballot is moved to an Unmarked status if an election official notices an error with the associated absentee ballot documents such as a name or address error, missing signature, or missing signature verification. Election officers are required to contact voters if their ballot requires a cure– correction to the information accompanying the ballot. If the cure is not provided, the ballot will not be counted. Some voters choose to have a new ballot mailed to them if a cure is required, in which case a ballot in the Unmarked state will be spoiled and marked Deleted in the system. This is one of the reasons we see voters having one or more Deleted ballots associated with them in the DAL files.
Deleted ballots are not supposed to be processed (counted). We believe these are officially referred to as “spoiled ballots. The process to keep these separate from countable ballots is an interest area for election integrity observers. The most common reason for ballots to get Deleted (spoiled) is voter error. Examples: mistake when filling out a ballot in person resulting in the first ballot being spoiled and a new ballot issued, or a voter surrendering an absentee ballot to vote in person or receive a new one via the mail.
More accurate voter registration recordsMAY reduce the volume of initial Not Issued and Deleted ballots. Our post-election observations and recommendations will address this issue. Our initial hypothesis – changes in residency, relocation within Localities, ineligible voters requesting ballots, and voters passing away probably account for most of the unexpectedly large values of ballots in an “at risk” state.
Individual voters should NEVER have more than one (1) active ballot. If this occurs, there is a risk that human error by an election official will result in a voter having more than one ballot counted.
Virginia has 226 individuals with two or more active ballots according to the Daily Absentee List file as of 28 October, 6 AM. This is occurring in nearly half of the Localities in Virginia – 59 out of 133.
This is a process issue – either procedural, or ballot tracking. The process should make it impossible for more than one vote to be counted.
It is possible that these will be caught before they get counted … but mistakes are made when people get overloaded or distracted. Process software should prevent the possibility of this “defect” occurring to prevent the perception of malfeasance.
The number of ballots in “Provisional” status is growing. This is to be expected because Virginia began allowing “same day voter registration” on 1 October, and same-day votes are to be labeled Provisional.
A handful of ballots were Provisional status prior to 1 October, and this ought to be explained. The steady increase of Provisional ballots started on 19 October. The count of Provisional ballots is currently growing by approximately 200 ballots each day. This number is expected to grow exponentially as we approach election day.
The root cause of the Provisional ballot increase is most likely “same day registration and voting” but a detailed study has not yet been performed.
Recently I’ve started downloading all of the data from the TX secretary of state website multiple times per day. Each time I download the data I grab new versions of files representing how many Mail-In or In-Person votes have happened since mail-in votes have started to be accepted, according to the TX SOS. Note that this TX Early Voting return data, which is required by law to be publicly posted daily, is supposed to reflect the number of voted ballots (either In-Person or Mail-In) per the previous days in the ongoing election and serves as the official public record of these ballot transactions.
The TX SOS does site not post the cumulative results, but instead has individual links by day that show the totals of each category of voted ballot. I have downloaded copies all of this data over multiple days.
Now you would think, that if the TX SOS data was trustworthy and accurate, that I shouldn’t see differences in the historical data on the TX SOS site day to day. I should see new data as a newly available download, but the data associated with previous days results should stay the same.
… except it doesn’t.
In the gallery below are 3 separate graphs of the data pulled from the TX SOS site. Each pull of the data grabbed the entire history of the data.
If you play the images in sequence you will notice that between the 1st (captured on 10/26 @ ~3pm) and second image (captured 10/27 @ ~7am) there are a few thousand ballots that suddenly appear in the Mail-In ballot trace attributed to 10/22. Between the second and third image (captured 10/27 @ ~9PM) you will see that there are a handful (~10) of ballots that get retrospectively added to the In-Person ballot totals attributed to 10/17 and 10/18.
What is the explanation for these additions?
I’m happy to supply the raw downloaded and timestamped files to anyone who is interested. Feel free to contact me and I will send the latest zip files and source code used to download.
Similar to the statistics that I have been computing for the VA Daily Absentee List (DAL), I have also been collecting the daily early voting reports from the GA secretary of states website. These are also a set of cumulative files that track the status of early and absentee ballots.
I’m using the same set of processing techniques on the GA data as I am doing with the VA datafiles, save for some slight tweaks due to differences in the datasets. (1) GA doesn’t have the ‘Marked’ or ‘Pre-Processed’ distinction in how they track their mail in ballots like VA does, those records are all simply labeled as ‘Mail-In’. (2) GA has an ‘Electronic’ category for ballots, which I’m assuming is the equivalent of the ‘FWAB’ category in VA.
There are two plots below representing the same data, one plot with a linear y-axis and the other with a logarithmic y-axis. The x-axis is the date that each DAL file processed was archived and pulled from the Dept of Elections servers. Solid traces are directly extracted data from the DAL files. Dashed traces are computed metrics such as the number of “vanished” voters detected. Red datapoints are placed on traces that exhibit questionable behavior, for example if the number of “approved” and “countable” ballots ever decreases, etc. Vertical dotted lines indicate important dates.
All of the latest plots for every locality and precinct as well as the corresponding underlying CSV data files will be updated daily, and you can download them here.
The semilog versions of the plots for all localities or precincts that appear in the DAL data per locality are shown in the gallery below. The image carousel below might take a moment to load, btw.
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Here is the changes to the voter registration numbers for each VA locality over the course of the 2022 general election. These files will be updated automatically as the data becomes available. The first graph below is the percent change with the color coding clamped to +/- 3 x the standard deviation, and the second is the absolute percent change.
Update 10-17-2022: There has been an issue with the VERIS system (the database that runs behind the scenes at the VA department of elections) where updates to the DAL files have not progressed since 10/14. On 10/17 there was a published change to the data files but the report generated was incomplete and cutoff halfway through its listing of CARROL COUNTY data. I had a phone conversation on 10/17 with ELECT and they are aware of the issue and working to correct it. Also I have included a new gallery at the bottom of the page of all of the individual localities or precincts that are automatically flagged as having issues of concern. Issues detected include any number of “vanishing” voters as defined below, “On Machine” ballot counts that decrease day-to-day, “Marked” OR “Pre-Processed” counts that decrease day-to-day, etc.
Update 10-18-2022: The publication of the DAL files has resumed. I have queries in to the department of elections as to the exact cause of the issues and will update accordingly as I find out more information.
Below is the current set of statistics from the 2022 VA General Election Daily Absentee List (DAL) file records. There are two plots below representing the same data, one plot with a linear y-axis and the other with a logarithmic y-axis. The x-axis is the date that each DAL file processed was archived and pulled from the Dept of Elections (ELECT) servers. Solid traces are directly extracted data from the DAL files. Dashed traces are computed metrics such as the number of “vanished” voters detected (described below). Red datapoints are placed on traces that exhibit questionable behavior, for example if the number of “approved” and “countable” ballots ever decreases, etc. Vertical dotted lines indicate important dates.
There are two very important fields in the DAL file that we want to pay attention to here: the APP_STATUS field, and the BALLOT_STATUS field.
DAL records with APP_STATUS = “Approved” and BALLOT_STATUS = “Issued” indicate a ballot that has been mailed to a voter.
DAL records with APP_STATUS = “Approved” and BALLOT_STATUS = “Marked” indicate a mail-in ballot that has been mailed to a voter, and then subsequently returned.
DAL records with APP_STATUS = “Approved” and BALLOT_STATUS = “Pre-Processed” indicate a mail-in ballot that has been mailed to a voter, returned and the ballot envelope has been opened and the ballot processed.
DAL records with APP_STATUS = “Approved” and BALLOT_STATUS = “On Machine” indicate a ballot record from a voter who physically walked into an early voting site and cast their vote on a tabulator machine.
DAL records with APP_STATUS = “Approved” and BALLOT_STATUS = “FWAB” indicate a Federal Worker Absentee Ballot (FWAB) mail-in ballot that has been received.
The combination of all ballots that have APP_STATUS=Approved and BALLOT_STATUS = “Marked” | “Pre-Processed” | “On Machine” | “FWAB” we term as “Countable” ballots.
I’ve computed the number of countable records that have an invalid BALLOT_RECIEPT_DATE or an invalid APP_RECIEPT_DATE. (For example if the BALLOT_RECIEPT_DATE is before the start of early voting, etc.)
I am also attempting to detect the number of duplicate voter IDs in a “countable” (as described above) state, if any, for each DAL file.
Additionally, I’ve computed and plotted the number of “Vanished” voters seen as we process the DAL files in chronological order. As each publication of the the DAL file is intended to capture information on all of the absentee ballots to date during an election, we would expect that once a unique voter ID becomes a record in the DAL file, that all subsequent DAL files should have an entry for that ID, regardless of its status. However, we know there are multiple instances where a voter ID will show up in the DAL record on a given date, and then be completely missing from a future DAL file.
Upon asking the department of elections for clarification as to how this can occur, their answer given was that if the voter has their registration cancelled for any reason, they are also removed from the DAL file. This holds true, even if live ballots had been issued for that voter, or if the voters vote has already been fed into a tabulator. This means that there is NO ACCOUNTING for these ballots in the DAL record. Note that the department of elections also does the same thing with the Voter History List (VHL) and the List of Those Who Voted (LTWV) data files. This is apparently standard operating procedure for the VERIS database(s) at ELECT, and (I quote) “… nothing unusual …” or to be concerned about as far as the department of elections is concerned. I vehemently disagree, and think that removing these records from the DAL while the election is ongoing is extremely problematic, to put it politely.
I will continue to update these plots as the election progresses. as more data comes in I will also be publishing these types of graphs for selected localities and precincts.
All of the latest plots for every locality and precinct as well as the corresponding underlying CSV data files will be updated daily, and you can download them here.
The semilog versions of the plots for all localities or precincts that appear in the DAL data that have flagged issues of concern are shown in the gallery below. The image carousel below might take a moment to load, btw.
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Since I know there have been a number of registrars and volunteers around the state that have been working to try and improve the maintenance of the VA voters rolls, I thought people would be interested in some summary results that I can now compute. While there is still plenty of work to be done, we have at least made some progress.
Below you will find the graph of the number of detected duplicate records in the statewide VA Registered Voter Lists as pulled on 2021-11-06 and 2022-09-22. The duplicate detection is based on an exact match of the join of (First Name + Middle Name + Last Name + Suffix + DOB + Gender) fields.
The total number of duplicate records detected in the 2021-11-06 RVL was 1882, and the total number of duplicate records in the 2022-09-22 RVL was 1471. Thats a 21% reduction in the number of duplicated voter records!
Interestingly enough, many of the duplicates actually cross over between multiple localities, so it is possible that many of these duplicated records are people that legitimately moved, but for some reason were given a new voter ID in the new locality and the old locality never removed their record. (A persons voter ID is supposed to be unique to them and ‘follow’ them throughout moves, etc.)
Any registrars who are interested in reviewing the specific records identified, please feel free to contact us.
Since I posted my initial analysis of the Henrico CVR data, one comment was made to me by a member of the Texas election integrity group I have been working with: We have been assuming, based on vendor documentation and the laws and requirements in various states, that when a cast vote record is produced by vendor software the results are sorted by the time the ballot was recorded onto a scanner. However, when looking at the results that we’ve been getting so far and trying to figure out plausible explanations for what we were seeing, he realized it might be the case that the ordering of the CVR entries are being done by both time AND USB stick grouping (which is usually associated with a specific scanner or precinct) but then simply concatenating all of those results together.
While there isn’t enough information in the Henrico CVR files to breakout the entries by USB/Scanner, and the Henrico data has record ID numbers instead of actual timestamps, there is enough information to break out them by Precinct, District and Race, with the exception of the Central Absentee Precincts (CAP) entries where we can only break them out by district given the metadata alone. However, with some careful MATLAB magic I was able to cluster the results marked as just “CAP” into at least 5 different sub-groupings that are statistically distinct. (I used an exponential moving average to discover the boundaries between groupings, and looking at the crossover points in vote share.) I then relabeled the entries with the corresponding “CAP 1”, “CAP 2”, … , “CAP 5” labels as appropriate. My previous analysis was only broken out by Race ID and CAP/Non-CAP/Provisional category.
Processing in this manner makes the individual distributions look much cleaner, so I think this does confirm that there is not a true sequential ordering in the CVR files coming out of the vendor software packages. (If they would just give us the dang timestamps … this would be a lot easier!)
I have also added a bit more rigor to the statistics outlier detection by adding plots of the length of observed runs (e.g. how many “heads” did we get in a row?) as we move through the entries, as well as the plot of the probability of this number of consecutive tosses occurring. We compute this probability for K consecutive draws using the rules of statistical independence, which is P([a,a,a,a]) = P(a) x P(a) x P(a) x P(a) = P(a)^4. Therefore the probability of getting 4 “heads” in a row with a hypothetical 53/47 weighted coin would be .53^4 = 0.0789. There are also plotted lines for a probability 1/#Ballots for reference.
Results
The good news is that this method of slicing the data and assuming that the Vendor is simply concatenating USB drives seems to produce much tighter results that look to obey the expected IID distributions. Breaking up the data this way resulted in no plot breaking the +/- 3/sqrt(N-1) boundaries, but there still are a few interesting datapoints that we can observe.
In the plot below we have the Attorney Generals race in the 4th district from precinct 501 – Antioch. This is a district that Miyares won handily 77%/23%. We see that the top plot of the cumulative spread is nicely bounded by the +/- 3/sqrt(N-1) lines. The second plot from the top gives the vote ratio in order to compare with the work that Draza Smith, Jeff O’Donnell and others are doing with CVR’s over at Ordros.com. The second from bottom plot gives the number k of consecutive ballots (in either candidates favor) that have been seen at each moment in the counting process. And the bottom plot raises either the 77% or 23% overall probability to the k-th power to determine the probability associated with pulling that many consecutive Miyares or Herring ballots from an IID distribution. The most consecutive ballots Miyares received in a row was just over 15, which had a .77^15 = 0.0198 or 1.98% chance of occurring. The most consecutive ballots Herring received was about 4, which equates to a probability of occurrence of .23^4 = 0.0028 or 0.28% chance. The dotted line on the bottom plot is referenced at 1/N, and the solid line is referenced at 0.01%.
But let’s now take a look at another plot for the Miyares contest in another blowout locality with 84% / 16% for Miyares. The +/- 3/sqrt(N-1) limit nicely bounds our ballot distribution again. There is, however, an interesting block of 44 consecutive ballots for Miyares about halfway through the processing of ballots. This equates to .84^44 = 0.0004659 or a 0.04659% chance of occurrence from an IID distribution. Close to this peak is a run of 4 ballots for Herring which doesn’t sound like much, but given the 84% / 16% split, the probability of occurrence for that small run is .16^4 = 0.0006554 or 0.06554%!
Moving to the Lt. Governors race we see an interesting phenomenon where where Ayala received a sudden 100 consecutive votes a little over midway through the counting process. Now granted, this was a landslide district for Ayala, but this still equates to a .92^100 = 0.000239 or 0.0239% chance of occurrence.
And here’s another large block of contiguous Ayala ballots equating to about .89^84 = 0.00005607 or 0.0056% chance of occurrence.
Tests for Differential Invalidation (added 2022-09-19):
“Differential invalidation” takes place when the ballots of one candidate or position are invalidated at a higher rate than for other candidates or positions. With this dataset we know how many ballots were cast, and how many ballots had incomplete or invalid results (no recorded vote in the cvr, but the ballot record exists) for the 3 statewide races. In accordance with the techniques presented in [1] and [2], I computed the plots of the Invalidation Rate vs the Percent Vote Share for the Winner in an attempt to observe if there looks to be any evidence of Differential Invalidation ([1], ch 6). This is similar to the techniques presented in [2], which I used previously to produce my election fingerprint plots and analysis that plotted the 2D histograms of the vote share for the winner vs the turnout percentage.
The generated the invalidation rate plots for the Gov, Lt Gov and AG races statewide in VA 2021 are below. Each plot below is representing one of the statewide races, and each dot is representing the ballots from a specific precinct. The x axis is the percent vote share for the winner, and the y axis is computed as 100 – 100 * Nvotes / Nballots. All three show a small but statistically significant linear trend and evidence of differential invalidation. The linear regression trendlines have been computed and superimposed on the data points in each graph.
To echo the warning from [1]: a differential invalidation rate does not directly indicate any sort of fraud. It indicates an unfairness or inequality in the rate of incomplete or invalid ballots conditioned on candidate choice. While it could be caused by fraud, it could also be caused by confusing ballot layout, or socio-economic issues, etc.
[1] Forsberg, O.J. (2020). Understanding Elections through Statistics: Polling, Prediction, and Testing (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781003019695
[2] Klimek, Peter & Yegorov, Yuri & Hanel, Rudolf & Thurner, Stefan. (2012). Statistical Detection of Systematic Election Irregularities. Proceedings of the National Academy of Sciences of the United States of America. 109. 16469-73. https://doi.org/10.1073/pnas.1210722109.
Update 2022-08-29 per observations by members of the Texas team I am working with, we’ve been able to figure out that (a) the vendor was simply concatenating data records from each machine and not sorting the CVR results and (b) how to mostly unwrap this affect on the data to produce much cleaner results. The results below are left up for historical reference.
For background information, please see my introduction to Cast Vote Records processing and theory here: Statistical Detection of Irregularities via Cast Vote Records. This entry will be specifically documenting the results from processing the Henrico County Virginia CVR data from the 2021 election.
As in the results from the previous post, I expanded the theoretical error bounds out to 6/sqrt(N) instead of 3/sqrt(N) in order to give a little bit of extra “wiggle room” for small fluctuations.
However the Henrico dataset could only be broken up by CAP, Non-CAP or Provisional. So be aware that the CAP curves presented below contain a combination of both early-vote and mail-in ballots.
The good news is that I’ve at least found one race that seems to not have any issues with the CVR curves staying inside the error boundaries. MemberHouseOfDelegates68thDistrict did not have any parts of the curves that broke through the error boundaries.
The bad news … is pretty much everything else doesn’t. I cannot tell you why these curves have such differences from statistical expectation, just that they do. We must have further investigation and analysis of these races to determine root cause. I’ve presented all of the races that had sufficient number of ballots below (1000 minimum for the race a whole, and 100 ballot minimum for each ballot type).
