Election Data Analysis Election Forensics Election Integrity mathematics technical Uncategorized

VA 2024 March Primary Election Fingerprints


Examining the Election Night Reporting data from the VA 2024 March Democratic and Republican primaries provides supporting evidence that the Republican primary was impacted and skewed by a large number of Democratic “crossover” voters, resulting in an irregular election fingerprint when the data is plotted.


The US National Academy of Sciences (NAS) published a paper in 2012 titled “Statistical detection of systematic election irregularities.” [1] The paper asked the question, “How can it be distinguished whether an election outcome represents the will of the people or the will of the counters?” The study reviewed the results from elections in Russia and other countries, where widespread fraud was suspected. The study was published in the proceedings of the National Academy of Sciences as well as referenced in multiple election guides by USAID [2][3], among other citations.

The study authors’ thesis was that with a large sample sample of the voting data, they would be able to see whether or not voting patterns deviated from the voting patterns of elections where there was no suspected fraud. The results of their study proved that there were indeed significant deviations from the expected, normal voting patterns in the elections where fraud was suspected, as well as provided a number of interesting insights into the associated “signatures” of various electoral mechanism as they present themselves in the data.

Statistical results are often graphed, to provide a visual representation of how normal data should look. A particularly useful visual representation of election data, as utilized in [1], is a two-dimensional histogram of the percent voter turnout vs the percent vote share for the winner, or what I call an “election fingerprint”. Under the assumptions of a truly free and fair election, the expected shape of the fingerprint is of that of a 2D Gaussian (a.k.a. a “Normal”) distribution [4]. The obvious caveat here is that no election is ever perfect, but with a large enough sample size of data points we should be able to identify large scale statistical properties.

In many situations, the results of an experiment follow what is called a ‘normal distribution’. For example, if you flip a coin 100 times and count how many times it comes up heads, the average result will be 50. But if you do this test 100 times, most of the results will be close to 50, but not exactly. You’ll get almost as many cases with 49, or 51. You’ll get quite a few 45s or 55s, but almost no 20s or 80s. If you plot your 100 tests on a graph, you’ll get a well-known shape called a bell curve that’s highest in the middle and tapers off on either side. That is a normal distribution.

In a free and fair election, the plotted graphs of both the Turnout percentage and the percentage of Vote Share for Election Winner should (again … ideally) both resemble Gaussian “Normal” distributions; and their combined distribution should also follow a 2-dimensional Gaussian (or “normal”) distribution. Computing this 2 Dimensional joint distribution of the % Turnout vs. % Vote Share is what I refer to as an “Election Fingerprint”.

Figure 1 is reprinted examples from the referenced National Academy of Sciences paper. The actual election results in Russia, Uganda and Switzerland appear in the left column, the right column is the modeled expected appearance in a fair election with little fraud, and the middle column is the researchers’ model of the as-collected data, with any possible fraud mechanisms included.

Figure 1: NAS Paper Results (reprinted from [1])

As you can see, the election in Switzerland (assumed fair) shows a range of voter turnout, from approximately 30 – 70% across voting districts, and a similar range of votes for the winner. The Switzerland data is consistent across models, and does not show any significant irregularities.

What do the clusters mean in the Russia 2011 and 2012 elections? Of particular concern are the top right corners, showing nearly 100% turnout of voters, and nearly 100% of them voted for the winner.

Both of those events (more than 90% of registered voters turning out to vote and more than 90% of the voters voting for the winner) are statistically improbable, even for very contested elections. Election results that show a strong linear streak away from the main fingerprint lobe indicates ‘ballot stuffing,’ where ballots are added at a specific rate. Voter turnout over 100% indicates ‘extreme fraud’. [1][5]

Note that election results with ‘outliers’ – results that fall outside of expected normal voting patterns – while evidentiary indicators, are not in and of themselves definitive proof of outright fraud or malfeasance. For example, in rare but extreme cases, where the electorate is very split and the split closely follows the geographic boundaries between voting precincts, we could see multiple overlapping Gaussian lobes in the 2D image. Even in that rare case, there should not be distinct structures visible in the election fingerprint, linear streaks, overly skewed or smeared distributions, or exceedingly high turnout or vote share percentages. Additional reviews of voting patterns and election results should be conducted whenever deviations from normal patterns occur in an election.

Additionally it should be noted that “the absence of evidence is not the evidence of absence”: Election Fingerprints that look otherwise normal might still have underlying issues that are not readily apparent with this view of the data.

Results on 2024 VA March Primaries:

Figure 2 and Figure 3 are the computed election fingerprints for the Democratic and Republican VA 2024 March Primaries, respectively. They were computed according to the NAS paper and using official state reported voter turnout and votes for the statewide winner and reported per voting Locality with combined In-Person Early, Election Day, Absentee and Provisional votes. Figures 4 and 5 perform the same process, except each data point is generated per individual precinct in a locality. The color scale moves from precincts with low counts as deep blue, to precincts with high numbers represented as bright yellow. Note that a small blurring filter was applied to the computed image for ease of viewing small isolated Locality or Precinct results.

The upper right inset in each graphic image was computed per the NAS paper; the bottom left inset shows what an idealized model of the data could or should look like, based on the reported voter turnout and vote share for the winner. This ideal model is allowed to have up to 3 Gaussian lobes based on the peak locations and standard deviations in the reported results. The top-left and bottom-right inset plots show the sum of the rows and columns of the fingerprint image. The top-left graph corresponds to the sum of the rows in the upper right image and is the histogram of the vote share for the winner across precincts. The bottom right graph shows the sum of the columns of the upper right image, and is the histogram of the percentage turnout across voting localities.

Figure 2 Democratic primary, accumulated per Locality:
Figure 3 Republican primary, accumulated per Locality:
Figure 4 Democratic primary, accumulated per Precinct:
Figure 5 Republican primary, accumulated per Precinct:


As can be seen in Figure 2 and 4, the Democratic primary fingerprint looks to fall within expected normal distribution. Even though the total vote share for the winner (Biden) is up around 90%, this was not unexpected given the current set of contestants and the fact that Biden is the incumbent.

The Republican primary results, as shown in Figure 3 and 5, show significant “smearing” of the percent of total vote share for the winner. The percent of voter turnout (x-axis) does however show a near Gaussian distribution, which is what one would expect. The republican primary data does not show the linear streaking pattern that the authors in [1] correlate with extreme fraud, but significant smearing of the distribution is observed.

A consideration that might partially explain this smearing of the histogram, is that there was at least 17% of “crossover voters” who historically lean Democrat but voted in the Republican primary (see here for more information). Multiple news reports and exit polling suggest that this was due in part to loosely organized efforts by the opposing party to cast “Protest Votes” and artificially inflate the challenger (Haley) and dilute the expected (Trump) margin of victory for the winner, with no intention of supporting a Republican candidate in the General Election. (This is completely legal in VA, by the way, as VA does not require by-party voter registration.)

If we categorize each locality as being either Democratic or Republican leaning based on the average results of the last four presidential elections, and then split the computation of the per precinct results into separate parts accordingly, we can see this phenomenon much clearer.

Figure 6 shows the per-precinct results for only those locality precincts that belong to historic Republican leaning localities. It depicts a much tighter distribution and has much less smearing or blurring of the distribution tails. We can see from the data that Republican base in historically Republican leaning localities seems solidly behind candidate Trump.

Figure 7 shows the per-precinct results for only those locality precincts that belong to historic Democratic leaning localities. It can clearly be seen by comparing the two plots that the major contributor to the spread of the total republican primary distribution is the votes from historically Democratic leaning localities.

Figure 6 Republican primary, accumulated per Precinct in Republican leaning localities:
Figure 7 Republican primary, accumulated per Precinct in Democratic leaning localities:


Election Data Analysis Election Integrity technical Uncategorized

Technical issues with Enhanced Voting Election Night Return JSON files

As I was going through and processing the (new) VA election night reporting data provided by Enhanced Voting, I noticed a number of technical issues with the data feed. I’ve tried to capture them here in the attempt to help assist the VA Department of Elections in correcting bugs and implementation issues with their new reporting format.

While the new ENR data feed is commendable in that it presents the data for the state in an easily obtainable JSON formatted file, the following issues were observed in my processing of the data. I am happy to provide specific examples of these issues to the Enhanced Voting development team in order to help address them.

  • Inconsistent JSON formats being returned. Sometimes locality group results information is a cell of structures, sometimes an array.
  • Occasional mal-formed JSON, missing opening or closing parentheses or brackets causing the file to not be able to be parsed by JSON importing functions in python, MATLAB, etc.
  • Occasional duplicated locality precinct group result information
Election Integrity mathematics technical Uncategorized

Ranked Choice Voting: An Example of a Perverse Social Choice Function

The below is based on the discussion of “Single Transferrable Vote” (“STV”) methods in [1], published in 1977. STV has more recently been called “Ranked Choice Voting” (RCV) or “Instant Runoff Voting” (IRF), among other names, by lobbying groups that are currently pushing for its incorporation into our voting systems. Irrespective of the name used, it represents a family of voting methods, with slightly different variants depending on how votes are removed and/or redistributed in each successive round of voting. [2][5]

What does STV/RCV/IRV entail, in general:

The core system is a proportional voting system, where voters are required to rank order their preferred candidate selections and all ballots are collected and centralized tabulation is performed in multiple rounds until winner(s), or candidates that have support above a specified quota (or “threshold”), are allocated.

A common definition of the quota utilized in STL/RCV/IRV systems is the “Droop quota”, and is defined as:

q = FLOOR( # of Voters / (# of Seats + 1) + 1)

In a given round the candidate with the least support is eliminated from further evaluation. Surplus votes from candidates that go over the droop threshold and votes from eliminated candidates can be distributed amongst remaining candidates for subsequent rounds. Surplus vote distribution is only applicable when multiple winners are allowed in a contest.

Vote allocation procedure for STV/RCV/IRV. Reprinted from [1].

The arguments used to support and push for RCV have not significantly changed since the time that the original paper was published, but the terms and language utilized have been modified. The authors note that much of the rationale in pushing for STV was centered around the ideas of inclusivity and making sure voters are able to cast “effective” ballots.

“Modem proponents emphasize the system’s effective representation of minorities, its sensitivity and accuracy in ‘measuring changes in popular will,’ and its tendency to encourage independent (nonparty line) voting.”

Doron, G., & Kronick, R. (1977) [1]

The same arguments have been recently repeated and pushed to legislators and the media. The name has changed from “Single Transferrable Vote” to “Ranked Choice Voting” or “Instant Runoff Voting”, but the argument remains largely the same, as can be seen by simply visiting the websites and promotional material for any of the current groups that are lobbying for RCV to be incorporated [3][4].

The issue pointed out by Doron & Kronick:

The authors in [1] note that the STV/RCV/IRV system allows for a “perversion” (their words, not mine) whereby a candidates chances to be selected as a winner can potentially be negatively impacted even when receiving increased support.

“… a function that permitted an increased vote for a candidate to cause a decline in that candidate’s rank in the social ordering-would probably strike most of us as a rather absurd, even perverse, method of arriving at a social choice. Consequently, some writers refer to this condition as the ‘Non-Perversity’ condition. All of the democratic social choice functions that have been considered in the literature were assumed to guarantee this condition, but the Single Transferrable Vote system does not.”

Doron, G., & Kronick, R. (1977) [1]

The authors present a hypothetical example to demonstrate the issue. Suppose we have 3 candidates (Candidate X, Candidate Y, Candidate Z) and two different voting groups, which we will refer to as group D and D’. Both D and D’ are fairly similar and only disagree on the relative ranking of two specific candidates.

In the tables below, recreated from [1], the only difference in the two voting group selections is that candidate X receives more support than candidate Y in group D’. However, if using the voting rules as described above candidate X wins in D, and loses in D’ even though X has increased support in D’.

# of VotersFirst ChoiceSecond ChoiceThird Choice
Voting group D selections. Reprinted from [1].
# of VotersFirst ChoiceSecond ChoiceThird Choice
Voting group D’ selections. Reprinted from [1].

There are 17 voters in each case, and only 1 seat available. Therefore, the Droop quota/threshold is 9 votes required in order to declare a winner.

In group D it is candidate Z that has the least amount of votes in the first round and is eliminated, therefore advancing 5 second-choice votes for X into the next round. Candidate X passes the threshold and wins in the second round.

In group D’, where candidate X received more support than candidate Y, it is candidate Y that has the least amount of votes in the first round and is eliminated, therefore advancing 4 second-choice votes for Z into the next round. Candidate Z then passes the threshold and wins in the second round.


  1. Doron, G., & Kronick, R. (1977). Single Transferrable Vote: An Example of a Perverse Social Choice Function. American Journal of Political Science, 21(2), 303–311.
  5. Brandt F, Conitzer V, Endriss U, Lang J, Procaccia AD, eds. Handbook of Computational Social Choice. Cambridge: Cambridge University Press; 2016.
Election Data Analysis Election Forensics Election Integrity programming technical Uncategorized

Records of Early Ballots Cast Do Not Have Corresponding Registration Records in VA 2023 General Election Data

Update (2023-12-14 12:00:00 EST) : Special thank you to Rick Michael of the Chesterfield Electoral board for checking their records on issues #1 and #2 below. There were 3 x Issue #1 records and 9 x Issue #2 records identified in Chesterfield County.

According to Rick, the records in question were populated and visible when looking via the electronic VERIS (the states election database) login available to the Registrar. The 3 x Issue #1 records can be found and are Active records in the electronic system, and the 9 x Issue #2 records had an update that moved the records from Inactive to Active that were not reflected in the data supplied to us.

That implies that the data that we purchased (for approximately $12,000) directly from the department of elections is inaccurate and incomplete. Our initial purchase and download of the June 30 Registered Voter List (RVL) database does not show the registrants identified in Issue #1, even though the Registrar can see them in their electronic terminal. And our Monthly Update Subscription (MUS) we receive is missing the updates showing the registrant records identified in Issue #2 being moved from Inactive to Active status.

The department of elections is required by federal law (NVRA, HAVA) to keep and maintain accurate election records AND to make those records accessible for inspection and verification, and for use by candidates and political parties. Additionally, we have paid (twice!) for this data; once as taxpayers, and once again as a 501c3 entity. If the data we, and other campaigns and candidates are receiving is not representative of the actual records in the database, incomplete and inaccurate … that needs to be addressed and fixed.

  • Issue #1: There are 99 records of ballots cast, according to the VA Department of Elections (ELECT) Daily Absentee List (DAL) data file that do not have corresponding voter ID listed in Registered Voter List (RVL) data.
  • Issue #2: There are 380 records of ballots cast in the DAL where the corresponding RVL record has been listed as “Inactive” since June-30-2023 and no modification to the RVL record has taken place.
  • Issue #3: There are 18 records of ballots cast in the DAL where the corresponding RVL record is listed as “Inactive” as of Dec-01-2023, but there has been previous modifications to the RVL record since June-30-2023.
  • We are currently reaching out in attempts to contact the VA AG’s office and to provide them the details of this analysis in order to have these anomalies further investigated.
Data files utilized for this analysis:

Our 501c3 EPEC purchased and downloaded the full statewide VA RVL on June-30-2023 from ELECT. We additionally purchased the Monthly Update Service (MUS) package from ELECT, where on the 1st of each month we are provided a list of all of the changes that have occurred to the RVL in the previous month. By applying these changes to our baseline data file, we are able to update our copy of the RVL to reflect the latest state as per ELECT. We can also create a cumulative record of all entries associated with a particular voter ID by simply concatenating all of these datafiles.

Additionally, during the VA 2023 General Election, we purchased access to the Daily Absentee List (DAL) file generated by ELECT that documents all of the transactions associated with early mail-in or in-person voting during the 45 day early voting period. The DAL file we utilized for this analysis was downloaded from ELECT on Nov-13-2023 at 6am EST.

Identification of ballots cast via the DAL file can be performed by checking for rows of the DAL data table that have the APP_STATUS field set to “Approved” and have the BALLOT_STATUS field set to any of the following: “Marked” | “Pre-Processed” | “On Machine” | “FWAB” | “Provisional”.

Once cast ballots are identified in the DAL, the Voter Identification Number can be used to lookup all of the corresponding records in our cumulative RVL data. The data issues summarized above can be directly observed using this process. Due to VA law, we cannot publish the full specific records here in this blog but have summarized, captured and described our process and results.

For Issue #1: If there does not exist a corresponding registration record for cast ballots, then the voter should not have been able to have their mail-in ballot approved or issued, or been able to check-in to an early voting precinct to vote on-machine. If the voter record actually does exists, then why is it not reflected in the data that we purchased from ELECT. Note that all provisional and Same Day Registration (SDR) ballots were required to be entered into the states database (“VERIS”) by the Friday after the election (Fri Nov-10-2023). We specifically waited until we received the Dec-01-2023 MUS data update from ELECT to attempt to perform this or similar analysis in order to ensure that we would not be missing any last minute registrations or RVL updates.

For Issue #2: There are 380 records of ballots being cast in the DAL where the baseline June-30-2023 RVL data file shows the registrant as inactive, and there has been no modifications or adjustments to the record presented in the MUS data files. Therefore these registrants should still have been listed as “Inactive” during the early voting period which started in September through Election Day (Nov 7).

For issue #3: There are 18 records that show the cast ballot is from a registrant that is currently listed as “Inactive” but there has been adjustments to the registration record over the last 6 months. An example of such is below. Note that I have captured the MUS data file generation dates in the 5th column to note when the file was generated and received by us.

In the example given below, the first invalidation operation on the registration record appears in the MUS file dated Sep-01, with the earliest transaction date listed as Aug-29-2023. The ballot application was not received until Sept 26 according to the DAL, so the application should never have been approved or the ballot issued as the registrant status should have been “Invalid” according to the states own data.

(Also … yes … I know there is a typo in the spelling of “APP_RECIEPT_DATE” in the tables below … but this is the data as it comes from ELECT).

“2023-09-26 00:00:00”Approved“2023-10-19 00:00:00”Pre-Processed
Example Extract of a DAL data record for a mail-in ballot cast during early voting.
30-June-202312:12:00BASELINEBASELINEBaseline RVL
28-Jul-202309:34:03MODIFYChange OutMUS 08/01/2023
28-Jul-202309:34:04MODIFYAddress ChangeMUS 08/01/2023
28-Jul-202309:34:04MODIFYChange InMUS 09/01/2023
28-Jul-202309:34:03MODIFYChange OutMUS 09/01/2023
28-Jul-202309:34:04MODIFYAddress ChangeMUS 09/01/2023
28-Jul-202309:34:04MODIFYChange InMUS 09/01/2023
29-Aug-202311:55:49MODIFYInactivateMUS 09/01/2023
29-Aug-202311:55:49MODIFYInactivateMUS 10/01/2023
Extract of RVL Cumulative Data Records for Voter ID in above DAL entry

Fluctuations in election night reporting ballot counts for multiple races in VA 2023 General Election

Well, election day came and went and everyone was glued to the internet to find out the results.

I took the extra step of logging all of the election night return files posted by the VA department of elections (“ELECT”) at 5 minute increments, as I wanted to plot the results over time as the numbers came in.

The data is from this link on ELECT’s website:

I used a simple wget script to grab this file once every 5 minutes (approximately).

However, when I went to plot the results, I found some data curves that I can’t quite explain. Take, for example, the VA House of Delegates race in the 22nd District:

Now … last I checked, when accumulating counts of ballots … you wouldn’t expect the totals to go down, let alone oscillate back and forth.

This is not the only race where I found ballot curves that have a decrease in one of the ballot count after a data update. (The gallery is posted below.) Of the 183 races I looked at so far, 79 had a ballot trace that had its count total reduced after a data update. (I haven’t looked at all of the races yet.)

Now, one expects there to be some issues and corrections that have to be made to the election night reporting data. But when 43% of the races sampled have obvious data quality issues like this … I think that deserves some explanation.

So … can ELECT please address this:

  • Why do 79 (and counting) races (~43% of races sampled) in the VA election night reporting have obvious issues where the vote totals decreased after a data reporting update?
  • What was the cause?
  • Why was it not caught by your QA/QC procedures?
  • How will you be addressing it going forward?
Election Data Analysis Election Integrity Uncategorized

VA Daily Absentee List

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 records MAY 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.


Multiple Active Ballots

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.

Election Data Analysis Uncategorized

VA “Provisional” Ballots

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.


Variances Observed in TX Early Vote Data

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.


2022 General Election GA Daily Absentee Report Statistics

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.