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Election Data Analysis Election Forensics Election Integrity Interesting programming technical

Statistical Detection of Irregularities via Cast Vote Records

There has been a good amount of commotion regarding cast vote records (CVRs) and their importance lately. I wanted to take a minute and try and help explain why these records are so important, and how they provide a tool for statistical inspection of election data. I also want to try and dispel any misconceptions as to what they can or can’t tell us.

I have been working with other local Virginians to try and get access to complete CVRs for about 6 months (at least) in order to do this type of analysis. However, we had not had much luck in obtaining real data (although we did get a partial set from PWC primaries but it lacked the time-sequencing information) to evaluate until after Jeff O’Donnell (a.k.a. the Lone Raccoon) and Walter Dougherity did a fantastic presentation at the Mike Lindell Moment of Truth Summit on CVRs and their statistical use. That presentation seems to have broken the data logjam, and was the impetus for writing this post.

Just like the Election Fingerprint analysis I was doing earlier that highlighted statistical anomalies in election data, this CVR analysis is a statistics based technique that can help inform us as to whether or not the election data appears consistent with expectations. It only uses the official results as provided by state or local election authorities and relies on standard statistical principles and properties. Nothing more. Nothing less.

What is a cast vote record?

A cast vote record is part of the official election records that need to be maintained in order for election systems to be auditable. (see: 52 USC 21081 , NIST CVR Standard, as well as the Virginia Voting Systems Certification Standards) They can have many different formats depending on equipment vendor, but they are effectively a record of each ballot as it was recorded by the equipment. Each row in a CVR data table should represent a single ballot being cast by a voter and contain, at minimum, the time (or sequence number) when the ballot was cast, the ballot type, and the result of each race. Other data might also be included such as which precinct and machine performed the scanning/recording of the ballot, etc. Note that “cast vote records” are sometimes also called “cast voter records”, “ballot reports” or a number of other different names depending on the publication or locality. I will continue to use the “cast vote record” language in this document for consistency.

Why should we care?

The reason these records are so important, is based on statistics and … unfortunately … involves some math to fully describe. But to make this easier, let’s try first to walk through a simple thought experiment. Let’s pretend that we have a weighted, or “trick” coin, that when flipped it will land heads 53% of the time and land tails 47% of the time. We’re going to continuously flip this coin thousands of times in a row and record our results. While we can’t predict exactly which way the coin will land on any given toss, we can expect that, on average, the coin will land with the aforementioned 53/47 split.

Now because each coin toss constitutes an independent and identically distributed (IID) probability function, we can expect this sequence to obey certain properties. If as we are making our tosses, we are computing the “real-time” statistics of the percentage of head/tails results, and more specifically if we plot the spread (or difference) of those percentage results as we proceed we will see that the spread has very large swings as we first begin to toss our coin, but very quickly the variability in the spread becomes stable as more and more tosses (data) are available for us to average over. Mathematically, the boundary on these swings is inversely proportional to the square root of how many tosses are performed. In the “Moment of Truth” video on CVRs linked above, Jeff and Walter refer to this as a “Cone of Probability”, and he generates his boundary curves experimentally. He is correct. It is a cone of probability as its really just a manifestation of well-known and well-understood Poisson Noise characteristic (for the math nerds reading this). In Jeff’s work he uses the ratio of votes between candidates, while I’m using the spread (or deviation) of the vote percentages. Both metrics are valid, but using the deviation has an easy closed-form boundary curve that we don’t need to generate experimentally.

In the graphic below I have simulated 10 different trials of 10,000 tosses for a distribution that leans 53/47, which is equivalent to a 6% spread overall. Each trial had 10,000 random samples generated as either +1 or -1 values (a.k.a. a binary “Yes” or “No” vote) approximating the 53/47 split and I plotted the cumulative running spread of the results as each toss gets accumulated. The black dotted outline is the 95% confidence interval (or +/-3x the standard deviation) across the 10 trials for the Nth bin, and the red dotted outline is the 3/sqrt(n-1) analytical boundary.

So how does this apply to election data?

In a theoretically free and perfectly fair election we should see similar statistical behavior, where each coin toss is replaced with a ballot from an individual voter. In a perfect world we would have each vote be completely independent of every other vote in the sequence. In reality we have to deal with the fact that there can be small local regions of time in which perfectly legitimate correlations in the sequence of scanned ballots exist. Think of a local church who’s congregation is very uniform and they all go to the polls after Sunday mass. We would see a small trend in the data corresponding to this mass of similar thinking peoples going to the polls at the same time. But we wouldn’t expect there to be large, systematic patterns, or sharp discontinuities in the plotted results. A little bit of drift and variation is to be expected in dealing with real world election data, but persistent and distinct patterns would indicate a systemic issue.

Now we cannot isolate all of the variables in a real life example, but we should try as best as possible. To that effect, we should not mix different ballot types that are cast in different manners. We should keep our analysis focused within each sub-group of ballot type (mail-in, early-vote, day-of, etc). It is to the benefit of this analysis that the very nature of voting, and the procedures by which it occurs, is a very randomized process. Each sub-grouping has its own quasi-random process that we can consider.

While small groups (families, church groups) might travel to the in-person polls in correlated clusters, we would expect there to be fairly decent randomization of who shows up to in-person polls and when. The ordering of who stands in line before or after one another, how fast they check-in and fill out their ballot, etc, are all quasi-random processes.

Mail-in ballots have their own randomization as they depend on the timing of when individuals request, fill-out and mail their responses, as well as the logistics and mechanics of the postal service processes themselves providing a level of randomization as to the sequence of ballots being recorded. Like a dealer shuffling a deck of cards, the process of casting a mail-in vote provides an additional level of independence between samples.

No method is going to supply perfect theoretical independence from ballot to ballot in the sequence, but theres a general expectation that voting should at least be similar to an IID process.

Also … and I cannot stress this enough … while these techniques can supply indications of irregularities and discrepancies in elections data, they are not conclusive and must be coupled with in-depth investigations.

So going back to the simulation we generated above … what does a simulation look like when cheating occurs? Let’s take a very simple cheat from a random “elections” of 10,000 ballots, with votes being representative of either +1 (or “Yes”) or -1 (or “No”) as we did above. But lets also cheat by randomly selecting two different spots in the data stream to place blocks of 250 consecutive “Yes” results.

The image below shows the result of this process. The blue curve represents the true result, while the red curve represents the cheat. We see that at about 15% and 75% of the vote counted, our algorithm injected a block of “Yes” results, and the resulting cumulative curve breaks through the 3/sqrt(N-1) boundary. Now, not every instance or type of cheat will break through this boundary, and there may be real events that might explain such behavior. But looking for CVR curves that break our statistical expectations is a good way to flag items that need further investigation.

Computing the probability of a ballot run:

Section added on 2022-09-18

We can also a bit more rigor to the statistics outlier detection by computing the probability of the length of observed runs (e.g. how many “heads” did we get in a row?) occurring as we move through the sequential entries. We can 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.

Starting with my updated analysis of 2021 Henrico County VA, I’ve started adding this computation to my plots. I have not yet re-run the Texas data below with this new addition, but will do so soon and update this page accordingly.

Real Examples

UPDATE 2022-09-18:

  • I have finally gotten my hands on some data for 2020 in VA. I will be working to analyze that data and will report what I find as soon as I can, but as we are approaching the start of early voting for 2022, my hands are pretty full at the moment so it might take me some time to complete that processing.
  • As noted in my updates to the Henrico County 2021 VA data, and in my section on computing the probability of given runs above, the Texas team noticed that we could further break apart the Travis county data into subgroups by USB stick. I will update my results below as soon as I get the time to do so.

So I haven’t gotten complete cast vote records from VA yet (… which is a whole other set of issues …), but I have gotten my Cheeto stained fingers on some data from the Travis County Texas 2020 race.

So let us first take a look at an example of a real race where everything seems to be obeying the rules as set out above. I’ve doubled my error bars from 3x to 6x of the inverse square standard (discussed above) in order to handle the quasi-IID nature of the data and give some extra margin for small fluctuating correlations.

The plot below shows the Travis County Texas 2020 BoardOfTrusteesAt_LargePosition8AustinISD race, as processed by the tabulation system and stratified by ballot type. We can see that all three ballot types start off with large variances in the computed result but very quickly coalesce and approach their final values. This is exactly what we would expect to see.

Now if I randomly shuffle the ordering of the ballots in this dataset and replot the results (below) I get a plot that looks unsurprisingly similar, which suggests that these election results were likely produced by a quasi-IID process.

Next let’s take a look at a race that does NOT conform to the statistics we’ve laid out above. (… drum-roll please … as this the one everyone’s been waiting for). Immma just leave this right here and just simply point out that all 3 ballot type plots below in the Presidential race for 2020 go outside of the expected error bars. I also note the discrete stair step pattern in the early vote numbers. It’s entirely possible that there is a rational explanation for these deviations. I would sure like to hear it, especially since we have evidence from the exact same dataset of other races that completely followed the expected boundary conditions. So I don’t think this is an issue with a faulty dataset or other technical issues.

And just for completeness, when I artificially shuffle the data for the Presidential race, and force it to be randomized, I do in fact end up with results that conform to IID statistics (below).

I will again state that while these results are highly indicative that there were irregularities and discrepancies in the election data, they are not conclusive. A further investigation must take place, and records must be preserved, in order to discover the cause of the anomalies shown.

Running through each race that had at least 1000 ballots cast and automatically detecting which races busted the 6/sqrt(n-1) boundaries produces the following tabulated results. A 1 in the right hand column indicates that the CVR data for that particular race in Travis County has crossed the error bounds. A 0 in the right hand column indicates that all data stayed within the error bound limits.

RaceCVR_OOB_Irregularity_Detected
President_VicePresident1
UnitedStatesSenator1
UnitedStatesRepresentativeDistrict101
UnitedStatesRepresentativeDistrict171
UnitedStatesRepresentativeDistrict211
UnitedStatesRepresentativeDistrict251
UnitedStatesRepresentativeDistrict350
RailroadCommissioner1
ChiefJustice_SupremeCourt1
Justice_SupremeCourt_Place6_UnexpiredTerm1
Justice_SupremeCourt_Place71
Justice_SupremeCourt_Place81
Judge_CourtOfCriminalAppeals_Place31
Judge_CourtOfCriminalAppeals_Place41
Judge_CourtOfCriminalAppeals_Place91
Member_StateBoardOfEducation_District51
Member_StateBoardOfEducation_District101
StateSenator_District210
StateSenator_District241
StateRepresentativeDistrict471
StateRepresentativeDistrict481
StateRepresentativeDistrict491
StateRepresentativeDistrict501
StateRepresentativeDistrict510
ChiefJustice_3rdCourtOfAppealsDistrict1
DistrictJudge_460thJudicialDistrict1
DistrictAttorney_53rdJudicialDistrict1
CountyJudge_UnexpiredTerm1
Judge_CountyCourtAtLawNo_91
Sheriff1
CountyTaxAssessor_Collector1
CountyCommissionerPrecinct11
CountyCommissionerPrecinct31
AustinCityCouncilDistrict20
AustinCityCouncilDistrict40
AustinCityCouncilDistrict60
AustinCityCouncilDistrict70
AustinCityCouncilDistrict101
PropositionACityOfAustin_FP__2015_1
PropositionBCityOfAustin_FP__2022_1
MayorCityOfCedarPark0
CouncilPlace2CityOfCedarPark0
CouncilPlace4CityOfCedarPark0
CouncilPlace6CityOfCedarPark0
CouncilMemberPlace2CityOfLagoVista0
CouncilMemberPlace4CityOfLagoVista0
CouncilMemberPlace6CityOfLagoVista0
CouncilMemberPlace2CityOfPflugerville0
CouncilMemberPlace4CityOfPflugerville0
CouncilMemberPlace6CityOfPflugerville0
Prop_ACityOfPflugerville_2169_0
Prop_BCityOfPflugerville_2176_0
Prop_CCityOfPflugerville_2183_0
BoardOfTrusteesDistrict2SingleMemberDistrictAISD0
BoardOfTrusteesDistrict5SingleMemberDistrictAISD0
BoardOfTrusteesAt_LargePosition8AustinISD1
BoardOfTrusteesPlace1EanesISD1
Prop_AEanesISD_2246_0
BoardOfTrusteesPlace3LeanderISD0
BoardOfTrusteesPlace4LeanderISD0
BoardOfTrusteesPlace5ManorISD1
BoardOfTrusteesPlace6ManorISD0
BoardOfTrusteesPlace7ManorISD1
BoardOfTrusteesPlace6PflugervilleISD1
BoardOfTrusteesPlace7PflugervilleISD1
BoardOfTrusteesPlace1RoundRockISD1
BoardOfTrusteesPlace2RoundRockISD0
BoardOfTrusteesPlace6RoundRockISD0
BoardOfTrusteesPlace7RoundRockISD0
BoardOfTrusteesWellsBranchCommunityLibraryDistrict0
Var1470
BoardOfTrusteesWestbankLibraryDistrict0
Var1500
Var1510
DirectorsPlace2WellsBranchMUD0
DirectorsPrecinct4BartonSprings_EdwardsAquiferConservationDistr0
PropositionAExtensionOfBoundariesCityOfLakeway_1966_0
PropositionB2_yearTermsCityOfLakeway_1973_0
PropositionCLimitOnSuccessiveYearsOfServiceCityOfLakeway_1980_0
PropositionDResidencyRequirementForCityManagerCityOfLakeway_1980
PropositionEOfficeOfTreasurerCityOfLakeway_1994_0
PropositionFOfficialBallotsCityOfLakeway_2001_0
PropositionGAuthorizingBondsCityOfLakeway_2008_0
PropositionALagoVistaISD_2253_0
PropositionBLagoVistaISD_2260_0
PropositionCLagoVistaISD_2267_0
PropositionAEmergencyServicesDistrict1_2372_0

References and further reading:

Categories
Election Data Analysis Election Forensics Election Integrity technical

Distribution of VHL Entries as compared to CSV and Turnout statistics

Second time’s the charm! See here for my (corrected) first attempt. My apologies for the coding error in my first effort to compute the 2020 CSV Estimates.

Per a question that I received I took a look at the difference between the number of voters that the VA department of elections (“ELECT”) says participated in a given election (via their Voter History List files), the number of ballots that ELECT says were counted in an election (via the public CSV Election Results files), and the amount of turnout that occurred in a given election (via the ELECT public Turnout CSV files).

Theoretically, all of these sources should give the same (or extremely similar) results. Note that the VHL is counting people, and the CSV is counting votes cast in the respective races, but the numbers should still be roughly similar between the two sources. Also the Results CSV “Total Vote” field, and the Turnout CSV files used for this analysis should be including overvotes, write-ins, etc so that should not the source of the discrepancies. Furthermore, the 11-06-2021 VHL data file is identical to the VHL datafile I downloaded on 12-14-2021, so there is no “missing data” from the VHL file for the 2021 election, unless ELECT has a significant lag in their updating of voter credit. From my conversations with multiple registrars and elections staff, the voter credit is applied when the canvas is completed and the results are certified, so this also should not be a source of error. Update 2022-07-31 23:05 per email discussion with ELECT staff there IS a significant lag in the updating of the VHL data from the time of canvas and certification. This explains the incomplete 2021 VHL. The voter credit is supposed to be applied as of the canvas and certification, but there is often lag in the data being entered and/or replicated. I am not exactly sure how long of a lag is to be expected. Also, while the links for the DAL file given out by ELECT after a user purchases the data are “live” and updated daily over a 30 day window, the links for the RVL and VHL are NOT! So all of this data should be considered as of Nov 6 2021.

Finally, note that by definition the Voter History List information will slightly undercount the number of voters that participated in previous elections, because voters that have been removed from the registered voter list in between the end of an election period and when the a given VHL file is downloaded (11-6-2021 and 12-14-2021 in this case) will have had their corresponding records also deleted from the VHL. This means that as the date of the VHL file you are using gets further and further away from the election that you are interested in, the VHL data becomes more and more inaccurate by design. This seems like a pretty bad way to do record keeping in my opinion, but thats the way ELECT is handling the data.

Results:

One interesting thing to note, is that even though ELECT and registrars state that the “Voter Credit” (i.e. updates to the VHL) are applied at the conclusion of the canvas and before certification of the election, it appears that there are significant differences with the VHL for 2021 from the Turnout and Result CSV files. The VHL file I am using was first downloaded on 11-06-2021 after the canvas and certification was completed, and I continued to download and archive versions of the the live URL link I received from ELECT until 12-14-2021. There was no change in the content of the VHL file for that entire period. (See my update note above) An example from King George County is shown below.

Another interesting thing to observe is that the CSV Election Results and CSV Turnout Results tend to agree for the most part, while the VHL data discrepancies trend negatively as we move further and further away from past elections. As discussed above, this might be attributable to the fact that changes to the voter registrations are mirrored in updates to the Voter History List. An example from Highland County that shows this small but increasing deviation as we move into the past is shown below.

One specific interesting datapoint is the below plot from Page county, where the 2020 Results CSV and Turnout CSV numbers significantly deviate, with a much smaller deviation between the VHL numbers and the Results CSV. I don’t have any explanation as to why this occurs in the data.

Another specifically interesting datapoint is that the turnout numbers from 2019 in Rockbridge County are significantly less than the number of voters that supposedly participated in the elections according to the VHL. (Note that there were no congressional district races in 2019 November General.) This is particularly interesting considering that we presume, per discussion above, that the VHL undercounts the number of participating voters as we look further and further into the past, as the VHL has voters removed as they are purged from the voter roles. So how does the number of participating voters (from VHL) outnumber the number of votes tallied by ~2400?

A similar occurrence in New Kent County in 2020 can be observed below, with a deviation of nearly -6000 estimated ballots. The VHL file shows there were ~6000 more voters that participated in the election than the Turnout CSV file records, yet the CSV file for the number of votes cast is within 265 votes of the VHL estimate. Whats going on here?

A similar situation occurs in Shenandoah County in 2018, but by a much smaller margin.

Complete Gallery:

Below is a complete gallery of every Locality’s computed results for completeness.

Categories
Election Data Analysis Election Forensics Election Integrity programming technical

Number of duplicated voter records in each VA locality

Computed below is the number of duplicated voter records in each locality as of the 11/06/21 VA Registered Voter List (RVL). The computation is based on performing an exact match of LAST_NAME, DOB and ADDRESS fields between records in the file.

Note: If the combination of the name “Jane Smith”, with DOB “1/1/1980”, at “12345 Some Road, Ln.” appears 3 times in the file, there are 3 counts added to the results below. If the combination appears only once, there are 0 counts added to the results below, as there is no repetition.

Additionally I’ve done an even more restrictive matching which requires exact match on FIRST, MIDDLE and LAST name, DOB and ADDRESS fields in the second graphic and list presented below.

The first, more lenient, criteria will correctly flag multiple records with the same first or middle name, but misspelled such as “Muhammad” vs “Mahammad”, but could also include occurrences of voting age twins who live together or spouses with the same DOB.

The second, more strict, criteria requires that multiple rows flagged have exactly the same spelling and punctuation for FIRST, MIDDLE, LAST, DOB and ADDRESS fields. This has less false positive, but more false negatives, as it will likely miss common misspellings between entries, etc.

There are no attempts to match for common misspellings, etc. I did do a simple cleanup for multiple contiguous whitespace elements, etc., before attempting to match.

I have summarized the data here so as not to reveal any personally identifiable information (PII) from the RVL in adherence to VA law.

Update 2022-07-13 12:30: I have sent the full information, for both the lenient and strict criteria queries, to the Prince William County and Loudoun County Registrars. The Loudoun deputy registrar has responded and stated that all but 1 of the duplications in the stricter criteria had already been caught by the elections staff, but he has not yet looked at the entries in the more lenient criteria results file. I have also attempted to contact the Henrico County, Lynchburg City, and York County registrars but have not yet received a response or request to provide them with the full data.

Update 2022-07-31 23:03: I have also heard back from the PWC Registrar (Eric Olsen). Most of the entries that I had flagged in the 11/6/2021 RVL list have already been taken care of by the PWC staff already. There were only a couple that had not yet been noticed or marked as duplicates. Also, per our discussion, I should reiterate and clarify that the titles on the plots below simply refer to duplicated entries of the data files according to the filtering choice. It is a technically accurate description and should not be read as I am asserting other than the results of the matching operation.

Locality NameNumber of repeated entries
ACCOMACK COUNTY64
ALBEMARLE COUNTY311
ALEXANDRIA CITY225
ALLEGHANY COUNTY16
AMELIA COUNTY32
AMHERST COUNTY84
APPOMATTOX COUNTY18
ARLINGTON COUNTY446
AUGUSTA COUNTY119
BATH COUNTY2
BEDFORD COUNTY170
BLAND COUNTY4
BOTETOURT COUNTY45
BRISTOL CITY22
BRUNSWICK COUNTY30
BUCHANAN COUNTY30
BUCKINGHAM COUNTY32
BUENA VISTA CITY10
CAMPBELL COUNTY82
CAROLINE COUNTY67
CARROLL COUNTY42
CHARLES CITY COUNTY18
CHARLOTTE COUNTY28
CHARLOTTESVILLE CITY80
CHESAPEAKE CITY545
CHESTERFIELD COUNTY948
CLARKE COUNTY40
COLONIAL HEIGHTS CITY18
COVINGTON CITY2
CRAIG COUNTY4
CULPEPER COUNTY114
CUMBERLAND COUNTY8
DANVILLE CITY88
DICKENSON COUNTY22
DINWIDDIE COUNTY44
EMPORIA CITY6
ESSEX COUNTY25
FAIRFAX CITY38
FAIRFAX COUNTY2962
FALLS CHURCH CITY39
FAUQUIER COUNTY203
FLOYD COUNTY28
FLUVANNA COUNTY36
FRANKLIN CITY23
FRANKLIN COUNTY84
FREDERICK COUNTY210
FREDERICKSBURG CITY54
GALAX CITY0
GILES COUNTY24
GLOUCESTER COUNTY52
GOOCHLAND COUNTY84
GRAYSON COUNTY18
GREENE COUNTY32
GREENSVILLE COUNTY16
HALIFAX COUNTY48
HAMPTON CITY285
HANOVER COUNTY316
HARRISONBURG CITY40
HENRICO COUNTY676
HENRY COUNTY74
HIGHLAND COUNTY4
HOPEWELL CITY34
ISLE OF WIGHT COUNTY98
JAMES CITY COUNTY217
KING & QUEEN COUNTY13
KING GEORGE COUNTY42
KING WILLIAM COUNTY43
LANCASTER COUNTY10
LEE COUNTY24
LEXINGTON CITY12
LOUDOUN COUNTY1245
LOUISA COUNTY74
LUNENBURG COUNTY26
LYNCHBURG CITY165
MADISON COUNTY12
MANASSAS CITY64
MANASSAS PARK CITY24
MARTINSVILLE CITY14
MATHEWS COUNTY18
MECKLENBURG COUNTY54
MIDDLESEX COUNTY12
MONTGOMERY COUNTY159
NELSON COUNTY30
NEW KENT COUNTY26
NEWPORT NEWS CITY329
NORFOLK CITY411
NORTHAMPTON COUNTY18
NORTHUMBERLAND COUNTY22
NORTON CITY6
NOTTOWAY COUNTY12
ORANGE COUNTY70
PAGE COUNTY47
PATRICK COUNTY28
PETERSBURG CITY68
PITTSYLVANIA COUNTY84
POQUOSON CITY28
PORTSMOUTH CITY186
POWHATAN COUNTY55
PRINCE EDWARD COUNTY43
PRINCE GEORGE COUNTY77
PRINCE WILLIAM COUNTY1159
PULASKI COUNTY59
RADFORD CITY14
RAPPAHANNOCK COUNTY10
RICHMOND CITY300
RICHMOND COUNTY14
ROANOKE CITY133
ROANOKE COUNTY233
ROCKBRIDGE COUNTY28
ROCKINGHAM COUNTY113
RUSSELL COUNTY28
SALEM CITY58
SCOTT COUNTY18
SHENANDOAH COUNTY48
SMYTH COUNTY40
SOUTHAMPTON COUNTY28
SPOTSYLVANIA COUNTY345
STAFFORD COUNTY410
STAUNTON CITY14
SUFFOLK CITY194
SURRY COUNTY10
SUSSEX COUNTY14
TAZEWELL COUNTY52
VIRGINIA BEACH CITY922
WARREN COUNTY46
WASHINGTON COUNTY78
WAYNESBORO CITY26
WESTMORELAND COUNTY24
WILLIAMSBURG CITY22
WINCHESTER CITY42
WISE COUNTY40
WYTHE COUNTY35
YORK COUNTY178
Locality NameNumber of repeated entries
ACCOMACK COUNTY0
ALBEMARLE COUNTY4
ALEXANDRIA CITY0
ALLEGHANY COUNTY0
AMELIA COUNTY0
AMHERST COUNTY2
APPOMATTOX COUNTY0
ARLINGTON COUNTY10
AUGUSTA COUNTY0
BATH COUNTY0
BEDFORD COUNTY4
BLAND COUNTY0
BOTETOURT COUNTY0
BRISTOL CITY0
BRUNSWICK COUNTY0
BUCHANAN COUNTY2
BUCKINGHAM COUNTY0
BUENA VISTA CITY0
CAMPBELL COUNTY2
CAROLINE COUNTY0
CARROLL COUNTY0
CHARLES CITY COUNTY0
CHARLOTTE COUNTY0
CHARLOTTESVILLE CITY0
CHESAPEAKE CITY8
CHESTERFIELD COUNTY8
CLARKE COUNTY0
COLONIAL HEIGHTS CITY0
COVINGTON CITY0
CRAIG COUNTY0
CULPEPER COUNTY0
CUMBERLAND COUNTY0
DANVILLE CITY0
DICKENSON COUNTY0
DINWIDDIE COUNTY0
EMPORIA CITY0
ESSEX COUNTY0
FAIRFAX CITY0
FAIRFAX COUNTY54
FALLS CHURCH CITY0
FAUQUIER COUNTY2
FLOYD COUNTY0
FLUVANNA COUNTY0
FRANKLIN CITY3
FRANKLIN COUNTY0
FREDERICK COUNTY6
FREDERICKSBURG CITY0
GALAX CITY0
GILES COUNTY2
GLOUCESTER COUNTY0
GOOCHLAND COUNTY0
GRAYSON COUNTY0
GREENE COUNTY0
GREENSVILLE COUNTY0
HALIFAX COUNTY0
HAMPTON CITY8
HANOVER COUNTY0
HARRISONBURG CITY0
HENRICO COUNTY24
HENRY COUNTY2
HIGHLAND COUNTY0
HOPEWELL CITY2
ISLE OF WIGHT COUNTY4
JAMES CITY COUNTY0
KING & QUEEN COUNTY0
KING GEORGE COUNTY0
KING WILLIAM COUNTY0
LANCASTER COUNTY0
LEE COUNTY0
LEXINGTON CITY0
LOUDOUN COUNTY23
LOUISA COUNTY0
LUNENBURG COUNTY0
LYNCHBURG CITY16
MADISON COUNTY0
MANASSAS CITY0
MANASSAS PARK CITY0
MARTINSVILLE CITY0
MATHEWS COUNTY2
MECKLENBURG COUNTY0
MIDDLESEX COUNTY0
MONTGOMERY COUNTY2
NELSON COUNTY0
NEW KENT COUNTY0
NEWPORT NEWS CITY0
NORFOLK CITY0
NORTHAMPTON COUNTY0
NORTHUMBERLAND COUNTY0
NORTON CITY0
NOTTOWAY COUNTY0
ORANGE COUNTY0
PAGE COUNTY0
PATRICK COUNTY0
PETERSBURG CITY0
PITTSYLVANIA COUNTY4
POQUOSON CITY0
PORTSMOUTH CITY0
POWHATAN COUNTY0
PRINCE EDWARD COUNTY0
PRINCE GEORGE COUNTY0
PRINCE WILLIAM COUNTY8
PULASKI COUNTY0
RADFORD CITY0
RAPPAHANNOCK COUNTY0
RICHMOND CITY10
RICHMOND COUNTY0
ROANOKE CITY0
ROANOKE COUNTY11
ROCKBRIDGE COUNTY0
ROCKINGHAM COUNTY4
RUSSELL COUNTY0
SALEM CITY0
SCOTT COUNTY0
SHENANDOAH COUNTY0
SMYTH COUNTY0
SOUTHAMPTON COUNTY0
SPOTSYLVANIA COUNTY4
STAFFORD COUNTY4
STAUNTON CITY0
SUFFOLK CITY4
SURRY COUNTY0
SUSSEX COUNTY0
TAZEWELL COUNTY2
VIRGINIA BEACH CITY4
WARREN COUNTY0
WASHINGTON COUNTY0
WAYNESBORO CITY0
WESTMORELAND COUNTY2
WILLIAMSBURG CITY0
WINCHESTER CITY0
WISE COUNTY0
WYTHE COUNTY2
YORK COUNTY0
Categories
Election Data Analysis Election Forensics Election Integrity Interesting technical

Distribution of VHL Entries for selected VA Localities

MAJOR CORRECTION (2022-07-17): The below analysis incorrectly computed the CSV totals for each of the 3 counties. I had an indexing error into my CSV file list and erroneously computed the 2020 totals from the CSV’s. I have since updated and recomputed all of the VHL and CSV results, and have additionally added a check against the Turnout report numbers as reported by ELECT. These new results will be forthcoming in an upcoming blog post. While there are still discrepancies between the VHL and CSV, their magnitude is not as large as was originally presented here. My apologies for the error. Corrected numbers for the 2020 CSV totals are shown in red below with my original estimates in strikethrough font.

(Edited 2022-07-10 17:31 EST to add better explanation of VHL and CSV files to first paragraph.)

Per Request – Prince William, Fairfax and Loudoun County

According to the CSV file hosted on the ELECT servers and downloaded on 11-30-2020, the number of votes cast in Prince William County in 2020 Presidential race was 228,267 (corrected) 137,874, which is significantly different than the number of Voters that cast ballots (223,404) in 2020 as generated from the Voter History List (VHL). The 2021 CSV file, downloaded from ELECT on 12-11-2021, gives 153,218 voters as compared to 152,166 given by the VHL data. Why the discrepancies? Note that the VHL is counting people, and the CSV is counting votes cast in the races for President(2020)/Gov(2021), but the numbers should still be roughly similar between the two sources. Also the CSV “Total Vote” field should be including overvotes, write-ins, etc so that is not the source of the discrepancies. Furthermore, the 11-06-2021 VHL data file is identical to the VHL datafile downloaded on 12-14-2021, so there is no missing data from the VHL file for the 2021 election. Finally, note that by definition the Voter History List information will undercount the number of voters that participated in the 2020 election, because voters that have been removed from the registered voter list in between the end of the 2020 election period and when the file was downloaded (11-6-2021) will have had their corresponding records deleted from the VHL.

According to the CSV file hosted on the ELECT servers and downloaded on 11-30-2020, the number of votes cast in Fairfax County was 601,243 (corrected) 487232, which is significantly different than the number (589282) generated from the VHL. The 2021 CSV file, downloaded from ELECT on 12-11-2021, give 441262 voters as compared to 439344 given by the VHL data. Why the discrepancies?

According to the CSV file hosted on the ELECT servers and downloaded on 11-30-2020, the number of votes cast in Loudoun County was 224,976 (corrected) 111655, which is significantly different than the number (220758) generated from the VHL. The 2021 CSV file, downloaded from ELECT on 12-11-2021, give 161793 voters as compared to 161449 given by the VHL data. Why the discrepancies?

Categories
Election Data Analysis Election Forensics Election Integrity programming technical

Nationwide Nursing Home, Hospice Care and Assisted Living Facilities listing

Many election integrity investigators are looking through registration records and trying to find suspicious registrations based on the number of records attributed to a specific address as an initial way of identifying records of interest and of need of further scrutiny. This can often produce false positives for things like nursing homes, college dormitories, etc. Additionally, one of the concerns that has been raised is the risk of potential elder abuse, ID theft, manipulation or improper use of ballots for occupants of nursing home, hospice care or assisted living facilities.

According to https://npino.com : “The National Provider Identifier (NPI) is a unique identification number for covered health care providers (doctors, dentists, chiropractors, nurses and other medical staff). The NPI is a 10-digit, intelligence-free numeric identifier. This means that the numbers do not carry other information about healthcare providers, such as the state in which they live or their medical specialty. The NPI must be used in lieu of legacy provider identifiers in the HIPAA standards transactions. Covered health care providers and all health plans and health care clearing houses must use the NPIs in the administrative and financial transactions adopted under HIPAA (Health Insurance Portability and Accountability Act).”

I’ve compiled a list of every nursing home, hospice care, or assisted living facility in the country based on their current NPI code. I have mirrored and scraped the entire https://npino.com site as of 5-23-2022 and compiled the list of nationwide Nursing homes, Assisted Living and Hospice Care facilities into the below CSV file and am presenting it here in the hopes that it is useful for other researchers. I did do a small amount of regular expression based cleanup to the entries (e.x. // replacing “Ste.” with “Suite”, fixing whitespace issues, etc.) as well as manually addressing a handful of obviously incorrect addresses (e.x. // repeated/spliced street addresses, etc.).

Categories
Election Data Analysis Election Forensics Election Integrity Interesting programming technical

2021 VA Election Fingerprints

I finally had some time to put this together.

For additional background information, see here, here and here. As a reminder and summary, according to the published methods in the USAID funded National Academy of Sciences paper (here) that I based this work off of, an ideal “fair” election should look like one or two (depending on how split the electorate is) clean, compact Gaussian distributions (or “bulls-eye’s”). Other structural artifacts, while not conclusive, can be evidence and indicators of election irregularities. One such indicator with an attributed cause is that of a highly directional linear streaking, which implies an incremental fraud and linear correlation. Another known and attributed indicator is that of large distinct and extreme peaks near 100% or 0% votes for the candidate (the vertical axis) that are disconnected from the main Gaussian lobe which the authors label a sign of “extreme fraud”. In general, for free and fair elections, we expect these 2D histograms to show a predominantly uncorrelated relationship between the variables of “% Voter Turnout” (x-axis) and “% Vote Share” (y-axis).

The source data for this analysis comes directly from the VA Department of Elections servers, and was downloaded shortly after the conclusion and certification of the 2021 election results on 12/11/2021 (results file) and 12/12/2021 (turnout file). A link to the current version of these files, hosted by ELECT is here: https://apps.elections.virginia.gov/SBE_CSV/ELECTIONS/ELECTIONRESULTS/2021/2021%20November%20General%20.csv and https://apps.elections.virginia.gov/SBE_CSV/ELECTIONS/ELECTIONTURNOUT/Turnout-2021%20November%20General%20.csv. The files actually used for this analysis, as downloaded from the ELECT servers on the dates mentioned are posted at the end of this article.

Note that even though the republican (Youngkin) won in VA, the y-axis of these plots presented here was computed as the % vote share for the democratic candidate (McCauliffe) in order to more easily compare with the 2020 results. I can produce Yougkin vote share % versions as well if people are interested, and am happy to do so.

While the 2021 election fingerprints look to have less correlations between the variables as compared to 2020 data, they still look very non-gaussian and concerning. While there is no clearly observable “well-known” artifacts as called out in the NAS paper, there is definitely something irregular about the Virginia 2021 election data. Specifically, I find the per-precinct absentee [mail-in + post-election + early-in-person] plot (Figure 6) interesting as there is a diffuse background as well as a linearly correlated set of virtual precincts that show low turnout but very high vote share for the democratic candidate.

One of the nice differences about 2021 VA data is that they actually identified the distinctions between mail-in, early-in-person, and post-election vote tallies in the CAP’s this year. I have broken out the individual sub-groups as well and we can see that the absentee early-in-person (Figure 9) has a fairly diffuse distribution, while the absentee mail-in (Figure 7) and absentee post-election (Figure 8) ballots show a very high McAuliffe Vote %, and what looks to be a linear correlation.

For comparison I’ve also included the 2020 fingerprints. All of the 2020 fingerprints have been recomputed using the exact same MATLAB source code that processed the 2021 data. The archive date of the “2020 November General.csv” and “Turnout 2020 November General.csv” files used was 11/30/2020.

I welcome any and all independent reviews or assistance in making and double checking these results, and will gladly provide all collated source data and MATLAB code to anyone who is interested.

Figure 1 : VA 2021 Per locality, absentee (CAP) + physical precincts

Figure 2 : VA 2021 Per locality, physical precincts only:

Figure 3 : VA 2021 Per locality, absentee precincts only:

Figure 4 : VA 2021 Per precinct, absentee (CAP) + physical precincts:

Figure 5 : VA 2021 Per precinct, physical precincts only:

Figure 6 : VA 2021 Per precinct, absentee (CAP) precincts only:

Figure 7 : VA 2021 Per precinct, CAP precincts, mail-in ballots only:

Figure 8 : VA 2021 Per precinct, CAP precincts, post-election ballots only:

Figure 9 : VA 2021 Per precinct, CAP precincts, early-in-person ballots only:

Comparison to VA 2020 Fingerprints

Figure 10 : VA 2020 Per locality, absentee (CAP) + physical precincts:

Figure 11 : VA 2020 Per locality, physical precincts only:

Figure 12 : VA 2020 Per locality, absentee precincts only:

Figure 13 : VA 2020 Per precinct, absentee (CAP) + physical precincts:

Figure 14 : VA 2020 Per precinct, physical precincts only:

Figure 15 : VA 2020 Per precinct, absentee (CAP) precincts only:

Source Data Files:

Categories
Election Data Analysis Election Forensics Election Integrity Interesting programming technical Uncategorized

Vanishing Voter ID’s in sequential 2021 DAL files

During the 2021 election I archived multiple versions of the Statewide Daily Absentee List (DAL) files as produced by the VA Department of Elections (ELECT). As the name implies, the DAL files are a daily produced official product from ELECT that accumulates data representing the state of absentee votes over the course of the election. i.e. The data that exists in a DAL file produced on Tuesday morning should be contained in the DAL file produced on the following Wednesday along with any new entries from the events of Tuesday, etc.

Therefore, it is expected that once a Voter ID number is listed in the DAL file during an election period, subsequent DAL files *should* include a record associated with that voter ID. The status of that voter and the absentee ballot might change, but the records of the transactions during the election should still be present. I have confirmed that this is the expected behavior via discussions with multiple former and previous VA election officials.

Stepping through the snapshots of collected 2021 DAL files in chronological order, we can observe Voter IDs that mysteriously “vanish” from the DAL record. We can do this by simply mapping the existence/non-existence of unique Voter ID numbers in each file. The plot below in Figure 1 is the counts of the number of observed “vanishing” ID numbers as we move from file to file. The total number of vanishing ID numbers is 429 over the course of the 2021 election. Not a large number. But it’s 429 too many. I can think of no legitimate reason that this should occur.

Now an interesting thing to do, is to look at a few examples of how these issues manifest themselves in the data. Note that I’m hiding the personally identifiable information from the DAL file records in the screenshots below, BTW.

The first example in the screenshot below is an issue where the voter in question has a ballot that is in the “APPROVED” and “ISSUED” state, meaning that they have submitted a request for a ballot and that the ballot has been sent out. The record for this voter ID is present in the DAL file up until Oct 14th 2021, after which it completely vanishes from the DAL records. This voter ID is also not present in the RVL or VHL downloaded from the state on 11/06/2021.

This voter was apparently issued a real, live, ballot for 2021 and then was subsequently removed from the DAL and (presumably) the voter rolls + VERIS on or around the 14th Oct according to the DAL snapshots.  What happened to that ballot? What happened to the record of that ballot? The only public record of that ballot even existing, let alone the fact that it was physically issued and mailed out, was erased when the Voter ID was removed from DAL/RVL/VHL records.  Again, this removal happened in the middle of an election where that particular voter had already been issued a live ballot!

A few of these IDs actually “reappear” in the DAL records.  ID “230*****” is one example, and a screenshot of its chronological record is below.  The ballot shows as being ISSUED until Oct 14th 2021.  It then disappears from the DAL record completely until the data pull on Oct 24th, where it shows up again as DELETED.  This status is maintained until Nov 6th 2021 when it starts oscillating between “Marked” and “deleted” until it finally lands on “Marked” in the Dec 5 DAL file pull.  The entire time the Application status is in the “Approved” state for this voter ID.  From my discussions with registrars and election officials the “Marked” designation signifies that a ballot has been received by the registrar for that voter and is slated to be tabulated.

I have poked ELECT on twitter (@wwrkds) on this matter to try and get an official response, and submitted questions on this matter to Ashley Coles at ELECT, per the advice of my local board of elections chair. Her response to me is below:

I will update this post as information changes.

Categories
Election Data Analysis Election Forensics Election Integrity programming technical

Slides from Loudoun Election Integrity Meeting

Presented 2022-04-14 at Patriot Pub in Hamilton VA. Note that I mis-quoted the total registration error number off the slide deck during the voice track of my presentation. The slides are correct … my eyes and memory just suck sometimes! I said ~850K. The actual number is ~380K.

Video can be found here: https://www.patriotpuballiance.com

Categories
Election Data Analysis Election Forensics Election Integrity programming technical

Updates on the In-Person Early Vote records added after the close of Early Voting in VA 2021

Previously I wrote about finding In-Person Early Vote records inserted into the Daily Absentee List (DAL) records after the close of early voting in VA in 2021. Well, theres been quite a bit of activity since then and I have somme updates to share.

I originally discovered this issue and began digging into it around Nov 8th 2021, and finally published it to my blog on Dec 10th 2021. At the same time, queries were sent through the lawyers for the RPV to the ELECT Chairman (Chris Piper) and to a number of registrars to attempt to determine the cause of this issue, but no response was supplied. I also raised this issue to my local Board of Elections chair, and requested that ELECT comment on the matter through their official twitter account.

Since that time I have continued to publish my findings, have continued to request responses from ELECT, and have offered to work with them to address and resolve these discrepancies. I know ELECT pays attention to this site and my twitter account, as they have quietly corrected both their website and data files after I have pointed out other errors and discrepancies. Additionally, Chris Piper has continued to publicly insist that there were no major issues in either the 2020 or 2021 election (including under questioning by the VA Senate P&E Committee), and neither he nor any member of ELECT has publicly acknowledged any of the issues I have raised … besides the aforementioned changing of their site contents, of course. I have thankfully had a few local board of elections members work with me, as well as a few local registrars … but I did not see any meaningful response or engagement from anyone at ELECT until Feb 23rd 2022 as discussed below.

On Feb 22nd 2022, I was invited to participate in a meeting arranged by VA State Senator Amanda Chase with the VA Attorney Generals office to discuss election integrity issues. I specifically cited a number of the issues that I’ve documented here on my blog, including the added DAL entries, as justification for my belief that there is an arguable case to be made for there being criminal gross negligence and maladministration at ELECT with respect to the administration and handling of VA election data.

That meeting apparently shook some things loose. Good.

The day after that meeting Chris Piper finally sent a response at 10:45am to our inquiry on the subject of the added DAL entries. It is quoted below:

While I am glad that he finally responded, his technical reasoning does not address all of the symptoms that are observed:

  • He states the cause was due to the EPB’s from one vendor, but the data shows DAL additions being attributed to multiple localities that use different EPB vendors.
  • His explanation does not address the distribution of the numbers of DAL additions across all of the precincts observed. A misconfigured or malfunctioning poll book would affect all of the check-ins entered on it, not just a sporadic few.
  • This also does not seem like a minor issue as its affecting thousands of voters ballots and voter history. So I’m rather concerned with Mr. Piper’s attitude toward this issue, as well as others. It needs to be addressed as a logic and accuracy testing issue, as a matter of procedures and training, in addition to simply asking vendors to add checks to their software. Also, will this be addressed in VERIS at all, or if/when a replacement system is put in place?
  • In response to his last paragraph, I will simply note that I have been actively and consistently working to raise all of these issues through official channels … through official requests via the RPV, working with local election board members and registrars, and asking for input from elect through social media. I have not made accusations of malicious or nefarious intent, but I do think there is plenty of evidence to make the case of incompetence +/or gross negligence with our elections data … which is actually a crime in VA … and Chris Piper has been the head of ELECT and the man responsible for ensuring our election data during that time (he is stepping down effective March 11th).

Since his response was sent, a few additional things have occurred:

(A) The AG’s office informed us that they are actually required by law to treat ELECT as their client and defend them from any accusations of wrongdoing. This is frustrating as there does not seem to be any responsive cognizant authority that is able to act on this matter in the interest of the public. This is not a local jurisdictional issue as it affects voters statewide, and is therefore in the purview of the AG as the Department of Elections has been heretofore dismissive and non-responsive of these matters. I am not a lawyer, however.

(B) I was able to connect with the Loudoun County Deputy Director of Elections (Richard Keech) as well as the Prince William County registrar (Eric Olsen) and have been working through the finer details of Chris’s explanation to verify and validate at the local level. [Note: I previously had Richard erroneously listed here as the Registrar instead of the Deputy Directory]. Both Richard and Eric are continuing to look into the matter, and I continue to work with them to get to the bottom of this issue.

  • Richard confirmed his belief that the bad OS system date on Election Day EPBs was responsible for the errors, however with some slight differences in the details from Piper’s description. There were multiple vendors affected, not just one. Per Richard, the problem appeared to be that a number of Loudoun poll-books (regardless of vendor) that were used for Election Day had been in storage so long that their batteries had completely depleted. When they were finally powered up, their OS system clocks had a wildly incorrect date. The hardware used was a mixture of Samsung SM-T720 and iPad tablets, depending on poll-book application vendor. The hardware was purchased separately through local contracts with CDW, and the software was uploaded and configured by the vendors.
  • In Loudoun, all of the EPBs went through logic and accuracy testing before the election per Richard, but it does not appear that the procedures for the Logic and Accuracy testing had any specific checks for OS date settings.
  • In Prince William County the registrar (Eric) was not aware of any issues with the system clocks on the poll-books, and he was skeptical of the distribution of the small numbers of added DAL entries. He noted, as I did above, that if a poll book was misconfigured it would affect all of the records that passed through it, not just a small handful. He also noticed that there was a discrepancy with the attribution of polling place names that I had extracted from the DAL files, where some of the names did not correspond with actual polling places in PWC. He has stated he will look into the matter and get back to me. I will update my blog when he does so.
  • From my communications with Richard, the VERIS system imports a text based file for processing voter credit and does not have any special checks against the dates for in-person vs early voting records. Hence, why this issue can impact multiple vendors if their applications use the system clock to date-stamp their exported txt files for upload into VERIS.

(C) I have reworked my code per my conversations with Ricky and Eric, and fixed a few bugs and parsing errors along the way. Most notably, there are a number of missing or malformed field values in the raw DAL files that were being parsed into ‘undefined’ categorical values by the default csv MATLAB parser. These ‘undefined’ values, even when located in unimportant fields in the row of a MATLAB table, can cause the entire row to be incomparable to other entries when performing logical operations. I have adjusted my parser and logic to account for +/or ignore these entries as necessary. Additionally I had previously looked for new entries by comparing values across entire rows, but have adjusted to now only look at voter ID numbers that have not been seen previously, in order to omit those entries that had simply been adjusted (address changes, etc) after the fact, or that contained ‘undefined’ field elements as mentioned previously. Also I noticed that some of the dal files had duplicate records of Approved and On-Machine records for the same voter ID. While that is an issue in itself, I de-duplicated those entries for this analysis. This new logic gives the updated results presented below, with a total number of discrepancies now at 2820.

I will note that I am still a little skeptical of the “bad date” explanation as being a complete answer to this issue, as it does not adequately explain the distribution of small numbers of discrepancies attributed to multiple precincts, for one thing. While the bad date may explain part of the issue, it does not adequately account for all of the observed effects, IMO. For example, in Loudoun there are 26 precincts listed below that have inserted DAL records attributed to them. Many of these precincts having only 1 or 2 associated records. If the bad OS date explanation is to blame, then (a) there must have been at least 26 poll-books, one at each precinct, in Loudoun with misconfigured and incorrect dates AND (b) many of these poll-books were used to only check in 1 or 2 people total, as anyone checked in on a misconfigured poll-book would have their voter credit/DAL file entries affected. This would have to have been replicated at ALL of the precincts in ALL of the localities listed below. While the above scenario is admittedly possible, I find it rather implausible.

Update 2022-03-20

I’ve heard back from both Ricky Keech (Loudoun) and Eric Olsen (PWC).

Eric looked into the 10 entries for PWC and all of them were Military voters who did actually walk in, in person, to the registrars office and vote on machine absentee after the close of early voting, as allowed for by state law. So everything checks out for PWC.

Per discussion with Ricky, there were two issues: The first being a number of pollbooks for four specific precincts that were used for Election Day having the wrong date setting as discussed above. The other issues was possibly the connectivity issue of the Pollbook to the servers in South Riding during the early voting period that had to be hand corrected. Ricky’s explanation e-mail to me is copied below.

Hi Jon,

Following up on our conversation the other day.  So, I did some more digging and was able to figure out what happened.  The bulk of the voters (1213) were from the four precincts that had a tablet with the wrong date on it.  That accounts for all voters in precincts 214, 416, 628, and 708.  I had a staff person go back and pull out the tablets used in those precincts, and we confirmed that each of those four had one table with the wrong date.

That leaves 141 voters that received ‘credit’ after election day.  Once we had narrowed it down, I looked for patterns and noticed all the remaining precincts were in South Riding.  That jogged my memory and led me to the solution.  When I ran the final reports on the Sunday before the election, I noticed that the number showing as voting early seemed to be off by 100 or so.  This was odd because our daily checked in count and voted count reconciled at every site every day.  So, I went back and compared the voters checked into our pollbooks at early voting to the voters with early voting (On Machine) credit in VERIS and found that there were 137 voters who voted on October 19 at the Dulles South EV site and for some reason did not have credit.  I worked on this Monday to make sure it was right, and it was, none of those voters had credit.  This could either have been a connectivity issue at Dulles South EV site OR an issue with VERIS when the data was uploaded to mark the voters.  I can say definitively that the number checked in on the pollbooks at that site on that day and the number of people who put ballots into the machine was correct, we check that constantly and the observers on site checked as well.  I can guarantee that if there had been a discrepancy, we’d have heard about it right away.

So, after determining that was exactly what happened I uploaded credit for those voters at 2:06:51pm on Wednesday, November 3 and the upload completed processing at 2:07:07pm.

When we spoke the other day, I thought it was likely a connectivity issue, but now I’m not entirely sure that’s the case, as if the connection wasn’t working the numbers should have been off.  And they were correct on the devices at the EV site and my laptop here at the office.  Everything matched.

So long story short, we did an audit, discovered missing credit from one early voting site on one day, and corrected it.

The other four voters were people who voted an emergency early voting ballot on Monday, November 1.

Richard Keech, Deputy Director of Elections for Loudoun County, Mar 11 2022 email to Jon Lareau
LocalityCOUNT
LOUDOUN COUNTY1344
HANOVER COUNTY1302
CHESAPEAKE CITY92
PRINCE WILLIAM COUNTY10
HENRICO COUNTY7
WINCHESTER CITY7
CHARLES CITY COUNTY5
CAMPBELL COUNTY3
CHARLOTTE COUNTY3
FAUQUIER COUNTY3
LUNENBURG COUNTY3
WASHINGTON COUNTY3
ALEXANDRIA CITY2
AMELIA COUNTY2
AMHERST COUNTY2
BATH COUNTY2
CAROLINE COUNTY2
FALLS CHURCH CITY2
HENRY COUNTY2
NORTHAMPTON COUNTY2
ORANGE COUNTY2
ROANOKE CITY2
VIRGINIA BEACH CITY2
ALLEGHANY COUNTY1
APPOMATTOX COUNTY1
BLAND COUNTY1
CHARLOTTESVILLE CITY1
CLARKE COUNTY1
CULPEPER COUNTY1
ESSEX COUNTY1
FAIRFAX CITY1
FLOYD COUNTY1
KING GEORGE COUNTY1
MIDDLESEX COUNTY1
NELSON COUNTY1
NOTTOWAY COUNTY1
POQUOSON CITY1
STAFFORD COUNTY1
STAUNTON CITY1
LOCALITYPRECINCTCOUNT
HANOVER COUNTY704 – ELMONT667
HANOVER COUNTY602 – LEE DAVIS635
LOUDOUN COUNTY416 – HAMILTON443
LOUDOUN COUNTY214 – SUGARLAND NORTH344
LOUDOUN COUNTY708 – SENECA319
LOUDOUN COUNTY628 – MOOREFIELD STATION97
LOUDOUN COUNTY319 – JOHN CHAMPE21
LOUDOUN COUNTY313 – PINEBROOK16
LOUDOUN COUNTY112 – FREEDOM13
LOUDOUN COUNTY122 – HUTCHISON FARM11
LOUDOUN COUNTY126-GOSHEN POST10
CHESAPEAKE CITY055 – GEORGETOWN EAST8
LOUDOUN COUNTY107 – LITTLE RIVER8
CHESAPEAKE CITY053 – FAIRWAYS7
LOUDOUN COUNTY121 – TOWN HALL7
LOUDOUN COUNTY316 – CREIGHTON’S CORNER7
LOUDOUN COUNTY318 – MADISON’S TRUST7
CHESAPEAKE CITY008 – SOUTH NORFOLK RECREATION6
CHESAPEAKE CITY012 – GEORGETOWN6
LOUDOUN COUNTY114 – DULLES SOUTH6
CHESAPEAKE CITY018 – INDIAN RIVER5
LOUDOUN COUNTY124 – LIBERTY5
LOUDOUN COUNTY320 – STONE HILL5
CHESAPEAKE CITY029 – TANGLEWOOD4
CHESAPEAKE CITY042 – PARKWAYS4
CHESAPEAKE CITY059 – CLEARFIELD4
CHESAPEAKE CITY065 – WATERWAY II4
LOUDOUN COUNTY119 – ARCOLA4
LOUDOUN COUNTY322-BUFFALO TRAIL4
CHESAPEAKE CITY005 – CRESTWOOD3
CHESAPEAKE CITY022 – NORFOLK HIGHLANDS3
CHESAPEAKE CITY057 – CYPRESS3
LOUDOUN COUNTY120 – LUNSFORD3
LOUDOUN COUNTY123 – CARDINAL RIDGE3
WINCHESTER CITY101 – MERRIMANS3
AMHERST COUNTY501 – MADISON2
CHARLES CITY COUNTY101 – PRECINCT 1-12
CHARLES CITY COUNTY301 – PRECINCT 3-12
CHARLOTTE COUNTY702 – BACON/SAXE2
CHESAPEAKE CITY010 – OSCAR SMITH2
CHESAPEAKE CITY014 – GRASSFIELD2
CHESAPEAKE CITY015 – GREENBRIER MIDDLE SCHOOL2
CHESAPEAKE CITY016 – HICKORY GROVE2
CHESAPEAKE CITY023 – OAK GROVE2
CHESAPEAKE CITY024 – OAKLETTE2
CHESAPEAKE CITY031 – CARVER SCHOOL2
CHESAPEAKE CITY032 – PROVIDENCE2
CHESAPEAKE CITY034 – HICKORY MIDDLE SCHOOL2
CHESAPEAKE CITY043 – PLEASANT CROSSING2
CHESAPEAKE CITY056 – GREEN TREE2
FALLS CHURCH CITY003 – THIRD WARD2
LOUDOUN COUNTY302 – ROUND HILL2
LOUDOUN COUNTY308 – ST LOUIS2
LOUDOUN COUNTY309 – ALDIE2
LOUDOUN COUNTY617 – OAK GROVE2
ORANGE COUNTY101 – ONE WEST2
PRINCE WILLIAM COUNTY409 – TYLER2
PRINCE WILLIAM COUNTY513 – LYNNWOOD2
PRINCE WILLIAM COUNTY712 – LEESYLVANIA2
WASHINGTON COUNTY701 – HIGH POINT2
WINCHESTER CITY201 – VIRGINIA AVENUE2
ALEXANDRIA CITY110 – CHARLES HOUSTON CENTER1
ALEXANDRIA CITY201 – NAOMI L. BROOKS SCHOOL1
ALLEGHANY COUNTY101 – ARRITT1
AMELIA COUNTY301 – NUMBER THREE1
AMELIA COUNTY501 – NUMBER FIVE1
APPOMATTOX COUNTY401 – COURTHOUSE1
BATH COUNTY101 – WARM SPRINGS1
BATH COUNTY201 – HOT SPRINGS1
BLAND COUNTY301 – HOLLYBROOK1
CAMPBELL COUNTY102 – NEW LONDON1
CAMPBELL COUNTY402 – COURT HOUSE1
CAMPBELL COUNTY602 – CONCORD1
CAROLINE COUNTY202 – SOUTH MADISON1
CAROLINE COUNTY401 – DAWN1
CHARLES CITY COUNTY201 – PRECINCT 2-11
CHARLOTTE COUNTY201 – RED OAK WYLLIESBURG1
CHARLOTTESVILLE CITY102 – CLARK1
CHESAPEAKE CITY006 – DEEP CREEK1
CHESAPEAKE CITY009 – BELLS MILL1
CHESAPEAKE CITY011 – GENEVA PARK1
CHESAPEAKE CITY020 – E W CHITTUM1
CHESAPEAKE CITY033 – WESTOVER1
CHESAPEAKE CITY046 – BELLS MILL II1
CHESAPEAKE CITY048 – JOLLIFF MIDDLE SCHOOL1
CHESAPEAKE CITY049 – WATERWAY1
CHESAPEAKE CITY050 – RIVER WALK1
CHESAPEAKE CITY051 – COOPERS WAY1
CHESAPEAKE CITY062 – FENTRESS1
CHESAPEAKE CITY063 – POPLAR BRANCH1
CHESAPEAKE CITY064 – DEEP CREEK II1
CLARKE COUNTY301 – MILLWOOD1
CULPEPER COUNTY303 – CARDOVA1
ESSEX COUNTY201 – NORTH1
FAIRFAX CITY001 – ONE1
FAUQUIER COUNTY202 – AIRLIE1
FAUQUIER COUNTY404 – SPRINGS VALLEY1
FAUQUIER COUNTY501 – THE PLAINS1
FLOYD COUNTY301 – COURTHOUSE1
HENRICO COUNTY105 – GREENDALE1
HENRICO COUNTY209 – GLEN LEA1
HENRICO COUNTY304 – JACKSON DAVIS1
HENRICO COUNTY316 – COLONIAL TRAIL1
HENRICO COUNTY416 – SPOTTSWOOD1
HENRICO COUNTY506 – EANES1
HENRICO COUNTY513 – PLEASANTS1
HENRY COUNTY203 – HORSEPASTURE #21
HENRY COUNTY501 – BASSETT NUMBER ONE1
KING GEORGE COUNTY101 – COURTHOUSE1
LOUDOUN COUNTY108 – MERCER1
LOUDOUN COUNTY118 – MOOREFIELD1
LOUDOUN COUNTY401 – WEST LOVETTSVILLE1
LUNENBURG COUNTY301 – ROSEBUD1
LUNENBURG COUNTY501 – REEDY CREEK1
LUNENBURG COUNTY502 – PEOPLES COMMUNITY CENTER1
MIDDLESEX COUNTY501 – WILTON1
NELSON COUNTY401 – ROSELAND1
NORTHAMPTON COUNTY201 – PRECINCT 2-11
NORTHAMPTON COUNTY401 – PRECINCT 4-11
NOTTOWAY COUNTY201 – PRECINCT 2-11
POQUOSON CITY001 – CENTRAL1
PRINCE WILLIAM COUNTY103 – GLENKIRK1
PRINCE WILLIAM COUNTY210 – PENN1
PRINCE WILLIAM COUNTY305 – PATTIE1
PRINCE WILLIAM COUNTY311 – SWANS CREEK1
ROANOKE CITY014 – Crystal Spring1
ROANOKE CITY019 – Forest Park1
STAFFORD COUNTY702 – WHITSON1
STAUNTON CITY301 – WARD NO 31
VIRGINIA BEACH CITY030 – RED WING1
VIRGINIA BEACH CITY063 – CULVER1
WASHINGTON COUNTY302 – SOUTH ABINGDON1
WINCHESTER CITY301 – WAR MEMORIAL1
WINCHESTER CITY402 – ROLLING HILLS1

The MATLAB code for generating the above is given below. The raw time-stamped DAL data files, as downloaded from the ELECT website, are loaded from the ‘droot’ directory tree as shown below, and I only utilize the latest daily download of DAL file data for simplicity.


warning off all
% Data directory root
droot = 'SourceData/DAL/2021/';

% Gets the list of DAL files that were downloaded from the ELECT provided
% URL over the course of the 2021 Election.
files = dir([droot,'raw/**/Daily_Absentee_List_*.csv']);
matc = regexp({files.name}, 'Daily_Absentee_List_\d+(T\d+)?.csv','match');
matc = find(~cellfun(@isempty,matc));
files = files(matc);

% Only process the last updated DAL for each day.  I downloaded multiple
% times per day, but we will just take the last file downloaded each day
% for simplicity here.
matc = regexp({files.name}, '(\d+)(T\d+)?','tokens');
fd = []; pc = 0; ic = 0; idx = [];
for i=1:numel(matc)
    if isempty(regexp(matc{i}{1}{1},'^2021.*'))
        fd(i) = datenum(matc{i}{1}{1},'mmddyyyy');
    else
        fd(i) = datenum(matc{i}{1}{1},'yyyymmdd');
    end
    datestr(fd(i))
    if pc ~= fd(i)
        ic = ic+1;
    end
    idx(ic) = i;
    pc = fd(i);
end
files = files(idx);

% Now that we have our list of files, lets process.
seen = [];
firstseen = [];
astats = [];
astatsbc = {};
cumOnMachine = [];
T = [];
for i = 1:numel(files)
    % Extract the date of the ELECT data pull.  Note that the first few
    % days I was running the script I was not including the time of the
    % pull when I was pulling the data fro the ELECT url and writing to
    % disk, so there's soe special logic here to handle that issue.
    matc = regexp(files(i).name, '(\d+)(T\d+)?','tokens');
    matc = [matc{1}{:}];
    if isempty(regexp(matc,'^2021.*'))
        fdn = datenum(matc,'mmddyyyy')+.5;
    else
        fdn= datenum(matc,'yyyymmddTHHMMSS');
    end
    fds = datestr(fdn,30)
    fdt = datetime(fdn,'ConvertFrom','datenum');

    % Move a copy to the 'byDay' folder so we can keep a reference to the
    % data that went into this analysis.
    dal2021filename = [files(i).folder,filesep,files(i).name];
    ofn = [droot,'byDay/Daily_Absentee_List_',fds,'.csv'];
    if ~exist(ofn)
        copyfile(dal2021filename,ofn);
    end

    % Import the DAL file
    dal2021 = import2021DALfile(dal2021filename, [2, Inf]);

    % Cleanup and handle undefined or imssing values.  
    dal2021.CITY(isundefined(dal2021.CITY)) = 'UNDEFINED';
    dal2021.STATE(isundefined(dal2021.STATE)) = 'UNDEFINED';
    dal2021.ZIP(isundefined(dal2021.ZIP)) = 'UNDEFINED';
    dal2021.COUNTRY(isundefined(dal2021.COUNTRY)) = 'UNDEFINED';

    % Do some basic indexing of different DAL status categories and combinations
    appvd = dal2021.APP_STATUS == 'Approved' ;
    aiv2021 = appvd & dal2021.BALLOT_STATUS == 'Issued';
    amv2021 = appvd & dal2021.BALLOT_STATUS == 'Marked';
    aomv2021 = appvd & dal2021.BALLOT_STATUS == 'On Machine';
    appv2021 = appvd & dal2021.BALLOT_STATUS == 'Pre-Processed';
    afwv2021 = appvd & dal2021.BALLOT_STATUS == 'FWAB';
    appmv2021 = amv2021 | aomv2021 | appv2021 | afwv2021; % Approved and Countable

    % Accumulate the stats for each DAL file
    rstats = table(fdt,sum(aiv2021),sum(amv2021),sum(aomv2021),...
        sum(appv2021),sum(afwv2021),sum(appmv2021),'VariableNames',{'DALFileDate',...
        'NumIssued','NumMarked','NumOnMachine','NumPreProcessed','NumFWAB','NumCountable'});
    astats = [astats; rstats];

    % Write out the entries that were approved and countable in this DAL
    % file
    ofn = [droot,'byDayCountable/Daily_Absentee_List_Countable_',fds,'.csv'];
    if ~exist(ofn)
        writetable(dal2021(appmv2021,:),ofn);
    end

    % Write out the entries that were approved, countable and marked as 'On
    % Machine' (i.e. an Early In-Person Voter Check-In) in this DAL file
    ofn = [droot,'byDayCountableOnMachine/Daily_Absentee_List_Countable_OnMachine',fds,'.csv'];
    if ~exist(ofn)
        writetable(dal2021(aomv2021,:),ofn);
    end

    % Since the DAL file grows over time, we're going to try and figure out
    % which On-Machine entries in each new file:
    %       (a) We've seen before and are still listed.
    %       (b) We haven't seen before (a NEW entry).
    %       (c) We've seen before but the listing is missing (a DELETED
    %           entry).
    if isempty(T)
        % Only applicable for the first file we process.
        T = dal2021(aomv2021,:);

        % There are sometimes duplicate uid numbers in the approved and
        % on-machine counts!  This is a problem in and of itself, but not
        % the problem I'm trying to focus on at the moment.  So I'm going
        % to remove any duplicated rows based on UID.
        [uid, ia, ib] = unique(T.identification_number);
        T = T(ia,:);
        inew = (1:size(T,1))';
        ideleted = [];
        firstseen = repmat(fdt,size(T,1),1);
    else
        dOM = dal2021(aomv2021,:);

        % There are sometimes duplicate uid numbers in the approved and
        % on-machine counts!  This is a problem in and of itself, but not
        % the problem I'm trying to focus on at the moment.  So I'm going
        % to remove any duplicated rows based on UID.
        [uid, ia, ib] = unique(dOM.identification_number);
        dOM = dOM(ia,:);

        %[~,ileft,iright] = innerjoin(T,dOM);
        [~,ileft,iright] = intersect(T.identification_number,dOM.identification_number);

        % 'inew' will be a boolean vector representing those entries in
        % 'dOM' that are new On-Machine records
        inew = true(size(dOM,1),1);
        inew(iright) = false;

        % 'ideleted' will be a boolean vector representing those entries in
        % 'T' that are missing On-Machine records in 'dOM'
        ideleted = true(size(T,1),1);
        ideleted(ileft) = false;
        T = [T;dOM(inew,:)];
        firstseen = [firstseen; repmat(fdt,sum(inew),1)];
    end
    clf;
    plot(astats.DALFileDate,astats{:,2:end},'LineWidth',2);
    grid on;
    grid minor;
    legend(astats.Properties.VariableNames(2:end),'Location','NorthWest');
    xlabel('Date of DAL file pull from ELECT');
    ylabel('Counts');
    drawnow;
end
ofn = [droot,'byDayStats.csv'];
writetable(astats,ofn);
T.FirstSeen = firstseen;
ofn = [droot,'onMachineRecords.csv'];
writetable(T,ofn);
ofn = [droot,'onMachineRecords_missing.csv'];
writetable(T(ideleted,:),ofn);
cutoffDate = datetime('2021-11-01');
ofn = [droot,'onMachineRecords_after_20211101.csv'];
writetable(T(T.FirstSeen >= cutoffDate,:),ofn);
Ta = T(T.FirstSeen >= cutoffDate,:);
[ulocality,ia,ib] = unique(Ta.LOCALITY_NAME);
clocality = accumarray(ib,1,size(ulocality));
Tu = table(ulocality,clocality,'VariableNames',{'Locality','COUNT'});
ofn = [droot,'numOnMachineRecords_after_20211101_byLocality.csv'];
writetable(Tu,ofn);
Ta = T(T.FirstSeen >= cutoffDate,:);
[uprecinct,ia,ib] = unique(join([string(Ta.LOCALITY_NAME),string(Ta.PRECINCT_NAME)]));
cprecinct = accumarray(ib,1,size(uprecinct));
Tu = table(Ta.LOCALITY_NAME(ia),Ta.PRECINCT_NAME(ia),cprecinct,'VariableNames',{'LOCALITY','PRECINCT','COUNT'});
ofn = [droot,'numOnMachineRecords_after_20211101_byLocalityByPrecinct.csv'];
writetable(Tu,ofn);

The adjusted MATLAB parser function is listed below:

function dal = import2021DALfile(filename, dataLines)
%IMPORTFILE Import data from a text file
%  DAILYABSENTEELIST10162021 = IMPORTFILE(FILENAME) reads data from text
%  file FILENAME for the default selection.  Returns the data as a table.
%
%  DAILYABSENTEELIST10162021 = IMPORTFILE(FILE, DATALINES) reads data
%  for the specified row interval(s) of text file FILENAME. Specify
%  DATALINES as a positive scalar integer or a N-by-2 array of positive
%  scalar integers for dis-contiguous row intervals.
%
%  Example:
%  dal = import2021DALfile("SourceData/DAL/Daily_Absentee_List_10162021.csv", [2, Inf]);
%
%  See also READTABLE.
%
% Auto-generated by MATLAB on 16-Oct-2021 14:19:26
%% Input handling
% If dataLines is not specified, define defaults
if nargin < 2
    dataLines = [2, Inf];
end
%% Set up the Import Options and import the data
opts = delimitedTextImportOptions("NumVariables", 38);

% Specify range and delimiter
opts.DataLines = dataLines;
opts.Delimiter = ",";

% Specify column names and types
opts.VariableNames = ["ELECTION_NAME", "LOCALITY_CODE", "LOCALITY_NAME", "PRECINCT_CODE", "PRECINCT_NAME", "LAST_NAME", "FIRST_NAME", "MIDDLE_NAME", "SUFFIX", "ADDRESS_LINE_1", "ADDRESS_LINE_2", "ADDRESS_LINE_3", "CITY", "STATE", "ZIP", "COUNTRY", "INTERNATIONAL", "EMAIL_ADDRESS", "FAX", "VOTER_TYPE", "ONGOING", "APP_RECIEPT_DATE", "APP_STATUS", "BALLOT_RECEIPT_DATE", "BALLOT_STATUS", "identification_number", "PROTECTED", "CONG_CODE_VALUE", "STSENATE_CODE_VALUE", "STHOUSE_CODE_VALUE", "AB_ADDRESS_LINE_1", "AB_ADDRESS_LINE_2", "AB_ADDRESS_LINE_3", "AB_CITY", "AB_STATE", "AB_ZIP", "BALLOTSTATUSREASON", "Ballot_Comment"];
opts.VariableTypes = ["string", "string", "categorical", "string", "string", "string", "string", "string", "string", "string", "string", "string", "categorical", "categorical", "categorical", "categorical", "categorical", "string", "string", "string", "categorical", "string", "categorical", "string", "categorical", "double", "string", "categorical", "categorical", "categorical", "string", "string", "string", "string", "string", "string", "string", "string"];

% Specify file level properties
opts.ExtraColumnsRule = "ignore";
opts.EmptyLineRule = "read";

% Specify variable properties
opts = setvaropts(opts, ["ELECTION_NAME", "LOCALITY_CODE", "PRECINCT_CODE", "PRECINCT_NAME", "LAST_NAME", "FIRST_NAME", "MIDDLE_NAME", "SUFFIX", "ADDRESS_LINE_1", "ADDRESS_LINE_2", "ADDRESS_LINE_3", "EMAIL_ADDRESS", "FAX", "VOTER_TYPE", "APP_RECIEPT_DATE", "BALLOT_RECEIPT_DATE", "PROTECTED", "AB_ADDRESS_LINE_1", "AB_ADDRESS_LINE_2", "AB_ADDRESS_LINE_3", "AB_CITY", "BALLOTSTATUSREASON", "Ballot_Comment"], "WhitespaceRule", "preserve");
opts = setvaropts(opts, ["ELECTION_NAME", "LOCALITY_CODE", "LOCALITY_NAME", "PRECINCT_CODE", "PRECINCT_NAME", "LAST_NAME", "FIRST_NAME", "MIDDLE_NAME", "SUFFIX", "ADDRESS_LINE_1", "ADDRESS_LINE_2", "ADDRESS_LINE_3", "CITY", "STATE", "COUNTRY", "INTERNATIONAL", "EMAIL_ADDRESS", "FAX", "VOTER_TYPE", "ONGOING", "APP_RECIEPT_DATE", "APP_STATUS", "BALLOT_RECEIPT_DATE", "BALLOT_STATUS", "PROTECTED", "CONG_CODE_VALUE", "STSENATE_CODE_VALUE", "STHOUSE_CODE_VALUE", "AB_ADDRESS_LINE_1", "AB_ADDRESS_LINE_2", "AB_ADDRESS_LINE_3", "AB_CITY", "AB_STATE", "BALLOTSTATUSREASON", "Ballot_Comment"], "EmptyFieldRule", "auto");

% Import the data
dal = readtable(filename, opts);

% Perform some cleanup on commonly found issues...
dal.Ballot_Comment = strrep(dal.Ballot_Comment,char([13,10]),". ");
dal.BALLOTSTATUSREASON = strrep(dal.BALLOTSTATUSREASON,char([13,10]),". ");
dal.LOCALITY_NAME = categorical(strtrim(string(dal.LOCALITY_NAME)));
dal.PRECINCT_NAME = categorical(regexprep(strtrim(string(dal.PRECINCT_NAME)),'^(\d+)( +)(\w+)','$1 - $3'));
end
Categories
Election Data Analysis Election Forensics Election Integrity technical

There’s More to the Story …

A while back I posted about the fact that the official turnout numbers on the VA Dept of Elections (“ELECT”) website we’re incorrect. ELECT’s webpage showed the turnout for the 2020 election was 81.48%, when their own stats on the same page clearly show the results should be 75.08%.

I first published this discrepancy on Aug 13 2021 via twitter (here), and then again on Sep 09 2021 on this blog (as well as twitter) in the first section of my 2020 analysis report (here). ELECT subsequently (and quietly) updated their results page by Sept 10th, but made no public notice as to the error and revision, and never responded to my questions to them as to how the error came about in the first place, why was it still on their official results page for almost a year, and what were they doing to ensure these types of errors didn’t happen again. I subsequently updated my 2020 analysis report to include the silent update of the results by ELECT (here).

In going over and reviewing some of my 2020 work and trying to make sure all of my documentation is complete, I’ve come across another tidbit of interesting information regarding the results on the ELECT site:

There were other silent updates to the official results that predate my finding the wrong turnout numbers.

Using the wayback machine (here), the first record I can find that has the 2020 results is from March 10 2021. Subsequent changes made to the values that can be seen with the wayback machine data are summarized below.

Wayback Machine DateYearTotal RegisteredPercent Change from Previous YearTotal VotingTurnout (% Voting of Total Registered)Voting Absentee (included in Total Voting)
2021-09-1020205,975,6966.18%4,486,82175.08%2,687,304
2021-09-0820205,975,6966.18%4,486,82181.48%2,687,304
2021-04-0920205,975,6966.18%4,486,82181.48%2,687,304
2021-04-0220205,975,8026.18%4,413,38873.85%2,614,927
2021-03-1020205,975,8026.18%4,413,38873.85%2,614,927
Statistics posted on https://www.elections.virginia.gov/resultsreports/registrationturnout-statistics/ (as per the WayBack Machine) at different times since the close of the 2020 election.

From the table above there looks to have been a change sometime between April 2nd and April 9th to the number of registered voters, the total number of voters in the 2020 election, the turnout statistic, and the number of voters voting absentee. There also was a change sometime between Sept 8th and Sept 10th to the turnout statistic (presumably in response to my documenting the error and publishing it).

The entry from 2021-03-10 agrees with the official Election 2020 Summary Report dated 2021-01-25. The entry 2021-09-10 agrees with the Election 2020 Summary Report dated 2021-10-01. To their credit, ELECT has at least put a notice on the page for the Election Summary Reports that the report was updated on Oct 1st, but there is no explanation as to the reasoning for the revisions.

I would like to know why was ELECT modifying the results for months after the close of the election? Where did the extra ~73K ballots come from that were added to the results on April 9th? Where did the 81% come from?