Analysis of 2024 election results in Clark County indicates manipulation

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This isn't news, it's a paid press release.

https://github.com/DevrathIyer/ballotproof/tree/master

Apparently, Shaotran removed his name from the repo and is now listed as a private user, but it can be found in the archive of the devpost page linked from the repo: https://devpost.com/software/ballotproof-vision

https://web.archive.org/web/20250204131222/https://devpost.c...

Info from: https://bsky.app/profile/cartwright776.bsky.social/post/3lhr...

Could this be used to spoof the results, and are there artifacts that could be discovered to directly link it or rule it out?

Here is an image from the Election Truth Alliance analysis that shows a possible artifact: https://img1.wsimg.com/isteam/ip/9087f51c-d3bd-4002-9943-797...

PDF of their analysis: https://electiontruthalliance.org/clark-county%2C-nv

Link to the full report: https://electiontruthalliance.org/clark-county%2C-nv

At the end of the full report, there are two histograms of the vote shares earned by Harris and by Trump, respectively, overlaid by a gaussian ("normal") distribution. Election shares tend to follow gaussian distributions [1]. In Clark County, NV, the election shares have a strong discontinuity that favors Trump, but only in Early Voting results.

This discontinuity only appears to exist on machines that tabulated a lot of results.

Past elections that are suspected to have been fraudulent (a few in Russia and one in Uganda, prior to 2012) show these exact same types of discontinuities, whereas elections that are generally regarded as secure do not show these types of discontinuities [1].

Are there other potential explanations for these discontinuities? ("Russian Tails" as they are colloquially known [2].) Yes! Election Truth Alliance lists them out as any proper data scientist would. Furthermore, ETA lists and links their datasets, and invites anyone to use them. This is what a model data scientist should do.

It seems that they are trying to get more data from more contested areas. What will they find? I don't know, but it seems that the more this phenomenon presents itself, then some of the alternate hypotheses can be ruled out.

[1] https://pmc.ncbi.nlm.nih.gov/articles/PMC3478593/

[2] https://www.rferl.org/a/georgia-election-manipulation-russia... *[Interesting that Elon wants to get rid of Radio Free Europe. -ed]

"But there's evidence this time and there wasn't in 2020!" Well, the other side would argue the opposite. We're not going to get anywhere this way.

Look: regardless of the the extent to which these alleged voting shenanigans are real, we need to fix the system. The legitimacy of the state is at risk.

Just as Caesar's wife must be above reproach, our voting system must be above reproach. There are plenty of common sense things officials can take to bolster the public's faith in the system. For example, we should ban electronic voting machines. The real threat to democracy isn't election manipulation but officials who refuse to enact measures that would dispel even the appearance of manipulation.

https://www.newsweek.com/2024-election-rigged-donald-trump-e...

  # Download and extract zip file
  import requests
  import zipfile
  import io

  # Get raw data from Clark County website
  zip_url = "https://elections.clarkcountynv.gov/electionresultsTV/cvr/24G/24G_CVRExport_NOV_Final_Confidential.zip"

  # Download the zip file
  response = requests.get(zip_url)
  zip_file = zipfile.ZipFile(io.BytesIO(response.content))

  # Extract to the current working directory
  zip_file.extractall()

  # Close the zip file
  zip_file.close()

  import pandas as pd
  import matplotlib.pyplot as plt
  import numpy as np

  # Read the actual data, skipping the first three header rows and excluding downballot races
  df = pd.read_csv('/content/24G_CVRExport_NOV_Final_Confidential.csv', skiprows=3, usecols=range(21), low_memory=False)

  # Find the Trump and Harris columns
  trump_col = "REP"
  harris_col = "DEM"

  # Convert to numeric
  df[trump_col] = pd.to_numeric(df[trump_col], errors='coerce')
  df[harris_col] = pd.to_numeric(df[harris_col], errors='coerce')

  # Filter for early voting
  early_voting = df[df['CountingGroup'] == 'Early Voting']

  # Group by tabulator and calculate percentages
  tabulator_stats = early_voting.groupby('TabulatorNum').agg({
      harris_col: 'sum',
      trump_col: 'sum'
  }).reset_index()

  # Calculate total votes and percentages
  tabulator_stats['total_votes'] = tabulator_stats[harris_col] + tabulator_stats[trump_col]
  tabulator_stats['harris_pct'] = tabulator_stats[harris_col] / tabulator_stats['total_votes'] \* 100
  tabulator_stats['trump_pct'] = tabulator_stats[trump_col] / tabulator_stats['total_votes'] \* 100

  # Create subplots
  fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))

  # Plot Harris histogram
  ax1.hist(tabulator_stats['harris_pct'], bins=50, edgecolor='black', color='blue', alpha=0.7)
  ax1.set_title('Distribution of Harris Votes by Tabulator (Early Voting Only)')
  ax1.set_xlabel('Percentage of Votes for Harris')
  ax1.set_ylabel('Number of Tabulators')

  # Plot Trump histogram
  ax2.hist(tabulator_stats['trump_pct'], bins=50, edgecolor='black', color='red', alpha=0.7)
  ax2.set_title('Distribution of Trump Votes by Tabulator (Early Voting Only)')
  ax2.set_xlabel('Percentage of Votes for Trump')
  ax2.set_ylabel('Number of Tabulators')

  plt.tight_layout()
  plt.show()

> According to the theory, in fair elections, turnout indicators typically follow a regular graphical representation that resembles a bell shape. If anomalies appear in the data -- for example, forms different from the bell shape or the bell "grows a tail" -- this indicates unfair elections.

There is *no* respectable statistician who would *ever* draw a conclusion from data. You could say 'may indicate evidence of unfair elections,' but it is impossible to deem whether something occurred or not with 100% certainty.

Now to address their "indicators."

> While in cities the data representation mostly follows a bell shape, in the regions it is anomalously deviated

All statistics is just a representation of data, it tells us nothing of logic. Logic is also based in axioms and it's important to identify what those are.

Here the author is saying the data is "anomalously deviated" with the axiomatic assumption that all voters must follow the same distribution, but why should this assumption be held true? It ignores other logical factors that may sway these distributions.

If I'm a candidate for a political position that would give a rural precinct free money, wouldn't that heavily affect that population's voting outcomes? Surely what's said in a political campaign can sway these things, yet there's nothing in the article that mentions that.

> According to the theory, if a number of stations emerge where one candidate -- the "beneficiary" -- is unusually high, this might indicate falsification. There's a high risk of manipulation where increased overall voter turnout is reflected only in support of the "beneficiary."

So the belief is that because there are 2 distributions for the number of precincts with a certain percentage of "yes" votes, that automatically "indicates falsification"? There needs to be more information than this to make a conclusion. What one holds as axioms are a matter of opinion.

The only way to get something useful out of a counterargument to this is for a proponent to exhaustively list all the votes they think are legitimate, and for the opponent to show the same patterns can occur there as well.

Similarly for a proponent to convince detractors, they'd have to show that other elections they see as legitimate *never* indicate those patterns. Otherwise this is just an opinion piece on data.

[1] https://news.ycombinator.com/item?id=42996898

I happened to have detailed day-by-day voting and registration data for the 2024 Nevada election as it was happening. Harris losing Nevada was telegraphed in the data almost from the beginning. Forget the top line totals, the underlying structure of where and who was registering and voting in real-time made the outcome all but a foregone conclusion weeks before the actual election. Nevada was some of the easiest money on the prediction markets.

Much is explained by an unusually robust turnout of low-propensity voters for Trump. They often don't care about the rest of the ballot, so it skews results albeit in a predictable way. (This is also the risk for Republicans; they are unlikely to show up for mid-terms and may sit on the sidelines again in four years.)

Nevada has consistently clean election processes. I am only aware of a single anomaly over the last several Federal elections that was clearly inexplicable. It wasn't this election, that instance looked like a system failure rather than fraud, and it didn't change the outcome. Some other States are rife with anomalies that persist even with sophisticated analysis most cycles.

If I was going to pick a State to look for Federal election irregularities, Nevada would be pretty low on my list. It is easy for amateurs to fool themselves into thinking they've found election fraud when really they just don't understand what is required to find a signal that holds up under sophisticated analysis. Same thing happened in 2020.

I also used to live in Nevada and am pretty attuned to the local politics. There are local bellwether statistics that are traditionally pretty reliable for indicating how an election will break. Harris was upside down on these too. The tortured rationalization by Nevada election pundits like Jon Ralston to reinterpret those as "good actually" was uncritically repeated widely in the national media.