It is possible to reproduce one of the key claims in this post -- the "Russian tail" in the early voting tallies -- straight from the raw data hosted on the Clark County, NV website. This code can be run in a Colab notebook:
# 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()
“Normal” people can’t actually buy these high-end Rolexes, even if they have the money.
It is true that some/many Rolex AD’s will allocate the most desirable watches to customers with an existing purchase history, and that some customers therefore buy less desirable models in order to earn goodwill with the AD.
However, it is not the case that the most desirable watches are necessarily (or even on average) the most expensive models. For instance, it is generally the steel models that are the most desirable and command the highest markup from MSRP on the secondary market. The Submariner, the Daytona, the GMT-Master II: almost all of Rolex’s most iconic, most in-demand, most "flippable" watches are the full steel versions, which are the cheapest versions of those model families.
To give a concrete example, it is generally considered easier to get a full-gold GMT (~$43k) or a two-tone (half steel, half gold) GMT (~$18k) at an Authorized Dealer than it is to get the full steel version ($11k).