The Final Countdown: 2024 Electoral Battlegrounds: A Statistical Analysis of a Razor-Thin Race
Analysis Shows Trump’s Narrow Lead But Multiple Paths to Victory Remain
RCP Polling Data Processor
https://github.com/tatsuikeda/rcp_polling_data_processor
Tatsu Ikeda’s final prediction: November 4, 2024, 17:08:24
Harris: 241, Trump: 297
In what promises to be one of the most closely watched elections in recent American history, former President Donald Trump currently holds a narrow but significant lead over Vice President Kamala Harris in the race for the White House. However, comprehensive analysis suggests the race remains remarkably fluid, with multiple paths to victory for both candidates.
The Current State of Play
Recent polling data across seven battleground states shows Trump leading in five states and Harris ahead in two, with margins so narrow that several states could easily flip in the final days. Our latest projection shows Trump at281.3 ± 27.1 electoral votes at 90% CI: (230.0, 312.0), Harris at 256.7 electoral votes.
Win Probability - Trump: 62.5%
Win Probability - Harris: 37.5%
Probability of Recount Scenario: 12.3%
Current State-by-State Margins
Arizona:
Mean Margin: +3.1%
90% Confidence Interval: (+0.3%, +5.8%)
Win Probability - Trump: 96.3%, Harris: 3.7%
Georgia:
Mean Margin: +1.6%
90% Confidence Interval: (-1.2%, +4.4%)
Win Probability - Trump: 83.5%, Harris: 16.5%
Michigan:
Mean Margin: -0.5%
90% Confidence Interval: (-3.2%, +2.3%)
Win Probability - Trump: 38.9%, Harris: 61.1%
Nevada:
Mean Margin: +0.9%
90% Confidence Interval: (-1.8%, +3.7%)
Win Probability - Trump: 71.6%, Harris: 28.4%
North Carolina:
Mean Margin: +1.5%
90% Confidence Interval: (-1.3%, +4.2%)
Win Probability - Trump: 81.5%, Harris: 18.5%
Pennsylvania:
Mean Margin: +0.4%
90% Confidence Interval: (-2.3%, +3.2%)
Win Probability - Trump: 60.9%, Harris: 39.1%
Wisconsin:
Mean Margin: -0.6%
90% Confidence Interval: (-3.4%, +2.2%)
Win Probability - Trump: 35.9%, Harris: 64.1%
The Demographic Divide
The 2024 election reveals complex demographic patterns that differ significantly from previous cycles:
Gender Gap and Early Voting
Women making up 54% of early ballots in battleground states
Strong female turnout potentially reshaping traditional voting patterns
Party Loyalty and Crossover Votes
Harris securing 8% of Republican crossover voters
Trump showing unexpected strength among Hispanic voters, particularly in Arizona and Nevada
Black voter support for Harris at 78% - lower than traditional Democratic numbers but still substantial
Educational and Geographic Divides
Harris maintaining strong support among college-educated voters and in suburban areas
Educational divide becoming more pronounced than in previous elections
Significant urban-rural split persisting
Blue Wall States
Michigan
8-point decrease in white non-college voters since 2008
Strong early voting turnout among women (54%)
Harris currently leading by 0.8%
Wayne County (Detroit) showing incremental Republican gains
Wisconsin
Dane County growth exceeding Biden’s 2020 margin of 20,682 votes
Brown County remains divided between urban Democratic and rural Republican voters
Waukesha County showing Republican erosion in suburban areas
Harris leading by 0.6%
Pennsylvania
Democrats showing 58% vs 35% advantage among early voting seniors
Erie County remains a key bellwether
Weather forecast appears favorable for turnout
Sun Belt States
Georgia
Projected 395,000 new residents, mostly in Democratic-leaning metro Atlanta
Latino voters have doubled since 2008
Cobb County continuing Democratic shift due to diversification
Arizona
Maricopa County adding 337,000 new residents
Latino voters increased by 10 points
Trump currently leading by 3%
Nevada
Clark County projected to gain 125,000 new residents
Latino share up 7 points
AAPI voters now represent nearly 10% of eligible voters
North Carolina
Wake County (Raleigh) showing increasing Democratic margins
Latino voters quadrupled since 2008
Growth in red counties offsetting blue suburban growth
The data suggests a tightening race with demographic shifts generally favoring Harris in the Blue Wall states while showing mixed impacts in the Sun Belt.
Turnout Analysis
Current turnout data shows intense voter engagement:
Early Voting Trends
Over 78 million Americans have already cast ballots
Democrats showing strong early voting numbers in Pennsylvania
Republicans increasing their early voting share compared to 2020 levels
Turnout Scenario Impacts
Three distinct scenarios emerge from our analysis:
High Turnout: Favors Harris (+0.6% net effect)
Base Turnout: Neutral impact
Low Turnout: Favors Trump (+1.2% net effect)
The Pennsylvania Factor
Pennsylvania emerges as the critical tipping point state, with several unique characteristics:
8.2% undecided voters (highest among battleground states)
Significant regional variations between Philadelphia suburbs and western PA
Complex early voting patterns
High sensitivity to turnout operations
Campaign Resource Allocation
Current campaign investments show intense focus on key states:
Pennsylvania: Leading in ad spending ($27.6M/week)
Michigan: Intensive ground game operations
Arizona: Experiencing late surge in campaign activity
Georgia: Heavy early vote focus
Late Breaking Factors
Four key factors could shift the race in its final days (with weighted importance):
Economic Indicators (35%)
Campaign Events (25%)
Advertisement Saturation (20%)
Ground Game Effectiveness (20%)
Possible Scenarios
Our model identifies three primary scenarios that could materialize:
Scenario 1: High Turnout + Harris Late Break (28% probability)
Projected outcome: Harris 281 EV, Trump 257 EV
Key drivers:
Strong suburban turnout
Higher-than-expected youth vote
Successful GOTV operations in urban areas
Weather cooperation in key northern states
Scenario 2: Low Turnout + Trump Late Break (32% probability)
Projected outcome: Trump 312 EV, Harris 226 EV
Key drivers:
Weather suppression in urban areas
Lower turnout among young voters
Strong rural turnout
Late momentum in swing states
Scenario 3: Weather Impact + Regional Correlation (24% probability)
Projected outcome: Trump 289 EV, Harris 249 EV
Key drivers:
Rust Belt state correlation
Weather impacts in Upper Midwest
Regional voting patterns holding firm
Traditional demographic splits
Critical Uncertainties
Several factors could significantly impact the final outcome:
Weather Impact Analysis (November 5, 2024)
Recent weather forecasts for key battleground states show potentially significant election day conditions:
Pennsylvania (16° C, Cloudy)
Mild temperatures but cloudy conditions
No precipitation expected
Generally favorable for turnout
Evening temperatures remaining stable
Michigan (19° C, Windy)
Mild temperatures with windy conditions
35% chance of precipitation
9mm rain predicted
Wind could impact elderly voter turnout
Moderate weather-related turnout risk
Wisconsin (19° C, Cloudy)
Cloudy with significant precipitation risk
65% chance of rain early in the day
23mm rain predicted
Highest weather-related turnout risk
Temperature dropping in evening hours
Weather Impact on Scenarios
These conditions could particularly affect our Scenario 3 (Weather Impact + Regional Correlation):
Wisconsin’s heavy rain could suppress turnout in urban areas
Michigan’s wind and rain may impact elderly voter participation
Pennsylvania’s mild conditions might benefit from stable turnout
The weather patterns suggest:
Morning voting likely to see higher turnout than evening
Greatest impact potential in Wisconsin
Moderate impact risk in Michigan
Minimal weather effect in Pennsylvania
Historical Weather Impact Analysis
Weather conditions show significant electoral implications:
For every inch of rain above normal, Republican candidates historically gained 2.5% more votes
Snow increases Republican vote share by 0.6% per inch above normal
The 1960 election, with its remarkably clear weather nationwide, likely benefited Kennedy over Nixon
Temperature changes affect turnout: every 10-degree rise reduces voting probability by 0.6%
Regional Turnout Modeling
Key battleground regions show distinct patterns:
Upper Midwest
Wisconsin’s Dane County has grown faster than any other large county, exceeding Biden’s 2020 margin of 20,682 votes
Brown County (Green Bay) remains divided between urban Democratic and rural Republican voters
Sun Belt States
Georgia projected to add 395,000 new residents, mostly in Democratic-leaning metro Atlanta
Maricopa County, Arizona expected to add 337,000 residents by election day
Clark County, Nevada projected to gain 125,000 new residents
Early Voting Analysis: Battleground States 2024 vs 2020
Current early voting shows significant shifts:
78 million total early votes cast (42.6M in-person, 35.3M mail-in)
Party registration breakdown:
Democrats: 14.8M (37.9%)
Republicans: 14.1M (36%)
Unaffiliated/Other: 10.2M
Women comprise 54% of early voters in tracked states
Arizona
Early Voting 2024: 4.31 million
Early Voting 2020: 5.35 million
Change: -19.32%
Party Registration 2024: Democrats 35.4%, Republicans 42.1%, Other 22.5%
Party Registration 2020: Democrats 37.4%, Republicans 37.0%, Other 25.6%
Key Shifts: Surge in new male Republican voters; significant changes in turnout dynamics
Georgia
Early Voting 2024: 4.00 million
Early Voting 2020: 2.70 million
Change: +48.15%
Notable Trends: More diverse early voter population; drop in youth turnout (18-29); 700,000 new voters who didn’t participate in 2020
North Carolina
Early Voting 2024: 4.20 million
Early Voting 2020: 3.63 million
Change: +15.70%
Party Registration 2024: Republicans 31%, Democrats 30%, Other 39%
Party Registration 2020: Democrats 38%, Republicans 30%, Other 32%
Nevada
Early Voting 2024: 1.03 million
Early Voting 2020: 1.11 million
Change: -7.55%
Party Registration 2024: Democrats 34%, Republicans 39%, Other 27%
Party Registration 2020: Democrats 45%, Republicans 39%, Other 16%
Key Trends: 11-point rise in nonpartisan voters; increased Republican early voting
Pennsylvania
Early Voting 2024: 0.43 million
Party Registration 2024: Democrats 58%, Republicans 42%
Key Trends: Higher women participation; strong 65+ Democratic turnout
Wisconsin
Early Voting 2024: 1.34 million
Early Voting 2020: 1.96 million
Change: -31.61%
Party Registration 2024: Democrats 55%, Republicans 40%, Other 5%
Party Registration 2020: Democrats 51%, Republicans 45%, Other 4%
Notable Shift: Youth voter surge (22% after Harris rally)
Michigan
Early Voting 2024: 2.79 million
Early Voting 2020: 2.84 million
Change: -1.73%
Key Trends: Increased women participation; rise in Republican early voting; shift from traditional urban Democratic dominance
Key Takeaways
Biggest Changes:
Largest increase: Georgia (+48.15%)
Largest decrease: Wisconsin (-31.61%)
Party Registration Shifts:
Republican gains in Arizona and Nevada
Democratic strength in Wisconsin and Pennsylvania
Growing independent voter share in multiple states
Demographic Trends:
Increased women participation across multiple states
Variable youth turnout (drop in Georgia, surge in Wisconsin)
New voter engagement particularly strong in Georgia
Overall Volume Trends:
Mixed pattern compared to 2020
Southern states generally showing increases
Some Midwest states showing decreases
Demographic Shifts in Key Counties
Several crucial demographic changes are emerging:
Sun Belt Changes
White voters decreased by double digits since 2008 in Nevada, Arizona, and North Carolina
Latino voters increased by:
10 points in Arizona
7 points in Nevada
Doubled in Georgia
Quadrupled in North Carolina
Urban-Suburban Shifts
Fort Bend County (Texas) showing Democratic gains due to increased diversity and education levels
Miami-Dade County experiencing Hispanic voter realignment
Wayne County (Detroit) showing incremental Republican gains during the Trump era
These patterns suggest a highly competitive election with weather potentially playing a decisive role in close states, particularly given the projected precipitation in key battleground regions on election day.
Late Decision Makers
Significant portion of voters still undecided
Historical tendency for late shifts in close races
Potential impact of final campaign events
Legal and Process Factors
Potential legal challenges could delay final results
Recount scenarios in close states (10.8% probability)
Varying state procedures for counting and certification
Win Probability Analysis
Current model shows:
Trump: 62.5% win probability
Harris: 37.5% win probability
However, these probabilities remain fluid and could shift significantly based on turnout patterns, regional correlations, and late-breaking developments.
Projected Outcomes
Based on the combination of these factors:
Pennsylvania: Likely to favor Trump due to:
Clear weather means no weather-related turnout suppression
Trump currently leads by 0.5% in polling
Strong Republican early voting compared to 2020
Michigan: Toss-up but slightly favoring Harris because:
Weather impact is moderate
Democratic advantage among new female voters
However, wind could affect elderly turnout
Wisconsin: Likely to favor Trump due to:
Significant rain forecast potentially suppressing urban turnout
Historical weather impact patterns favoring Republicans
Razor-thin current margin (Harris +0.6%)
However, it's important to note that these projections come with significant uncertainty due to the large number of unaffiliated voters and the extremely close margins in all three states
Looking Ahead
While Trump currently holds a slight advantage, the race remains remarkably fluid. The high number of states with razor-thin margins suggests that election night could be just the beginning of determining the final outcome. Key factors to watch in the final days include:
Pennsylvania turnout patterns
Weather impacts in the Upper Midwest
Hispanic vote trends in Arizona and Nevada
Early vote patterns in Georgia and North Carolina
Methodology Note
This script employs both traditional and modern statistical techniques to process and analyze polling data:
1. Traditional Poll Aggregation Methods
Pollster Quality Ratings
Pollsters are weighted based on their historical accuracy and methodology:
POLLSTER_RATINGS = {
'A+': 1.0,
'A': 0.85,
'A-': 0.75,
'B+': 0.65,
'B': 0.55,
'B-': 0.45,
'C+': 0.35,
'C': 0.25,
'C-': 0.15,
'D': 0.1
}
Time Decay Weighting
Recent polls are given more weight using an exponential decay function:
POLL_DECAY_RATE = 0.05 # Decay rate parameter
time_weight = 1 / (1 + POLL_DECAY_RATE * days_old)
Sample Size and Type Weighting
Polls are weighted based on sample size and likely voter (LV) vs. registered voter (RV) methodology:
base_weight = 1 / np.sqrt(1/sample_size) if sample_size > 0 else 0
sample_type_weight = 1.5 if 'LV' in str(row['SAMPLE']) else 1.0
Historical Error Adjustment
Each state’s historical polling errors are weighted to account for systematic bias:
weighted_error = (
0.7 * hist_errors['2020'] +
0.2 * hist_errors['2016'] +
0.1 * hist_errors['2012']
)
Regional Correlations
State outcomes are correlated based on geographic regions:
REGIONAL_CORRELATIONS = {
'Northeast': {'Northeast': 1.0, 'Midwest': 0.8, 'South': 0.7, 'Southwest': 0.6},
'Midwest': {'Northeast': 0.8, 'Midwest': 1.0, 'South': 0.75, 'Southwest': 0.7},
'South': {'Northeast': 0.7, 'Midwest': 0.75, 'South': 1.0, 'Southwest': 0.65},
'Southwest': {'Northeast': 0.6, 'Midwest': 0.7, 'South': 0.65, 'Southwest': 1.0}
}
Undecided Voter Allocation
Sophisticated allocation of undecided voters considering:
Challenger advantage (reduced in final weeks)
Current polling strength
Historical polling errors
Diminishing returns on strength adjustments
challenger_base = 0.52
strength_adjustment = np.tanh(relative_strength) * 0.08
error_adjustment = np.tanh(historical_error / 10) * 0.04
challenger_share = min(max(challenger_share - error_adjustment, 0.48), 0.56)
2. Modern Statistical Enhancements
Regularization Techniques
LASSO (Least Absolute Shrinkage and Selection Operator)
Ridge Regression
Elastic Net
Regularized correlation matrices for state relationships
models = {
'lasso': LassoCV(cv=5, random_state=42),
'ridge': RidgeCV(cv=5),
'elastic_net': ElasticNetCV(cv=5, random_state=42)
}
Machine Learning Ensemble
Random Forest Regressor
Gradient Boosting Regressor
Weighted model averaging
Cross-validation for model performance
models.update({
'rf': RandomForestRegressor(n_estimators=100, random_state=42),
'gbm': GradientBoostingRegressor(n_estimators=100, random_state=42)
})
Hierarchical/Multilevel Modeling
Region-level effects
Shrinkage estimation
Borrowing strength across similar states
Prior strength adjustment
shrinkage = weights[i] / (weights[i] + 100) # 100 is prior strength
shrunk_estimate = (
shrinkage * raw_estimate +
(1 - shrinkage) * (results.params[0] + region_effect)
)
Bootstrap Methods
Resampling with replacement
Robust confidence intervals
Non-parametric uncertainty estimation
def bootstrap_confidence_intervals(data: pd.DataFrame, n_bootstrap: int = 1000) -> dict:
results = []
for _ in range(n_bootstrap):
bootstrap_sample = data.sample(n=len(data), replace=True)
weighted_mean = np.average(
bootstrap_sample['margin'],
weights=bootstrap_sample['Weight']
)
results.append(weighted_mean)
Missing Data Imputation
Iterative imputation
Multiple regression iterations
Preservation of relationships between variables
Bounded value constraints
imputer = IterativeImputer(
max_iter=10,
random_state=42,
min_value=0,
max_value=100
)
3. Combined Monte Carlo Simulation
The simulation now integrates both traditional and modern methods:
Traditional Components
Poll weights based on quality, recency, and sample size
Historical error adjustments
Regional correlations
Undecided voter allocation
Modern Enhancements
Ensemble model predictions
Hierarchical state estimates
Bootstrap confidence intervals
Regularized correlation structure
Multiple Sources of Uncertainty
Base polling error
Historical error patterns
Sampling uncertainty
Model uncertainty
Systematic bias
state_results[:,i] = (
mean_estimate +
random_effects[:,i] * uncertainty +
systematic_bias * hist_error * 0.1
)
4. Final Estimation Formula
The complete weight calculation combines all factors:
# Traditional poll weight
base_poll_weight = (
base_weight *
time_weight *
sample_type_weight *
pollster_rating *
historical_error_adjustment
)
# Combined modern estimates
combined_estimates[state] = (
0.4 * ensemble_predictions[state] +
0.4 * hierarchical_estimates[state] +
0.2 * bootstrap_results[state]['mean']
)
5. Recount Probability
Calculates the probability of extremely close electoral college outcomes:
recount_zone = np.sum(np.abs(trump_ev - 269.5) <= 5) / n_sims