Poll Analyst

#Definition

A poll analyst in prediction markets specializes in political markets by systematically analyzing polling data, building probability models, and identifying discrepancies between model outputs and market prices. These traders develop positions well before mainstream attention crystallizes, capturing value as markets gradually incorporate polling information.

Poll analysts combine quantitative skills with political science knowledge to transform raw polling data into actionable trading insights.

#Why Traders Use This Approach

Poll analysis attracts traders because:

• Rich data availability: Polling data is publicly available but requires skill to interpret correctly
• Systematic mispricings: Markets often over- or underweight individual polls relative to aggregates
• Long time horizons: Election cycles provide months to build and adjust positions
• Compounding edge: Early positioning captures value as information gradually reaches markets
• Defensible methodology: Analysis can be backtested against historical elections

Platforms like Polymarket and Kalshi feature extensive election markets where poll analysis provides significant trading edge.

#Tools of the Trade

• Aggregators: 538, RealClearPolitics (RCP), The Hill/DDHQ.
• Raw Data Repositories: Individual pollster websites or PDFs for crosstabs.
• Modeling Software: R or Python for Monte Carlo simulations.

#Pollster Ratings and Quality

Not all polls are created equal. Understanding pollster quality is essential:

#FiveThirtyEight Pollster Ratings

#Methodology Quality Hierarchy

#Sample Size and Margin of Error

Understanding sampling statistics helps evaluate poll reliability:

#Margin of Error by Sample Size

#What Margin of Error Actually Means

Example: Poll shows Candidate A at 52%, Candidate B at 48%
Sample size: 1,000 (MoE ±3.1%)

95% Confidence Interval for Candidate A: 48.9% to 55.1%
95% Confidence Interval for Candidate B: 44.9% to 51.1%

These ranges OVERLAP, meaning:
- The race could actually be tied
- Either candidate could be ahead
- A "4-point lead" is NOT statistically significant at this sample size

For a lead to be "significant" with n=1,000:
Lead must exceed 2 × MoE = 2 × 3.1% = 6.2%

#Historical Polling Errors by Race Type

Understanding typical polling errors helps set realistic expectations:

#Notable Historical Misses

#Detecting Herding

Herding occurs when pollsters adjust their results to match other polls, reducing the informational value of the poll ecosystem.

#Signs of Herding

#Why Herding Matters for Traders

When polls herd, the aggregate is less reliable because:

• Polls are not truly independent data points
• True uncertainty is higher than aggregate suggests
• A systemic error affects ALL polls simultaneously

Trading implication: In herding environments, increase your uncertainty estimates. A "5-point lead" in a herded environment might only be worth 60% probability, not 85%.

#How It Works

Strategy Complexity: High

Poll analysis follows a data-driven research process:

1. Collect and aggregate data

   • Gather polls from multiple sources
   • Weight polls by recency, sample size, and pollster quality
   • Track polling averages over time
   • Poll Weighting: Not all polls are equal. Analysts weight by sample size (N > 1000 is better), methodology (Probability-based > Opt-in Online), and pollster rating (A+ vs C). See political bettor for broader context.

2. Apply adjustments

   • Correct for known pollster biases (house effects)
   • Adjust for likely voter screens and methodology differences
   • Account for historical polling errors in similar races

3. Build probability models

   • Convert polling margins to win probabilities
   • Incorporate uncertainty based on time until election
   • Model correlated outcomes across related races

4. Compare to market prices

   • Identify markets where prices diverge from model estimates
   • Calculate expected value for potential positions
   • Prioritize opportunities with largest edge

5. Position and monitor

   • Enter positions in underpriced markets
   • Update models as new polls arrive
   • Adjust positions based on changing estimates

#Polling to Probability Conversion

Converting a polling margin to win probability:

Polling margin: +5% (Candidate A leads by 5 points)
Days until election: 60
Historical polling error (standard deviation): 3.5%

Accounting for uncertainty:
- Current lead in standard errors: 5% / 3.5% = 1.43
- Probability from normal distribution: ~92%

Adjusting for time:
- Polls 60 days out have higher uncertainty
- Additional error factor: ~1.5%
- Adjusted standard error: 5%
- Adjusted lead in standard errors: 5% / 5% = 1.0
- Time-adjusted probability: ~84%

If market prices: 75%
Expected edge: 84% - 75% = 9%

#When to Use It (and When Not To)

#Suitable Conditions

• Races with regular, reliable polling
• Sufficient time until election for positions to work
• Market prices that deviate from polling fundamentals
• Understanding of local political dynamics

#Unsuitable Conditions

• Races without quality polling data
• Very short timeframes where poll-based positions can't adjust
• Markets already efficiently pricing polling information
• Elections where polling has historically been unreliable

#Examples

#Example 1: Primary Election Positioning

A poll analyst identifies opportunity months before a primary:

• Current polling: Candidate A at 35%, Candidate B at 30%
• Market prices: Candidate A at $0.45, Candidate B at$0.35
• Historical analysis: Candidates in A's position win nomination 60% of time
• Model estimate: Candidate A at 55-60%, market underpricing by 10%+

The analyst builds a position on Candidate A, planning to add if polls strengthen.

#Example 2: General Election Aggregation

A market on presidential election outcomes:

• Individual polls show conflicting results
• Market reacts strongly to each new poll
• Poll analyst maintains aggregate showing consistent 52-48 lead
• When market overreacts to outlier polls, analyst trades against the move

#Example 3: Senate Race Portfolio

An analyst builds positions across multiple Senate races:

• Model identifies 8 races where markets misprice by 5%+
• Positions sized based on edge magnitude and uncertainty
• Portfolio captures aggregate edge even when individual races surprise
• Correlations between races considered for risk management

#Risks and Common Mistakes

• Over-fitting to recent polls: Weighting latest polls too heavily versus longer-term trends
• Ignoring polling errors: Assuming polls are accurate when historical errors are substantial
• Sample quality issues: Treating all polls equally when methodologies vary significantly
• Herding with aggregates: Blindly following aggregators without understanding their methodology
• Partisan bias: Unconsciously favoring polls that support personal political views
• Late-breaking shifts: Failing to account for undecided voters and late movement

#Practical Tips

• Build your own aggregates: Don't rely entirely on public aggregators; understand the underlying methodology
• Study historical accuracy: Know how polls in similar races have performed
• Adjust for house effects: Track individual pollster biases and correct accordingly
• Model uncertainty properly: Wider confidence intervals further from election day
• Follow quality pollsters: Weight higher-rated pollsters more heavily
• Watch for methodological changes: Pollsters adjust methods; track how changes affect results
• Backtest rigorously: Test models against past elections before trading real capital

#Related Terms

• Political Bettor
• Prediction Market
• Expected Value
• Sharp Money
• Information Aggregation
• Risk Management

#FAQ

#How accurate are polls in predicting election outcomes?

Poll accuracy varies by race type, timing, and methodology. National polls for presidential elections typically fall within 2-3 percentage points of final results. State-level and down-ballot polling tends to be less accurate. Historical analysis shows polls have systematic tendencies to over- or underestimate certain candidate types. Successful poll analysts understand these patterns.

#Should poll analysts use prediction market prices as inputs?

Some analysts incorporate market prices as one signal among many, treating them as reflecting other traders' probability estimates. However, relying too heavily on market prices can create circular reasoning. Most poll analysts use markets primarily as comparison points rather than model inputs, identifying edge where their fundamental analysis diverges from prices.

#How do poll analysts handle polling errors?

Sophisticated analysts model polling error explicitly, using historical data to estimate error distributions. Rather than treating poll results as precise measurements, they treat them as noisy estimates with known uncertainty properties. This approach produces probability ranges rather than point estimates, leading to more robust trading decisions.

#When should poll analysts exit positions?

Exit decisions depend on strategy. Some analysts hold to election resolution, accepting that their edge compounds through patient positioning. Others exit when market prices reach their model estimates, capturing edge without waiting for resolution. Exit timing also depends on new information—if fundamentals change significantly, positions should adjust regardless of original timeline.