A major 2026 academic study (292 million trades, 327,000 binary contracts across Kalshi and Polymarket) reveals that prediction market calibration is not uniform — it is a structured, multidimensional phenomenon. Understanding these biases gives sophisticated Polymarket trading bots a massive, repeatable edge.

Key Calibration Findings

The paper decomposes calibration variance into four components explaining 87.3% of observed patterns on Kalshi (with strong replication on Polymarket):

1. Universal Horizon Effect — Prices become more extreme as time-to-resolution shortens.
2. Domain-Specific Biases (14.6% variance) — The biggest structural finding:
   • Politics: Persistent underconfidence — prices chronically compressed toward 50% (mean recalibration slope 1.31 on Polymarket).
   • Weather & Entertainment: Mild overconfidence — prices too extreme.
   • Sports & Crypto: Near well-calibrated (slopes ~1.08).
3. Domain-by-Horizon Interactions (16.5% variance).
4. Trade-Size Scale Effect — Larger trades amplify underconfidence in politics on Kalshi, but this does not strongly replicate on Polymarket (platform microstructure difference).

Practical Implications for Polymarket Trading Bots

1. Politics Markets (Biggest Edge)

• Market prices are systematically too close to 50%.
• A contract priced at $0.60 may truly imply ~65–70% probability.
• Bot adjustment: Apply domain-specific recalibration p* = σ(θ · logit(p)) where θ > 1 for politics.

2. Time-to-Resolution Awareness

• Short-horizon markets (<24h) show different biases than long-horizon ones.
• 5m/15m crypto rounds and buzzer sniping benefit from horizon-aware models.

3. Trade-Size & Microstructure Awareness

• On Polymarket, large trades do not amplify bias as much as on Kalshi.
• This favors aggressive sizing on high-conviction political edges.

Production Implementation Example

def recalibrate_probability(raw_price: float, domain: str, horizon_hours: float) -> float:
    # Simplified from paper's decomposition
    base_slope = 1.0
    if domain == "politics":
        base_slope = 1.31
    elif domain in ["weather", "entertainment"]:
        base_slope = 0.91  # overconfidence correction

    # Horizon adjustment (example coefficients)
    horizon_factor = 1 + 0.08 * np.log(max(1, horizon_hours))

    logit_p = np.log(raw_price / (1 - raw_price))
    adjusted_logit = base_slope * horizon_factor * logit_p
    return 1 / (1 + np.exp(-adjusted_logit))

Use this adjusted probability for:

• EV filtering
• Kelly sizing
• Shadow market making quote skew
• Combinatorial arbitrage detection

Bot Strategy Recommendations

• Overweight political edges after recalibration — the crowd’s underconfidence is your alpha.
• Underweight extremes in weather/entertainment (prices too aggressive).
• Track domain + horizon in your feature pipeline.
• Combine with structural plays (binary hedging, buzzer sniping, Negative Risk) for multi-layered edge.

Well-calibrated bots don’t treat all $0.65 prices the same — they adjust for domain, time, and platform effects. This is how top 1% performers consistently extract profits while 70% of users lose.

The crowd is wise on average, but systematically biased by domain. Exploit it.

If you have more questions, please feel free to contact me at any time: https://t.me/FatherSon97

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