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Market 81.0% against model 75.5%. Resolves in 57d 11h, data updated 4d ago.
Decision layer
The market is worth monitoring, but the current edge or evidence does not justify an actionable label.
Expected value after costs, not raw probability spread.
How much support the model sees across available inputs.
Thin markets can erase apparent edge through spread and slippage.
Resolution ambiguity, timing, and data quality pressure the decision.
usable feature coverage.
Why / why not trade
This public box mirrors the internal diagnostic style without exposing execution controls: decision, probability gap, cost-adjusted edge, blocker, and next thing to monitor.
side NO
75.5% model / 81.0% market
fees, spread, slippage, risk
Interesting disagreement, but the full action threshold is not met.
Watch whether the market price moves toward or away from the model.
Model 75.5% vs market 81.0%.
Raw disagreement is reduced by fees, spread, slippage, and risk controls.
Watch, do not force it
The market is directionally interesting, but at least one evidence, edge, liquidity, or risk condition is not strong enough.
Sign in to return to this exact question, review governed evidence, and record an append-only probability without exposing private thesis text.
Volume $139,784
The model estimates a 5-point lower probability than the market, primarily driven by historical base rate.
| FACTOR | SIGNAL | WEIGHT | LOG-ODDS ΔLog-odds contribution measures how much each factor shifted the model's probability estimate in log-odds space — the mathematically correct way to stack independent evidence. Formula: Δlog-odds = weight × signal. Positive values push the probability up; negative values push it down. Log-odds are converted back to probability via the logistic function at the end. | DIRECTION | DESCRIPTION |
|---|---|---|---|---|---|
| Historical base rate | 75% | — | +1.093 | Bullish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
No comparable events matched for this market.
Taiwanese local elections are scheduled to be held on November 28, 2026. This market will resolve according to the candidate who becomes the next Mayor of Taipei as a result of this election. Temporary, interim, or placeholder Mayors appointed before the election will not be considered. If the result of this election isn't known, or no candidate has been elected, by June 30, 2027, 11:59 PM ET, the market will resolve to "Other". This market will resolve based on the results of the elections as indicated by a consensus of credible reporting. If there is ambiguity, this market will resolve based solely on the official results as reported by the Taiwanese government, specifically the Central Election Commission (https://db.cec.gov.tw/).
ambiguity 40/100analyzed by heuristicTaiwanese local elections are scheduled to be held on November 28, 2026. This market will resolve according to the candidate who becomes the next Mayor of Taipei as a result of this election. Temporary, interim, or placeholder Mayors appointed before the election will not be considered. If the result of this election isn't known, or no candidate has been elected, by June 30, 2027, 11:59 PM ET, the market will resolve to "Other". This market will resolve based on the results of the elections as indicated by a consensus of credible reporting. If there is ambiguity, this market will resolve based solely on the official results as reported by the Taiwanese government, specifically the Central Election Commission (https://db.cec.gov.tw/).
- Undefined edge case The criteria themselves acknowledge unresolved edge cases.
- Oracle dependency Resolution depends on a single named source continuing to publish the metric.
Resolves Sun, 29 Nov 2026 04:59:00 GMT. The contract pays on these exact criteria, not on the thesis.
Paper position only. No real-money execution
| 0.00 |
| 0.20 |
| 0.000 |
| Neutral |
| Linked venue pricing the same event higher/lower (V2 scanner); masked without an approved SAME_EVENT or INVERSE link. |
| Recent price momentum | −0.07 | 0.35 | −0.023 | Neutral | Drift of the market's own implied probability over its last 8 price updates (typically hours; the key's "7d" is historical) — recent moves carry information. |
| BTC/ETH 7-day momentum | —This factor was not available for this market. This factor applies to crypto markets only. | 0.20 | — | — | 7-day Bitcoin or Ethereum return, normalized. Applied to crypto-category markets only. |
| Rate surprise | —This factor was not available for this market. This factor applies to Fed, CPI, and macro markets only. | 0.25 | — | — | 2-year Treasury yield reaction in the 48 hours after the most recent scheduled release — a proxy for how markets interpreted the data versus expectations. |
| Yield curve shift | —This factor was not available for this market. This factor applies to Fed, CPI, and macro markets only. | 0.15 | — | — | 30-day change in the 10-year minus 2-year Treasury spread. A flattening curve signals tightening expectations; steepening signals easing. |
| News signal | —This factor was not available for this market. No news signal available for this market in the past 14 days. | 0.25 | — | — | Reliability-weighted direction of relevant news from the past 14 days. Official sources (filings, agency statements) carry more weight than commentary. |
| Crowd forecast | —This factor was not available for this market. Insufficient forecasters to compute crowd signal. Requires at least 5 calibration-weighted estimates. | 0.20 | — | — | Calibration-weighted average of user probability estimates. Only applied when 5 or more weighted forecasters have submitted estimates. |
| Model probability | 74.5% | Prior: 75% · Market: 81.0% | |||
| Confidence (λ)Confidence λ (lambda) controls how much weight to give the model vs. the market. Formula: p_final = λ·p_model + (1−λ)·p_market. λ is derived from data quality, factor agreement, and liquidity. When inputs are weak, the model shrinks toward the market — not toward 50%. | 0.84 | Final: 75.5% = λ·model + (1−λ)·market | |||
Since the first stored model read on 2026-09-13, the market has moved from 91.5% to 81.0%.
This is a directional diagnostic for unresolved markets, not final performance. Resolved outcomes still determine the official live record.
Missing: News signal, Crowd forecast
When features are unavailable, the model increases uncertainty and weights the final estimate closer to the market price. Lower data quality does not mean the market is wrong. It means the model is being appropriately humble.
| Inverse liquidity | 22 | |
| Price volatility | 32 | |
| Resolution proximity | 0 | |
| Data quality | 6 | |
| Category base risk | 55 | |
| Resolution ambiguity | 40 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 26/100, higher = riskier.
| Market | Mkt | Delta |
|---|---|---|
| Category context | ||
| Will Pam Alexis win the next Abbotsford-Mission by-election? category context: same category + wording overlap | 2.8% | +35pt |
| Will Sven Schulze be the next Minister-President of Sachsen-Anhalt? category context: same category + wording overlap | 15.5% | -- |
| Trump out as President before GTA VI? category context: same category + wording overlap | 49.5% | +1pt |
| Will Ulrich Siegmund be the next Minister-President of Sachsen-Anhalt? category context: same category + wording overlap | 67.5% | -- |
| Will the price of Bitcoin be above $90,000 on October 2? category context: wording overlap | 0.1% | +18pt |
Divergences > 5pt flagged in amber. For cross-venue pricing, see the Scanner.