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Market 99.1% against model 83.3%. Resolves in resolved, data updated 28d ago.
Decision layer
The model disagreement survives the current gates. This is still research context, not financial advice.
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
83.3% model / 99.1% market
fees, spread, slippage, risk
Model edge survives the current public research gates.
Watch whether the market price moves toward or away from the model.
Model 83.3% vs market 99.1%.
Raw disagreement is reduced by fees, spread, slippage, and risk controls.
Model leans NO
The model-market gap currently survives the decision gates, but it is still research context and must be judged against the public track record.
Sign in to return to this exact question, review governed evidence, and record an append-only probability without exposing private thesis text.
Volume $38,255
The model estimates a 16-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 | 79% | — | +1.347 | Bullish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
No comparable events matched for this market.
The Piauí gubernatorial election is scheduled to take place in Brazil on October 4, 2026, with a runoff on October 25, 2026, if no candidate receives a majority of the valid votes in the first round. This market will resolve according to the candidate who wins this election. Temporary, interim, or placeholder governors appointed by any means before the specified election will not be considered. If the result of this election isn't known by June 30, 2027, 11:59 PM ET, the market will resolve to "Other". This market will resolve based on the result of the election, 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 Brazilian government, specifically the Superior Electoral Court (Tribunal Superior Eleitoral, TSE) (e.g., www.tse.jus.br/eleicoes/resultados-eleicoes).
ambiguity 40/100analyzed by heuristicThe Piauí gubernatorial election is scheduled to take place in Brazil on October 4, 2026, with a runoff on October 25, 2026, if no candidate receives a majority of the valid votes in the first round. This market will resolve according to the candidate who wins this election. Temporary, interim, or placeholder governors appointed by any means before the specified election will not be considered. If the result of this election isn't known by June 30, 2027, 11:59 PM ET, the market will resolve to "Other". This market will resolve based on the result of the election, 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 Brazilian government, specifically the Superior Electoral Court (Tribunal Superior Eleitoral, TSE) (e.g., www.tse.jus.br/eleicoes/resultados-eleicoes).
- 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, 04 Oct 2026 06:00:00 GMT. The contract pays on these exact criteria, not on the thesis.
Paper position only. No real-money execution
| —This factor was not available for this market. No approved link prices the same event on another venue for this market. |
| 0.20 |
| — |
| — |
| Whether the same event is priced differently on another venue. A gap may signal an opportunity or a structural difference. |
| Recent price momentum | —This factor was not available for this market. This factor was not available for this market. | 0.35 | — | — | Drift in the market's own implied probability over its last 8 price updates, typically a few hours. Recent directional 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 | 79.4% | Prior: 79% · Market: 97.9% | |||
| 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%. | 0.79 | Final: 83.3% = λ·model + (1−λ)·market | |||
Since the first stored model read on 2026-09-07, the market has moved from 99.0% to 99.1%.
This is a directional diagnostic for unresolved markets, not final performance. Resolved outcomes still determine the official live record.
Missing: Cross-market divergence, Recent price momentum, 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 | 27 | |
| Price volatility | 6 | |
| Resolution proximity | 100 | |
| Data quality | 24 | |
| Category base risk | 55 | |
| Resolution ambiguity | 40 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 25/100, higher = riskier.
| Market | Mkt | Delta |
|---|---|---|
| Category context | ||
| Will Camilo Santana win the 2026 Brazilian presidential election? adjacent contract: nearby expiry + wording overlap | 0.1% | +17pt |
| Will Cleitinho Azevedo win the 2026 Minas Gerais gubernatorial election? adjacent contract: nearby expiry + wording overlap | 66.5% | -6pt |
| Will Lula win the most votes in the next Brazil presidential election from Paraíba? adjacent contract: nearby expiry + wording overlap | 94.1% | -13pt |
| Will Augusto Cury win the 2026 Brazilian presidential election? category context: similar expiry window + same category | 0.1% | +17pt |
| Will Geraldo Alckmin win the 2026 Brazilian presidential election? category context: similar expiry window + same category | 0.1% | +17pt |
Divergences > 5pt flagged in amber. For cross-venue pricing, see the Scanner.