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Market 93.8% against model 78.3%. Resolves in 20d 1h, data updated 2d 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
78.3% model / 93.8% 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 78.3% vs market 93.8%.
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 $32,598
The model estimates a 15-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 | 76% | — | +1.139 | Bullish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
No comparable events matched for this market.
Gubernatorial elections are currently scheduled to be held in Bryansk Oblast on September 20, 2026. This market will resolve according to the listed candidate who wins the 2026 Bryansk Oblast gubernatorial elections. Any interim or caretaker Governor will not qualify. This market includes any potential second round. If the results are not known definitively by June 30, 2027, 11:59 PM ET, this market will resolve to “Other”. This market will resolve based on the results of this election, as indicated by a consensus of credible reporting. If there is ambiguity, this market will resolve solely based on official results as reported by government sources of Bryansk Oblast and Russia, including the Election Commission of Bryansk Oblast and the Central Election Commission of Russia.
ambiguity 40/100analyzed by heuristicGubernatorial elections are currently scheduled to be held in Bryansk Oblast on September 20, 2026. This market will resolve according to the listed candidate who wins the 2026 Bryansk Oblast gubernatorial elections. Any interim or caretaker Governor will not qualify. This market includes any potential second round. If the results are not known definitively by June 30, 2027, 11:59 PM ET, this market will resolve to “Other”. This market will resolve based on the results of this election, as indicated by a consensus of credible reporting. If there is ambiguity, this market will resolve solely based on official results as reported by government sources of Bryansk Oblast and Russia, including the Election Commission of Bryansk Oblast and the Central Election Commission of Russia.
- 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, 20 Sep 2026 23:59:00 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); 0 without an approved link. |
| 7-day price momentum | 0.00 | 0.35 | 0.000 | Neutral | 7-day drift of the market's own implied probability — sustained 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 | 75.7% | Prior: 76% · Market: 93.8% | |||
| 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%. | 0.86 | Final: 78.3% = λ·model + (1−λ)·market | |||
Since the first stored model read on 2026-08-29, the market has moved from 93.0% to 93.8%.
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 | 5 | |
| Resolution proximity | 0 | |
| Data quality | 28 | |
| Category base risk | 55 | |
| Resolution ambiguity | 40 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 21/100, higher = riskier.
| Market | Mkt | Delta |
|---|---|---|
| Category context | ||
| Trump out as President before GTA VI? category context: same category + wording overlap | 49.5% | +1pt |
| Will Microsoft have the best AI model at the end of August 2026? category context: wording overlap | 0.1% | -- |
| US ceasefire against Iran continues through August 31? category context: wording overlap | 0.1% | +50pt |
| Will Google have the best AI model at the end of August 2026? category context: wording overlap | 0.1% | -- |
| Will Meta have the best AI model at the end of August 2026? category context: wording overlap | 0.3% | -- |
Divergences > 5pt flagged in amber. For cross-venue pricing, see the Scanner.