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Market 64.0% against model 61.4%. Resolves in 23d 22h, data updated 7d ago.
The model can still be informative here, but one or more gates blocks a trade call.
Decision layer
The model may still be informative, but at least one gate blocks an action-style signal.
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.
no side selected
61.4% model / 64.0% market
fees, spread, slippage, risk
No edge after fees and slippage — the market price is fair within costs.
No edge after fees and slippage — the market price is fair within costs.
Model 61.4% vs market 64.0%.
Raw disagreement is reduced by fees, spread, slippage, and risk controls.
No trade
The model may disagree with price, but the gates say the disagreement is not actionable right now.
Sign in to return to this exact question, review governed evidence, and record an append-only probability without exposing private thesis text.
Volume $55,534
Declining to trade is a feature: most markets are priced fairly within costs, and the risk gates run before any edge is considered.
The model estimates a 3-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 | 61% | — | +0.439 | Bullish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
No comparable events matched for this market.
Parliamentary elections to elect the 250 members of the National Assembly of Serbia are scheduled to take place on October 25, 2026. This market will resolve to the electoral list that wins the greatest number of seats in the National Assembly of Serbia as a result of this election. Electoral lists will be primarily ranked by the number of seats won in the specified election. If two or more lists are tied on seats, ties will be broken by the total number of valid votes received, with higher vote totals ranking higher. If lists remain tied, ties will be broken by alphabetical order of the listed names. This market will resolve to the list that occupies the highest finishing position after applying this ranking. This market's resolution will be based solely on the number of seats won by the named electoral list, as it appears on the ballot and official proclamations, regardless of which parties make up the list or how its members later organize themselves in the National Assembly. If the results of the specified election are not definitively known by March 31, 2027, 11:59 PM ET, this market will resolve to "Other". This market will resolve based on the results of the elections, as indicated by a consensus of credible reporting. In the case of ambiguity, this market will resolve solely based on the results as reported by the Republic Electoral Commission of Serbia (https://www.rik.parlament.gov.rs/).
ambiguity 40/100analyzed by heuristicParliamentary elections to elect the 250 members of the National Assembly of Serbia are scheduled to take place on October 25, 2026. This market will resolve to the electoral list that wins the greatest number of seats in the National Assembly of Serbia as a result of this election. Electoral lists will be primarily ranked by the number of seats won in the specified election. If two or more lists are tied on seats, ties will be broken by the total number of valid votes received, with higher vote totals ranking higher. If lists remain tied, ties will be broken by alphabetical order of the listed names. This market will resolve to the list that occupies the highest finishing position after applying this ranking. This market's resolution will be based solely on the number of seats won by the named electoral list, as it appears on the ballot and official proclamations, regardless of which parties make up the list or how its members later organize themselves in the National Assembly. If the results of the specified election are not definitively known by March 31, 2027, 11:59 PM ET, this market will resolve to "Other". This market will resolve based on the results of the elections, as indicated by a consensus of credible reporting. In the case of ambiguity, this market will resolve solely based on the results as reported by the Republic Electoral Commission of Serbia (https://www.rik.parlament.gov.rs/).
- 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, 25 Oct 2026 23:59:00 GMT. The contract pays on these exact criteria, not on the thesis.
The engine sizes NO TRADE markets to zero. Sizing never overrides the risk gates.
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 | 60.8% | Prior: 61% · Market: 64.0% | |||
| 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%. | 0.81 | Final: 61.4% = λ·model + (1−λ)·market | |||
Since the first stored model read on 2026-09-26, the market has moved from 68.0% to 64.0%.
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 | 20 | |
| Price volatility | 16 | |
| Resolution proximity | 0 | |
| Data quality | 43 | |
| 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 Eduardo Bolsonaro win the 2026 Brazilian presidential election? category context: same category + wording overlap | 0.1% | -- |
| Will Eduardo Leite win the 2026 Brazilian presidential election? category context: same category + wording overlap | 0.1% | -- |
| Will Aldo Rebelo win the 2026 Brazilian presidential election? category context: same category + wording overlap | 0.1% | -- |
| Will Michelle Bolsonaro win the 2026 Brazilian presidential election? category context: same category + wording overlap | 0.1% | -- |
| Will Pablo Marçal win the 2026 Brazilian presidential election? category context: same category + wording overlap | 0.1% | +18pt |
Divergences > 5pt flagged in amber. For cross-venue pricing, see the Scanner.