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Market 23.5% against model 33.7%. Resolves in 38d 5h, 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.
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 YES
33.7% model / 23.5% 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 33.7% vs market 23.5%.
Raw disagreement is reduced by fees, spread, slippage, and risk controls.
Model leans YES
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.
weak feature coverage.
Volume $149,265
The model estimates a 10-point higher 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 | 36% | — | −0.567 | Bearish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
| Cross-market divergence | —This factor was not available for this market. No approved cross-venue link exists 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. |
| 7-day price momentum | —This factor was not available for this market. This factor was not available for this market. |
No comparable events matched for this market.
Parliamentary elections to elect the Abgeordnetehaus of Berlin are scheduled to take place in Berlin on September 20, 2026. This market will resolve to the political party that wins the greatest number of seats in the formal deliberative assembly of Berlin (Abgeordnetenhaus) as a result of this election. If voting in the Berlin election for the Abgeordnetenhaus does not occur by December 31, 2026, this market will resolve to "Other". In the case of a tie between this party or coalition and any other for the most seats won, this market will resolve in favor of the party or coalition whose listed abbreviation comes first in alphabetical order. This market's resolution will be based solely on the number of seats won by the named party or coalition. This market will resolve based on the election results, 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 Election Office of Berlin (Landeswahlleiter Berlin https://www.berlin.de/wahlen/)
ambiguity 52/100analyzed by heuristicParliamentary elections to elect the Abgeordnetehaus of Berlin are scheduled to take place in Berlin on September 20, 2026. This market will resolve to the political party that wins the greatest number of seats in the formal deliberative assembly of Berlin (Abgeordnetenhaus) as a result of this election. If voting in the Berlin election for the Abgeordnetenhaus does not occur by December 31, 2026, this market will resolve to "Other". In the case of a tie between this party or coalition and any other for the most seats won, this market will resolve in favor of the party or coalition whose listed abbreviation comes first in alphabetical order. This market's resolution will be based solely on the number of seats won by the named party or coalition. This market will resolve based on the election results, 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 Election Office of Berlin (Landeswahlleiter Berlin https://www.berlin.de/wahlen/)
- Undefined edge case The criteria themselves acknowledge unresolved edge cases.
- Oracle dependency Resolution depends on a single named source continuing to publish the metric.
- Deadline without timezone A deadline is stated without a timezone — the cutoff moment is undefined.
Resolves Sun, 20 Sep 2026 00:00:00 GMT. The contract pays on these exact criteria, not on the thesis.
Paper position only. No real-money execution
| 0.35 |
| — |
| — |
| 7-day drift in the market's own implied probability. Sustained 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 | 36.2% | Prior: 36% · Market: 23.5% | |||
| 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%. | 0.80 | Final: 33.7% = λ·model + (1−λ)·market | |||
Since the first stored model read on 2026-08-10, the market has moved from 27.0% to 23.5%.
This is a directional diagnostic for unresolved markets, not final performance. Resolved outcomes still determine the official live record.
Missing: Cross-market divergence, 7-day 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 | 22 | |
| Price volatility | 9 | |
| Resolution proximity | 0 | |
| Data quality | 9 | |
| Category base risk | 55 | |
| Resolution ambiguity | 52 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 25/100, higher = riskier.
| Market | Mkt | Delta |
|---|---|---|
| Category context | ||
| Will BN win the 2026 Negeri Sembilan general elections? category context: same category + wording overlap | 99.9% | -- |
| Will Abdul El-Sayed win the 2026 Michigan Democratic Primary? category context: same category + wording overlap | 98.6% | -18pt |
| Will John James win the 2026 Michigan Governor Republican primary election? category context: same category + wording overlap | 95.0% | -15pt |
| Will Abdul El-Sayed win Macomb County in the Michigan Senate Democratic primary? category context: same category + wording overlap | 82.5% | -- |
| Will Abdul El-Sayed win Wayne County in the Michigan Senate Democratic primary? category context: same category + wording overlap | 80.5% | -10pt |
Divergences > 5pt flagged in amber. For cross-venue pricing, see the Scanner.