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Market 52.5% against model 45.1%. Resolves in 23h, data updated 14m 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
45.1% model / 52.5% market
fees, spread, slippage, risk
Interesting disagreement, but the full action threshold is not met.
Watch resolution risk, timing, and data quality before trusting the gap.
Model 45.1% vs market 52.5%.
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 $97,240
The model estimates a 7-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 | 44% | — | −0.253 | Bearish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
No comparable events matched for this market.
Primary elections in Florida are scheduled to be held on August 18, 2026. This market will resolve according to the share of valid votes James Fishback wins in the 2026 Florida Republican Gubernatorial Primary. If the reported value falls exactly between two brackets, then this market will resolve to the higher range bracket. If the results of the specified election are not known definitively by November 30, 2026, 11:59 PM ET, this market will resolve to the lowest bracket. The primary resolution source for this market will be information from the State of Florida, such as official statewide results published by the Florida Department of State (https://dos.fl.gov/); however, an overwhelming consensus of credible reporting may suffice. If a recount is initiated before the vote total has been made official, the market will remain open until the recount is completed and the vote is made official.
analyzed by heuristicPrimary elections in Florida are scheduled to be held on August 18, 2026. This market will resolve according to the share of valid votes James Fishback wins in the 2026 Florida Republican Gubernatorial Primary. If the reported value falls exactly between two brackets, then this market will resolve to the higher range bracket. If the results of the specified election are not known definitively by November 30, 2026, 11:59 PM ET, this market will resolve to the lowest bracket. The primary resolution source for this market will be information from the State of Florida, such as official statewide results published by the Florida Department of State (https://dos.fl.gov/); however, an overwhelming consensus of credible reporting may suffice. If a recount is initiated before the vote total has been made official, the market will remain open until the recount is completed and the vote is made official.
- Oracle dependency Resolution depends on a single named source continuing to publish the metric.
Resolves Tue, 18 Aug 2026 23:59:00 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 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. | 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 | 43.7% | Prior: 44% · Market: 51.5% | |||
| 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%. | 0.82 | Final: 45.1% = λ·model + (1−λ)·market | |||
Since the first stored model read on 2026-08-16, the market has moved from 39.5% to 52.5%.
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 | 18 | |
| Price volatility | 44 | |
| Resolution proximity | 93 | |
| Data quality | 57 | |
| Category base risk | 55 | |
| Resolution ambiguity | 18 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 41/100, higher = riskier.
| Market | Mkt | Delta |
|---|---|---|
| Category context | ||
| Will David Crowley win the Wisconsin Democratic Governor primary by 1–1.2%? category context: same category + wording overlap | 0.5% | -- |
| Will David Crowley win the Wisconsin Democratic Governor primary by less than 0.2%? category context: same category + wording overlap | 0.9% | -- |
| Will David Crowley win the Wisconsin Democratic Governor primary by less than 1%? category context: same category + wording overlap | 99.8% | -16pt |
| Will Francesca Hong win the Wisconsin Democratic Governor primary by at least 30%? category context: same category + wording overlap | 37.5% | +2pt |
| Will Darline Graham Nordone be the new republican nominee for Senate in South Carolina? category context: same category + wording overlap | 40.6% | +13pt |
Divergences > 5pt flagged in amber. For cross-venue pricing, see the Scanner.