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Market 1.7% against model 18.4%. Resolves in 815d 24h, data updated 2h 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
18.4% model / 1.7% 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 18.4% vs market 1.7%.
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.
usable feature coverage.
Volume $8,189,238
The model estimates a 17-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 | 21% | — | −1.329 | Bearish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
| Cross-market divergence | 0.00 | 0.20 |
| Event | Outcome | Relevance |
|---|---|---|
| Prediction-market favorites in national elections | Favorites at 60–70¢ won less often than priced in low-liquidity markets | Demo market — synthetic data; favorite-longshot bias applies. |
Node probabilities are conditional on the parent; hover for cumulative path probability. Leaf EV is per $1 YES contract at the current price, before fees (fee-adjusted EVs in the table on the left).
| Path | Path prob. | YES pays | EV (YES, after costs) |
|---|---|---|---|
| Threshold hit in first half of window | 8.0% | $1 | +95.6c |
| Threshold hit in second half | 9.8% | $1 | +95.6c |
| Never reaches threshold in window | 82.2% | $0 | -4.4c |
Root-implied probability 17.8% reconciles with the model's 18.4% (±1pt invariant).
A 16.3% probability gap at a 1.6% price translates to 13.5% expected value per dollar of payout exposure after costs on the YES side. EV — not the raw probability gap — is the comparable number: the same gap is worth very different amounts at 50¢ and at 92¢.
The model's edge depends on its inputs being right. Concretely: the base rate of 20.9% may not apply if this event differs structurally from its reference class; the pm.cross_market_divergence factor could be noise rather than information at this horizon; and with confidence at 0.84, the model itself concedes meaningful estimation error. Resolution risk remains: the contract pays on the precise criteria — "The 2028 US Presidential Election is scheduled to take place on November 7, 2028. This market will resolve to the person who wins the 2028 …" — not on the thesis.
The 2028 US Presidential Election is scheduled to take place on November 7, 2028. This market will resolve to the person who wins the 2028 US Presidential Election. The resolution source for this market is the Associated Press, Fox News, and NBC. This market will resolve once all three sources call the race for the same candidate. If all three sources haven’t called the race for the same candidate by the inauguration date (January 20, 2029) this market will resolve based on who is inaugurated.
analyzed by heuristicThe 2028 US Presidential Election is scheduled to take place on November 7, 2028. This market will resolve to the person who wins the 2028 US Presidential Election. The resolution source for this market is the Associated Press, Fox News, and NBC. This market will resolve once all three sources call the race for the same candidate. If all three sources haven’t called the race for the same candidate by the inauguration date (January 20, 2029) this market will resolve based on who is inaugurated.
- Deadline without timezone A deadline is stated without a timezone — the cutoff moment is undefined.
Resolves Tue, 07 Nov 2028 00:00:00 GMT. The contract pays on these exact criteria, not on the thesis.
Paper position only. No real-money execution
| 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 | 20.9% | Prior: 21% · Market: 1.7% | |||
| 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%. | 0.87 | Final: 18.4% = λ·model + (1−λ)·market | |||
Since the first stored model read on 2026-07-20, the market has moved from 1.6% to 1.7%.
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 | 11 | |
| Price volatility | 0 | |
| Resolution proximity | 0 | |
| Data quality | 42 | |
| Category base risk | 55 | |
| Resolution ambiguity | 20 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 17/100, 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.