Will the Carolina Hurricanes win the 2026 NHL Stanley Cup?
Market 100.0% against model 80.8%. Resolves in 3d 3h, data updated 12d ago.
Why this is not actionable
The model can still be informative here, but one or more gates blocks a trade call.
Decision layer
No-trade decision
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
One decision layer for the market read.
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
80.8% model / 100.0% market
fees, spread, slippage, risk
Market price 100.0% is at or beyond the effectively-resolved threshold (99%) — contract is priced as settled, no liquid opposing side exists.
Market price 100.0% is at or beyond the effectively-resolved threshold (99%) — contract is priced as settled, no liquid opposing side exists.
Read this market in three passes
Model 80.8% vs market 100.0%.
Raw disagreement is reduced by fees, spread, slippage, and risk controls.
No trade
Why this read matters
The model may disagree with price, but the gates say the disagreement is not actionable right now.
Will the Carolina Hurricanes win the 2026 NHL Stanley Cup?
Volume $2,107,433
Why the engine declines to trade this market
- - Market price 100.0% is at or beyond the effectively-resolved threshold (99%) — contract is priced as settled, no liquid opposing side exists.
Declining to trade is a feature: most markets are priced fairly within costs, and the risk gates run before any edge is considered.
Market-implied vs model probability
Factor attribution
The model estimates a 19-point lower probability than the market, primarily driven by historical base rate and rate surprise.
| 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 | 80% | — | +1.384 | Bullish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
| Cross-market divergence | 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 | −0.49 | 0.25 | −0.122 | Bearish | 2y-yield reaction in the 48h after the latest scheduled release — the observable proxy for surprise vs consensus. |
| 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 | 77.9% | Prior: 80% · Market: 100.0% | |||
| 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%. | 0.87 | Final: 80.8% = λ·model + (1−λ)·market | |||
Comparable eventsseeded prior 80% - 0 matches (min 8 for historical)
| Event | Outcome | Relevance |
|---|---|---|
| Recession-within-a-year markets since 2008 | Persistently overpriced vs realized frequency | Macro doom trades carry a structural premium. |
Scenario treeEngine template
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 | 36.4% | $1 | -2.6c |
| Threshold hit in second half | 44.4% | $1 | -2.6c |
| Never reaches threshold in window | 19.2% | $0 | -102.6c |
Root-implied probability 80.8% reconciles with the model's 80.8% (±1pt invariant).
Why this mattersTemplate (no LLM key)
A 19.2% probability gap at a 100.0% price translates to 16.5% expected value per dollar of payout exposure after costs on the NO side. EV — not the raw probability gap — is the comparable number: the same gap is worth very different amounts at 50¢ and at 92¢.
What could make this wrongTemplate (no LLM key)
The model's edge depends on its inputs being right. Concretely: the base rate of 80.0% may not apply if this event differs structurally from its reference class; the macro.rate_surprise factor could be noise rather than information at this horizon; and with confidence at 0.87, the model itself concedes meaningful estimation error. Resolution risk remains: the contract pays on the precise criteria — "This market will resolve to “Yes” if the Carolina Hurricanes win the 2026 NHL Stanley Cup. Otherwise, this market will resolve to “No”. Thi…" — not on the thesis.
- - Risk score 15/100 — composite of liquidity, volatility, time-to-resolution, data quality and category risk.
- - Factor agreement 1.00: factors broadly agree, but shared blind spots are possible.
- - Data quality 0.69 (simulated input in MVP).
- - Simulated model values — this brief demonstrates structure, not live research.
Description
This market will resolve to “Yes” if the Carolina Hurricanes win the 2026 NHL Stanley Cup. Otherwise, this market will resolve to “No”. This market will resolve to “No” if it becomes impossible for this team to win the 2026 NHL Stanley Cup based off the rules of the NHL. The resolution source for this market will be information from the NHL.
Resolution criteria (verbatim, with analyzer flags)
analyzed by heuristicThis market will resolve to “Yes” if the Carolina Hurricanes win the 2026 NHL Stanley Cup. Otherwise, this market will resolve to “No”. This market will resolve to “No” if it becomes impossible for this team to win the 2026 NHL Stanley Cup based off the rules of the NHL. The resolution source for this market will be information from the NHL.
Resolves Tue, 30 Jun 2026 00:00:00 GMT. The contract pays on these exact criteria, not on the thesis.
Suggested paper position
The engine sizes NO TRADE markets to zero. Sizing never overrides the risk gates.
Paper position only. No real-money execution
Live open-market tracking
Since the first stored model read on 2026-06-15, the market has moved from 100.0% to 100.0%.
This is a directional diagnostic for unresolved markets, not final performance. Resolved outcomes still determine the official live record.
Data quality76/100 - usable
Missing: Yield curve shift, 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.
Risk factor breakdownsim
| Inverse liquidity | 6 | |
| Price volatility | 0 | |
| Resolution proximity | 78 | |
| Data quality | 54 | |
| Category base risk | 50 | |
| Resolution ambiguity | 8 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 20/100, higher = riskier.