Apple announces AR glasses at WWDC 2026?
Market 0.0% against model 5.0%. Resolves in resolved, data updated 14d ago.
Decision layer
Actionable research signal
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.
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.
side YES
5.0% model / 0.0% market
fees, spread, slippage, risk
Model edge survives the current public research gates.
Watch resolution risk, timing, and data quality before trusting the gap.
Read this market in three passes
Model 5.0% vs market 0.0%.
Raw disagreement is reduced by fees, spread, slippage, and risk controls.
Model leans YES
Why this read matters
The model-market gap currently survives the decision gates, but it is still research context and must be judged against the public track record.
Apple announces AR glasses at WWDC 2026?
Volume $115,600
Market-implied vs model probability
Factor attribution
The model estimates a 5-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 | 18% | — | −1.516 | Bearish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
| Model probability | 5.0% | Prior: 18% · Market: 1.0% | |||
| 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%. | 0.62 | Final: 5.0% = λ·model + (1−λ)·market | |||
Comparable eventsseeded prior 18% - 0 matches (min 8 for historical)
No comparable events matched for this market.
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 | 2.3% | $1 | +96.7c |
| Threshold hit in second half | 2.8% | $1 | +96.7c |
| Never reaches threshold in window | 95.0% | $0 | -3.3c |
Root-implied probability 5.0% reconciles with the model's 5.0% (±1pt invariant).
Description
Resolves based on Apple announcing a standalone augmented reality glasses product at WWDC 2026.
Resolution criteria (verbatim, with analyzer flags)
analyzed by heuristicResolves YES if Apple announces a standalone augmented reality glasses product (distinct from Vision Pro) at WWDC 2026.
Resolves Thu, 11 Jun 2026 03:48:52 GMT. The contract pays on these exact criteria, not on the thesis.
Suggested paper position
Paper position only. No real-money execution
Live open-market tracking
Since the first stored model read on 2026-04-27, the market has moved from 22.0% to 0.0%.
This is a directional diagnostic for unresolved markets, not final performance. Resolved outcomes still determine the official live record.
Data quality60/100 - usable
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 | 32 | |
| Price volatility | 27 | |
| Resolution proximity | 100 | |
| Data quality | 36 | |
| Category base risk | 40 | |
| Resolution ambiguity | 8 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 43/100, higher = riskier.