Will Belgium win the 2026 FIFA World Cup?
Market 1.7% against model 19.0%. Resolves in 23d 1h, data updated 9d 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
19.0% 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.
Read this market in three passes
Model 19.0% vs market 1.7%.
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.
Will Belgium win the 2026 FIFA World Cup?
Volume $55,555,688
Market-implied vs model probability
Factor attribution
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% | — | −1.315 | Bearish | 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 | —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 signalsim | 0.00 | 0.25 | 0.000 | Neutral | Reliability-weighted mean of extracted news direction labels, past 14 days. |
| 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 | 21.2% | 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%. | 0.89 | Final: 19.0% = λ·model + (1−λ)·market | |||
Comparable eventsseeded prior 21% - 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 | 8.5% | $1 | +95.9c |
| Threshold hit in second half | 10.4% | $1 | +95.9c |
| Never reaches threshold in window | 81.0% | $0 | -4.2c |
Root-implied probability 19.0% reconciles with the model's 19.0% (±1pt invariant).
Why this mattersTemplate (no LLM key)
A 17.3% probability gap at a 1.7% price translates to 14.8% 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¢.
What could make this wrongTemplate (no LLM key)
The model's edge depends on its inputs being right. Concretely: the base rate of 21.2% 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 0.89, the model itself concedes meaningful estimation error. Resolution risk remains: the contract pays on the precise criteria — "This market will resolve according to the national team that wins the 2026 FIFA World Cup. If at any point it becomes impossible for this t…" — not on the thesis.
- - Risk score 16/100 — 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 0.68 (simulated input in MVP).
- - Simulated model values — this brief demonstrates structure, not live research.
Description
This market will resolve according to the national team that wins the 2026 FIFA World Cup. If at any point it becomes impossible for this team to win the FIFA World Cup based on the rules of FIFA (e.g., they are eliminated in the knockout stage), this market will resolve immediately to “No”. If the 2026 FIFA World Cup is permanently canceled or has not been completed by October 13, 2026, 11:59 PM this market will resolve to “Other”. The primary resolution source will be official information from FIFA, however, a consensus of credible reporting may also be used.
Resolution criteria (verbatim, with analyzer flags)
analyzed by heuristicThis market will resolve according to the national team that wins the 2026 FIFA World Cup. If at any point it becomes impossible for this team to win the FIFA World Cup based on the rules of FIFA (e.g., they are eliminated in the knockout stage), this market will resolve immediately to “No”. If the 2026 FIFA World Cup is permanently canceled or has not been completed by October 13, 2026, 11:59 PM this market will resolve to “Other”. The primary resolution source will be official information from FIFA, however, a consensus of credible reporting may also be used.
- 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 Mon, 20 Jul 2026 00:00:00 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 2026-06-15, the market has moved from 1.9% to 1.7%.
This is a directional diagnostic for unresolved markets, not final performance. Resolved outcomes still determine the official live record.
Data quality68/100 - usable
Missing: Rate surprise, Yield curve shift, 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 | 0 | |
| Price volatility | 1 | |
| Resolution proximity | 0 | |
| Data quality | 59 | |
| Category base risk | 50 | |
| Resolution ambiguity | 30 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 16/100, higher = riskier.