Pokémon ETB Forecast Accuracy and Model Limits
A forecast is useful only when its uncertainty is visible. Sealed Value publishes walk-forward results and model limits because a precise-looking line can create false confidence. The model is a best-effort comparison with historical ETBs, not a promise that a future buyer will pay the displayed target.
Current walk-forward results
| Market | 12-month samples | Typical absolute error | Median bias | Displayed range coverage |
|---|---|---|---|---|
| Pokémon Center ETBs | 69 | 54.5% | -2% | 56.5% |
| Standard ETBs | 69 | 33.7% | 5.6% | 53.6% |
| Booster Bundles | 0 | Not enough history | Not enough history | Not enough history |
| Booster Boxes | 42 | 53.9% | 53.9% | 90.5% |
Typical absolute error describes the median size of the miss, regardless of direction. Median bias shows whether forecasts tended to land high or low. Range coverage shows how often the realized outcome landed inside the displayed likely range. None of these metrics guarantees that the next forecast will behave similarly.
Why Pokémon Center error is higher
Exclusive ETBs can have thinner transaction histories, larger premiums, and stronger supply shocks. A small number of unusual releases can produce wide outcomes even after robust outlier filtering. The model widens the displayed range using observed historical error, but it cannot turn sparse data into certainty.
Known failure modes
- Reprints and restocks: unannounced supply can interrupt the historical lifecycle.
- Demand shocks: a new collecting trend can make older averages temporarily irrelevant.
- Thin sales: a market price based on few transactions may not be executable at scale.
- Outlier sets: a unique hit can move far beyond the typical cohort, while a weak set can lag it.
- Costs: displayed ROI excludes fees, shipping, taxes, storage, damage, and the value of time.
- Product mismatch: each forecast applies only to the exact product family selected; it does not predict cards or the full set.
How Sealed Value avoids overstating confidence
New or incomplete products can show PASS while the model waits for more reliable data. The suggestion combines the model’s entry and holding tests. Outliers are filtered from the cohort center. Forecast ranges widen with observed error. Every page identifies the data-through date and the exact product family being modeled.
Responsible use
Use the forecast as one screening tool. Verify the actual product, examine recent sold listings, estimate your net proceeds, consider position size, and decide what loss you can tolerate. If a purchase only works when the center forecast is exactly right, the decision has no margin for model error.
Read the full forecast guide, then compare a strong modeled setup such as Mega Evolution — Mega Lucario with a lower-confidence or pending set. The difference between those pages shows why the suggestion should always be read with its forecast range.