In a recent study, a scenario model examined 64 English authorities slated for the May 7 2026 local elections. Six distinct scenarios—ranging from no swing to a +4 pp challenger surge—were layered onto a historical baseline. The analysis produced 1,536 rows, each with point estimates and calibrated P10, P50, and P90 bands drawn from 2,000 bootstrap draws.

Backtests were treated not merely as pass‑fail metrics but as empirical error distributions. Tier‑level mean‑centered residuals from the 2014–2018 training window and the 2018–2022 backtest were pooled, then resampled to generate the uncertainty bands. This approach keeps the bands rooted in how the model has actually mis‑predicted past cycles.

When shocks were compared to the median P10–P90 width, the largest scenario—S3’s +4 pp challenger surge—moved the central estimate by only 13 % of the noise band. S1’s continuation of recent churn shifted estimates by 6 %, while S2’s partial recovery moved them by 5 %. All shocks fell well inside historical error envelopes.

The findings underscore that scenario shocks are dwarfed by calibrated uncertainty. Ranking scenarios based on point estimates alone would mislead analysts into seeing false precision. By grounding uncertainty in historical residuals, the model offers a realistic gauge of what electoral swings truly matter for decision makers.