The Washington Post
Graded against the actual result across 56 races (from 116 polls, through 2024).
Head-to-head vs VotePredictor Elections
The fair, apples-to-apples test: on the 50 races The Washington Post actually polled, how its final poll's margin compared to what VotePredictor Elections predicted for those same races.
| Model | Avg miss (pts) | Called right |
|---|---|---|
| The Washington Post | 3.60 | 76% |
| VotePredictor Elections | 2.32 | 94% |
VotePredictor Elections aggregates all the pollsters, so it's expected to beat any single one on margin — that's the value of averaging. The honest comparison among forecasters is on the combined board.
Every race (56)
Each race The Washington Post polled, scored on its final poll — the call right before the vote — against the actual Dem−Rep result. Click a race for its full detail.
Accuracy by time to election
Lower is better. Time to election runs right (election week) to left (~2 months out).
By the numbers
| Time to election | Polls | Avg miss | vs field | Called right |
|---|---|---|---|---|
| ≤1 wk | 6 | 2.13 | -2.11 | 67% |
| 1–3 wk | 54 | 3.06 | -2.00 | 74% |
| 3–6 wk | 30 | 3.17 | -2.53 | 70% |
| 6–9 wk | 26 | 4.53 | -1.61 | 85% |
vs field is this pollster's average miss minus all pollsters' at the same lead time — green beats the field, redtrails it. Our historical polls reach ~2 months out; earlier polling isn't in the record.
Track record by cycle — getting better?
| Year | Polls | Avg miss | Lean (house effect) |
|---|---|---|---|
| 2000 | 16 | 3.1 | R+1.3 |
| 2004 | 21 | 3.3 | R+1.2 |
| 2006 | 4 | 2.3 | R+0.1 |
| 2008 | 15 | 2.5 | R+0.9 |
| 2009 | 3 | 9.4 | D+9.4 |
| 2010 | 4 | 5.0 | D+3.0 |
| 2012 | 18 | 2.5 | D+0.1 |
| 2016 | 6 | 1.6 | D+1.1 |
| 2020 | 15 | 4.8 | D+4.4 |
| 2024 | 8 | 2.0 | D+1.9 |
Do we credit a pollster for fixing its bias? Each cycle, the model re-estimates every pollster's lean from all its earlier polls (walk-forward) and subtracts it before using the poll. We tested weighting recent cycles more — it doesn't help: a pollster's lean in one cycle barely predicts the next (correlation 0.28), so the swings above are mostly noise, and averaging over more history beats chasing the latest cycle. The all-time estimate we use came out within ~0.5% of the best option.