The New York Times/Siena College
Graded against the actual result across 146 races (from 210 polls, through 2024).
Head-to-head vs VotePredictor Elections
The fair, apples-to-apples test: on the 145 races The New York Times/Siena College 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 New York Times/Siena College | 4.23 | 75% |
| VotePredictor Elections | 3.87 | 79% |
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 (145)
Each race The New York Times/Siena College 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 | 24 | 3.54 | -0.70 | 67% |
| 1–3 wk | 84 | 4.05 | -1.01 | 69% |
| 3–6 wk | 54 | 5.40 | -0.30 | 82% |
| 6–9 wk | 48 | 5.76 | -0.38 | 58% |
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) |
|---|---|---|---|
| 2016 | 15 | 4.9 | D+4.3 |
| 2018 | 94 | 4.5 | R+2.0 |
| 2020 | 69 | 5.8 | D+5.6 |
| 2022 | 12 | 1.9 | D+0.0 |
| 2024 | 19 | 3.8 | D+3.7 |
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.