Remington Research Group
Graded against the actual result across 41 races (from 77 polls, through 2024).
Head-to-head vs the VotePredictor model
The fair, apples-to-apples test: on the 40 races Remington Research Group actually polled, how its final poll's margin compared to what the VotePredictor model predicted for those same races.
| Model | Avg miss (pts) | Called right |
|---|---|---|
| Remington Research Group | 6.09 | 75% |
| VotePredictor | 4.46 | 73% |
VotePredictor 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 (40)
Each race Remington Research Group 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 |
|---|---|---|---|---|
| 1–3 wk | 50 | 4.96 | -0.11 | 70% |
| 3–6 wk | 15 | 8.94 | +3.20 | 67% |
| 6–9 wk | 9 | 9.00 | +2.85 | 56% |
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) |
|---|---|---|---|
| 2014 | 6 | 7.5 | D+2.0 |
| 2016 | 38 | 6.2 | D+3.8 |
| 2018 | 8 | 4.4 | D+0.2 |
| 2020 | 11 | 9.5 | D+3.8 |
| 2022 | 11 | 4.0 | R+2.1 |
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.