Predictions
Predictions are per-voter probabilities produced by machine-learning models trained on your organization’s own roll, vote history, and knockback data. They are signals, not verdicts — a “likely to turn out” voter is still one who needs a knock, and a “lean R” voter is still one who needs a conversation.
What gets predicted
Section titled “What gets predicted”| Model | Target | Range |
|---|---|---|
| Turnout | Probability this voter casts a ballot in the target election | 0.0 – 1.0 |
| R-ballot | Probability this voter pulls a Republican ballot in the next primary | 0.0 – 1.0 |
| R-lean | Soft score for general-election Republican alignment | 0.0 – 1.0 |
All three are calibrated — a voter at 0.8 should, over a large sample, turn out about 80% of the time. Calibration holds within your organization’s own data; models are never shared between organizations.
Where you see them
Section titled “Where you see them”Predictions surface wherever you target: per-voter probabilities on the voter record, rollups by precinct and by street segment for route batching, and an organization-wide summary with an expected turnout count. The R-Lean view ranks your roll by general-election alignment so you can cut a targeting universe directly from it.
The R-Lean view — your roll ranked by predicted alignment, ready to cut into a targeting universe.
Training cadence
Section titled “Training cadence”- On a fresh import — the turnout model auto-retrains after a roll import that adds a meaningful number of new rows, per organization.
- On demand — a retrain can be run whenever you want a fresh read; ask your Voterra team if you don’t see the option.
- Every cycle — at minimum, retrain after each primary and each general; adding vote history is the single biggest lift to model accuracy.
Every training run records how accurate the model was on held-out data, so “how well is our model doing” always has a concrete, current answer.
Using predictions in the field
Section titled “Using predictions in the field”Predictions feed route building and universe selection, not door-level decisions:
- Route scoring — the default route builder blends the turnout model’s output with the tier score; a high-scoring, low-turnout voter is still worth a knock, but the walking order may rank them slightly lower.
- Universe cuts — “likely R-ballot, low turnout” is the highest-leverage GOTV universe. Filter to voters with a high R-ballot probability and a low turnout probability, and route the result.
- Post-election debrief — compare predicted turnout to actual on the next cycle’s imported roll; the delta drives the next retrain and surfaces model drift.