CM ELECTION PREDICTIONS
Forecasts
Model-based race ratings.
Ratings combine historical results, polling where available, and structural factors such as incumbency, retirements, and national environmental factors.
About CM Election Predictions
Election forecasting built for clarity.
CM Election Predictions tracks U.S. elections with model-based ratings and live election results. The goal is simple: make the electoral landscape easier to read, and make results easier to understand.
Results
Live election results.
Election night tools are designed to give users the ability to easily understand results as they come in, and what they mean for wider results.
Accountability
Historical performance tracking.
Forecasts are meant to be evaluated over time. Past projections and actual outcomes are tracked on the Historical Results page.
Methodology
How the project works.
How does the Model work?
How does the Model work?
The Model incorporates historical election results, available polling data, and adjustments for national environmental factors such as incumbency, retirements, and election cycles, including differences between midterm and presidential elections. It estimates likelihoods, not certainties.
Inputs are applied consistently. Ratings are not manually overridden to match conventional wisdom, even when polling data is sparse.
Polling data published by The New York Times is used under the terms of the Creative Commons Attribution 4.0 International license.
Historical election data used in the Model includes data obtained from the Redistricting Data Hub and data produced by the Voting and Election Science Team (VEST). Analyses incorporating Redistricting Data Hub data are conducted using data obtained from the Redistricting Data Hub. Dave’s Redistricting App has also been used as a resource for evaluating and validating historical election data.
How accurate is the Model?
How accurate is the Model?
You can view the full performance record, including past projections and actual outcomes, on the Historical Results page.
Why do some predictions differ from conventional expectations?
Why do some predictions differ from conventional expectations?
Early in a cycle, polling data may be limited or unavailable. During that period, the Model relies more heavily on historical results and structural factors, which can produce ratings that look different from conventional expectations.
As polling volume increases, the forecast updates to reflect the new information. States with unusual recent voting histories may remain more competitive in the model than someone may assume from recent elections.
How often are forecasts updated?
How often are forecasts updated?
Prediction pages update around every 60 minutes.
Who runs CM Election Predictions?
Who runs CM Election Predictions?
CM Election Predictions is an independent election forecasting project created by Collen Mahoney. It is built to apply consistent methods to election forecasting and make the data easier to understand.