HuffPost Pollster begins by collecting every publicly released poll on the 2014 Senate races. We then use a statistical model to estimate the trend in support for each candidate based on all the survey data, adjusting for sample size and pollsters’ “house effects.” Interactive charts of those support trends are available here and on the HuffPost Pollster home page.
By running a series of simulations (known commonly as the Monte Carlo method), the model allows us to quantify the uncertainty associated with the current polling snapshot. That uncertainty comes from multiple sources: sampling error in the polls themselves, uncertainty about the house effect corrections, and uncertainty about how quickly vote intentions are changing.
The model then calculates a “win probability” for each race that is displayed in the graphics on this page. This probability takes three factors into account:
The time remaining between the current snapshot and the election.
The possibility that the polls could be wrong or that some sort of major event could shake up a race in ways that the current polls can’t measure.
The proportion of “undecided” voters in the polls. If the undecided proportion is high relative to the expected margin between the candidates, the outcome of that race must be less certain.
Lastly, we combine the win probabilities from each race and perform another set of Monte Carlo simulations to calculate the likelihood of each outcome — giving us the probabilities of Democrats keeping control of the Senate or of Republicans taking over.
Move the sliders to see how different win probabilities for the most competitive seats affect the overall chance that each party will control the Senate. The highlighted bar shows the most likely scenario.
Your selected probabilities would give Republicans a 61% chance of taking control of the Senate and Democrats a 39% chance of keeping control.
Each pollster tends to produce estimates that lean toward one candidate or the other relative to the overall polling average. As new polls are added, our model recalculates these house effects along with a 95 percent confidence interval. Download Data (CSV)
By Aaron Bycoffe, Jay Boice and Hilary Fung. Statistical model created by Natalie Jackson and Mark Blumenthal, building on the work of Simon Jackman.