Roll... to the White House

Predictions for the 2012 National Elections:
A Bayesian Approach

The ideas and methods used in this site all stem from this paper written by Steven E. Rigdon, Sheldon H. Jacobson, Wendy K. Tam Cho, Edward C. Sewell, and Christopher J. Rigdon. This paper provides an analysis of the prediction results from the 2008 Presidential Election.

Background and Motivation

The results from the 2000 and 2004 United States Presidential Election suggested that it can be difficult to predict the winner of the presidential election based on popular vote. In fact, it is possible that the popular vote and the electoral college vote can lead to significantly different results.

To address this, Rigdon et. al created a new prediction model based on the electoral college vote to determine the winner of the next presidential election. This model was used to successfully predict the outcome of the 2008 Presidential Election (

For the 2012 elections, the Election Analytics Team at the University of Illinois has extended the model to handle Senate races in addition to the Presidential race. This will allow for predictions of which party will control the White House and the Senate. New prediction results will be posted as new polling data is made available.

Technical Analysis and Assumptions

The mathematical model employs Bayesian estimators that use available state poll results (at present, this is being taken from Rasmussen, Survey USA, and Quinnipiac, among others) to determine the probability that each presidential candidate will win each of the states (or the probability that each political party will win the Senate race in each state). These state-by-state probabilities are then used in a dynamic programming algorithm to determine a probability distribution for the number of Electoral College votes that each candidate will win in the 2012 presidential election. In the case of the Senate races, the individual state probabilities are used to determine the number of seats that each party will control.

Polling data for each state is weighted based on how recently the poll was conducted. If three or more polls are available within the past two weeks, then polls within the past week have a weighting factor of 1, polls between one and two weeks old have a weighting factor of 0.5, and polls older than two weeks have a weighting factor of 0. If two or fewer polls are available within the most recent two weeks, then the three most recent polls are used, with polls within the past week have a weighting factor of 1 and polls older than one week have a weighting factor of 0.5. If no polls comparing the two candidates are available for a state, the results of the last election are used to estimate the outcome in the upcoming election.

Swing Scenarios

Undecided voters can have a significant role on the outcome of elections. In fact, they are likely to be the ultimate deciders of who will win this presidential election. To address this source of error, five different scenarios are considered:

Each of these scenarios is considered individually. The Neutral Scenario provides an unbiased handling of undecided voters. The Strong Democrat and Republican Swing Scenarios provide two extreme envelopes around which the results obtained can be judged and evaluated. The Mild Democrat and Republican Swing Scenarios provide realistic possibilities if late breaking information surfaces that shifts voter preferences.

Other Considerations

Polls show figures that are for likely voters, as opposed to registered voters. If a greater number of registered voters show up to vote on November 6, this means that the poll numbers may not be representative of the actual voters.

Maine and Nebraska split their electoral college votes (4 and 5, respectively) based on their congressional districts. At present, these two states are treated like every other state (all or nothing).

Limitations of Results

The results presented are a direct function of the quality of the state poll data being used. Any biases in this data can lead to misleading and false results, and hence, invalid conclusions. The results of this analysis have been obtained as part of an academic, educational exercise to demonstrate the power of statistics and operations research to analyze data of significant importance and practical interest.

University of Illinois
at Urbana-Champaign

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