After examining 50 Illinois House races from the 2014 General Election, I noticed a new pattern.
My algorithm produces a High Turnout Projection (the Green line) and a Low Turnout Projection (The Gray line).
In my previous post, I contend that during primary cycles, the Low Turnout Projection closely emulates the actual voter turnout.
After adding an additional operator to my algorithm, I found something that looks pretty special.
An average between the High and Low projections produces a number that more closely represents the actual turnout.
This is visible as the blue line (prediction) and yellow line (actual) meet at several points.
In the Illinois 31st House, 26,394 voters cast a ballot. The algorithm predicts 26,848 ballots– a difference of only 1.7 percent.
In the Illinois 45th House, 32,001 voters cast a ballot. The algorithm predicts 31,805 ballots. This is a difference of 0.6 percent, barely 1 half of a percent off.
In the Illinois 5th House, 27,155 voters cast a ballot. The algorithm predicts 27,093 ballots. That is a difference of 0.2 percent.
I’ll keep working. Let me know what you think!
PS> All data used in this post is public record. Using data sets from proprietary voting files delivers even greater accuracy. In my previous application during a live campaign, when campaign data was inserted into the algorithm, it outlined not only an accurate win number, but also purposed an accurate vote total for *Each candidate. I would be very curious to continue this experimentation.