Ohio Primary Election Forecast

Tomorrow voters will go to the polls in Ohio to determine who will represent their party.  PoliticIt has applied the It Score algorithm to find out who the likely representatives will be.

The It Score is a measure of a digital influence that correlates with election results (GigaOM).

The score was used in the 2012 election to correctly predict the outcomes of every major federal race with 92% accuracy, and it correctly predicted every race in the most recent Utah conventions.

If a candidate has a higher It Score relative to their opponent then they will likely win.

Scores for candidates running against each other are calculated on a relative basis.  An asterisk next to a candidate’s name indicates that they lack sufficient data to calculate a correct score.

Methodology for its calculation is contained at this end of this article.

Congressional District 1

Democratic Race

Candidate It Score
Fred Kundrata 51%
Jim Prues 49%

Fred Kundrata will likely defeat Jim Prues in tomorrow’s primary.

Congressional District 2

Democratic Race

Candidate It Score
Ronny Richards 35%
John Sheil 33%
Marek  Tyszkiewicz 28%
William Smith* 4%

This race will likely be close.  Ronny Richards is projected to win.

Congressional District 8

Democratic Race

Candidate It Score
Tom Poetter 52%
Matthew Guyette* 32%
Robert Crow* 9%
Mort Meier* 7%

Tom Poetter is forecasted to win, however, all of his opponents lack sufficient data to calculate accurate scores.

Republican Race

Candidate It Score
John Boehner 64%
J.D. Winteregg 26%
Matthew Ashworth* 6%
Eric Gurr 4%

John Boehner will likely win the nomination tomorrow.

Congressional District 14

Republican Race

Candidate It Score
Dave Joyce 77%
Matt  Lynch 23%

Dave Joyce is projected to defeat Matt Lynch in tomorrow’s primary.


Some races were omitted completely.  This was due to insufficient data.

The It Score is a machine learning algorithm that gathers chatter surrounding a political candidate from social, and traditional media sources in order to provide a gauge of their digital influence.  It accounts for tone, how people are reacting to a politician, the buzz surrounding the candidate, and what people are saying about the politician.

Whatever candidate has the highest It Score will likely win in the election.

The machine learning algorithm was trained off of actual primary election results.  The original algorithm was predicting at 67% accuracy, but overtime it learned.  Currently it is predicting at 92% accuracy, and has a standard error of 5.

One limitation the algorithm faces is it’s inability to predict races where candidates lack a presence online.  PoliticIt omits these races because there is insufficient data.

PoliticIt’s hope is to refine this algorithm so that political candidates can use it to receive real-time feedback on campaign performance.

(Photo Credit: Flickr via J. Stephen Conn)

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