Election Forecast

West Virginia Primary Election Forecast

Today voters will go to the polls in West Virginia 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 an accurate score.

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

U.S. Senate Primary Election Forecast

Democratic candidates:

Candidate It Score
Dennis Melton* 4%
Natalie Tennant 93%
David Wamsley* 4%

Natalie Tennant will likely win the nomination.

Republican candidates:

Candidate It Score
Larry Butcher* 17%
Shelley Capito 68%
Matthew Dodrill* 15%

Shelley Capito will likely win the nomination.

District 2 Primary Election Forecast

Republican candidates:

Candidate It Score
Robert Fluharty* 4%
Steve Harrison 7%
Charlotte Lane* 20%
Alex Mooney 38%
Jim Moos* 4%
Ken Reed* 18%
Ron Walters 9%

Alex Mooney will likely win the Republican nomination for congressional district 2.

Democratic candidates:

Candidate It Score
Nick Casey 54%
Meshea Poore 46%

Nick Casey will likely win the Democratic nomination.

District 3 Primary Election Forecast

Democratic candidates:

Candidate It Score
Nick Rahall 65%
Richard Ojeda* 35%

Nick Rahall will likely win the Democratic nomination for district 3.


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 Traci Gardner)

Josh Light

Josh Light

Josh Light was CEO and Co-Founder of PoliticIt.
Josh Light


I enjoy tweeting about startups, and economics.
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