Florida Election Forecast Congressional District 13


Tomorrow Alex Sink, David Jolly, Lucas Overby, and Michael Levinson will compete for congressional district 13.  PoliticIt has applied the It Score algorithm to find out who will likely win in this special election.

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

The It Score was used in the 2012 election to correctly predict the outcomes of every major federal race with 92% accuracy.  Last week the It Score forecasted the Texas primary election…predicting the races with 91% accuracy.

If a candidate has a higher It Score relative to their opponent then they will likely win on March 4.

Scores for candidates running against each other are calculated on a relative basis. Methodology for its calculation is contained at this end of this article.

Florida Election Forecast

Candidate It Score
Alex Sink 45
David Jolly 39
Lucas Overby 12
Michael Levinson 4

The It Score is indicating that Alex Sink has a slight edge in tomorrow’s election, and will likely win.  Sink outperformed her opponents across the board online.

David Jolly performed well online, but fell behind Sink on nearly every metric.  If Jolly chooses to challenge again in the future, then he should invest earlier in building his social networks if he hopes to stand a chance against Sink online.

Lucas Overby had an impressive online presence relative to libertarian candidates in other states.  His biggest weakness is his inability to attract earned media as effectively as his opponents.

Michael Levinson really doesn’t have much of a digital influence.


This Florida election forecast was generated by the It Score algorithm.

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.

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


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