Tomorrow voters will go to the polls in Indiana 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.
Unfortunately…both Don Bates, and Kelly Mitchell are lacking sufficient data to calculate an accurate score. The current forecast is predicting a victory for Wayne Seybold, but this forecast isn’t going to be as accurate due to insufficient data.
Congressional District 3
Justin Kuhnle is forecasted to win tomorrow…due to insufficient data this race won’t be as accurate.
Congressional District 4
John Dale is forecasted to win tomorrow. Due to insufficient data…this election forecast won’t be as accurate.
Congressional District 5
Susan Brooks is forecasted to win the Republican nomination. Her challenger will likely be Lane Siekman, but all of Siekman’s Democratic opponents lack sufficient data to generate an accurate score.
Congressional District 7
J.D. Miniear is forecasted to win, however, this race lacks sufficient data to generate accurate scores.
Congressional District 8
Larry Bucshon is projected to win the Republican nomination.
Congressional District 9
The It Score is forecasting a victory for Bill Bailey.
Connie Lawson (Secretary of State), Suzanne Crouch (State Auditor), Peter Visclosky (Congressman of District 1), Jackie Walorski (Congresswoman District 2), and Luke Messer (Congressman District 6) went unchallenged this year.
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 Steve Baker)