Data Analytics Data Science Elections Politics Statistics

Did Clyburn Help Biden in South Carolina?

By: Dr. Ikjyot Singh Kohli The conventional wisdom by the political pundits/analysts who are seeking to explain Joe Biden’s massive win in the 2020 South Carolina primary is that Jim Clyburn’s endorsement was the sole reason why Biden won. (Here is just one article describing this.) I wanted to analyze the data behind this and […]


Optimal Strategies for Winning The Democratic Primaries

By: Dr. Ikjyot Singh Kohli Election season is upon us again, and a number of people from political analysts to campaign advisors are making a huge deal about winning the Iowa caucuses. This seems to be the standard “wisdom”. I decided to run some analysis on the data to see if it was true. I […]

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The Relationship Between The Electoral College and Popular Vote

An interesting machine learning problem: Can one figure out the relationship between the popular vote margin, voter turnout, and the percentage of electoral college votes a candidate wins? Going back to the election of John Quincy Adams, the raw data looks like this: Electoral College Party Popular vote  Margin (%) Turnout Percentage of EC John […]

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Protected: Physics, Data, and The 2016 US Elections

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Data Analytics Data Science Elections Mathematics Politics

2016 Michigan Primary Predictions

Using the Monte Carlo techniques I have described in earlier posts, I ran several simulations today to try to predict who will win the 2016 Michigan primaries. Here is what I found: For the Republican primaries, I predict: Trump: 89.64% chance of winning Cruz: 5.01% chance of winning Kasich: 3.29% chance of winning Rubio: 2.06% […]

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Canadian Federal Election Predictions for 10/19/2015

Tomorrow is the date of the Canadian Federal Elections. Here are my predictions for the outcome: That is, I predict the Liberals will win, with the NDP trailing very far behind either party.