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 looked at every Democratic primary since 1976 and tried to find which states are absolutely “must-win” for a candidate to be the Democratic presidential nominee. Because the data from a data science perspective is scarce, I had to run Monte Carlo bootstrap sampling on the dataset to come up with the results.
Interestingly, irrespective of the number of bootstrap samples, three classification tree results kept coming up, which I now present:
Very interestingly, from the classification tree above, one sees that actually the most important state for a candidate to win to ensure the highest probability of being the Democratic nominee is Illinois.
The other result from bootstrap sampling was as follows:
Here we see that winning Texas is of paramount importance. In fact, all subsequent paths to the nomination stem from winning Texas.
There is also a third result that came from the bootstrap simulation:
We see that in this simulation, once again Illinois is of prime importance. However, even if a candidate does lose Illinois, evidently a path to the nomination is still possible if that candidate wins Maryland and Arizona.
Conclusion: We see that from analyzing the data that Iowa and New Hampshire are actually not very important in becoming the Democratic party nomination. Rather, Illinois and Texas are much more important to ensure a candidate of a high probability of being the Democratic nominee.
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:
Predicted Percentage of Electoral College Votes (+/- 0.04996417)
One sees that even for an extremely low voter turnout (30%), at this point Hillary Clinton can expect to win the Electoral College by a margin of 61.078% to 71.07013%, or 328 to 382 electoral college votes. Therefore, what seems like a relatively small lead in the popular vote (6.1%) translates according to this neural network model into a large margin of victory in the electoral college.
One can see that the predicted percentage of electoral college votes really depends on popular vote margin and voter turnout. For example, if we reduce the popular vote margin to 1%, the results are less promising for the leading candidate:
Voter Turnout %
E.C. % Win
E.C% Win Best Case
E.C.% Win Worst Case
One sees that if the popular vote margin is just 1% for the leading candidate, that candidate is not in the clear unless the popular vote exceeds 60%.
A great deal of noise has been made in the previous weeks about the surge in the polls of Donald Trump and Bernie Sanders. This has led some people to question whether Hillary Clinton will actually end up being the Democratic party nominee in 2016. This was further evidenced by the fact that Sanders is now leading Clinton in the latest New Hampshire polls.
However, running an analysis on current polling data, I still believe that even though it is very early, Hillary Clinton still has the best chance of being the Democratic party nominee. In fact, running some algorithms against the current data, I found that:
Hillary Clinton: chance of winning Democratic nomination.
Bernie Sanders: chance of winning Democratic nomination.
These numbers were deduced from an algorithm that used non-parametric methods to obtain the following probability density functions.
Thanks to Hargun Singh Kohli for data compilation and research.