Election 2016 might shake our faith in data science

First off – I am about as independent and undecided as one can get in this country. In general I am fiscally conservative and socially liberal. I am not a big fan of Clinton, Sanders or Trump – and can’t even decide who I dislike the least. But – as the primaries wind down, I am starting to follow the election with great interest. This is not merely political interest or for entertainment – it just gets me all geeked out on the potential of data science to help these candidates.

Trump is the “presumptive” candidate for GOP. That does not mean anything about his chance of winning. He is not anywhere close on any dimension to the kind of GOP candidates that went before him in past elections. So the idea of “red states” and “blue states” as it exists today do not really matter to figure out how he can win.

I am not sure if his campaign used extensive data analysis so far. On TV, it seemed like he just used his big personality (and the free media coverage in drew) as the primary weapon and it threw off the conventional campaigns of his competitors. Even the grand daddy of all data scientists who cover elections – Nate Silver – was thrown off his game by the Trump campaign. But now that he is the presumptive candidate – his campaign will probably take a more data driven approach to his approach in general elections.

The modeling approaches could get very complex for figuring out what Trump should do to attract votes. I read somewhere that his big voter base is white males without a college degree. Since education has generally increased over the years, It will be interesting how the percentages work in each state. The general theory is that black community do not like Trump because of him challenging Obama’s birth certificate issue. But if all things remain equal, even if he takes a small chunk of black community votes – he might carry the state. But then women don’t like him either apparently – which adds to the complexity of a predictive model . Past polling data and all kinds of analysis that RNC must have done – my bet is that it won’t be of much use and new models will need to be created and tweaked.

Its not any easier to predict what works best for Clinton to get the 270 magic number in general election. Math looks to be in her favor to win the primaries of her party. That said – Bernie Sanders has an extremely loyal base , especially amongst young voters. Even if Bernie himself endorsed Clinton at the convention as I think he will – I am not sure whether his supporters will care for Clinton. In that case – will they vote for her, stay home or god forbid, vote for Trump ? Also – just as GOP data scientists  will have to find what exactly works for Trump – Clinton camp will need to find messages that work against him. Given no history exists for a candidate like Trump – this exercise should wear out a lot of keyboards.

While Trump is famous for his gut instinct driving his primary wins, there is one aspect that makes me think there is a bit of data analysis that has helped his cause. His idea of tagging Bush as “Low energy”, Rubio as “Little”, Ted Cruz as “Lying” and finally Clinton as “Crooked” seems to me like a data driven strategy. May be it did not start that way and his gut instinct gave him the idea to begin with Jeb Bush. But my best guess is that his team picked up on it and tested the other adjectives before he used them effectively in his debates and stumps. Of course I can only guess – and I would love to see what comes out after the election cycle is over and someone writes a book.

I am sure a book or two will be written – this election will put to test a lot of data science and its practitioners. I can’t wait.





Published by Vijay Vijayasankar

Son/Husband/Dad/Dog Lover/Engineer. Follow me on twitter @vijayasankarv. These blogs are all my personal views - and not in way related to my employer or past employers

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