Ideas for the online revolution.

Predicting the NY Democratic Primary

My county model (or more accurately one of the models - as the situation is more of a continuum) of primary only states came up with a prediction of Clinton 62.0 / Sanders 38.0 for NY.

That said, the main goal was to predict county values that would let me use my real-time county swing analyzer to predict the state swing.

Democratic Primary - State Model and Data Release

I decided to create a state level model for predicting the 2016 Democratic nomination race.

So I've aggregated my county level data into state level variables. I also added election results for WY (actual votes - from, KS, and AK (state delegates).

Note the election results for WA are legislative district delegates, and for ME they are state delegates - not popular vote.

My various state models give different results for NY. I've got Sanders at anywhere from 33% to 46%.


Predicting NY 2016 Democratic Primary

I think that given the number of polls, it is likely that a purely polling based forecast will prove the most accurate for NY.

Currently Pollster has it at 56.2% Clinton / 43.8% Sanders.

My demographics models are significantly more pro-Clinton, with Clinton being at around 66%. It is possible that the truth will like somewhere in between in which case we could see a 60/40 split.

2016 Democratic Nomination - Data Release

If you want to make your own county-level model for the 2016 Democratic nomination race, you can use my data set.

Download the Data Set

I would LOVE to hear from anyone who is using this. What does your model look like? What variables are you including? What are your predictions? What additional factors are you adding to the model that I don't have?

WY Caucus prediction

Using my county-level model and a caucus model (excludes the primaries), which has a much smaller standard error than when I combine primaries with caucuses - I am predicting WY - Sanders: 70.5% / Clinton 29.5% (of the Clinton + Sanders vote).

I am hoping that this will be within 2-3 percent of the final result, but no guarantees. My WI forecast was off by 5.85%.

This is based on race, income, age, sex, old FB like data (could be an issue), education, density, and past election results. It also includes Google Search trends for the last 7 days (the latest 7 days possible).

Wisconsin Democratic Primary Prediction - 2016

I've been updating my county-level model to predict the outcome of the democratic primary.

I recently added a Google search trend variable that uses the last seven days before the election (but not including the actual election date) and is equal to Sanders / (Sanders + Clinton). While Sanders dominates the search engine trends (typically 2:1), there is a strong positive correlation between the percent of searches that he gets and the outcome. As of this past hour, Sanders is getting 73% of the searches in Wisconsin - which is a strong showing.

Improving my Turnout Model - Latest Predictions

Previously I was relying upon Obama presidential 2008 vote to be solely predictive of turnout. However I've now created a turnout model for primaries with additional variables.

When I apply this to my predicted Sanders vote percentages (county-level), I get some small to medium sized changes in my predictions. Old values in parentheses.

Sanders Predicted Vote Share:
CT: 45.2% (45.3)
DE: 39.6% (35.5) <--- biggest change
IN: 53.4% - no change
KY: 44.3% (44.0)
MD: 27.6% (28.1)
NY: 33.1% (33.9)
OR: 71.8% (71.7)
PA: 42.7% (42.5)

Improving my Democratic Primary Prediction Model and Mapping Sanders Support

I'm learning a lot and have made significant improvements to my model.

Notably I've added a turnout variable - and am assuming that turnout will be proportional to Obama's presidential vote in 2012. While this is likely flawed, I don't have a better idea on how to predict turnout.

I added FB likes by county. Interestingly the FB likes by state are still significant.

I created a caucus-only model which has a much smaller confidence interval for its estimates (40% of the general model's interval).


Prediction Models for the Democratic 2016 Primary

Prediction Models for the Democratic 2016 Primary

There are a lot of people creating models for the 2016 primary. I decided to focus on the Democratic primary as it should be easier to predict the outcome in what is primarily a two person race. While I do prefer Sanders over the other candidates, I am currently not planning on voting for him as I prefer to vote for left-wing candidates (Green

My model for predicting the 2016 Democratic Primary/Caucus Results

I'm developing a model for predicting the Sanders vote share in the upcoming Democratic Party primaries and caucuses (2016). Unlike others, I've chosen to create a county level model. Ultimately this can be used to do real-time analysis of votes as they come in on election night. If anyone wants to work on developing this model, I would LOVE to hear from you. I've got an early version of the model (and a real-time "vote swing" analyzer), but it needs work. Notably I need a method for estimating county level turnout so I can translate the county swings into a state wide swing.

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