Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics
    How Data Analytics Can Help You Construct A Financial Weather Map
    4 Min Read
    financial analytics
    Financial Analytics Shows The Hidden Cost Of Not Switching Systems
    4 Min Read
    warehouse accidents
    Data Analytics and the Future of Warehouse Safety
    10 Min Read
    stock investing and data analytics
    How Data Analytics Supports Smarter Stock Trading Strategies
    4 Min Read
    predictive analytics risk management
    How Predictive Analytics Is Redefining Risk Management Across Industries
    7 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Simple Methods and Ensemble Forecasting of Elections
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Predictive Analytics > Simple Methods and Ensemble Forecasting of Elections
Collaborative DataPredictive Analytics

Simple Methods and Ensemble Forecasting of Elections

mvgilliland
mvgilliland
6 Min Read
SHARE

Two enduring principles of forecasting are that simple methods can work as well as fancy methods, and that combining (averaging)  forecasts, also known as “ensemble forecasting,” will usually result in more accurate predictions than the individual methods being averaged. We saw a good demonstration of these principles in Tuesday’s election forecasts by Nate Silver on his FiveThirtyEight blog, and PollyVote.com. But let me digress…

Contents
  • Six Methods of Election Forecasting
  •  A Win for the Quants

Two enduring principles of forecasting are that simple methods can work as well as fancy methods, and that combining (averaging)  forecasts, also known as “ensemble forecasting,” will usually result in more accurate predictions than the individual methods being averaged. We saw a good demonstration of these principles in Tuesday’s election forecasts by Nate Silver on his FiveThirtyEight blog, and PollyVote.com. But let me digress…

Six Methods of Election Forecasting

 There are at least six kinds of methods used in election forecasting:

  • Nonsense: Basing the forecast on an observed historical correlation between the election outcome and a causally irrelevant variable. For example, the “Redskins rule,” which asserted that when the Washington Redskins football team wins their last home game prior to the election, the party that holds the White House wins the election. When this rule failed for the first time in 2004, it was amended to assert that when the Redskins win, the party that won the popular vote in the previous election wins the election. (Recall Bush v. Gore in 2000.) Result: On November 4 the Redskins lost their home game, thus foretelling a Romney win.
  • Punditry: One step beyond nonsense (but just a baby step), are the forecasts of the once employed politicians (Newt Gingrich I, Newt Gingrich II), once relevant consultants (Dick Morris), once funny comedians (Jim Cramer), and current intellectual leaders (Rush Limbaugh). Such forecasts are based on the nebulous concepts of “experience” and “gut feel.”  If you have a lot of money, there is no shortage of Washington, DC operatives willing to sell you their opinions, and part you from your political contributions. A laudable attribute of the pundits is that they don’t let data and scientific evidence get in the way of their viewpoints. (See Karl Rove vs. the quants at the Fox News Decision Desk.)
  • Econometric Models: The University of Colorado model stresses state-level economic data, including unemployment and changes in per capita income. This approach didn’t do so well. It forecast a Romney win with 330 electoral votes, and correctly called just 3 of 13 battleground states (with Florida still to be determined). Yale economist Ray Fair’s model is interesting in that it is claimed to have correctly predicted 21 of 24 presidential elections from 1916 through 2008. What should give one pause, however, is that Ray Fair wasn’t born until 1942, so how did his model “predict” those elections that occurred before the model existed? Even if he were a child forecasting prodigy and perfected his model in time for the 1944 elections, that would be 7 fewer election predictions to brag about. In fact, the model was first used only in 1980, and miscalled 1992, 2000, and now 2012 (Obama 49%), making it correct in just 6 of 9 elections, or just a little bit better than tossing a fair coin. (Note: To be fair, Fair has stated that the 2012 prediction is within the margin of error, so too close to call, but rendering the model irrelevant.)
  • Prediction Markets: Relying on the “wisdom of crowds,” the Iowa Electronic Market and Intrade are the two best known examples. On Monday Intrade priced Obama’s chances at 72.4%, and IEM at about 75.7% (average price for the day in the winner-take-all market). Of course, just like in predicting the weather, if you don’t forecast something (like rain) as either 0% or 100%, you can never be proven wrong. IEM’s vote share market averaged 50.9% for Obama on Monday, so that was pretty good.
  • Combination Models: PollyVote.com is an unweighted average of forecasts from five sources: polls, the IEM vote share prediction market, econometric models, expert surveys, and indexes based on voter perception and candidate biographies. In just its 3rd presidential election, PollyVote has always come within 0.5% of the two-party vote percentages, and was about 0.2% off this year (giving Obama 51.0% of the two-party vote).
  • Polling: The definitive source for election news is, of course, The Colbert Report, where New York Times blogger Nate Silver explained his prediction methodology: “Go and look at the polls and take an average and add up the states and see who has 270 electoral votes. It’s not really that complicated, but people treat it like it’s Galileo or something.” Just as I predicted on Tuesday, someone would pretty much nail the results and become famous, and Nate Silver is the one. 

 A Win for the Quants

While the PollyVote and Nate Silver results were certainly a win for the principles of forecasting, remember one more important principle:

More Read

“The term BI has been stretched and widened to encapsulate a lot of different techniques, tools and…”
Who I Want at the Business Intelligence Table
Unlocking Big Data Means Truly Understanding the Customer Journey [INFOGRAPHIC]
“We have talked about a new department on cyber-physical…
How to Set up a Predictive Maintenance Project that is Set for Success

Don’t jump to too many conclusions based on just one data point!

Sometimes good (or bad) results are just due to chance.

 

TAGGED:Barack Obamaelectionensemble forecasting; election forecasting; nate silverfivethirtyeight blogIntradeiowa electronic markets
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

protecting patient data
How to Protect Psychotherapy Data in a Digital Practice
Big Data Exclusive Security
data analytics
How Data Analytics Can Help You Construct A Financial Weather Map
Analytics Exclusive Infographic
AI use in payment methods
AI Shows How Payment Delays Disrupt Your Business
Artificial Intelligence Exclusive Infographic
financial analytics
Financial Analytics Shows The Hidden Cost Of Not Switching Systems
Analytics Exclusive Infographic

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

2012 Presidential Elections Popular Vote

1 Min Read

Analytics Overkill: Dashboards, Analysis and Big Data in the US Election

5 Min Read

Thanks, Big Data: America’s Drinking Habits Predict the Election

5 Min Read

2012: The Year of Big Data in American Politics

4 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?