DavidMRothschild on March 02, 2014 @ 8:17PM
DavidMRothschild on March 01, 2014 @ 3:02PM
I always emphasis four traits to any good indictor or prediction: accurate, relevant, timely, and scalable. Accurate means a small error and well calibrated. Relevant means that the values the right value for the stakeholders. Timely means that the values starts early and update regularly. Scalable means that the methods and platforms allow many different values to be generated with minimal marginal cost.
DavidMRothschild on February 21, 2014 @ 10:13AM
Over the summer I got together with my research assistant Deepak Pathak and my colleague Miro Dudik to take a look at four different types of Oscar data: fundamentals, polling, prediction markets, and experts. While there are certainly meaningful things to learn from all of the data sources, properly translated prediction market prices were, by far, the most superior data source for creating continuously updating and accurate forecasts for all 24 Oscar categories.
Where did the other data go wrong?
DavidMRothschild on September 26, 2013 @ 9:01AM
We obsess about the aggregated prices that emerge from markets, whether it is oil, the Dow Jones, or the prediction market contract on who will be the next president of the United States. The price is a reflection of the subjective beliefs of individual traders, and we spend too little time considering the individual traders’ expectations, strategies, and motivations that combine to create that price. Rajiv Sethi of Barnard College and I were very lucky to examine a unique dataset this summer, which allowed us to learn more about how individual traders behave in markets; specifically, we examined trade-level data for all trades that occurred in the final two weeks of the 2012 election for either Obama or Romney to win on Intrade, the largest political prediction market in 2012.
DavidMRothschild on September 04, 2013 @ 8:05AM
On August 4 I tweeted that “Smart money is on de Blasio edging out [Bill] Thompson” for the second spot on the runoff. I followed that up by noting that Christine Quinn’s trajectory was troubling; it is not a good sign for a runoff if you are heading in the wrong direction. Both statements proved prescient as de Blasio was fourth in the polls on August 4 and is the current heavy favorite to be the next mayor of New York City. Meanwhile Quinn’s downward trajectory may push her out of a potential runoff, or even ameliorate the need for a runoff. But, Twitter does lead a little too much to the imagination, so here are some more details on the New York City mayoral contest.
DavidMRothschild on June 11, 2013 @ 10:49AM
We start with three different types of data …
DavidMRothschild on May 18, 2013 @ 10:54AM
May 18 at 6:05 ET: Halfway through the voting and only two viable countries left Denmark (89%) and Ukraine (7%). Of course, this was our initial top and second predictions for first place.
5:58 PM via ET Twitter: Calling it for Denmark with 17 of 39 countries voting! #ev2013
DavidMRothschild on March 02, 2013 @ 7:10PM
I judge my predictions on four major attributes: relevancy, timeliness, accuracy, and cost-effectiveness. I am very proud of my 2013 Oscar predictions, because they excelled in all four attributes: they predicted all 24 categories (and all combinations of categories), moved in real-time, were very accurate, and built on a scalable and flexible prediction model.
Relevant, real-time, accurate, and scalable: 2013 Oscar predictions are a win for predictive science
DavidMRothschild on February 25, 2013 @ 12:33AM
Predicting the Oscars for me is not about the Oscars per se, but the science of predicting. The challenge was to make predictions in all 24 categories, when most predictions only do 6. The challenge was to make predictions that move in real-time during the time period between the nominations and the Oscars, when most predictions are static. The challenge was to make to predictions that were accurate, not just in the binary correctness, but in calibrated probabilities. The challenge was to make these cost effective predictions, so that they could not only scale to 24 categories, but be useful in making predictions in varying domains.Prediction market data, including Betfair, Hollywood Stock Exchange, and Intrade, combined with some user generated data from WiseQ, allowed me to meet all of these challenges.
DavidMRothschild on February 24, 2013 @ 9:26PM