Oddhead Blog

A collection of (relatively) recent yellow bricks on the road to widespread use of prediction markets:

• Wired Magazine features an article on political prediction markets infused with a mostly healthy if occasionally misguided skepticism. I was struck most by the nonchalant subtext that prediction markets have gone mainstream: “As you’ve no doubt heard…”
• Two nice articles at MSNBC and Business Week describe the context and opportunity presented by the CFTC’s mayday asking for guidance on regulating prediction markets in the US.
• In May, twenty-two economists, including two with Nobel Prize credentials (expect that number to grow), published a position paper in Science calling on the US government to legalize and regulate socially valuable prediction markets.
• The International Institute of Forecasters has set up a new Special Interest Group on Prediction Markets as part of its Forecasting Principles portal. The prediction market content is maintained by Andreas Graefe and contains a number of valuable resources including publications, press, events, links, and a nice news feed. (Some oddhead-centric items: coverage of Dimitrov and Sami’s EC’08 paper in ScienceDaily, slides and abstracts from the 3rd Workshop on Prediction Markets, and a preview of a session on prediction markets at INFORMS.) Andreas is also involved in Pollyvote, an election forecasting aggregator that uses IEM prices as one of its four components and, according to the site, “provided near-perfect predictions for the 2004 election”.

Plus a Murphy’s Law Alert:

Mr. Murphy may be hard at work orchestrating one of his signature ironies. Picture this: Prediction market proponents (including me) aren’t careful and get what they wish for: The CFTC takes prediction markets under its regulatory purview. Then, efforts to legalize Internet gambling in the US succeed, opening up an enormous and fabulously lucrative business that the “socially good” prediction market operators are legislated out of, mired instead in a separate regulatory goop of their own making.

Sorry folks, I just released 26 comments from purgatory where they had been sitting for as long as 58 days. All pending comments have now been approved and posted. I’ll try to go through them soon and respond where appropriate.

About two months ago I changed my WordPress configuration and it turns out that comments were piling up for moderation without email notification, and I failed to spot the growing queue until now.

Since I’m using re-captcha and have turned off trackbacks, I shouldn’t need to moderate comments going forward, so I’ve turned off moderation (fingers crossed).

I’m happy to announce the public beta launch of Yoopick, a sports prediction contest with a twist.

You pick any range you think the score difference or point spread of the game will fall into, for example you might pick Pittsburgh wins by between 2 and 11 points.

The more your prediction is viewed as unlikely by others, and the more you’re willing to stake on your prediction, the more you stand to gain. Of course it’s all for fun: you win and lose bragging rights only.

You can play with and against your friends on Facebook.

You can settle a pick even before the game is over, much like selling a stock in the stock market. Depending on what other players have done in the interim, you may be left with a gain or loss. You gain if you were one of the first to pick a popular outcome.

If you run out of credit, you can “work off your debt” by helping to digitize old books via the recaptcha project.

Those are the highlights if you want to go play the game. If you’re interested in more details, read on…

Motivation, Design, and Research Goals

There are a great many sources of sports predictions, including expert communities, statistical number crunchers, bookmakers, and betting exchanges. Many of these sources are highly accurate, however they typically focus on predicting the outright or spread-adjusted winner of the game. Our goal is to obtain more information about the final score, including the relative likelihood of each point spread. For example, if our system is working, on average there should be more weight put on point spreads of 3 and 7 in NFL games than on 2,4,6, or 8.

We chose sports as a test domain to tap into the avid fan base and the armies of arm chair (and Aeron chair) prognosticators out there. However, the same approach should translate well to any situation where you’d like to predict a number, for example, the vote share of a politician or the volume of sales of your company’s widget. In addition to giving you the expected value of the number, our approach gives you the confidence or variance of the prediction — in fact, it gives you the entire probability distribution, or the likelihood of every possible value of the number.

Underneath the hood, Yoopick is a type of combinatorial prediction market where the possible outcomes are the values of the point spread, and each pick is a purchase of a bundle of outcomes in a given interval. We use Hanson’s logarithmic market scoring rules market maker to price the picks — that is, to set the risk/reward ratio. This pricing mechanism also determines the gain or loss when picks are settled early.

Wins and losses on Yoopick are measured in milliyootles, a social currency useful for expressing thanks.

Our market maker can — and we expect will — lose yootles on average. Stated another way, we expect players as a whole to gain on average. At the same time, we actively work to improve our market maker to limit its losses to control inflation in the game.

Because the outcomes of a game are tied together in a unified market, picks in one region automatically affect the price of picks in other regions in a logically consistent way. Players have considerable flexibility in how and what information they can inject into the market. In particular, players can replicate the standard picks like outright winner and spread-adjusted winner if they want, or they can go beyond to pick any interval of the point spread. No matter the form of the pick, all the information flows into a single market that aggregates everything in a unified prediction. In contrast, at venues from Wall Street to Churchill Downs to High Street to Las Vegas Boulevard, markets with many outcomes are usually split into independent one-dimensional markets.

Our goal is to test whether our market design is indeed able to elicit more information than traditional methods. We hope you have fun playing in our Petri dish.

Sharad Goel
David Pennock
Daniel Reeves
Prasenjit Sarkar
Cong Yu