Mercury's Blog

How did 2009 turn out?

Early this year I posted my predictions for 2009. In the best spirit of Robin Hanson (getting better predictions by simply tracking how close predictions matched reality), I want to see how I did.

• “Prediction markets in 2009 are going to become even more well-known and wide-spread, but there will be no single event that brings them to the attention of the public. It’s going to be a slow, but steady, growth.” – This was spot on. All of the vendors appear to be doing well, but there was no big “event” that brought everyone attention.
• “All of the prediction market vendors will mature their business offering/proposition.” – Not having been on the receiving end of any of their sales pitches, I can’t say this one is true for sure. But from their blog posts and public statements I would assess this as likely.
• “HubDub will continue to only be the only strongly popular play-money prediction market.” – Put me down as wrong on this one. Nigel and the creators of HubDub have focused their time and effort on FanDuel instead. (Rightly, for revenue reasons!) So while HubDub is still active, it’s not the hive of activity it was for a while.

That said, I’m still a fan of niche, public, popular sites like HSX. They managed to turn play-money prediction markets into a real-money revenue stream by analyzing trader behaviour (which can only be seen by administrators) and selling that business intelligence to the studios. This could be replicated in many industries, such as video games or television.

• “While a couple additional software vendors may appear, I get the feeling that the market for prediction market software is largely saturated.” – This was also spot on.
• “I’m looking forward to see how the CantorExchange develops.” – Not so much a prediction, but a hope that it proved interesting. It’s taken a long time to get up and running apparently, and won’t be widely launched for real-money contracts until 2010. (If I read the website correctly.)

A great development from InTrade

Just yesterday John Delaney of InTrade posted on his blog that InTrade will soon be offering some historical market data to the public for free. (As he notes, this means they’re losing a source of potential revenue, as historical data can be quite valuable.) This is a great development, and should be a solid source of data for people to dig in and get interested in how traders operate in a prediction market. Kudos to John for doing this.

It might not be the most ground-breaking innovation out there, but I really like what Inkling has done recently with their trading widgets. They recently demonstrated how your employees and partners can predict project management milestones within Basecamp, a very popular web application for project management.

Basecamp is used a lot in small and medium businesses, and I’ve heard that it’s becoming more common in niches of larger businesses. (It’s easy to put the relatively small monthly bills on corporate purchase cards.) It’s popularity means there’s a great population of potential customers that can start taking advantage of this new source of information.

(Inkling’s full post on how to implement prediction markets within Basecamp can be seen by clicking here.)

I’ve written before that I think there is a LOT of value in using prediction markets to forecast project milestones. (It’s generally done poorly, if it’s done at all.) One of my more popular posts according to Google Analytics is “RAG status – people lie, prediction markets don’t“. I dreamt of a day where prediction markets would be run on all project milestones, and the results of those markets would drive a project’s “RAG status”. That is now easily done with Inkling’s Basecamp widget.

There are probably still a few details to be ironed out with the actual implementation, but this is a great step forward for businesses that want better intelligence from their employees and partners. I look forward to hearing more from Inkling about how customers have taken advantage of this and if/how it’s worked for them.

From their blog post, here are two screenshots of what the screens look like before and after you trade:

The “trading” screen:

The results screen:

I recently found out about a new competitor in corporate prediction markets: CrowdClarity.

I’m a little partial to these guys, mainly because they come from my alma mater, the University of Michigan. The key people look to be a mix of entrepreneurial students and professionals. The company itself was started a year and a half ago, but has had success with early pilot projects.

In fact, three slides included in their “Learn more” online slideshow are quite powerful statements as to why prediction markets can be useful. They were predicting car sales in the winter of 2008/2009, during what was one of the most volatile months the industry has seen in many, many years. And the prediction market beat the internal forecast made at the beginning of the month, and the expert forecast made at the end of the month.

Here are the slides:

To recap, the prediction market beat the official GM forecast (made at the beginning of the month) easily, which isn’t hugely surprising considering the myopic nature of internal forecasting. But the prediction market also beat the Edmunds.com forecast. This is particularly interesting, as Edmunds would have had the opportunity to review almost the entire month’s news and data before making their forecast at the end of the month.

Examining the numbers

Let’s quickly quantify this error. Assume an average Chevrolet sells for $18,000. After dealer markup, assume that GM/Chevrolet receives $16,000 per vehicle.

Within the first week of November 2008, the prediction market would have warned Chevrolet that they were going to miss their revenue targets by $800 million in the Chevrolet division alone. And depending upon the exact product mix, this could have easily exceeded $1billion.

Now Chris can blather on about corporate prediction markets, but he’s simply wrong. Assume that even with three weeks’ early warning Chevrolet was only able to save 10% of that gap, it’s still $80million in savings. Even if a corporate prediction market for a giant company like GM cost $200,000 a year, that would still be a return on investment of 40,000 %. And again, that’s in the Chevrolet division alone. (Note: It would be a rare prediction market that cost $200k/year to run.)

Now not every problem should be solved by a prediction market. This is where management expertise comes in: are the errors large enough to warrant the cost of reducing those errors? But big problems with big numbers are often very suitable to address with a prediction market.

Summary

I’d like to wish good luck to the CrowdClarity team. It’s great to see Wolverine entrepreneurs working on prediction markets. There are more and more players each year in the corporate prediction market scene, but with case studies like this behind their belts, they’ll be well-placed to pick up some business.

I’ve been blogging here about prediction markets for two and a half years. In that time, a lot of discussion has taken place both here and elsewhere about specific prediction market contracts; what happened on those markets and when.

One of the problems with prediction markets is that the information from them is not just valuable when it’s “live” but also after the market has finished and been closed out. It would be useful to go back and observe changes in markets over time, and even more valuable to compare markets to each other. Unfortunately, the data is largely useless to the various vendors and typically gets deleted after a certain period of time. (Though the real-money markets like InTrade do tend to protect their data as a valuable asset.)

I’ve recently found a solution that could prove quite useful to the prediction market industry: Timetric.

The Future

Timetric is essentially YouTube for time-series data. Their current library of charts (nearly 100k time-series) includes everything from exchange rates to crime data to Twitter replies. These can be manipulated, compared, layered on top of each other, and more. Their standard charts can be easily dropped into blog posts, and the raw data easily exported for manipulation elsewhere. (Full disclosure: I’m friends with the founders, three very smart PhD’s from Cambridge University.)

In my ideal future Timetric would be the home for all prediction market data. Users could compare how InTrade, NewsFutures, and Inkling performed over time on the 2008 election. Those political charts could be compared to economic data or virtually anything else to draw further conclusions. In fact, Timetric is now being used in Guardian blogs (a UK newspaper) to enhance coverage; an example is here.

This is an example of an embedded chart. (It doesn’t display correctly in Google Reader, so please click to see the original post.) Be sure to tick the “Multiple Axes” box for the full effect.

A year and a half ago at the London Prediction Markets conference a number of people talked about what the prediction market industry should do; one of the big ideas was a warehouse for old prediction market data. Now that warehouse doesn’t need to be built; Timetric has done it. All we need to do is fill it with data.

The Challenge for Prediction Market software vendors

My challenge to software vendors is to create feeds that can be easily imported into Timetric. Don’t throw away your data… give it away to someone who values it!

While it’s not explicity stated on their site, they can easily take RSS feeds (specifically Atom) of data. This means that as people trade on contracts the Timetric will be able to update data for the contract nearly automatically. Doing so will make prediction market data permanent, and widely available to academics and the public. It will enable individuals to do their own experimentation and potentially be a great tool for prediction market enthusiasts to debate the merits of methods and approaches for different markets.

Software vendors can also provide a simple CSV or XLS file with datetime information in the first column and values in the second column. While it won’t update like Atom feeds, it does provide the same data for easy import into Timetric.

So what prediction market software companies are open to creating an Atom feed of contract data?

What companies will provide data for a permanent archive?

Happy New Year to everyone! I hope that 2009 is happy, healthy and prosperous for you. I specifically wanted to thank you as a reader of this blog, and those who have linked to my posts here. Google recently refreshed their PageRank for Mercury’s Blog, and it now has a PageRank of 6/10! Though I don’t post often, I do try to write long-form news and analysis, and I’m glad you’ve found it useful.

What I expect for prediction markets in 2009

I’m probably going to regret doing this by the end of the year, but I’m going to put on my forecasting hat and try to predict what will happen this year in the field of prediction markets.

• Prediction markets in 2009 are going to become even more well-known and wide-spread, but there will be no single event that brings them to the attention of the public. It’s going to be a slow, but steady, growth.
• All of the prediction market vendors will mature their business offering/proposition. Both InklingMarkets and Xpree have made recent business development hires, and Inkling recently redesigned their website with a stronger business pitch and case studies. I think that business cases for prediction markets will be more clear and more developed in general because of this.
• HubDub will continue to only be the only strongly popular play-money prediction market. (HSX is probably similarly popular, but is a single-industry prediction market.) With Hubdub’s partner program and social networking features, they will see significant growth this year.
• While a couple additional software vendors may appear, I get the feeling that the market for prediction market software is largely saturated. People working on projects part-time and selling them may proliferate and be suitable for the lower end of the market, I don’t see many more serious prediction market software companies starting up in 2009.
• I’m looking forward to see how the CantorExchange develops. Will there be enough customers? Will the market see the effects of other studios trying to bid the prices of their competitors’ films down? Or will it be seen more as a financial hedging tool for institutions involved in the film industry? There are lots of open questions to evaluate here, but I still maintain it’s a great step forward for the prediction markets industry.

What to expect from this blog in 2009

I’m going to continue posting on both prediction markets, but continue talking more and more about how collective intelligence can be used in other contexts. Specifically, I’ve become really interested in how groups of people develop new ideas and new innovations.

As I’ve written before, I really don’t like the word “innovation.” Bruce Nussbaum at BusinessWeek has written a few things lately that I really like:

“Innovation” died in 2008, killed off by overuse, misuse, narrowness, incrementalism and failure to evolve. It was done in by CEOs, consultants, marketeers, advertisers and business journalists who degraded and devalued the idea by conflating it with change, technology, design, globalization, trendiness, and anything “new.” It was done it by an obsession with measurement, metrics and math and a demand for predictability in an unpredictable world.

and

“Innovation” is inadequate as a concept to deal with these changes. You have “game-changing” innovation, which is big but rare and incremental innovation which is small but common.

The truth about innovation is that it takes groups of people a lot of work to find and develop new ideas and turn them into innovations. Scott Berkun has a great chapter in his book “The Myths of Innovation” where he destroys the myth of the lone creative genius.

We know it takes groups of people to make innovation happen, so where are the tools for it? A lot of time, money and effort has gone into social networking software to connect people, such as Facebook, MySpace, etc. But I have to agree with Tim O’Reilly: we need to stop throwing sheep and do something worthy. But those tools can be very useful to us in helping foster innovation. I reviewed the software for idea and innovation software recently, but was left generally unimpressed.

These are some of my current thoughts. I plan on thinking out loud more in 2009 on these topics and seeing where it takes me. I look forward to your feedback.