Intrade (or any prediction exchange wanting to compete), please list global warming contracts!

The information will probably end up saving lives, as we can more efficiently allocate finite resources to global warming research and reduction (vs. preventative healthcare, disease control, micro loans, etc.). For instance, we spend a lot of money on the AIDS pandemic in Africa, but polluted water and malaria each claim more victims. The investment allocations could be tweaked, quite a bit.

In today&#8217-s WSJ, Goddard Institute of Space Studies finds that:

The latest twist in the global warming saga is the revision in data at NASA&#8217-s Goddard Institute for Space Studies, indicating that the warmest year on record for the U.S. was not 1998, but rather 1934 (by 0.02 of a degree Celsius).

Canadian and amateur climate researcher Stephen McIntyre discovered that NASA made a technical error in standardizing the weather air temperature data post-2000. These temperature mistakes were only for the U.S.- their net effect was to lower the average temperature reading from 2000-2006 by 0.15C.

The new data undermine another frightful talking point from environmentalists, which is that six of the 10 hottest years on record have occurred since 1990. Wrong. NASA now says six of the 10 warmest years were in the 1930s and 1940s, and that was before the bulk of industrial CO2 emissions were released into the atmosphere.

Those are the new facts. What&#8217-s hard to know is how much, if any, significance to read into them. NASA officials say the revisions are insignificant and should not be &#8220-used by [global warming] critics to muddy the debate.&#8221- NASA scientist Gavin Schmidt notes that, despite the revisions, the period 2002-2006 is still warmer for the U.S. than 1930-1934, and both periods are slightly cooler than 1998-2002.

Still, environmentalists have been making great hay by claiming that recent years, such as 1998, then 2006, were the &#8220-warmest&#8221- on record. It&#8217-s also not clear that the 0.15 degree temperature revision is as trivial as NASA insists. Total U.S. warming since 1920 has been about 0.21 degrees Celsius. This means that a 0.15 error for recent years is more than two-thirds the observed temperature increase for the period of warming. NASA counters that most of the measured planetary warming in recent decades has occurred outside the U.S. and that the agency&#8217-s recent error would have a tiny impact (1/1000th of a degree) on global warming.

If nothing else, the snafu calls into question how much faith to put in climate change models. In the 1990s, virtually all climate models predicted warming from 2000-2010, but the new data confirm that so far there has been no warming trend in this decade for the U.S. Whoops. These simulation models are the basis for many of the forecasts of catastrophic warming by the end of the century that Al Gore and the media repeat time and again. We may soon be basing multi-trillion dollar policy decisions on computer models whose accuracy we already know to be less than stellar.

What&#8217-s more disturbing is what this incident tells us about the scientific double standard in the global warming debate. If this kind of error were made by climatologists who dare to challenge climate-change orthodoxy, the media and environmentalists would accuse them of manipulating data to distort scientific truth. NASA&#8217-s blunder only became a news story after Internet bloggers played whistleblower by circulating the new data across the Web.

Cross-posted from CavBet

Read the previous blog posts by Caveat Bettor:

• The Democrat SC Showdown: Intrade v. Zogby
• Zogby beats Intrade in predicting Nevada caucus winner Clinton.
• The GOP SC and Dem NV Showdown: Intrade v. Zogby
• Latest Intrade v. Zogby contest is up.
• Who said prediction markets were perfect?
• Intrade markets and Zogby polls agree in New Hampshire
• The Iowa Showdown: Zogby v. Intrade

This is the fifth month the simExchange video game prediction market has traded contracts on console hardware and the second month, the simExchange has traded contracts on 10 software SKUs. Contracts are settled against the NPD Group&#8217-s monthly unit sales data.

Sony&#8217-s PS3 sales came in line with market expectations at 159,000 units. The simExchange market expected 168,000 units to be sold in the month of July. PSP sales were also inline, coming in at 214,000 units, the market expected 209,000 units. Both Nintendo&#8217-s Wii and Microsoft&#8217-s Xbox 360 surprised the market with 425,000 units and 170,000 units sold respectively. The market had only expected 353,000 units for the Wii and 137,000 units for the Xbox 360. Sales of the Nintendo DS disappointed the market, coming in at 405,000 units. The market had expected 473,000 units.

It appears the market was originally correct when it had forecast the Xbox 360 to outsell the PS3 despite the PS3&#8217-s price cut. The market sold off the Xbox 360 July future from the 160,000 units range after believing the leak of the Xbox 360&#8217-s upcoming price cut would deter potential buyers, which in retrospect was an overreaction.

Overall, July software sales came in below the market&#8217-s expectations at $419.2 million. The simExchange had expected sales about 12.8% higher, between $459 – $487 million. It appears traders were generally bullish this month, expecting 16.79% more in total units for all software SKUs tracked in July.

The following tables compare market expectations on the simExchange and actual results as reported by the NPD Group. Expectations by leading analyst Michael Pachter of Wedbush Morgan are also presented for comparison purposes.

* NPD Group sales data
** The simExchange trading data
*** Games Industry, August 20, 2007

How exactly does this work?

Gamers and developers sign up on the simExchange for a free trading account. Using virtual currency called DKP, players buy virtual futures contracts that are under-predicting sales and short sell futures that are over-predicting sales. This concept is widely known as &#8220-the Wisdom of the Crowd&#8221- and this system is known as a &#8220-prediction market.&#8221-

This article was crossposted from the simExchange Official Blog and The simExchange Research.

If you haven&#8217-t seen this take a look: http://conferences.oreillynet.com/pub/w/64/about.html

The relevant bit:

Sample sessions and topics for Money:Tech include:

• Prediction markets work better than that other market
• Prediction markets are finally coming of age, becoming spooking-effective at predicting everything from movements in financial markets to American Idol winners. They are poised to go mainstream, and here&#8217-s how.

In a brief email exchange with Paul Kedrosky, he said that he was struggling to find good content for a session like this.

Any thoughts or suggestions for him?

~alex

Predicting the shift in odds caused by knockout competitions can be extremely profitable and lends itself excellently to tennis.

The odds on at least one competitor winning a tournament will always be 100%. However, as the tournament progresses and the number of competitors lesson then so do the odds of the remaining competitors and the 100% gets redistributed as each round is completed. For example should someone trading at 4.00 be knocked out of a competition then there is suddenly 25% of probability that needs to be redistributed.

This strategy lends itself very well to tennis as competitors are always being knocked out at differing timescales and also you are looking for one of the favorites to progress to the next round early whilst his close rivals are still to play (it also helps if the player concerned has a relatively easy next round). You then back this player. If any of his rivals should be knocked out of the competition then the price on the player you have backed will come in significantly. And even if all of his close rivals do progress the price will only drift ever so slightly. This is an excellent low risk strategy as the following example shows.

Nikolay Davydenko was priced at 10.5 (9.52% chance of winning) 3^rd favourite for a very open tournament in
Paris. He had already qualified for the third round and all his close rivals were still to play so I backed him for ?25. Inevitably a couple of his rivals lost and his price went down to 8.00 (12.5% chance of winning). He was due to play the next round first and was a hot favourite priced around 1.2 I figured he would still progress further and his odds would shorten dramatically so to protect my poison I layed him for ?25 at 1.2 during this match. Which then meant if he went out I would lose nothing. He duly won and I lost ?5 however his price to win the tournament had now gone down to 5 (20% chance of winning tournament). Obviously I let this bet stand as once again all his rivals were yet to play and Yet again a close rival lost and his price again went down to 3.25 (30% chance of winning) It was then I greened up and made myself to ensure a ?55 profit whoever won. This ended up being a ?50 profit as I had lost ?5 during the earlier Lay.

As you can see providing you do your homework and keep an eye on the times the matches take place. (All this information is available on the atptennis.com ot the tournament website) this strategy can prove to be extremely profitable for very low risk. Obviously this strategy can work for any tournament in any sport for example should a team like Manchester United get through to the next round of the FA cup in a lunchtime kick off before everybody else is set to play it might be worth backing them in the hope that one or more of their rivals e.g. Chelsea, Liverpool and Arsenal lose that afternoon.

I will be using this principle and backing Maria Sharapova currently available at 5.9 on Betfair to win the womens US Open tennis which begins this week.

Should you require any further information go to

www.tradeonsports.co.uk

www.tradeonsports.blogspot.com

or e-mail [email protected]