YES, Harry Potter will survive J.K. Rowling’s 7th installment of the saga, The Deathly Hallows

Via Matt Drudge, via EITB 24, a hacker whose pseudonym is “Gabriel” tells you the ending of the book, The Deathly Hallows, yet to be published. He claims to have hacked one of the computers of the publisher. The bottom line is that Harry does not die, it seems.

I have just bought all the NewsFutures “Harry Potter will survive” contracts I could snap at $80. Plus, I was granted 10 pairs of contracts thru this program, and I sold the 10 “no” contracts to some misinformed trader at $20. Here’s my VIP page at NewsFutures. [I previously flip-flopped on this issue. :) ]

Just a short note on the NewsFutures prediction exchange: very usable. Our good doctor EJSS and his employees are detail oriented, and I think that’s the way to go. As I have written 10 times on Midas Oracle, this is about satisfying the sophisticated bettors, not the Joe Six-Packs. The folks at Pop Sci’s PPX should take notice. You know, I am on the receiving end of many youngsters’ claims to be “the next NewsFutures”. Well, my message to them: get up earlier, boys.

Here are all the blog posts and comments written about Harry Potter on Midas Oracle.

If I’m right to trust the hacker “Gabriel” and I win this game, then I’ll publish a victory blog post against Niall O’Connor and Michael Giberson. Niall O’Connor swallowed the William Hill story (All the money was on Harry Potter to die, so they stopped taking bets) like a lake carp swallows a peanut butter doughball. But, that’s too early. Let’s wait and see.

Harry Potter will survive The Deathly Hallows.

© NewsFutures

UPDATE: The sentiment of the majority favors the Niall O’Connor theory that puts William Hill at the receiving end of insider information (”Harry Potter dies”), lately. And the NewsFutures market-generated probability would reflect the (misinformed, on this one) “wisdom of crowds”, which kind of senses that an author for kids will not make the hero die. We’ll see.


milw0rm is a group of politically motivated “hacktivists” whose most famous exploit was penetrating the computers of the Bhabha Atomic Research Centre (BARC) in Bombay, the primary nuclear research facility of India, on June 3, 1998. They have anti-nuclear and pro-peace agendas and, in this case, anti-Harry Potter and pro-Pope Benedict XVI.

UPDATE: The contract of the Harry Potter event derivative at NewsFutures may be flawed.



Manipulation can affect prices

For the last two weeks a very interesting manipulation has been going on in Intrade’s “Hillary Clinton for President” contract.

1. The contract had been trading between 23 and 26 all year. It has consistently been about half the price of the “Hillary to get nominated” contract price. This ratio implies that, conditional on nominating Hillary, the Democrats have a 50 percent chance of winning the Presidency.

2. Comparing this with the unconditional probability of a Democratic victory (about 56 percent throughout 2007) suggests that Hillary is a slightly weaker general election candidate than the alternatives (Obama, mainly). [Note I say “suggests” because the comparison of conditional probabilities implies a correlation, but not necessarily that a Clinton nomination would cause a better outcome for the GOP. For more see the fifth question in this paper].

3. Around May 12, someone started buying “Hillary for President” pretty heavily, driving the price up to 40. This price is clearly ridiculous for two reasons:

3a. You could sell the President contracts of Hillary, Obama, Gore, and Edwards for a combined 69 (40+17+8+4) and buy the “Democrat to win” contract for 56.

3b. Since there was no movement in the nomination contract, the conditional probability of Hillary was now a ridiculous 40/52 = 77%, while the conditional probability of “Not Hillary” was 16/48 = 33%.

4. Unlike past manipulation attempts, this manipulator isn’t just dumping a ton of money in to move the price once. He (or she) is moving the price, and then providing support to keep the price high. Note that the price stayed at 40 for about a week (on higher than normal volume).

5. I mentioned the manipulation at the end of my talk at the Palm Desert prediction markets conference, figuring that there was no surer way to get a $100 bill picked up than to tell that crowd about it. Someone emailed Greg Mankiw and he blogged about it the next day. (Justin and I also just tipped off Tyler Cowen). Since then there has been some downward price pressure, but the manipulator isn’t throwing in the towel. He/she keeps replenishing the bid side of the order book, albeit giving ground in the process.

6. By my calculation, the manipulator has spent about $10k to push the Hillary contract up around 12 pts on average for 2 weeks, buying about 8,500 contracts in the process. [I’m assuming 26 is fair value and just summing up volume*(price – 26)].

7. So what do we learn from this?

7a. Manipulation doesn’t have to be as ham fisted as the 2004 Bush reelection contract manipulation.

7b. The manipulators are getting smarter. This manipulator was smarter in one sense by providing price support after the fact. But of course, he/she shouldn’t have pushed the price up to such an obviously ridiculous level (and should have bought and sold other contracts to keep the pricing relationships consistent). The same mistake probably won’t be made next time.

7c. By prediction markets standards, manipulation is expensive. But by political spending standards, manipulation could be reasonably cheap. That said, I can find only one media mention of the inflated Hillary price. $10k for one blog mention probably isn’t great value for money, but the Intrade prices get cited a lot these days, so the manipulator may just have been unlucky.

7d. None of this disputes Hanson and Oprea’s point that, if anticipated, manipulation could increase average prediction market accuracy. In their model, traders all have rational expectations about how much manipulation to expect. In the real world, they may need some help (hence this post).

7e. Although the Hillary price is down to 34.5 (bid-ask midpoint at time of writing), there are about 500 contracts bid above 33, so there is still plenty of free money there if you want it.

Hillary Clinton Chart 2007 Manipulations EZ

Super Bowl Analysis Highlights — Keith Jacks Gambles Second Turn

A bunch of Marginal Revolution commenters dared criticizing Keith Jacks Gamble&#8217-s Super Bowl Analysis Highlights (+ addendum). The impudence! The audacity! They called down the thunder- they should get ready for the boom. [*]

Yes, the results of individual plays depend on lots of factors including play selection, and there is no way my analysis can separate all of these factors. After all, football is a team sport, and thus I suspect that cleanly identifying a player&#8217-s sole contribution play-by-play to his team&#8217-s chance of winning may be impossible. Certainly the performance of the offensive line has a ton to do with a team&#8217-s success. But even with standard statistics for measuring a player&#8217-s performance these issues remain. Quarterbacks get credit for completions, passing yards, and throwing touchdowns even though the coach&#8217-s play calling, the line&#8217-s blocking, the receiver&#8217-s route running, ect have everything to do with creating those numbers. Attributing actions to the primary actors on a play is a natural way to compute statistics. At least my net probability points statistic provides some weight to the game situation when measuring a player&#8217-s impact on a given play. Certainly, a 3 yard run on 3rd and 2 at the opponent&#8217-s 4 yard line means more than a 3 yard run on 3rd and 10 at a team&#8217-s own 20. This difference isn&#8217-t captured in rushing yards, but it plays a major role in my numbers.

It&#8217-s not ludicrous to think that the error rate of a betting market is low. In fact, Professor Paul Tetlock&#8217-s research [PDF] shows that it is low. He finds that the implied probabilities (prices) for sports contracts on are very close to the frequency that these contracts payoff. See Figure 1 on page 41 and notice that for his data the differences between prices and frequencies are actually quite small- in the range of prices from 40 to 80 (where the prices were for most of the Super Bowl) he finds deviations of around 1 (1% in probability). Furthermore, a look at Table 1 on page 34 shows that the standard errors for the estimated deviations in this price range are too large to rule out &#8220-no deviation&#8221- as an unlikely truth.

True, before kickoff the betting market estimated that Florida had about a 30% chance of beating Ohio State, and certainly before kickoff the market would have estimated that Florida had only an very small chance of winning by so many touchdowns. However, I&#8217-m sure that the market&#8217-s estimate of Florida winning by so much was not zero. Thus, this one example doesn&#8217-t say much of anything about the error rate of betting markets. Small probability events do happen. I once saw a lady win $5,000 on a quarter slot machine in Vegas, but that doesn&#8217-t make it ludicrous for me to think that my chance of doing the same was extremely small.

I think that my analysis does account for bruising running, clock control, and ball control. If a Rhodes run wears on the defense, the market sees this fact and will raise the probability of the Colts winning more so than if he had just fallen down at the same spot without knocking into a defender. Also with clock control, if a Colts receiver stays in bounds to keep the clock moving to protect a lead, then the market sees this fact and will raise the probability of the Colts winning more so than if he had run out of bounds at the same spot. Certainly, Addai not fumbling will raise the probability of the Colts winning more so than if he had not controlled the ball. The reason that you don&#8217-t see these factors greatly boosting Rhodes&#8217- and Addai&#8217-s numbers in my analysis is that these things are to be expected of any NFL running back. All running backs pound defenders, stay in bounds when necessary, and hold on to the ball when most important. Thus, market prices only change a little when these actions are done successfully. Doing good things that are to be expected do not count that much towards a player&#8217-s performance in my analysis. However, doing something bad when something good is to be expected, such as fumbling, really hurts a player&#8217-s performance statistic. For example, Addai&#8217-s fumble and Manning&#8217-s interception hurt their overall net performance statistic.

I was surprised by how low Rhodes&#8217- performance measured in my analysis, and I think it&#8217-s in part due to poor play calling that unfortunately gets counted against Rhodes in my analysis. For example, when Rhodes ran for 8 yards on a 3rd and 10 inside the red zone, the market dropped the Colts chance of winning by 4.5% even though I think Rhodes getting 8 yards was a great outcome for a RUN inside the 10. The priced dropped because that play pretty much ended the Colts chance at scoring a touchdown on the drive given that the field goal team was now trotting onto the field. I think this price drop is suggestive that the Colts made a mistake in their play calling. There&#8217-s also a case in which Rhodes ran for 5 yards to midfield on a 2nd and 13, but the probability of the Colts winning dropped by 1%. Even though 5 yards is an above average outcome for a run, it&#8217-s not a good outcome for a play on a 2nd and 13, because the probability of a punt goes up. Although, not related to play calling, all of Rhodes&#8217- yards in the last 5 minutes of the game amounted in just a .5% increase in the Colts probability of winning because at this stage of the game the market was fully convinced that the Bears chance at a comeback was was less than 1%.

It&#8217-s a common misconception that betting lines are set up to get equal money on both sides. Betting lines are set up to maximize the sportsbooks&#8217- profits. See Steven Levitt&#8217-s paper [PDF] or just take a look at betting trends. NFL betters overbet on favorites, so I wouldn&#8217-t be surprised to see underdogs beating the spread a little more often than favorites. However, I would be shocked if underdogs can truly be expected to beat the spread more than 52.4% of the time since it would mean that an underdog better with a big pocketbook could expect to make a lot of money from the sportsbooks. Also, my data is taken from, an online exchange that takes no positions unlike a sportsbook. It&#8217-s a marketplace of betters, and so far, the evidence is that the prices there are pretty good estimates of probabilities. [&#8230-]