Trading on Markets vs. WeatherBill, Market Scoring Rule, BetFair Multiples

No Gravatar

I will try to be clearer.

PROBLEM: Other than on popular prediction markets at TradeSports-InTrade and BetFair, liquidity is a problem.

HANSON&#8217-S SOLUTION [*]: Market Scoring Rule. (See it in action: I’m betting the farm on AL Gore for the Oscars.) Cool, but not really customizable. In my example, I had the choice between three options, only. Quite poor.

WEATHER BILL SOLUTION: Forget trading on markets, forget automated market makers, forget mixing betting and trading (MSR), forget all this &#8212-impossible to go retail with these mechanisms. Weather Bill = a sophisticated way to take client orders, very precisely. Customers get what they really want. Weather Bill offers a highly customized hedging service. (Instead of going for re-insurance money, they pass the risks to some hedge funds via a CFTC-acknowledged EBOT, but that&#8217-s a back-office detail.)

HEDGESTREET CASE: HedgeStreet is a hedging/speculating service. (Note that they are regulated by the CFTC, not just acknowledged.) Let&#8217-s do a thought experiment. HedgeStreet (just like BetFair did recently) becomes a bookmaker, in addition to being an exchange. They adopt a WeatherBill-like user interface to take very precise client orders &#8212-as opposed to asking traders to fill in ask-and-bid orders and making use of an automated market makers (as HedgeStreet did, if I am correct). Of course, you would have two different user interfaces: one for the speculators and one for the hedgers. Then, do you see an algorithm/mechanism that would balance the two? And whom would they pass the risk to (if any)? And is there a way to have a mix exchange&#8211-bookmaker business model while satisfying the CFTC at the same time?

&#8212-

My point is that there is room for innovation, out there. BetFair has shown that you can go retail &#8212-providing that you cater to a population of sophisticated bettors. The fact that they now offer multiples show that the betting exchange business model has some limits. Sticking with it like a fundamentalist prevents you to servicing fully your customers.

As always, innovation is the key to customer satisfaction &#8212-and to profitability, mister Jason Ruspini.

Addendum: [*] Robin Hanson actually devised two versions of Market Scoring Rule. His combinatorial version might render this blog post totally pointless. Alas, no prediction exchange (betting exchange) is using his combinatorial Market Scoring Rule.

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 Tradesports.com 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 Sportsbook.com&#8217-s 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 Tradesports.com, 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-]

Super Bowl Analysis Highlights – MR Comments

No Gravatar

The Marginal Revolution commenters on Keith Jacks Gamble&#8217-s Super Bowl Analysis Highlights:

very interesting, but I disagree with his assignment of results to individual players. The results of individual plays, and their effect on the probabilities, depend on a lot of factors, not least play selection.
Posted by: Bernard Yomtov at Feb 11, 2007 12:33:55 PM

Its ludicrous to imagine that the error rate of a betting market is as low as implied. Consider that in the Florida-Ohio State game, the market was off by several touchdowns.
Posted by: Vish Subramanian at Feb 11, 2007 2:22:05 PM

This is an interesting idea, but it assigns no value to the worth of Dominic Rhodes wearing out the Bears&#8217- defensive players with his bruising running, or the clock control and just plain ball control Joseph Addai contributed to. It also fails to account for things like offensive line play.
Posted by: Mick at Feb 12, 2007 5:22:36 PM

Also, betting lines are set up to get equal money on both sides of the coin, so the betting public is supposed to be 50/50 winners and losers. Underdogs had a winning record in the NFL this year, and a runaway winning record against the spread.
Posted by: Mick at Feb 12, 2007 5:24:23 PM

Predictocracy: Market Mechanisms for Public and Private Decisionmaking – THE MARKET WEB

Predictocracy: Market Mechanisms for Public and Private Decisionmaking &#8211- by Michael Abramowicz &#8211- 2007-xx-xx &#8211- (fall)

Chapter: The Market Web &#8211- (towards the end of the book)

&#8212-

Michael Abramowicz:

If prediction markets should become commonplace, decisionmakers might link to them in their own analyses.

Will trading play-money and/or real-money event derivative contracts become commonplace? It&#8217-s likely, at the contrary, that trading will remain an elite occupation and that prediction markets with appropriate liquidity will remain scarce. Unless Google, Yahoo! (with Yootopia) and/or MicroSoft has/have a secret plan to popularize betting exchanges &#8212-which could well be since Bo Cowgill, David Pennock and Todd Proebsting are ambitious guys.

&#8212-

Michael Abramowicz:

For example, suppose that a corporation is deciding whether to build a new factory in a particular area. That decision might depend on variables like future interest rates and geographic patterns. And so, a decisionmaker might build a spreadsheet containing live links to prediction markets assessing these issues.

Interest rate prediction markets would help, for sure. As for geographic forecasting, maybe non-trading mechanisms could help &#8212-for real estate, I&#8217-m thinking of Zillow, or some improved mechanisms derived on Zillow.

&#8212-

Michael Abramowicz:

The Market Web

If prediction markets should become commonplace, decisionmakers might link to them in their own analyses. For example, suppose that a corporation is deciding whether to build a new factory in a particular area. That decision might depend on variables like future interest rates and geographic patterns. And so, a decisionmaker might build a spreadsheet containing live links to prediction markets assessing these issues. That way, as the market predictions change, the spreadsheet&#8217-s bottom line would change as well. Predictions in many prediction markets may be interrelated, and so market participants in one prediction market will often have incentives to take into account developments in other prediction markets. Prediction markets thus can affect one another indirectly, as participants in one update their models based on developments in another.

Sometimes, however, it might be desirable to construct links among prediction markets so that changes in one automatically lead to changes in another. Consider, for example, the possibility of a market-based alternative to class action litigation. In Chapter 8, each adjudicated case represented a separate prediction market, but often there will be issues in common across cases. Many thousands of cases may depend in part on some common factual issues, as well as on some distinct issues. Legal issues also may be the same or different across cases. Someone who improves the analysis of any common factual or legal issue can thus profit on that only by changing predictions in a very large number of cases. A better system might allow someone to make a change across a single market and have that change propagate automatically to individual cases.

The critical step needed to facilitate creation of the market web is to allow a market participant to propose a mathematical formula to be used for some particular prediction market. Some of the variables in that formula could be references to other, sometimes new, prediction markets. For example, a market participant might propose in a market determining how much amages the plaintiff should receive a formula dependent on variables such as the probability that the plaintiff states a cause of action, the probability that the plaintiff was in fact injured, the probability given injury that the defendant caused the injury, the probability given a cause of action that the defendant is subject to strict liability, the probability given no strict liability that the defendant was negligent, and the damages that the plaintiff should be awarded if liability is proved. This formula, for example, presumably would allow for no damages where the plaintiff probably does not state a cause of action. Each of the components of this formula might be assessed with a separate prediction market. We can easily build the market web by combining three existing tools. The first tool is a text-authoring market. The relevant text would be the formula itself, including specifications of other prediction markets that would be used to calculate specific variables. As with any text-authoring market, a timing market would determine when a proposal to change the text should be resolved. Other markets might become live only once proposals to take them into account were approved. Ex post decisionmakers would assess the wisdom of these markets&#8217- recommendations in some fraction of cases to discipline the market&#8217-s functioning.

The second tool would be a simple normative prediction market corresponding to the text-authoring market. It might also be possible to have computer software that automatically parses the formula and consults various sources, but the market sponsor need not build this tool. Rather, ex post decisionmakers will assess the appropriate value for the normative prediction market based on the formula. An advantage of this approach is that it would make it easy to use complicated formulas, as well as formulas that depend in part on numbers from sources other than prediction markets, or from prediction markets of other types. In addition, this approach makes it easy to collapse a formula into a single prediction market, if that should prove desirable. The formula text simply would be changed to a description of the market to be created, such as &#8220-adjudication of plaintiff&#8217-s liability in a particular case.&#8221-

Finally, the third tool necessary is a mechanism for determining the market subsidy. A separate subsidy would be needed for the text-authoring market and the normative prediction market. Each of these subsidies could be determined by additional normative prediction markets, perhaps with fixed subsidies. The subsidy for the text-authoring market in turn would be distributed by the text-authoring market to individuals who have proposed particular amendments, and individuals who have participated in the assessment of particular amendments. The text-authoring market also could allocate a subsidy to the first individual who creates the market and proposes some text for it. When the text-authoring market produces a new formula reflecting additional prediction markets, the subsidy for the main prediction market would fall (since calculating a formula based on other prediction markets will often be relatively easy).

A single node in the market web would thus consist of a text-authoring market describing the node and providing a formula for calculating it, a normative prediction market, and a set of additional prediction markets for determining how to distribute a subsidy to the different components of the node. The nodes collectively create a web because the formulas link to other nodes- software, of course, could easily make these links clickable. At the same time, a mechanism is needed to determine what portion of the market subsidy each node should receive. A simple approach would be for a prediction market to be used for every link, to determine the portion of the subsidy for each node that should be allocated to each node linked to it. The total should add up to less than 1, leaving some portion of the subsidy for the node itself.

With these markets established, software could easily distribute a single subsidy for the market as a whole to market participants who have traded on individual nodes when the market closes. Market participants working on one portion of the web, meanwhile, would not have to assess the relative importance of one node to nodes that are only distantly related. It would also be straightforward to have a continuously open market, periodically collecting and distributing money in accordance with individual participants&#8217- success on the market.

This assumes that the market web would be arranged on a single server. It is possible, though, that a node on one market web might link to a node on another market web. If market sponsors allowed such links, it could promote competition among prediction market providers. It also partially answers one potential criticism of using prediction markets for decisionmaking, that a software engineer might hijack the government by faking some prediction market results. Market participants at least will have incentives to identify fake prediction markets and not link to them. In principle, it is possible to have government decisions based entirely on decentralized prediction markets. A caveat is that the government might want to subsidized individual market web providers, and it might use centralized prediction markets to accomplish that.

Whether or not the markets themselves are decentralized, they would allow market participants to make it easier to assess the basis for market predictions. Indeed, the market web is in some ways a substitute for deliberative prediction markets, because both provide means of helping observers understand the basis for the market&#8217-s predictions. An observer could look at any individual node of the market web and understand how it has been calculated, though inevitably there must be some &#8220-leaf&#8221- nodes that themselves do not contain any formulas. At the same time, software might allow an observer to find all of the nodes that link to a particular node. So a market participant addressing a factual issue relevant to many cases could link to all of the cases represented by that factual issue. As a particular issue becomes increasingly important, the subsidy for that node should rise, and market participants can profit on their analysis of the issues relevant to that node without worrying about details of individual cases.

[…]

Brainy stuff. I&#8217-ll mind this for a while. I&#8217-m sure that the Midas Oracle readers will find this idea original &#8212-and maybe, interesting.