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Chris F. Masse August 8th, 2008

I like BetFair and the BetFair people very much. I was the only blogger to talk up the BetFair starting price system and the BetFair brand-new bet-matching logic. But the other face of the coin is that 2 aspects of their model are rotten to the core.

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BetFair was created in 1999 and started off in 2000. Since that time, 2 major things arrived on the world scene. Number one, we have seen the emergence of the prediction market approach. Number two, the Web has taken our lives, and Google has become the dominant Internet search engine. Here are how these 2 major trends are affecting BetFair negatively.

1. Decimal Odds (a.k.a. Digital Odds). - The prediction market approach means that we attack the public with the news and their associated probabilistic predictions, expressed in percentages, where high prices mean high probabilities of happening. BetFair, at the contrary, approach the public with a betting universe and an arcane vocabulary (”backing” and “laying”) where low prices mean high probabilities of happening. That is totally counter intuitive.
2. Non-Indexable Prediction Market Webpages. - Like it or not, Google is now the world’s #1 media. We “google” anything, first thing in the morning. None of the BetFair prediction market webpages can be indexed by Google and the other Internet search engines. That means that BetFair is missing out, in my estimation, on hundreds of thousands of Google visitors each year. Those Google visitors will favor other prediction exchanges (e.g., HubDub) whose prediction market webpages are indexed naturally by the Internet search engines.

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The British, who drive on the wrong side of the road, don’t have the 2 most important keys of the future.

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Tags: BetFair, business models, decimal odds, digital odds, Google, Google Search, Google Web Search, Internet search engines, odds, prediction market approach, probabilistic predictions, probabilities

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Chris F. Masse July 18th, 2008

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UPDATE: He took the bait…

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Tags: 2008 US presidential elections, Al Gore, Barack Obama, event derivative markets, Hillary Clinton, InTrade, prediction markets, probabilities, US presidential elections

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Eric Zitzewitz June 26th, 2008

BetFair is running markets on both who will be the next vice president and who will be nominated by the two parties.

As we’ve discussed before in other contexts, one can divide two probabilities like these to obtain a conditional probability: e.g., if the Democrats put X on the ticket, they will win the general election Y% of the time (where Y = odds of X becoming VP/odds of X being nominated).

These markets are thin, so the conditional probabilities should be taken with a grain of salt. But they are interesting nonetheless:

The pattern I see here is that conditional probabilities are higher for fresh faces (Webb, Sebelius; and arguably Bayh and Richardson despite their longer tenure) than for the old guard (Clinton, Nunn, Biden).

Of course, these should be viewed as correlations, not necessarily causal effects. For example, two possible explanations are: 1) putting a fresh face on the ticket helps Obama, either because there is less baggage or less of a contrast in national-politics resume length, or 2) Obama will only pick an old guard candidate in the state of the world in which he needs to shore up a weakness (i.e., picking Clinton to end a civil war, or Nunn to add foreign policy experience).
On the GOP side:

Huckabee has the highest conditional probability, and Pawlenty and Jindal are noticeably lower. Interpreting this one is harder: it depends on what aspect of Huckabee one thinks the market is expecting to be appealing (religion, likeability, Southernness, selective economic populism).

Technical note: the bids and asks reported above are actual quotes scrapped this AM; the mids are (bid+ask)/2, rescaled to add to 100 across all candidates.

Tags: 2008 US presidential elections, BetFair, conditional probabilities, event derivative markets, odds, prediction markets, probabilities, US presidential elections, vice president, vice presidents, VP, VP conditional probabilities

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Chris F. Masse June 3rd, 2008

Probabilities…

… and then… Prices.

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Talk first probabilities… because people interested in news get the concept and vocabulary.

Then, later on, when only the bettors and traders stay with you for the rest of the conversation, talk prices.

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BetFair’s Mark Davies croaked in French about “les prix des paris” (the prices of the bets) to 1.6 million frogs, on French TV, last Sunday.

I’m persuaded the frogs didn’t get the first clue about what he meant.

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It shows, once again, that BetFair is a laggard when it comes to adopting the prediction market approach.

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Tags: BetFair, event derivative markets, Mark Davies, prediction market approach, prediction markets, prices, probabilities

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Chris F. Masse May 10th, 2008

Take their latest political output: not a single word about HubDub’s prices / probabilities. So, what the HubDub blog does is simply political journalism, with a link to the related HubDub prediction market.

Examples of prediction market journalism:

• Justin Wolfers at Freakonomics & Wall Street Journal —spot that he cites InTrade’s prices as probabilities.
• the InTrade bulletin —spot that they publish some good, readable compound charts.
• my previous Obama+Clinton post —spot that I’m the only one in the Blogosphere to do a multi-exchange reporting.

The 3 examples above are not the richest forms of prediction market journalism, but it’s a good step in the right direction. (Small step for for a man; big step for humanity. )

I am disappointed that Nigel Eccles (who is a guy who has computed many things right, other than that) let his bloggers without a clear handbook.

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Tags: bloggers, Chris F. Masse, Chris Masse, event derivative markets, HubDub, HubDub blog, InTrade, Justin Wolfers, Midas Oracle, Nigel Eccles, prediction market journalism, prediction markets, prices, probabilities

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Chris F. Masse May 2nd, 2008

Excellent formulation by Mike Linksvayer.

Tells a lot about the usage of the prediction market probablities, and how we should market them to people. BetFair, InTrade and TradeSports, are you listening?

I check prices at Intrade most days, which gives me a more accurate and much more concise status update than any amount of time spent reading or watching commentary.

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Tags: event derivative markets, prediction markets, probabilities, usage

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Chris F. Masse May 1st, 2008

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Boris Johnson, next mayor of London:

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RELATED: BetFair’s “percentage vote share” prediction markets are illiquid, alas.

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BetFair blog’s London page — Tabloid style. I don’t like it much. I think politics should be treated more seriously.

Mike Robb’s index page —maybe he’ll publish something new today (if he finds inspiration after a good Guinness).

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MORE INFO: I read a story on Slate, yesterday, about the London mayoral race, but it was overall uninteresting. Also, I haven’t spotted any New York Times stories on that election, lately, that I could recommend to you. So, you’re left with digging Google News, if you want more to read.

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PERSONAL NOTE: I enjoyed London and the Londoners, last year. Was my first time there. (Knew well Bristol and Bath, for years.) Can’t wait returning to London.

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Tags: BetFair, Boris Johnson, decimal odds, digital odds, elections, event derivative markets, fractional odds, Ken Livingstone, London, mayor, percentages, prediction markets, probabilities

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Chris F. Masse January 30th, 2008

Odds that Hillary Clinton gets the 2008 Democratic nomination = 1.56 (digital odds taken at 9:15 AM EST)

To get the implied probability expressed in percentage:

• Take the number “1″;
• Divided it by the digital odds (here “1.56″);
• Then multiply the result by 100;
• 64.1% = ( 1 / 1.56 ) x 100

BetFair-generated implied probability is not far away from InTrade’s 62.1%.

Psstt… This present post was prompted by Niall O’Connor, who puts all his faith in the BetFair instant “over-round” —which indeed doesn’t add up to the virgin and perfect “100%” that Niall is seeking (like the Monthy Python were seeking the Holy Grail). Good luck for your quest, Niall.

The French Taunter:

Your mother was a hamster and your father smelt of elderberries!

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External Resource: Interpreting Prediction Market Prices as Probabilities - (PDF file) - by Justin Wolfers and Eric Zitzewitz

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NEXT: Implied Probability of an Outcome –BetFair Edition

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Tags: BetFair, digital odds, implied probability, probabilities, probability

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Chris F. Masse January 25th, 2008

Jed Christiansen outputs the best explainer on prediction markets I’ve seen in years. Go read it.

- Fundamentals of Prediction Markets
- Different types of Prediction Markets
- Problem #1 – Understanding Probabilities
- Problem #2 – Prediction timescale
- Problem #3 – Assessing accuracy
- Problem #4 - Compared to what?
- Summary – How have the political prediction markets really performed?

Tags: accuracy, event derivative markets, prediction markets, probabilities

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Chris F. Masse January 22nd, 2008

Lance Fortnow:

[...] Notice that when we have a surprise victory in a primary, like Clinton in New Hampshire, much of the talk revolves on why the pundits, polls and prediction markets all “failed.” Meanwhile in sports when we see a surprise victory, like the New York Giants over Dallas and then again in Green Bay, the focus is on what the Giants did right and the Cowboys and Packers did wrong. Sports fans understand probabilities much better than political junkies—upsets happen occasionally, just as they should.

Previously: Defining Probability in Prediction Markets - by Panos Ipeirotis - 2008

[...] Interestingly enough, such failed predictions are absolutely necessary if we want to take the concept of prediction markets seriously. If the frontrunner in a prediction market was always the winner, then the markets would have been a seriously flawed mechanism. [...]

Previously: Can prediction markets be right too often? - by David Pennock - 2006

[...] But this begs another question: didn’t TradeSports call too many states correctly? [...] The bottom line is we need more data across many elections to truly test TradeSports’s accuracy and calibration. [...] The truth is, I probably just got lucky, and it’s nearly impossible to say whether TradeSports underestimated or overestimated much of anything based on a single election. Such is part of the difficulty of evaluating probabilistic forecasts. [...]

Previously: Evaluating probabilistic predictions - by David Pennock - 2006

[...] Their critiques reflect a clear misunderstanding of the nature of probabilistic predictions, as many others have pointed out. Their misunderstanding is perhaps not so surprising. Evaluating probabilistic predictions is a subtle and complex endeavor, and in fact there is no absolute right way to do it. This fact may pose a barrier for the average person to understand and trust (probabilistic) prediction market forecasts. [...] In other words, for a predictor to be considered good it must pass the calibration test, but at the same time some very poor or useless predictors may also pass the calibration test. Often a stronger test is needed to truly evaluate the accuracy of probabilistic predictions. [...]

Tags: Predictions, probabilistic predictions, probabilities, probability

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