My remark to John Tierney:

InTrade got it [almost] spot on because they were wrong on Missouri (which was predicted to go for Obama but went to McCain) and wrong too on Indiana (which was predicted to go for McCain but went to Obama) —and those 2 opposite mistakes canceled themselves because those 2 states have the exact same number of electoral votes (11). Hence, I disagree with your method.

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APPENDIX:

Here&#8217-s a visual post-mortem of the 2008 US presidential elections.

Pay attention to Missouri and Indiana.

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A) InTrade, on November 5, 2008 (screen shot taken at 2:00 am):

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Prediction Markets &amp- State Polls, on November 4, 2008:

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B1) Prediction Markets (on November 4, 2008)

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InTrade (screen shot taken at mid-day ET, November 4, 2008):

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InTrade (screen shot taken in the morning, November 4, 2008):

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BetFair (screen shot taken in the morning, November 4, 2008):

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HubDub (screen shot taken in the morning, November 4, 2008):

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B2) State Polls (on November 4, 2008)

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Karl Rove (on November 4, 2008):

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CNN (on November 4, 2008):

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Pollster (on November 4, 2008):

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Electoral-Vote.com (on November 4, 2008):

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Nate Silver (on November 4, 2008):

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PREDICTION MARKET PROBABILITIES

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Explainer On Prediction Markets

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A prediction market is a market for a contract that yields payments based on the outcome of a partially uncertain future event, such as an election. A contract pays $100 only if candidate X wins the election, and $0 otherwise. When the market price of an X contract is $60, the prediction market believes that candidate X has a 60% chance of winning the election. The price of this event derivative represents the imputed perceived likelihood of the partially uncertain event (i.e., its aggregated expected probability). A 60% probability means that, in a series of events each with a 60% probability, the favored outcome is expected to occur 60 times out of 100, and the unfavored outcome is expected to occur 40 times out of 100.

Each prediction exchange organizes its own set of real-money and/or play-money markets, using either a CDA or a MSR mechanism &#8212-with or without an automated market maker.

Prediction markets enable us to attain collective intelligence. Prediction markets produce dynamic, objective probabilistic predictions on the outcomes of future events by aggregating disparate pieces of information that the traders bring when they agree on prices. The event derivative traders are informed by the primary indicators (i.e., the primary sources of information), like the polls, for instance. These informed speculators then execute their transactions based on their anticipations about the future &#8212-anticipations that will be either confirmed or infirmed.

The value of a set of prediction markets consists in the added accuracy that these prediction markets provide relative to the other forecasting mechanisms, times the value of accuracy in improved decisions, minus the cost of maintaining these prediction markets, relative to the cost of the other forecasting mechanisms. According to Robin Hanson, a highly accurate prediction market has little value if some other forecasting mechanism(s) can provide similar accuracy at a lower cost, or if very few substantial decisions are influenced by accurate forecasts on its topic.

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More Info:

– The Best Resources On Prediction Markets = The Best External Web Links + The Best Midas Oracle Posts

– Prediction Market Science

– The Midas Oracle Explainers On Prediction Markets

– All The Midas Oracle Explainers On Prediction Markets

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