Wisdom of the Crowd – Part 2

Last time we looked at the concept of crowd wisdom and how it applies to Betfair markets, being sobered by their likely accuracy and pondering how we can combat that accuracy when we bet.

For all the occasions where crowds have demonstrated to be brilliantly predictive, punters should find hope in all the times the crowd has been wrong.


In the run-up to the 1992 UK General Election, every major polling company predicted a win for Neil Kinnock’s Labour party.  Even considering the margin-for-error, all those polls were significantly inaccurate, with John Major’s Conservative Party winning a surprise parliamentary majority.

Questionable polling seems to be on the rise, with Britain’s vote to leave the European Union, the United States’ election of Donald Trump, and numerous Senate and House races in the US in the 2014 and 2016, not universally precited by pollsters.

It’s important not to overly state the failures of such polls – most had the UK referendum as a close-contest and, likewise, polls in the US predicted some scenarios where Trump could win – but nonetheless many were poor at predicting voter turnout, which led to feeble forecasting.

In his 2004 book, The Wisdom of Crowds, Surowiecki examines the failings of some polling, claiming that Iowa Electronic Markets (IEM), a tool used in the US by academics to give students real-world experience of markets which operates in a similar way to Betfair, had consistently outperformed polling companies since its inception.  IEM, though, didn’t do so well in the 2016 elections either: just two weeks before election day it viewed a Clinton win as a 93% near-certainty.



The failings of polls – and of markets that try to outperform polls – can be explained by revisiting the conditions that Surowiecki claims are necessary for a crowd to be wise: diversity, independence, and decentralisation.

Political polls are based on relatively small samples – often around 1,000 people.  Whilst polling companies attempt to ensure the diversity of those polled, this diversity is limited because only the type of people prepared to answer polling questions are ever considered.  Some have argued that voting to leave the European Union and voting for Donald Trump were, for many, dark secrets, that those polled would not want to share with pollsters.

Betting markets around elections typically have far more participants and, unlike polls, the participants are expressing an opinion about who will win, rather than how they will vote.  Both factors suggest that betting markets will be more predictive in forecasting votes.  The issue, though, is that punters in these markets are unlikely to be decentralised or independent.  Although there are lots of them, they are drawn from a relative narrow pool of those interested in both politics and betting.

Furthermore, they will be working from similar information – some of it supplied by polling companies – meaning that betting markets around elections can become an aggregated and refined version of perceived wisdom, rather than the diverse, independent, and decentralised crowd that Surowiecki claims is required to be truly predictive.

In a 2011 Swiss study by Schweitzer and others, researchers observed the failings that can occur within a crowd when their information sources lack diversity.  Asking participants to predict the length of the Italian-Swiss border, researchers found that the accuracy of the median prediction decreased as participants were given more information about other participants’ estimates.


Similar things happen in financial and betting markets.  Information cascades, where participants, all acting on the same information, artificially skew the pricing within a market, occur all the time, seen most catastrophically in financial market bubbles which, invariably, burst.

In racing betting, a horse’s odds shortening can often – counter-rationally – cause other punters to want to back the horse, in the belief that the market somehow “knows” something that they don’t, and that the horse must be a “sure thing”.

This kind of short-term skewing of betting markets can provide opportunities for the well-informed punter – who is able to adopt a more objective view of value – to profit.

The approach of questioning crowd wisdom is explored in the now little-read, but aptly-named, 1995 betting manual, Against the Crowd, by Alan Potts.  In it, Potts argues that to be profitable, a degree of contrariness is desirable.

Potts’ view is backed up by data.  A 2006 study looked at 65,000 horses who had run in the UK, categorising over 5,000 of them as either “steamers” (their odds shortened by 5% or more in the last two hours before the race) or “drifters” (their odds lengthened by 5% or more in the last two hours before the race).

It found that, although both groups of horses had similar strike-rates, winning around 20% of their races, backing the steamers would have resulted in a 349-point loss, whereas the drifters returned a 231-point profit.

Next time we’ll look at other ways that you can identify when the crowd is likely to get things wrong, and how you can use the principles of crowd wisdom to improve your own betting.  In the meantime, simply being aware of those unprofitable “steamers”, and betting against them, might be enough to increase your profitability.


About The Author – Jack Houghton 

As a passionate sports’ fan and punter, Jack has written about sports and betting for over a decade, winning the Martin Wills Award for racing journalism in 2002 and writing Winning on Betfair for Dummies, first published in 2006 and now in its second edition, having sold over 35,000 copies in two languages.


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