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Superforecasting: The Science of Making Money

April 12, 2017

‘Superforecasting’ is a term popularised from insights gained as part of a fascinating idea known as the ‘Good Judgment Project’, which consists of running tournaments where entrants compete to forecast the outcome of national and international events.

The key conclusion of this project is that an identifiable element of those taking part (so-called ‘Superforecasters’) were able to consistently and significantly out-predict their peers. To the extent that this ‘superforecasting’ is real, and it seems to be, it provides support for the belief that markets can not only be beaten but systematically so.

So what is special about these ‘Superforecasters’? A key distinguishing feature of these wizards of prediction is that they tend to update their estimates much more frequently than regular forecasters, and they do so in smaller increments. Moreover, they tend to break big intractable problems down into smaller tractable ones.

They are also much better than regular forecasters at avoiding the trap of underweighting new information or overweighting it. In particular, they are good at evaluating probabilities dispassionately using a so-called Bayesian approach, i.e. establishing a prior (or baseline) probability that an event will occur, and then constantly updating that probability as new information emerges, incrementally updating in proportion to the weight of the new evidence.

In adopting this approach, the Superforecasters are echoing the response of legendary economist, John Maynard Keynes, to a criticism made to his face that he had changed his position on monetary policy.

“When my information changes, I alter my conclusions. What do you do, Sir?”

In this, Keynes was one of the great ‘Superforecasters.’ Keynes went on to earn a fortune betting in the currency and commodity markets.

Superforecasters in the field of sports betting can benefit in particular from betting in-running, while the event is taking place. Their evaluations are also likely to be data-driven, and are updated as frequently as possible, taking into account variables some of which may not even exist pre-match.

They will be aware of players who tend to struggle to close the deal, whether in golf, tennis, snooker, or whatever, and who may be value ‘lays’ when trading in-running at short prices. Or shaky starters, like batsmen whose average belies their likely performance once they get into double figures. This information is only valuable, however, if the market doesn’t already incorporate it. So they gain an edge by access to and dispassionate analysis of large data sets. Moreover, they are very aware that patterns spotted, and conclusions derived, from small data sets can be dangerous, and potentially very hazardous to the accumulation of wealth.

Superforecasters also tend to use ‘Triage’. This is the process of determining the most important things from amongst a large number that require attention. Risk expert and Hedge Fund manager, Aaron Brown offers an example of how, when he first got interested in basketball in the 1970s there were data analysts who tried to analyse the game from scratch. He considered that a hard proposition compared to asking which team was likely to attract more betting interest. As Los Angeles was a rich and high-betting city, and the LA Lakers a glamorous team, he figured it wasn’t hard to guess that the betting public would disproportionately favour the Laker and that therefore the spread would be slanted against them. ‘Bet against the Lakers at home’ became his strategy, and he observes that it took a lot less effort than simulating basketball games.”

Could such a simple strategy work today, tweaked or otherwise? And in what circumstances would you apply it?  That’s a more nuanced issue, but Superforecasters (who are normally very keen on big data sets) would be alert to it.

Aaron Brown sees trading contracts on the future as striking the right balance between under- and over-confidence, between prudence and decisiveness. The hard part about this, he observes, is that confidence is negatively correlated to accuracy. Even experienced risk takers bet more when they’re wrong than when they’re right, he says, and the most confident people are generally the least reliable.

The solution, he maintains, is to keep careful, objective records, preferably by a third party.

That’s right – even experienced risk takers bet more when they’re wrong than when they’re right. If true, this is a critical insight.

So how might a Superforecaster go about constructing a sports forecasting model?

Let’s say he wants to construct a model to forecast the outcome of a football match or a golf tournament. In the former, he might focus on assessing the likely team line-up before its announcement, and draw on his hopefully extensive data set to eke out an edge from that. The football market is very liquid and likely to be quite efficient to known information, so any forecasting edge in terms of estimating future information, like team shape, can be critical. The same might apply to rugby, cricket, and other team games.

In terms of golf, he could include statistics on the average length of drive of the players, their tee to green percentages, their putting performance, the weather, the type of course, and so on. But where is the edge over the market?

He could try to develop a better model than others, including using new, state-of-the-art econometric techniques. In trying to improve the model, he could also seek to identify additional explanatory variables.

He might also turn to the field of ‘prospect theory’, a body of work pioneered by Daniel Kahneman and Amos Tversky. This states that people behave and make decisions according to the frame of reference rather than just the final outcome. Humans, according to prospect theory, do not think or think or behave totally rationally, and this could be built that into the model.

In particular, a key plank of prospect theory is ‘loss aversion’, the idea that people treat losses more harshly than equivalent gains, and that they view these losses and gains with regard to a sometimes artificial frame of reference.

An excellent seminal paper on this effect in golf (by Devin Pope and Maurice Schweitzer, in the American Economic Review), is a good example of the sort of way in which study of the economic literature can improve sports modelling.  The key contribution of the Pope and Schweitzer paper is that it shows how prospect theory can play a role even in the behaviour of highly experienced and well-incentivised professionals. In particular, they demonstrate, using a database of millions of putts, that professional golfers are significantly more likely to make a putt for par than a putt for birdie, even when all other factors, such as distance to the pin, break, are allowed for. But why? And how does prospect theory explain it?

To find the explanation, they examine a number of possible explanations, and reject them one by one until they determine the true explanation. The find it is because golfers see par as the ‘reference’ score, and so a missed par is viewed (subconsciously or otherwise) by these very human golfers as a significantly greater loss than a missed birdie. They react irrationally in consequence, and cannot help themselves from doing so even when made aware of it. The researchers show that equivalent birdie putts tend to come up slightly too short relative to par putts. This is valuable information for Superforecasters, or even the casual bettor. It is also valuable information for a sports psychologist. If only someone could stand close to a professional golfer every time they stand over a birdie putt and whisper in their ear ‘This is for Par’, it would over time make a significant difference to their performance and pay.

So Superforecasters will Improve their model by increments, taking into account factors which more conventional thinkers might not even consider, and will apply due weight to updating their forecasts as new information emerges.

In conclusion, how might we sum up the difference between a Superforecaster and an ordinary mortal? Watch them as they view the final holes of the Masters golf tournament. What’s the chance of Sergio Garcia sinking that 10-footer? The ordinary mortal will just see the putt, the distance to the hole and the potential break of the ball on the green. The Superforecaster is going one step further, and also asking whether the 10-footer is for par or birdie. It really does make a difference, and it’s why she is watching from the members’ area at the Augusta National Golf Club. She has earned her place there, and she knew it before anyone else.

Further Reading and Links

https://selectnetworks.net/

D.G. Pope and M.E. Schweitzer, 2011, Is Tiger Woods Loss-Averse? Persistent Bias in the Face of Experience, Competition and High Stakes, American Economic Review, 101(1), 129-157.

Philip Tetlock and Dan Gardner, Superforecasting: The Art and Science of Prediction, 2016, London: Random House.

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