Super Crunchers

Today, I re-read a book called Super Crunchers: How Anything Can Be Predicted

by Ian Ayres.

So what is supercrunching?

This is best explained by the chess example:

The book starts off with the example of Orley Ashenfelter, a Princeton

economics professor as well as founder and editor of the Journal

of Wine Economics who wanted to apply supercrunching techniques to

predict whether a wine from a particular year would be a good wine or

not. He ended up with the following equation:

You can imagine the commotion that followed. The wine experts brushed

off this theory and that numbers can predict the wine quality better

than they can. After all, “Just as it’s more accurate to see the

movie, shouldn’t it be more accurate to actually taste the wine?”

And yet, the equation did indeed make better predictions, especially

with the prediction that 1989 and 1990 wines would be

bestsellers.

Orley was able to make this analysis because he had access to data

about the weather and the wine quality. Ian explains that there are

two ways to get the data – it already exists (like surveys and census

or simply transaction logs of companies) or you create it using

randomized

trials.

The latter idea of creating data with the “flip of a coin” is such

a simple yet powerful concept. Techies would be familiar with this

already under a different name – “A/B

testing”.

Let’s take the example of JoAnn sewing machines:

The key is that:

To realize how valuable this methodology is, let’s take the case of

Progresa:

Best of all, the evidence of Progresa being a good thing was so

convincing that the new government kept it going but under a different

name for political reasons. Zedillo’s idea worked. And beautifully.

Ian goes on to demonstrate similarly how Don Berwick’s campaign

prevented an estimated 1,22,342 hospital deaths in eighteen months.

The campaign was just a few simple suggestions that were determined

based on statistics of how deaths occurred and these suggestions were

implemented by the participating hospitals. The suggestions included

regular washing of hands.

Ian quotes several real-world examples throughout the book and the

number of times that number crunching and data crunching beat human

expertise is staggering. But Ian says that this does not mean the end

of need for human intervention. Supercrunching can validate ideas but

the ideas and hypotheses themselves have to be formulated by us

humans.

He goes on to explain the 2SD

rule

and the Bayes’ theorem in layman terms. Just understanding these two

concepts would go a long way in helping anyone decipher statistics.

All in all, the book was a good inspiring read. I would highly

recommend the book for anyone (even non-techies) interested in how

computers and databases are changing how decisions are made. These

decisions are not limited to websites. As we have seen above, it is

changing everything from how government policy decisions are made to

how movie scripts are being

written.

The key takeaway for me is that data insights are hard and so is

intuition.  People who can straddle both will be important people in

future.  Learning to read the data will mean getting comfortable with

statistics, models and even neural networks (as explained in the

book).

If you’re not patient enough to read the book, you can watch the

Google Tech Talk by Ian

Ayres. You can also read

more of Ian Ayres’ supercrunching stories on the Freakonomics

blog.