The traditional view of campus divides the student body into “jocks” (who like sports), and “nerds” (who don’t). It seems that every paper I read about Big Data tries to appeal to the jocks by quoting sports statistics as the most compelling example.

The face that launched a thousand blog posts

Moneyball, and Brad Pitt’s good looks became the face that launched a thousand blog posts. I should know, I wrote a few myself.

man tastes wine

But what about some other examples, which might appeal to us Europeans who don’t endlessly pore over box scores, feeding on-base percentages into a Hadoop cluster. We are too busy drinking wine and watching Eurovision. This leads to questions such as “Can Big Data help me find a good bottle of wine?” or “Can it help predict the winner of Eurovision?”

It turns out it can help, as the book Supercrunchers explains, in the world of wine recommendations. Imagine that you are trying to determine if 2013 will be a good year for cabernet. This might be because you want to invest in wine futures, or you want to place an early order for a few cases of the good stuff from your wine merchant. The usual approach is to ask a wine connoisseur who has decades of personal experience and is well-respected. This expert uses “swish & spit” to expose the complex flavors and, let’s be frank, has a livelihood dependent on being the expert.

Orley Ashenfelter, an economist by day, decided a superior approach would be to “Run the Numbers”, and found that all that expertise can be beaten by a simple linear equation:

Wine quality = 12.145 / 0.00117 * Winter Rainfall + 0.0614 average growing season temp – 0.00386 harvest rainfall

It turns out that the mathematical approach was superior by correctly predicting the “Wines of the Century” in 1989 and 1990. The reaction of the traditional experts was the same as the old scouts around the table in Moneyball. The highly influential Robert Parker laughed off the approach with the comment, “I’d hate to be invited to his house to drink wine.” But Ashenfelter had the last laugh because he made lots of money for his advocates in wine futures by betting “against the house.”


What could be further away from American sport than Eurovision? Even something like Eurovision can be predicted with a remarkable degree of accuracy.

David Rothschild, an economist at Microsoft Research, harnessed powerful computer clusters to predict that Emmelie de Forest’s rendition of Only Teardrops would win for Denmark (54% probability). He was right. Data came from social media sites “I like Emilie” tweets, YouTube download statistics, polls, the spread betting market, even game theory which analyzed how European countries vote for each other (the Scandinavian effect).

Big Data can be used to answer many questions that typically were previously the domain of the expert. In the area of supply chain this could be:

  • What are the long term risks associated with this source of supply ?
  • I’ve not bought laminated aluminium sheets before. What suppliers do companies like mine typically use for this commodity?
  • What is the average hourly rate for a level II warehouseman in Chicago?

Now I’m going to open a bottle of Corbière and study the weather data from the Languedoc. After all, the subtitle of Supercrunchers is Why Thinking-by-Numbers Is the New Way to Be Smart.