From building a wine cellar to finding your happily ever after, modern life is increasingly ruled by number crunching and algorithms. Super Crunchers (2007) is about the sheer power of the large data sets that are fed into algorithms and the way they’re revolutionizing our businesses, medical treatment and even our governments.
Ian Ayres is a lawyer, econometrician and professor at both Yale’s Law School and School of Management. He’s also a columnist for Forbes, a regular commentator on Marketplace and the author of several books including Carrots and Sticks: Unlock the Power of Incentives to Get Things Done.
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Start free trialFrom building a wine cellar to finding your happily ever after, modern life is increasingly ruled by number crunching and algorithms. Super Crunchers (2007) is about the sheer power of the large data sets that are fed into algorithms and the way they’re revolutionizing our businesses, medical treatment and even our governments.
Super crunching might sound like something from a video game, but it refers to the statistical analysis of large data sets – and its applications are nearly endless. Let’s start with some surprising examples.
Super crunching can be used to predict the future quality of vintage wine – indispensable knowledge for wine dealers.
A high-quality red wine is an investment because it gets better as time goes on. This means wine dealers need to be able to predict a wine’s future value.
Orley Ashenfelter, an economist and wine enthusiast, has found a way to accurately predict the quality of Bordeaux wines by analyzing historical relationships between weather and the price of wine.
Ashenfelter has found that each centimeter of winter rain raises the expected price of a wine by 0.00117 dollars. His analysis is so accurate that wine dealers now use it to determine whether or not they should buy a wine, and at what price.
Super crunching can also be applied to baseball. Bill James, a baseball writer, has done for baseball what Ashenfelter did for wine.
James challenged the assumption that baseball experts could assess talent simply by watching the players. He postulated that data-based analysis of players’ previous form in the game would be superior – and he was right!
Traditional talent scouts had little faith in Jeremy Brown, an aspiring player who was overweight, but James’s calculations said otherwise. The Oakland A’s decided to sign Brown based on James’s analysis of his past runs and the decision turned out to be a wise one: Jeremy Brown had his major league debut in September 2006.