Predicting the Unpredictable

yummy_wineAfter graduating from college, I left the barren Arizona desert for Manhattan to take my first job. It didn’t take long for my new Manhattanite friends to inform me that it was time to upgrade to wine from beer, so I enrolled in a wine-tasting class. But while it was great fun, I don’t think that I was any better at assessing the quality of wine after I’d completed the class than I was going in, though I was much better at faking it.

It wasn’t until years later that I discovered the secret, and it came via a Princeton economist. Understanding the fact that wine is an agricultural product, and as such is dramatically affected by weather, Orley Ashenfelter used decades of weather data and auction prices to come up with this equation for Bordeaux wines:

“Wine quality = 12.145 + 0.00117 winter rainfall + 0.0614 average growing season temperature — 0.00386 harvest rainfall”

Assessing wine is considered an art rather than a science, but oftentimes creativity is about applying a little science to art — as Orley did by taking into account weather and auction data. In an effort to inspire entrepreneurs to also turn ill-practiced art into science, below I share a few other examples.

The Mathematics of War

Sean Gourley, a physicist by training, wanted to gain a deeper understanding of what was happening with the war in Iraq.  So he worked with a cross-functional group to understand “the mathematics of war.” What he found was fascinating, that guerilla wars around the world — in Iraq, Columbia, Peru, Indonesia and Afghanistan — could be reduced to this equation:


Gourley tells the entire story in the video below, including how he arrived at the fact that alpha (the slope of the line) is 2.56. As he explains it, guerilla war has evolved to a state of equilibrium that can be defined by an equation — that there is an optimal organizational structure for fighting an organized military. Guerillas either discover this structure, and implicitly, this formula, or they get killed off.

Mathletics, Sabermetrics and “Moneyball”

The story of Sabermetrics and statistics in baseball has been told many times, so I won’t repeat it here. Suffice to say that if you have any interest in baseball stats, “Moneyball” is a must-read.

A story not as widely told is that of Wayne Winston and the Dallas Mavericks. A few years ago, Winston, a decision sciences professor from Indiana University, consulted the Mavericks on a new rating system aimed at measuring the impact a player has on the entire team. Points or assists don’t offer much information in and of themselves; what’s far more valuable information for a team is answering the question: “When player x is on the court, does our lead grow or shrink?”

From a 2003 New York Times article about the system:

Ignoring every traditional statistic for players, Sagarin and Winston have designed a ranking that is modeled on hockey’s plus-minus system, in which players receive credit for being in the game when their team does well. Whether they actually score points or grab rebounds does not matter.

”Did you make the pass before the assist? Did you tip a ball to someone who made a shot? Did you set a pick? Did you take a charge?” said Winston, a fast-talking former ”Jeopardy” champion who, like Sagarin, grew up outside New York City rooting for the Knicks of the late 1960’s and early 70’s.

”Nobody’s got a stat for these,” Winston said. ”Ninety percent of basketball is made up of things there aren’t stats for.”

I just pre-ordered Winston’s new book, “Mathletics,” due out this fall.  I can’t wait to read it.

How to Develop a “Prediction Function”

Step 1:

Start with some insight about the relation between two things — like the fact that weather determines wine quality.

Step 2:

Identify “output” data to tune your prediction function — for example, historic auction prices as an approximation for wine quality.

Step 3:

Graph the data to examine the best way to extract the function.  Does the data look like it fits a line?  If so, do a simple linear regression (a very simple way to do this is the Regression function in Microsoft Excel’s Data Analysis package — unfortunately, it’s no longer available on Excel for the Mac, but you can do it elswhere).  Does the data look like an exponential curve?  If so, you can do a logarithmic regression (here is an online tool for a simple regression). And you can use much more sophisticated statistics to find the right equation, if one exists.

Step 4:


There are so many areas in which having the ability to make dramatically better predictions would enhance our lives — jobs, dating and health, to name a few.  I can’t wait to see what the future has in store.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.