Opening Day (Part 2)

To help me get ready for the upcoming baseball season, I recently purchased the 2008 Baseball Prospectus, an annual almanac of analysis and predictions from the folks who brought us 21^st century metrics like VORP (Value Over Replacement-level Player) and BABIP (Batting Average on Balls In Play).

I got to thinking about the evolving perception of analytics in a baseball context. While mentioning those metrics at the water cooler of 2008 might get you some confused looks, you're probably not in danger of being stuffed in a locker, because analytics have begun to earn respect from a wider baseball audience. How, exactly, did this happen? Off the top of my head, I came up with four possible drivers of this change (not mutually exclusive):

1. Boil the frog slowly – If On-Base Percentage really correlates better with runs scored than batting average, well, who am I to argue? And if that's true, then maybe I ought to listen to some of your other ideas...


2. Myth-busting – Some assertions have been controversial (e.g. “There’s no such thing as a clutch player”), but maybe they’re plausible and interesting enough to get attention


3. Case studies – Michael Lewis’ best-seller Moneyball and the 2004 and 2007 Boston Red Sox (World Series winners) have shined a public light on organizations that succeeded with analytics-friendly leadership


4. Audience evolution – People in general have better quantitative reasoning skills than they did, say, 20 years ago, and so are more open to evidence-based insights

Here’s my question, and it’s not about baseball: In your organization and mine, a major barrier to extracting value from analytics is a rejection of the methods and implications from (for lack of a better term) the “old school” crowd. What’s the best way to make the case for analytics in your organization? Take on a single cherished nugget of conventional wisdom and prove it wrong? Or is that too risky? Is it better to plug along cautiously, incrementally adding some objectivity and trickling new metrics into the soup until the organization is ready? Or is it the Moneyball approach – find one manager willing to try the Kool-Aid and make something happen?

Correlation, Causation, and Flat Tires

I was out of town this week and received a call from my wife. The rear tire on her car was flat, she couldn’t figure out how to change it, and ultimately called AAA. The culprit turned out to be a nail. “The tow truck guy said he’s seeing lots of these in our town. He thinks it has to do with all the home construction that’s going on.”

Always on the lookout for good causation / correlation examples, I apologized for not being home to change the tire myself and quickly Googled “flat tire correlation.” The first hit I got was from a discussion forum for BMW owners. The thread was discussing whether high-performance tires were more prone to flats. “I suspect there is a correlation between flats in general and construction activity in your area,” reported chuck92103.

Interesting, but was it chance, coincidence, or a pattern?

I took the car to NTB this morning to have the tire repaired, and the serviceman – un-prompted - provided more evidence for my fledgling theory. “Yep, we’ve been getting between 15 and 20 nails per day,” he said. “We’ve seen a lot more since all the home construction started back up.”

Well, that was all the proof I needed. Now, in addition to the sub-prime crisis, the high cost of gasoline, and whether my kid’s Thomas the Tank Engine is covered in lead paint, I had a new problem to worry about. Thousands of rogue nails, escaping from construction sites, hiding along the roads, and leaping up to impale themselves in the tires of unsuspecting minivan drivers throughout Metrowest Boston.

“I think I’ll take the train into town on Friday,” I thought. That is, until I saw this news item from yesterday’s paper. A freight-car loaded with building materials broke loose from a siding at a lumber yard, rolled three miles down the main track, and collided with a commuter rail train. 150 people were injured (fortunately, none seriously).

Could it be any more obvious? Increased home construction requires more lumber. More lumber means more freight cars. More freight cars increases the probability that one will break loose and (somehow) thwart the devices intended to prevent runaways. And more runaways, of course, means your ride home may be interrupted with potentially disastrous consequences. Not to mention all those flat tires.

My conclusion? Stop the McMansion-ization of the suburbs, increase transportation safety throughout the region!

Correlation analysis can be a powerful tool in determining the root causes of business performance. But like any tool, it has its limitations. Have you ever tried to correlate outputs and inputs and arrived at an unusual result?


NCAA update: The ScoreCard algorithm missed both first-round upsets. I correctly predicted Villanova over Clemson. But the Pitt Panthers guaranteed I wouldn’t have my office pool winnings available to pay for the flat tire by dropping their second-round game to Michigan State. Human intuition – 50%. Computer – 0%. Man does not win by analytics alone…