Take Me Out to the Ballgame: Insurance and Baseball Analytics

This is always one of my favorite weeks of the year because I'm a sports fan. The NCAA men's basketball tournament Final Four is set and in a few days we will be celebrating the return of baseball and Opening Day (capitalized, of course).

Now what do baseball and insurance technology have in common? A lot more than you might think, actually. As a baseball fan I enjoy reading about—without fully comprehending—some of the advanced statistics available to fans and baseball insiders, represented in large part by the Society of American Baseball Research (SABR). These sabremetricians, as they are known, do have something in common with insurers. They take huge amounts of data and turn it into actionable information. (Baseball may have even more data to analyze than insurers and it's certainly cleaner.)

Fortunately for the insurance world, executives are hungry for this information and are convinced that getting a 360 degree view of their customer—both agents and policyholders—is worthwhile. They actively take this information and turn it into strategies for attracting new business, rejecting old business, and sorting through claims.

This is why I never fully understand why many average baseball fans find reject the use of advanced statistical formulas—such as VORP (Value Over Replacement Player), WIN Shares, or even something as simple as OPS (Onbase Percentage Plus Slugging percentage).

One reason baseball fans shy away from some of these statistics is because they are an arrogant lot. They feel they know the game from watching and playing it for years and their eyes tell them more than statistics. There is something to be said for instincts, but you can't let gut feelings rule what your mind knows is probably wrong.

Analytics has changed the way insurers do business in all segments of the operation. Carriers value information and brag about how much data they actually possess—not to mention the amount they can obtain from third-party sources.

To bring the baseball angle to a close, one of my favorite baseball writers is Joe Posnanski of Sports Illustrated. In a recent blog entry he continued to shake his head at how what you and I would call predictive analytics is rejected by so many baseball fans. Many baseball fans love what is known as small ball—or what some advocates call smart ball—using  the sacrifice bunt to move a baserunner from first to second base , but giving up an out to do so.

Posnanski is not a fan of such strategy and writes: "They have been playing baseball for more than 100 years. And for more than 100 years, more runs have scored with a man on first and nobody out than with a man on second and one out. This has been true every single season for more than 100 years. Every single one. Not only that, but since expansion in 1969, your chance of scoring a single run is better with a runner on first and nobody out than with a runner on second and one out. Get that? Your percentage for scoring one run is better. Now, a manager may believe that these so-called numbers are wrong, that hundreds of thousands of innings and at-bats and situations are wrong, that what is right is the manager's own instinct for avoiding the double play and putting his RBI guy up in the right situation. I don't begrudge a manager for thinking that or a team for believing in that manager or fans for wanting it to be true. I just wouldn't call it smart ball."

Analytics makes us smarter. If you've got some examples of how smart they've made you look—or how dumb it's made someone else look—please share them here.

If you want to find out more analytics, this Wednesday, March 30 at 12 noon Eastern—the day before Opening Day—Tech Decisions will be hosting a Web seminar called Analytics in Action: Improving Business Results with Jim Dean of Robert E. Nolan Co. and Joel Reott of Georgia Farm Bureau Insurance speaking. Check it out.