What Baseball Can Teach Us About Innovation

In a chat last week, Boston Red Sox General Manager Theo Epstein explained why he wasn’t bothered by J.D. Drew’s relatively low number of runs batted in (quotes from Joe Posnanski’s blog):

“When you’re putting together a winning team, that honestly doesn’t matter. When you have a player who takes a ton of walks, who doesn’t put the ball in play at an above average rate, and is a certain type of hitter, he’s not going to drive in a lot of runs. Runs scored, you couldn’t be more wrong. If you look at a rate basis, J.D. scores a ton of runs.

And the reason he scores a ton of runs is because he does the single most important thing you can do in baseball as an offensive player. And that’s NOT MAKE OUTS … Look at his runs scored on a rate basis with the Red Sox or throughout his career. It’s outstanding.

You guys can talk about RBIs if you want … we ignore them in the front office … and I think we’ve built some pretty good offensive clubs.”

Business managers can learn a lot from how baseball general managers build and manage their talent portfolio by drawing on the findings of baseball’s Sabermetrics revolution. And the same is true for business managers trying to balance their innovation portfolios: how can they focus on the metrics that really matter?

According to the old-fashioned metrics, the run-batted in is a vital statistic. But smart general managers like Epstein recognize that the RBI is not a valuable measure of performance (it actually correlates with the on-base percentage of the hitters earlier in the lineup).

Innovation managers, too, need to look beyond “obvious” but potentially misleading statistics like first-year revenue, first-mover advantage, and leveraging core competency to hidden drivers of success, such as targeting non-consumption and minimizing first year losses.

A key enabler of the statistical revolution in baseball was not just better statistics, but the widespread availability of those statistics. Even before the internet made possible utterly fantastic websites such as Baseball-Reference, Fangraphs, and Baseball Prospectus (which is also an annual book), the bible for statistics was Macmillan’s Baseball Encyclopedia, introduced to widespread acclaim in 1969. (Alan Schwarz, in The Numbers Game: Baseball’s Lifelong Fascination with Statistics, quotes from Christopher Lehmann-Haupt’s review in the New York Times: “I got lost in it for nearly two days…. It’s still the book I’d take with me to prison.”)

Companies should create an internal encyclopedia in which they highlight the year they started work on each innovation, what type it was, how projections about its market potential changed through time, its key characteristics, and its ultimate performance. The encyclopedia would facilitate statistical analysis to help the company increase its success rate.

Even better would be a cross-industry research effort to develop a deeper and broader reference work. A researcher who painstakingly created a like-for-like database of efforts across multiple companies (made anonymous, of course) would do the innovation movement a great service.

Key to the effort would need to be a robust categorization scheme for classifying the type of innovation (incremental line extension, disruptive, and so forth), the target customer (high-end, mainstream, low-end, nonconsumer) and the market circumstances (nascent, rapidly growing, mature, declining).

Better metrics give Theo Epstein a competitive advantage over his rivals. And better metrics can give you an advantage over yours — and create better innovations that benefit all of us. What else do you think would be in an ideal innovation encyclopedia? Is there an open source way to create a “good enough” starting point?

For a more in-depth argument about what you can learn from baseball about building and managing your talent portfolio, see my article in this month’s Harvard Business Review.