Big Data! Metrics! Analytics!…a cautionary tale from the world of baseball

“In the information age, things that are precisely measured are rewarded disproportionally relative to impact.” – Theo Epstein, President of the Chicago Cubs

I work in telecommunications, and one of the major developments of recent years is “big data” (if you don’t know what big data is, then here’s an amusingly-titled explanation).  Personally, I think it’s more accurate to say that the development is that there’s now a name, “big data”, for the vast information troves that have been a part of business for many years, but I digress.

Sometimes worlds collide: a milestone birthday party at which your colleagues mingle with long-time friends and teammates from the local beer league; a cosmic-scale cataclysm that results in a moon; or the worlds of business and sport.  OK, that last example is poor – business and sport came together about a century ago – but be patient because I’m going somewhere with this.

The book Moneyball (What’s that you say?  It’s a movie, too?) by Michael Lewis, (also author of the wildly entertaining Boomerang) introduced many newcomers to the world of sports statistics.  Sure, casual fans were familiar with baseball stats like runs batted in (RBI), batting average, and so on, but those rudimentary stats barely scratch the surface.  Things get really fun when you get into things like wins above replacement (WAR) and start adjusting for ballpark and normalizing by historical variations.  To kill a few hours, pop over to Baseball-Reference and check out how players stack up across the ages.  Advanced stats and efficiency ratings are now becoming the norm in all sports, as teams seek both to gain every advantage and to invest wisely in talent.

But is there a downside to the stats-based approach (I mean, other than rendering the games we cherish into some boring optimization problem)?  A basic stat that has become a focal point in recent years is the pitch count.  This count is simply a measure of the number of pitches thrown by the pitcher during a game.  We’re in the midst of a period during which pitch counts are becoming outrageously important: managers are being fired for overworking young arms, hitters are berated for swing early in a count, and the number 100 has gained mythical status as the point at which a pitcher must come out of a game or risk injury.  But have we perhaps over-torqued?  It’s true that many young pitchers have suffered major injuries, and this is especially apparent when a young gun sees a major spike in innings worked from one season to the next.  OK, fine…but what about the batters?

[I’ll interject for a moment to say that if you’re into fun sports statistical analysis, then I recommend you read Scorecasting, by Tobias J. Moskowitz and L. Jon Wertheim]

Back to the batters…  Managers all over Major League Baseball are encouraging batters to “take a pitch”; that is, to just stand there and watch as the ball flies by, strike or not.  Why?  To up the pitch count.  The theory goes that you want to get the starting pitcher out of the game as soon as possible so you can tee up on relief pitchers (never mind that these guys are still terrific).  So batters who’ve trained their entire lives to smack a baseball stand idly by as it flies past.  But is this approach working?

In an interesting article over at Sports Illustrated, Tom Verducci makes a compelling case that this strategy is actually having a disastrous effect on offense in baseball.  In other words, taking a pitch to get the pitch count up is having the opposite of the intended outcome.  I won’t repeat the argument and evidence, here; rather, I’ll just wait while you pop over and read it.

So why do people still focus so intently on increasing the pitch count, particularly in this age in which it’s easy to measure and quantitatively prove that the strategy doesn’t work?  We come to the quote with which I began the article, from Chicago Cubs GM Theo Epstein, “In the information age, things that are precisely measured are rewarded disproportionally relative to impact.”  At least part of the reason why increasing the pitch count is still alive is because it’s an easy stat to measure.  In a game that generates billions of dollars in revenue, and getting into the playoffs can mean millions in extra revenue to a team, and everything is measured, managers still follow a losing strategy because a stat is easy to measure.

What can we outside of baseball learn from this?  In the business world (here’s that collision!), I see two immediate lessons:

  • Measuring something is (perhaps a necessary but absolutely) an insufficient condition for increasing organizational intelligence
  • Paying attention to the wrong things can be disastrous

Let’s look at the first bullet.  I can’t help but think of “big data” when I see it, and not least because I get 10 emails a day that talk about a big data solution, tradeshow, vendor, conference, etc.  In fact, I’m partly to blame since I developed the positioning for a product that helps communications service providers examine their big data to extract meaning.  Of course, such tools are only useful if they are put to use; the alternative is analysis paralysis (it’s true and it rhymes…double-win).  Collecting reams of data and then just staring at it is about as productive as making a to-do list and then not actually doing any of the items therein.  Big data’s fine and all, but unless it leads to big action then it’s a big waste of time.  You need to measure, you need to examine, and you need to act.

Keeping up momentum, let’s go onto the second bullet.  Goal-setting is an important part of growth, and good goal-setting involves objectives that are measurable (the M in SMART).  However, we must watch out not to lose sight of what’s actually important.  In baseball, people focused too much on a single metric, the pitch count, and forgot to see if the actual end result improved.  In business, there are things that we can measure easily (e.g. number of leads generated, webinars hosted, emails sent, website updates, deals closed, etc.) and things that are fiendishly difficult to measure (e.g. quality of leads, return on investment, etc.).  It can be sorely tempting to focus on the trivial at the expense of the important.  Why?  Because it’s easier to say “I sent 10 marketing bulletins this quarter”, even if they were lousy and turned customers off, than to try to measure the revenue return on sending three great bulletins that might lead to sale in 16 months.  Metrics are useful, and they can encourage productive behaviour and give us direction, but they’re not the be all and end all.  Sometimes it’s better to do what makes sense than what can be easily counted…so do exercise caution when creating your own team and organizational metrics.

Measure what matters, ignore what doesn’t, and, above all, act decisively when information demonstrates that you need to do so.  Heed Mr. Epstein’s words and the lesson from baseball, and don’t lose sight of what’s actually important.  Finally, remember what a wise man once said:

“Management is doing things right; leadership is doing the right things.” – Peter Drucker

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Posted in Everything, Leadership, Management, Sports
2 comments on “Big Data! Metrics! Analytics!…a cautionary tale from the world of baseball
  1. […] p86 talks about Goodhart’s Law (“When a measure becomes a target, it ceases to be a good measure.”), and reminds me of two of my own posts: When metrics are mistakes: common pitfalls and how to avoid them and Big Data! Metrics! Analytics!…a cautionary tale from the world of baseball […]

  2. […] strategies, and tactics, but in that post I didn’t mention metrics. I’ve also written before about some of the perils of metrics, but in that post I didn’t go too deep into the relationships to strategy. Well, it’s […]

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