In Baseball and in Business: “Adapt or Die”

by | Nov 9, 2017 | General BI

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November 1, 2017, may someday be remembered as the date that analytics irrevocably took over baseball. That’s when the Houston Astros won the World Series. Theirs is the latest championship clinched by a team relying on modern analytics in a sport filled with revered old traditions.

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Baseball has changed significantly in the past 15 years, with analytics upending 150 years of conventional wisdom. So far in this Practical Analysis series, we have focused on statistical tools and concepts.  Baseball provides a compelling example of how these can be applied, sometimes with astounding results. The rise of analytics in baseball also offers a cautionary tale about embracing – or ignoring – empirical analysis. In the words of one baseball insider, organizations must “adapt or die.”

The recent evolution in baseball has been extensively and entertainingly documented. Two examples are Moneyball, the book by Michael Lewis, later made into a Brad Pitt movie, and Competing on Analytics, Tom Davenport’s review of how analytics is disrupting many industries, including professional sports.


Lewis’ book and the movie adaptation do a wonderful job telling the story of “sabermetrics” and the 2002 Oakland Athletics. As a minor market team with a limited payroll, the A’s struggled to compete against richer franchises. The A’s were pretty good at developing players. But when players got good, they were snatched up by higher-paying teams, such as the Red Sox (Johnny Damon) and Yankees (Jason Giambi).

A’s General Manager Billy Beane knew he must find undervalued players. To identify them, he and assistants drew on the growing wisdom of an informal fan community dedicated to sabermetrics. This community of “stats geeks” had developed a new approach to analyzing the game via statistics and understanding the relationship between outcomes and factors that contribute to them. Their analysis showed on-base percentage and slugging percentages were better predictors of success than time-honored measures like batting averages and runs-batted-in. Relying on these new tools and others, the A’s built a low-budget team that went to the playoffs in 2002 and 2003 — and set in motion major changes in baseball and other professional sports.

Competing on Analytics

Davenport’s book Competing on Analytics chronicles the rise of the Red Sox as the team began embracing analysis over conventional wisdom. In late 2002, the team took a page from the A’s playbook by hiring Bill James, the acknowledged father of sabermetrics. The same month, the Sox promoted the analytically inclined Theo Epstein to GM. The two helped assemble a team capable of efficiently producing runs and wins. The rest is history: In 2004, the Sox won their first World Series in 86 years. Another title followed in 2007 and yet another in 2011. Then Epstein went on to the Chicago Cubs, another long-suffering franchise. Within five years, the Cubs had won their first championship in 108 years.

2017 Champions

Just last week, the Houston Astros won the World Series, the first since the franchise began in 1962. Their ascent has a familiar ring: the team suffered several seasons with abysmal win-loss records while sabermetric strategies were put to work identifying and developing promising players. This year, the team compiled an outstanding regular season record and then picked off three teams with the biggest payrolls on the way to the championship. Houston’s tactics included frequent batter changes and a deep bullpen with a dizzying number of pitcher switches. Were the Astros even so shrewd as to intentionally “tank” for several seasons to get top draft picks, as conjectured by the sports press?

We will never know for certain. But there is no doubt this team is from top to bottom one the most analytically driven in baseball, drawing talent and ideas from the world of economics, engineering, and physics, and led by a GM and manager with impressive academic credentials.

Of course, one manager does not win a championship any more than one player does. Teams win championships. And recent history shows that teams embracing quantitative analysis win more.


So what is the takeaway for other industries? Here are three things to consider.

  • Start with the data you have – There is a good chance the data you have contains diamonds in the rough. Remember sabermetrics used the same data that had been tracked via scorecards for 150 years. The analysts just looked at it from a different perspective.
  • Figure out which outcomes matter – In baseball, that’s easy: the goal is to win games. Elsewhere it can be harder to clearly define which outcomes matter the most. But determining this is worth the work, since your organization will succeed or fail based on certain outcomes.
  • It’s about more than just the numbers – As we have stressed in this series, analysis itself is not enough to create results. Interpretation, communication, and persuasion are necessary to effect change. Data and insights represent nuggets of gold. But you need to figure out what to do with them.

The bottom line

In business and in baseball, managers must constantly adjust and evolve. New ways of leveraging data may fly in the face of conventional wisdom, but as the 2017 World Series shows, it increasingly separates winners from losers.

In our next blog, we will explore tactics for zeroing in on the meaningful information within big data. Stay tuned.

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