A view from the front lines of baseball’s data-analytics revolution

Here is a brief excerpt from an interview of Jeff Luhnow for the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.

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In the second of a two-part interview, Houston Astros general manager Jeff Luhnow describes how analytics has changed baseball—from Moneyball in 2003 to the last out of the 2017 World Series.

“Whenever people talk baseball,” observed beloved Hall of Famer Willie Stargell, they don’t say, ‘Work ball.’ They say, ‘Play ball.’”

Perhaps. But for people who perform at baseball’s highest levels—the athletes, coaches, scouts, and executives who compete for a living—the stakes are considerably higher. In fact, a major-league umpire doesn’t even say, “Play ball.” Instead, he’ll typically point to the pitcher’s mound at starting time and announce, abruptly, “Let’s go.” The game is a business. And the business is to win

Fifteen years ago, Michael Lewis, in his book Moneyball: The Art of Winning an Unfair Game (W. W. Norton & Company, 2003), described the growing importance of data in the baseball business. That same year, Jeff Luhnow joined the St. Louis Cardinals as the team was starting to embrace the ideas behind Moneyball. In this interview, Luhnow reflects on the course of baseball analytics over the years, and the contributions of the Astros along the way. This is the second interview of a two-part series. The first is for analytics leaders seeking inspiration in the Astros’s transformation. This one is for baseball fans, whose ability to grasp the modern game depends on an understanding of analytics’ growing role.

Quarterly: Let’s go back to 2003, when you entered baseball with the Cardinals. What was the state of play at that time with respect to data analytics versus traditional scouting?

Luhnow: Our sport has been consistently the same for decades and, quite frankly, a century. There have been some changes in terms of mound height and some of the rules around the game, but it’s essentially the same. What’s really changed dramatically in the last 30 years is information, and how that information is used to make decisions. This started in the ’50s, when some innovative baseball people started to recognize that the traditional ways of evaluating player performance were not accurate. Branch Rickey wrote an article in Life magazine about the early version of on-base percentage. That continued to evolve and really started to accelerate in the ’90s and going into the turn of the century. Certain teams recognized that there was value in the information and in how they could use the information.

There’s no better example of that than the Oakland Athletics, who were a team that was struggling revenue-wise compared with the big-market teams. They were able to utilize the information to evaluate players in a different way, to capture an edge versus the bigger-market teams. They became one of the more successful teams around the turn of the century and made a lot of playoff appearances, in large part because they were finding undervalued players that they were able to recognize through their use of information and analytics. That was all captured in Michael Lewis’s Moneyball, published in 2003.

It’s interesting. In our industry, you could write a book about someone whose best practices are allowing them to compete in a unique and advantageous way, and yet most of the industry would pretty much ignore that and keep doing things the way they were. One person that didn’t ignore that was Bill DeWitt Jr., the principal owner of the St. Louis Cardinals since 1996. When he saw Michael Lewis’s book, he thought this was an opportunity for the Cardinals, who were a larger-revenue, larger-market team, to capture an edge. In 2003, Bill DeWitt Jr. decided to bring me in to the Cardinals and begin the exploration of how to transform into a modern organization that utilizes information.

And over time, there have been teams like the Boston Red Sox, the St. Louis Cardinals, and the recent Chicago Cubs and Houston Astros that have used information and analytics as one of their competitive weapons and have been successful in not only reaching the playoffs but also actually achieving championship status. That was one of the criticisms about Oakland. They could get to the playoffs, but the analytic approach didn’t help them ever win in the playoffs. But that changed when the Red Sox and the Cardinals started to win the World Series and the industry knew that they had a strong analytic bent. And obviously, in recent years, it’s been even more so with the Cubs and the Astros. Teams have always looked to those teams that succeed, that win championships. What did they do that we can do in the future?

Quarterly: How would you contrast the information in 2003 with what’s available now?

Luhnow: Oh, it’s worlds different. Back then, when I talk about analytics and information, it was looking at historical performance to predict future performance—very much like in the financial-services industry, where you’re looking at the performance of stocks and the economy and trying to figure out what factors are predictive and sticky and what the future might look like. At that point, there was very rudimentary baseball information, like walks and singles and doubles and strikes and balls. But there was a lot of predictive value inside of that, and you could begin to build models that would help you predict a certain player at a certain age that plays a certain position that has had this type of résumé in the minor leagues and in his early major-league career. This was roughly what we would project him to do next year and the year after.

Those projections became much more accurate than if you just used traditional methods of asking your scouts or your baseball evaluators: How many home runs is this guy likely to hit next year? How many runs is he likely to score? But while those projections ended up being substantially more accurate and predictive, they still have a huge error bar around them, because we’re trying to predict what human beings are going to do on a field of competition, and there are so many other variables that you can’t control for. That was the breakthrough in 2003 around that time.

Today, it’s completely different. We now have so much technology around the ballpark and information about the trajectory of the ball, the physics of the bat swing, the physics and the biomechanics of the pitcher’s delivery—so many components now that advanced sciences have worked into our game. It’s, quite frankly, overwhelming in terms of the amount of information that we have access to and intimidating to figure out how to analyze all that information, work through it, and come up with the takeaways that will allow you to continue to do what we tried to do back in 2003, which is to make better predictions about what players are going to do in the future on the field.

Quarterly: What other changes jump out at you over past 10 to 15 years?

Luhnow: In 2003, there were maybe four to five clubs that had analytics-dedicated people on their payroll, and typically they were in an office down the hall working on recommendations to people who may or may not pay any attention. I think what’s changed today is that every general manager has some background or interest in analytics, and the typical size of the group in the front office is probably somewhere between 12 and 15 full-time people who all have advanced degrees, whether it’s computer science or physics or mathematics or some other discipline. Along with that, there are data departments in organizations. Most organizations now have database folks and data scientists that are on their payroll and that are helping them not only store the information and organize it properly but also evaluate what it means.

There was also a trend in the past of using external companies to house data, like scouting reports or statistics. Most of that has now come in-house. When I was with the Cardinals, we used an outside provider, and when I got to the Astros, they were using an outside provider, but the response time and the customization was lacking. Most important, when you come up with a way of looking at the world and you want the external provider to build the model for you, you don’t want them to share it with the other 29 clubs. It’s difficult to have the confidence that it’s not going to be shared in some way, shape, or form. I think that’s led to most clubs believing that their way of handling data and information is a competitive advantage. It therefore becomes critical to have control over that in-house.

Quarterly: What were your immediate priorities when you joined Houston in 2011?

Luhnow: When you’re lagging, the first thing you need to do is figure out how to make sure you’re not losing ground anymore. In 2011, things were changing pretty rapidly in terms of the types of information that was out there, the types of analysts that you might want to hire, the data scientists—all of that. We felt we had an advantage relative to the infrastructure that had been built in St. Louis over the prior eight years. We had a clean slate.

So we were able to start with a fresh piece of paper and say, “OK, given what we think is going to happen in the industry for the next five years, how would we set up a department?” That’s where we started, “OK, are we going to call it ‘analytics’ or are we going to call it something else?”

We decided to name it “decision sciences.” Because really what it was about for us is how we are going to capture the information and develop models that are going to help the decision makers, whether it’s the general manager, the farm director who runs the minor-league system, or the scouting director who makes the draft decisions on draft day. How are we going to provide them with the information that they need and that will allow them to do a better job?

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Here is a direct link to the complete article.

Jeff Luhnow is the general manager of the Houston Astros. This interview was conducted by Aaron De Smet, a senior partner in McKinsey’s Houston office, and Jeff Hart, a partner in the Houston office.

 

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