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The Houston Astros and the Transformative Power of Analytics

George Springer of the Houston Astros and the American League celebrates while rounding the bases after hitting a solo home run in the tenth inning against the National League during the 89th MLB All-Star Game, at Nationals Park on July 17, 2018 in Washington, DC.

In 2013, the Houston Astros finished the season with a 51-111 record, the worst record in the history of the franchise. In 2017, the Astros won 101 games, finished first in the American League West Division, and went on to win their first ever World Series. How did the Astros go from worst team record to first World Series victory in only four years? The answer, in a nutshell, is analytics.

In December, 2011 the Astros hired Jeff Luhnow from the St Louis Cardinals as their General Manager. As VP of scouting and player development in St Louis, Mr. Luhnow built a strong analytics department and enjoyed great success as one of the top talent producers in baseball, helping the Cardinals win the 2011 World Series as well as reach the playoffs from 2012 to 2015. Mr. Luhnow then brought his analytics and management skills to Houston and proceeded to transform the team into a top contender in a short few years.

Today, just every major sports team makes extensive use of sports analytics, but none more so than Major League Baseball teams. Baseball has long been a game of statistics, collecting and analyzing vast amounts of data going back to the founding of the MLB in the years following the Civil War.

Top MLB players are constantly compared not just to other contemporaneous players, but to other top players across the long history of the game. Babe Ruth, Willie Mays, Mickey Mantle, Hank Aaron and countless other players are never forgotten in the world of baseball statistics. For those of us who are fans, as I am, not surprisingly given my Cuban roots, baseball’s rich history is a major part of our enjoyment of the game.

In the 1990s, baseball took analytics to the next level, most prominently with the work of Billy Beane, the Oakland Athletics general manager. Mr. Beane used sabermetrics, a set of analytics techniques to evaluate a player’s performance and project his likely career, to make his small-market team highly competitive against teams with much larger budgets. Oakland then began to achieve success, reaching the playoffs from 2000 to 2003. Mr. Beane’s use of sabermetrics was popularized by Michael Lewis in his 2003 bestseller Moneyball, later turned into a film in 2011, which helped explain the growing importance of data analytics to a wide, non-technical audience.

A few weeks ago I read a two-part McKinsey interview with Astro’s GM Jeff Luhnow, an excellent case study for any leader seeking to learn from the Astros’ transformation, including the challenges involved and how to overcome them.

In the first part of the McKinsey interview, Mr. Luhnow explained that when he first joined the team in 2011, there wasn’t much of a focus on analytics, and the then personnel was not receptive to the changes he was proposing. “There are hundreds of people that work in a baseball organization, including coaches, scouts, and hundreds of players that are signed at any one point in time,” he said. “They did not accept it right away. For certain elements of the analytics, we had to wait and be patient. Because if you can’t get the coaches and the players to buy into it, it’s not going to happen.”

First, he had to get the player-acquisition decision makers to leverage data to help them make better decisions. That was the relatively easier part. Much harder was convincing the coaches and players across the Astros’ organization, including their extensive minor leagues and player development system, to use all the available information in their every-day decisions, such as game-day lineups and defensive configurations. That took three to four years of really hard work. It’s not surprising that after embarking on such an analytics-based strategy, a number of sports teams give up after a couple of years, “because they couldn’t stand the heat in the kitchen.”

For example, new defensive configurations, based on a statistical analysis of where each player is most likely to hit the ball, are really disruptive to players and coaches. All of sudden the defense isn’t standing where they’ve always been since the players were in Little League. Even though the data shows that the new defensive positions work better most of the time, it feels weird, and when they don’t work, the players start to complain and the coaches eventually give up pushing back against the players.

This was the case in 2012 and 2013, the first two years of Astros’ transformation. Starting in 2014, the team spent a lot more time on education, sharing the data with the players and coaches, so they could better understand why they were being asked to change their behavior. The Astros management felt that sharing the data with the players was worth the risk of giving away part of their competitive advantage when a player got traded to another team. The team also hired extra coaches at each level of the organization who believed in and could explain the reasons for the changes to the players and coaches, thus playing the role of translators between the analytics and baseball worlds.

After another couple of years the translators were no longer needed because the regular coaches and managers became technology enabled and could now do the player education on their own. Said Mr. Luhnow: “The program of sending the people out and eventually changing over a large part of our hitting and pitching coaches and managers, quite frankly, to be a bit more open-minded, progressive group is when our implementation started to take root… To be able to change people’s behavior on the field and how they assess new information and use new technologies is very, very difficult to do. It’s been painful, and it’s taken a long time, but it’s going to be hard for other clubs to copy that.”

In the second part of the interview, Mr. Luhnow discussed how analytics has changed baseball in the 15 years since Moneyball was published. Back then, analytics was mostly used to look at the historical performance of baseball prospects to try predict their future performance, much as trying to predict the performance of stocks in the financial-services industry based on their past performance. Compared to what’s available today, Mr. Beane didn’t have a lot of data to make his player projections 20 years or so ago, before the big data revolution got underway. Mr. Beane’s projections were still more accurate than just relying on the traditional methods of asking scouts and player evaluators, but the projections were subject to big error bars because there were many variable you couldn’t control for while trying to predict how players would perform in the future.

“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.”

Another major difference is the growth of sports analytics. In 2003, only four or five clubs had analytics people on their staff, and they weren’t always listened to. Now every team is involved in analytics and they typically have a group of 12-15 full-time people, all of whom have advanced degrees. Mr. Luhnow believes that big data combined with AI is the next big wave in baseball. The Astros, as well as other clubs, are making big investments in the area, to help take advantage of all the data that’s now available.

When Moneyball first came out in 2003, many viewed it as a story about the conflict between the traditional approach of the scouts versus the new approaches being introduced by the statheads. We now have enough data to compare the performance of scouts versus more purely statistical approaches. Most scouts now use a hybrid approach combining statistics with whatever else they learn about the players. Statistics alone cannot tell you everything you want to know about a player, and the additional personal evaluations of the scouts make a significant difference.

“There’s always going to be a place for experience and judgment and wisdom in baseball in terms of evaluating players,” added Mr. Luhnow. “And we’ve proven that when you combine the information from the technology and analytics with the human opinion, you get the best possible result. Either one separately gives you suboptimal results.”

Irving Wladawsky-Berger worked at IBM for 37 years and has been a strategic advisor to Citigroup, HBO and Mastercard. He is affiliated with MIT and Imperial College, and is a regular contributor to CIO Journal.


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