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Houston Astros: winning the World Series with advanced analytics

The team’s embrace of advanced analytics provides lessons to business leaders on winning over skeptical employees and inserting big data into everyday work.
Aaron De Smet

Delivers growth, innovation, and organizational agility and is an expert on culture change, leadership development, team effectiveness, capability building, and transformation

Professional sports teams traditionally have used their time-earned instincts to make critical player and on-the-field decisions. Increasingly, they’re relying on advanced data analytics to help.

Consider the Houston Astros’ data-driven process that, in just four years, helped take the team from last place to winning Major League Baseball’s 2017 World Series.

This practice required getting the organization to embrace data and analytics, translate it into ideas that matter and remove barriers hampering the process. Analytics needs to be fully embedded across the entire organization, not just on the sidelines, which meant breaking down organizational silos. Attitudes and behaviors had to be transformed to make changes meaningful and durable. Getting buy-in from the non-analytic folks and making analytics part of everyday work was critical.

If that sounds familiar, it should come as no surprise that the Houston Astros’ successes and failures offer valuable lessons for business leaders. To recount them, we talked with Astros General Manager Jeff Luhnow, who, from the moment he was hired in 2011, employed analytic insight to make critical choices for the team.

Question: In scouting new talent, what were the Astros’ analytics strengths and weaknesses when you joined?

Luhnow: It was a traditional scouting organization, and the Astros did a nice job of scouting and developing some really good players. But if I were to rank the organization’s analytic capabilities, Houston would have been in the bottom five.

Q: How did you get buy-in?

Luhnow: We had to be patient because buy-in is essential. We had to get decision-makers on the scouting side to use the information to make the right decisions. The harder part was changing the behavior of coaches and players on the Astros and in our minor-league system. It took three or four years to get where we felt good about it. I was fortunate my boss, the team’s owner, supported us.

Q: More broadly, what changes made the organization particularly uncomfortable?

Luhnow: Here’s a great example. The pitcher throws a pitch. The ball gets hit to where the shortstop has been right behind him forever. All of the sudden, the shortstop’s not there because analytics tell us the shortstop should be on the other side of the base. It was hard to convince pitchers of this. The second year, we were a bit more forceful, but infielders and pitchers again complained, and our coaches lost the desire to continue to push back.

The next spring, we shared the data with the players. They needed to understand why this is beneficial. One younger pitcher kept complaining, but a veteran pitcher who had come around said, “Look, this is going to help you have a better chance to have a better career.” Once players advocate for these tools, it changes the whole equation.

Q: What else helped?

Luhnow: We hired an extra coach in the minor leagues at each level. We found enough players who had played in college, maybe one year in the minors and understood analytics. They could explain why we asked certain things of players, and the players began to trust them.

Q: Is that a competitive advantage?

Luhnow: We’re in a zero-sum industry where any advantage you gain must come at someone else’s disadvantage. A lot of industries feel that way. For us, implementation took root when we became a bit more open-minded and progressive. It’s been painful and taken a long time, but it’s going to provide us an advantage for the next 5-10 years and be hard for other clubs to copy.

Q: Do you try to combine the analytics, the head and the heart to make better decisions?

Luhnow: We’re always going to rely on our coaches and scouts for their opinions. We’ve proven that when you combine the information from the technology and analytics with human opinion, you get the best possible result. The key is how to combine them.

Q: What’s on the horizon?

Luhnow: Big data combined with artificial intelligence, and we’re just starting to scratch the surface. We now have information we didn’t dream of a few years back. Developing models from all that information will be critical to teams’ success ahead.

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