Daryl Morey loves good data, and lots of it. As general manager of the Houston Rockets, the Northwestern graduate has made a name for himself with his devotion to using data analytics to make team decisions—everything from where to shoot from on the floor to whom to acquire in a mid-season trade. Morey talks with Kellogg Insight about the importance of assembling a staff that understands analytics, how to ensure you are using the data wisely, and the need to always keep your eye on the prize when crunching the numbers.

To hear Morey discuss how the Rockets have incorporated data analytics into their organization, check out this month’s Insight In Person podcast. (Editor’s Note: This interview has been edited for length and clarity. Special thanks to Kellogg School faculty members Thomas Hubbard, Keith Murnighan, and Ned Smith for their assistance with the interview.)

Kellogg Insight: When collecting data, do you know the questions you want to ask beforehand, or do the questions arise from the data you’re able to collect? Daryl Morey: For us the questions are very simple. Everything is judged on the probability of winning a championship over a three to five year time horizon. If the data we gather or a decision we make can affect that, we’re going to do it. For us, the success function is pretty easy to figure out. It’s unfortunately a very daunting equation because your odds are pretty terrible in a league of 30 where only one wins every year. KI: At Kellogg, we have a new program on data analytics. We’ve adopted the perspective that data analytics is a leadership problem, not a statistical problem. What are the key challenges business leaders face when integrating data analytics into the rest of the organization? DM: At the Rockets, we have an owner who is a visionary guy, and has been his whole career. He absolutely believes in the value of data analysis to help drive decision making. He’s seen it work in his other businesses and he was the pioneer in basketball to say, “Hey, I’m going to go make a full commitment here.” At other teams, I hear a lot of frustration. The decision makers will go in other directions, and often in ways that don’t work, because they are not versed in using information.

“Everything is judged on the probability of winning a championship over a three to five year time horizon.”

KI: How do you create that comfort in an organization? DM: I think most of it really comes down to what you hire for and what you reward. We want to make sure people understand the value of information. You don’t always have to use data to help drive a decision, but you do always have to see if you can do that. I live that, embody that, and we hire for that. The people who move forward are the ones who make the best decisions.

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KI: With respect to data analytics, what insights from other industries cross over into your purview? DM: Sports are really a late adopter of using data to drive decision making. If you look at Wall Street, or you look at consumer or credit card companies, or you look at Procter & Gamble, all of these are actually quite a bit ahead in terms of using data to drive their decisions. Sports are late to the party. We have a bunch of contacts working at quant funds. They are dealing with very similar data sets to ours, in that they have data that changes pretty rapidly through time. We’re trying to forecast players; they are trying to forecast companies. KI: How can something as intangible as style of play be captured in analytics? DM: Well it turns out it is not very intangible. You can see it on the floor. The most advanced data that’s out there—25-frames-per-second positional data of all the players and the referees on the floor—that data is very granular but very rich. If you want to look at style of play, i.e., how quickly the players move, how much they get up the floor, how often they are spaced, how often they are clumped—that kind of data can really glean the different styles of play in the NBA, both at the team level and at the individual level. If you want to look at all the derivatives of movement in terms of position, velocity, acceleration, jerk—you can get all those things with the data that we now have.

“Everyone’s using data even if you don’t know it. It’s the information from the world that you’re processing in your own experience and the mental models you form around that.”