Leadership Feb 2, 2015
Podcast: Mining NBA Data for Leadership Lessons
Houston Rockets GM Daryl Morey and Kellogg School faculty talk data analytics and team composition.
In this episode, which originally appeared in February 2015, Insight looks to the NBA for leadership lessons on how to build a successful team, how to manage diversity, and how one NBA team has embraced data analytics to drive decision making across the organization.
Ned Smith, an associate professor of management and organizations at the Kellogg School, explains how strategic team composition can lead to long-term growth and success.
Keith Murnigan, a professor of management and organizations at the Kellogg School, defends the value of three-point shooters, surgeons, and other specialists.
And Houston Rockets General Manager Daryl Morey discusses the advantages and challenges of using a data-driven approach to running a basketball franchise.
Jessica LOVE: After four years in Cleveland, LeBron James has officially signed a 153.3 million dollar deal with the LA Lakers. And the move has everyone talking. What will the East Conference look like without James? And how much can a single player alter the balance of power in the league?
Hello, and welcome to the Kellogg Insight podcast. I’m your host, Jessica Love.
Here at Insight, James’s move to Tinseltown has us thinking a lot about team composition. So we’re bringing back one of our favorite episodes, which originally appeared in February 2015.
In that episode, we spoke with two Kellogg School professors, Ned Smith and the late Keith Murnighan. Murnighan died in June of 2016.
Both Smith and Murnighan used data from NBA players to study team composition more broadly.
We also spoke with Daryl Morey, the general manager of the Houston Rockets, whose devotion to using data analytics in decision-making has earned him the affectionate nickname “dork Elvis.”
We hope you enjoy.
LOVE: For our non-NBA superfan listeners, don’t worry. Dork Elvis’ comments apply way beyond basketball. As Morey says:
Daryl MOREY: Sports is really a late adopter of using data to drive decision-making. I mean, if you look at Wall Street, you look at consumer or credit-card companies, you look at Procter & Gamble, all these companies are actually quite a bit ahead of using data to drive their decisions.
LOVE: First, let’s take a step back and ask what may seem like an obvious question: Why are teams important to a well-functioning organization?
Ned SMITH: It’s funny when this question even comes up, and I think it reflects a myth that we have in popular culture about the individual as being the innovator, or the individual as being the one that really drives performance.
LOVE: That’s Ned Smith, an associate professor of management and organizations at the Kellogg School. Despite this popular belief, when researchers look at innovation within organizations, it’s rarely a single leader who’s responsible.
SMITH: I would venture to say nine times out of ten we realize that it’s really the group—the product of a group effort—that had brought together ideas from all over the place, and it was only in that synthesis that something emerged. Very seldom do we actually go back and find that, “Nope, in fact, it’s all in the head of one person.”
LOVE: So, teams—and particularly well-functioning teams—are vital to growing a successful organization. That makes sense. But why are Kellogg school professors studying the NBA to learn about team composition?
Keith MURNIGHAN: So, for me, basketball teams are perfect in many ways.
LOVE: That’s Keith Murnighan, a professor of management and organizations at the Kellogg School.
MURNIGHAN: Basketball is one of those wonderful microcosms where teamwork really does matter—it’s really obvious that it matters.
LOVE: Murnighan explains that Basketball teams are completely interdependent. Part of the excitement of the sport is that if you play well as a team, you can often beat the other guys who may have the bigger-name players. Plus, the NBA is overflowing with data, from cameras installed above the court to statistical minutia like the secondary assist.
Both Smith and Murnighan have mined this rich data to explore team composition. And both have looked specifically at the benefits of assembling teams that are diverse in terms of the skills their members bring to the court.
Here’s Smith on why such diverse teams are useful.
SMITH: Oftentimes, the best ideas, the most innovative solutions, come from the recombination of unique bits of knowledge, which we can only get from people with diverse experiences and backgrounds.
LOVE: Smith’s NBA research has focused on the question of diversity of playing style on a team. He consulted with college coaches and decided to use information about what college conference an NBA player came from as a measure of diversity, since different conferences have distinct playing styles. One might be more physical, another might use a different kind of defense.
Then Smith looked at a particular element of this diversity. He separated the top players from the bench players and looked at the diversity of college conferences among those two groups. What he found from more than two decades worth of data is that teams that had a diverse starting team, and a similarly diverse bench won more games. He calls this redundant heterogeneity.
In other words, the optimal team structure is one with a lot of diversity—but where the diversity of the bench mirrors the diversity of the starters, meaning each of the conferences represented among the starters is also represented on the bench.
Smith breaks this advantage down in a few ways.
SMITH: So we show it with respect to players in the core group getting injured. There is, as one would expect, a negative effect on the team’s performance, and then we show that teams that have a redundant counterpart with a similar skill set in the secondary group are less negatively affected by that injury.
LOVE: He also found, perhaps less intuitively, that having a high level of redundant heterogeneity actually prolongs the overall benefit of this sort of diversity. Over time—about four seasons to be exact—teams start to lose their diversity as players’ styles start to blend together. But, Smith says:
SMITH: We think that what might be going on here is when there’s somebody on the team who is like me, I actually maintain my personality or my unique skill set for longer periods of time, so overall, the team can continue to benefit from our diversity, our collective diversity, from this structure.
LOVE: And remember, Smith isn’t trying to offer leadership lessons to the NBA, though feel free to take note, Phil Jackson. This insight into redundant heterogeneity is applicable to any organization that has multiple tiers of employees, like a law firm with associates and partners.
LOVE: Keith Murnighan has been using NBA data to study a different aspect of team diversity. He’s interested in generalists versus specialists. The generalist being the person who is good at lots of things, versus the specialist, whose skills are finely honed in one particular area.
MURNIGHAN: Imagine if a hospital had no specialists, just generalists. It wouldn’t be a very effective hospital when unusual illnesses or maladies arrived. But when a hospital’s got a full range of specialists, they can handle anything. And that’s what you want teams to be able to be flexible enough to do.
LOVE: And while that may make perfect sense, it turns out that people have a bias against specialists and instead gravitate toward generalists.
MURNIGHAN: You can see why people naturally would be attracted to generalists, because they can do a little bit of everything, and when you’re in need, they can step up.
The problem with this is if you have a team full of generalists, it’s kind of boring and dull and mediocre, and you don’t have the kind of ... sum of the parts being greater than the individual elements.
LOVE: In his NBA research, Murnighan looked at specialists in the form of three-point shooters. He recruited basketball fans and anointed them team managers. He gave them a budget to pick their teams and made it clear that they were in need of good three-point shooters. Yet, these anointed managers didn’t pick three-point specialists for their teams. Even when that diversity of skill set was exactly— and explicitly—what their teams needed.
MURNIGHAN: The difficulty is when you look at three-point shooters, they’re not very impressive even visually. They tend to be shorter, they don’t jump, they don’t run fast, they don’t defend. OK? But they shoot, lights out, when they have the ball and they’re open.
LOVE: Again, this extends beyond the NBA.
MURNIGHAN: This is a problem for teams. They have to get beyond it and look toward somebody who’s not your perfect prototype, and that’s what a generalist is. What you want is a perfect specialist who really knows their stuff deeply and can bring to bear information that nobody else has. And for three-point shooters, they’re bringing a skill that nobody else has.
LOVE: Murnighan stresses that when putting together a team—on the court or off—a leader needs to always be thinking about what the long-term goal is and build a team toward that end.
MURNIGHAN: What is the ultimate goal that they’d like to achieve, and what is it going to take to get there?
Particularly for leaders, there are important elements within a team and important skills that they can’t provide themselves, that they have to bring in, and they have to understand and know and be cognizant of exactly what they need to achieve the goals that they’re after.
MOREY: For us the questions are very simple. Everything is judged on probability of championship over a three- to five-year time horizon.
LOVE: That’s Houston Rockets general manager Daryl Morey, who, since becoming general manager in 2007, has instilled a data-centric approach to decision-making throughout the ballclub. To do this, Morey makes sure he has the right people to work with, and that they understand that while data can provide some amazing insights, it has its limitations, too.
MOREY: I think most of it really comes down to what you hire for and then what you reward.
In our hiring and in our key roles, we want to make sure people understand the value of information, that you don’t always have to use data to help drive a decision, but you do always have to go looking to see if you can do that.
LOVE: Morey is not a legendary former player or zen master coach who has been bumped up into the front office, but rather a computer scientist who cut his teeth at STATS, a sports technology company that gathers and analyzes sports statistics—you may know them from “Moneyball.” In Morey’s current job, making informed decisions about personnel means crunching the increasingly rich streams of data that teams like the Rockets are collecting. That analysis takes talent.
MOREY: Obviously, I live that, embody that, and we hire for that. The people who move forward are the ones who make the best decisions.
LOVE: Teams have always used data in some form, be it game tapes or scouting reports, to recognize patterns and drive decisions.
From defensive matchups to what midseason trade may best position a team for a deep playoff run, the current data goldmine requires analysts with the talent to separate the signal from the noise.
MOREY: How can you know, okay, we’re dealing with something spurious here or we’re dealing with a real trend that we need to deal with. That kind of stuff happens all the time. A guy is hot in the first half. Is he picking really good shots, or are we giving good shots that we need to close down, or did we actually follow a game plan for a while and we were just not lucky in the first half and we should just stick to our game plan.
LOVE: So the data’s available in real time, which is great. But that data sometimes runs against the game’s conventional coaching wisdom.
As an example, when Jeff Van Gundy was the head coach of the Rockets, Morey approached him with data that showed when the clock was winding down towards the end of a quarter, it made more sense to try to hoist up two shots—however rushed—than to hold the ball and run down the clock before taking a single shot.
MOREY: Trying to convince coach Van Gundy—who is an analytically smart guy—but trying to convince him that the 2 for 1 was better was a bit of a challenge. Coaches historically had wanted to go for one good shot because they are always preaching one good shot.
It was a little incongruent to then say to them, now in this particular instance, now take two really bad shots and it’s better for us. Actually, Coach Van Gundy over time even became convinced that that was the right thing to do, even though he didn’t implement it always because he felt like the difference in winning or losing wasn’t big enough.
LOVE: Data can help teams address perennial questions like how do you execute your offense at the end of a quarter. Teams can also get a more nuanced and more accurate read on complicated, interdependent aspects of the game, like quantifying the style of play.
MOREY: If you take the most advanced data that’s out there, 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, in what ways...that kind of data can really glean the different styles of play in the NBA.
LOVE: While data analytics can provide insights, ultimately, deciding what to do with that information boils down to much more than blindly following the numbers.
MOREY: A lot of that is we have to use judgment, and then as you know, all the best decisions I made are a combination of obviously using the data and at the same time using a lot of domain knowledge that’s built up over time.
We’re very careful not to use data unless we know that in the past it’s been predictive. That’s one thing I predict in all these sports is people are going to start using the stuff wrong because it will be the trendy thing to use it, and then they’ll not know to use it only in certain instances and only in certain contexts.
LOVE: In pro sports, there’s not a lot of time for A/B testing or experimenting with a large number of lineup combinations. It’s also next to impossible to do things like test whether a player from another team would gel with your team. So Morey is left with both a great deal of uncertainty and a tall task.
MOREY: Every decision, we’re trying to up our probability of being the championship team. It’s unfortunately a very daunting equation to go against because your odds are pretty terrible in a league of 30 where only one wins every year.
LOVE: But Morey and the Rockets are ready to put their money where their data are.
MOREY: There is a big difference between “I believe something” and “I believe something, and I’m willing to put a lot of money behind it and a lot of investment behind it and my future career behind it.” Those are two very different things. We’ve obviously gone full in on certain things that other people maybe believe but didn’t really put anything behind.
LOVE: This program was produced by Jessica Love, Fred Schmalz, Emily Stone, and Michael Spikes. Special thanks to Daryl Morey and Kellogg School of Management faculty Keith Murnighan, Ned Smith, and Tom Hubbard. You can stream or download our monthly podcast from iTunes, or from our website, where you can read more from our interview with Daryl Morey. Visit us at insight.kellogg.northwestern.edu. We will be back next month with another Insight In Person podcast.