Podcast: Mining NBA Data for Leadership Lessons
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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.

Yevgenia Nayberg

Based on the research of

J. Keith Murnighan

Edward (Ned) Smith

Listening: NBA Team Composition

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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.


[Music intro]

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. 

[Music inter­lude]

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. 

[Music inter­lude]

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. 

[Music inter­lude]

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. 

[Music out­ro]

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​.kel​logg​.north​west​ern​.edu. We will be back next month with anoth­er Insight In Per­son podcast. 

Featured Faculty

J. Keith Murnighan

Member of the Department of Management & Organizations from 1996-2016

Edward (Ned) Smith

Associate Professor of Management & Organizations, Associate Professor of Sociology (Weinberg College, courtesy)

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