Invest Like the Best with Patrick O'Shaughnessy - Sam Hinkie – Data, Decisions, and Basketball - [Invest Like the Best, EP.88]

Episode Date: May 22, 2018

I came across this week’s guest thanks to the overlap of three passions of mine: data informed investing, value creation, and basketball. Sam Hinkie worked for more than a decade in the NBA with the Houston Rockets, and then most recently as the President and GM of the Philadelphia 76ers. He helped launch basketball's analytics movement when he joined the Houston Rockets in 2005, and is known for unique trade structuring and a keen focus on acquiring undervalued players. Today, he is also an investor and advisor to a limited number of young companies in which he feels his experience can improve outcomes.  Please enjoy this unique episode with Sam Hinkie.    Show Notes 3:24 – (First Question) Advantages of having a long view and how to structurally harness one 6:08 – Using technology to foster an innovative culture            6:18– Empire of the Summer Moon: Quanah Parker and the Rise and Fall of the Comanches, the Most Powerful Indian Tribe in American History 10:16 – Favorite example of applied innovation from Sam’s career 11:34 - Most fun aspect of doing data analytics early on the Houston Rockets 13:38 - Is there anything more important than courage in asymmetric outcomes 14:29 – How does Sam know when to let the art of decision making finish where the data started 16:29 - Pros and cons of a contrarian mindset 17:26 – Where he wanted to apply his knowledge in sports when first getting out of school and how his thinking is best applied in the current sports landscape 21:39 – How does he think about trying to find the equivalent of mispriced assets in the NBA 23:12 – Where tradition can be an impediment to innovation 25:07 – What did the team and workflow of the team look like in the front office 27:03 - The measure of truth in a sports complex 29:10 – What were the early factors coming out of the data that helped to shape NBA teams 30:42 – Best tactics for hiring 33:59 – Process of recruiting spectacular people 35:39 – Thoughts on fostering a good marriage 37:57 – Picking your kids traits in your spouse            38:02 – Selfish Reasons to Have More Kids: Why Being a Great Parent is Less Work and More Fun Than You Think 40:45 – What kind of markers does he look for when evaluating long term investment ideas 42:44 – His interest in machine learning 45:55 – What’s more exciting, the actual advances in machine learning or the applications that can be imagined as a result            47:15– International Justice Mission 48:11 – How he got started teaching negotiations and some of the points he makes in that class 49:16 – Effective techniques for negotiating 50:03 – Is negotiating contentious, do you need empathy 50:41 – A Rorschach test of Sam based on his reading of Lessons of History (book) 53:01 – Biggest risk Sam took in his career 54:37 – Biggest risks Sam took while with the 76ers 58:09 – Do people undervalue asymmetric outcomes in the NBA 1:00:11 – The players Sam has enjoyed watching over the years 1:02:45 – Why Robert Caro is a favorite author of his 1:04:30 – Kindest thing anyone has done for Sam

Hello and welcome, everyone. I'm Patrick O'Shaughnessy, and this is Invest like the Best. This show is an open-ended exploration of markets, ideas, methods, stories, and of strategies that will help you better invest both your time and your money. You can learn more and stay up to date at investorfield guide.com. Patrick O'Shaunicey is the CEO of O'Shaughnessy Asset Management. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Shaunacy Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of O'Shaughnessy asset management may maintain positions and the securities discussed in this podcast. I came across this week's guest thanks to the overlap of three passions of mine, data-informed investing, value creation, and basketball.

Sam Hinky worked for more than a decade in the NBA with the Houston Rockets and then most recently served as president and general manager of the Philadelphia 76ers. He helped launch basketball's analytics movement when he joined the Rockets in 2005 and is known for unique trade structuring and a keen focus on acquiring undervalued players. Today, he's also an investor and an advisor to a limited number of young companies in which he feels his experience can improve outcomes. At one point in our conversation, Sam mentions that he tracked success via future financial outcomes, much like we would in the investing world. So I did some research and found many interesting stats about the Sixers surrounding Sam's tenure. When he took over the franchise, it was 24th in ESPN's franchise rankings. Today, it is fourth. This is the result of an impressive crop of young talent, players like All-Star Joel Embed and Ben Simmons,

which resulted in large part from unconventional decisions that Sam and his team made. While I'm sure these estimates are imperfect, Forbes estimated the Sixers value at about $418 million when Sam took over, and $1.2 billion just a few months ago. Now, NBA teams in general have grown in value, so a lot of that appreciation is the equivalent of market. at beta, but given that the 76ers had the top percentage growth number more recently of any team in the league, some of it is alpha-2. While we can't parse the exact amount, it seems his unique approach to building a team clearly created some large amount of current franchise equity value.

If watching Ben Simmons and crew as any indication, it looks like dividends from those decisions will compound for many years to come. While basketball was where Sam plied his talents in the past, his approach is much more elemental. It is about finding great people using data and structure. making decisions that create the possibility of huge returns, be they financial or otherwise. I don't know what Sam will do next, be it investing in companies, running one, or taking over another team, but I know that it will be fun to watch. Please enjoy this unique episode with Sam Hinky. It would be fun to kind of

rebuild a bit your framework just for thinking about, really, I think of this as your framework for investing, really. We already talked about things like duration, having a very long view. There's things that you've written about innovation, having a contrarian mindset, having respect for sort of the place that you're working within. So maybe we could start talking about some of these concepts. Anything else on the long view, on the more nuanced side, obviously probably having a longer view puts you in a lower competition environment because a few people actually walk that walk. But anything, any other thoughts about, as you think about business investing, really anything, on the advantages of having a long view and how to structurally do that?

Yeah, I mean, I think you're just trying to get to sort of an inner scorecard kind of system where you're judging yourself and letting a set of other people that you respect to judge how you're going about something. I think some of this is just temperament-wise. I'm wired this way to think about the long term, to think about what I want to do X years down the path or what will matter. But I also think about it because it's actually what matters. It doesn't matter what happens the next two months or six months or something. What matters is what happens over the next two years or six years or 20 years or 60. One story I remember, we were having a meeting, a good meeting when I was at the Sixers.

and something came up about one of my colleagues, we were talking about, you know, we shouldn't take any shortcuts here or there, and we should stick to this. And this was kind of in the middle of what we were doing. And he said, we don't want to wake up three or four years from now and regret this. And I interrupted, and I said, 30 or 40. And he said, what? And I said, let's not kid ourselves that we won't wake up in 30 or 40 years

and think about that one opportunity we had at the Sixers and we shortcut it. I will. I don't know if that's on my deathbed, but let's hope I live that long. But that's how I will feel. And I think that's actually how we will all feel. if we recognize it like this, I think in some ways I'm just wired that way, so I don't have a ton of, other than you understand how competition winnows over time and you understand how incentives work, a deep understanding of how incentives work and how people think about incentives and how they react to them and all that sort of stuff. But I think in some level, I was like this when I was 15 about stuff, about my life. I remember no joke on the first date ever with my wife thinking a lot about and talking a lot about with some friends of mine that I thought I wanted to marry.

her. And the reason I thought I wanted to marry her was all about the long view and all about what that could mean. And now, I wasn't quite dumb enough to do it right then, right? We dated for a year or so, but I was very focused. I was very focused not on like, this is a great date. I was very focused on, oh my gosh, if this is what I think it is. If this is what I think it is, this could be a great life. This could be an amazing partner to spend your life with and all those things. And I would say almost all those things I was thinking about was not what the next 12 or 18 months would be like, but what life would be like in our 50s and 60s and 70s if that was the person you actually spent that much time with.

A second principle you've written a lot about is innovation. And we've shared back and forth very long lists of favorite overlapping books. And maybe one interesting one here since we've talked about a little bit on this visit is Empire of the Summer Moon to talk about technology and innovation and sort of your views on the importance of innovating what that means to you and how to foster that kind of culture and innovative culture. Yeah, I would say kind of at the highest level, I think, it is at one level it's like just like theft. It's just pay attention to what's happening elsewhere and try to apply what's working in other places to your place if it matters,

to your decision criteria if it makes sense and try to substitute your weak judgment when you don't know much about this space for someone else's strong judgment when you're convinced they know more about it. So I think that's one way to do it, which is be hungry for what's out there and what's working in other places. Just Lee Kuan-U-style. Step one is at work. It doesn't work. Why are we talking about this? I don't want to talk about it. But if it works there, then let's kick it around and let's talk about it because that might, let's at least start with something that works somewhere else and some other place in the universe and be able to go from there. That book in particular, yeah, I like that book a lot. It sort of resonated with me in a bunch of ways. But one is it's, to me, it's sort of like a book about tech transfer and adoption curves and the race that is that. And so the book is a lot about Comanche tribe that kind of stretched from almost northern Mexico to Nebraska, but mostly was in. Texas. And when all the folks in Texas were settling, they, you know, it sort of wake up one day and the band of Comanchees overnight just raided the village or raided your house or the town

or whatever. And what they realized, the way the book is written is over time, if you sort of zoom out a little bit from the book, the summary is the Spanish bring the horses and the Comanches breed them wonderfully and ride them amazingly and let their children start riding them at age three, boys and girls. And so you get all this like deliberate practice for decades on end. And so they They become these amazing horsemen. And, of course, now they're mobile and homesteaders and farmers are anything but mobile. Step one, build a house. So they come in and raid all the time.

But they'll raid around 100 miles and then turn around and run 100 miles back at night. Good luck catching them. Then everyone comes along with better tech. We thought better tech, which was bigger, stronger horses and rifles. But, hey, they're slow and slow. And if you want to use one, you've got to get off the other. So if you want to use the rifle, you had to step one, dismount. You dismount the commensies will be like three arrows near three.

throat, right? And you're done. And so this slaughter just ensues over and over. But along come, the way the book is written, I suspect this is true. S. C. Gwyn wrote it. And along comes the Texas Rangers who there is a guerrilla warfare, right? You're losing. And so now you break the norms and break some of the morals of the way you're supposed to do it. And they don't have any qualms about the way they'll fight this thing. And so they jump in with a different approach, which is, hey, they know how to ride horses. Maybe we should ride horses like them. Hey, every time they steal our horses, Maybe we should steal them back, but since they're experts at stealing horses, they decide they'll kill all the horses. So here the Texas Rangers kill all the horses.

That keeps the Comanches from stealing them back from us. And then what they really had was innovation from New York. Somebody invents the revolver. And so the only thing better than an amazing Comanchee warrior rider with all this deliberate practice is a pretty good Texas Ranger rider with a revolver. Right? And it's like all of a sudden you're at a different spot. And you're not at stasis anymore that now, the Rangers have fought back and the Comanchees are on the decline. And so they figure out their

patterns and chase them down. And over time, a bunch of other innovations come. Here comes the rifle and here comes meat markets and here comes buffalo hunters, which is a lot of the Comanche's food. And the ecosystem shifts the other way. But at some levels, don't fight with an enemy that when you're using all your resources for today, they've got friends back in New York with an R&D lab that might send them great weapons at any day. I don't think that was all intentional. This was happenstance, just happened in history. But it's the same kind of notion of if you're not planning for tomorrow where you don't have access to futuristic markets or you don't have access to futuristic technology, someone else will. And whoever does and whoever puts that to use the best or the fastest is likely to be the next temporary victor.

What's your favorite example of sort of applied innovation from your own career? I mean, I'll tell you a simple one that maybe sounds dumb if I have to call it my favorite, but it's the first one that comes to mind is I struggled for a long time with remote workers. and I struggled in a bunch of ways because we didn't ingest them very well and whatever, my charm that I had, worked much better around the water cooler than it did for remote employees, even if they came in regularly. And just simple tools, Slack, all of a sudden broke a lot of that down. And now we can communicate in real time, and now this meeting is all captured in Slack, or now this meeting happened in Slack. And so if you're living in San Francisco or you're living in Canada or you're living around the world,

you can be dialed into what's happening in our office, even if you haven't been in our office in many months. That's an innovation that I think just widened our net of talent that was possible so that even now, a lot of the folks that I worked with at my last stop, they live in San Francisco and work at Uber, or they live in Mountain View and work at Google X, or they live in Canada, or they work at a hedge fund in Chicago. And many of them were living in those places already and were big contributors to our team, even though they were remote. And it's just, it sounds so dumb to just say, we use. slack and that helped us. But what it really helped us is acquire a set of talent we wouldn't have otherwise been able to bring in-house. Or we'll talk a little bit later about duration mismatch problems and sort of principal agent problems. But first, I'd love to hear kind of what the most fun part of the early stages of doing this, more data-intensive analytical work, specifically at

the Houston Rockets. So early on, what was the most fun aspect of that job? I mean, at some level, it's still a people business in lots of ways. And so the most fun elements for me, personally, aren't maybe what you might think, is all the behind the scenes work in the trenches with other people trying to get to the right answer in a real search for truth and then having conviction to do something that people think is not the right thing to do. I remember one year we traded, when we were in Houston, it was at the trade deadline. We traded our 32-year-old starting point yard for a 22-year-old third stringer in Memphis. And that third stringer just happened to be named Kyle Lowry. And we had real conviction around Kyle Lowry and we had real conviction around

our backup, Aaron Brooks, that if we could get to the playoffs that Aaron Brooks had the kind of high variance game, that we could feed him possessions and let him play lots of pick and roll and let him be a score for us in just the way we were going to need. And we were able to do that. And when we were able to do that, everyone was like all over us, how can you trade your starting port guard on a playoff push? How can this happen? And so to make that, I remember when we made that decision and how excited I was that everyone had coalesced around that was the right idea and that we should do it. So I was probably as excited that day as I was, I don't know, 80 or 90 days later when Aaron Brooks went for like 25 in Portland and won a game for us,

that was sort of the results of what had happened. But I knew that day that it was just the right thing for us. And odds are, I didn't know how it would turn out exactly, but odds are it was the right thing. And I remember sending Daryl to text afterwards that how proud I was to work with him because he would have the courage to do what he knew was right, even if it was going to be unpopular for, in our case, people thought it would be unpopular for a month or two or maybe the rest of the season. When the truth is, as soon as Aaron played well and Kyle played really well,

it was unpopular for 10 or 12 days, and then it switched to popular pretty quickly. When you think about asymmetric outcomes, do you think there's anything as important as courage? Sadly, sometimes I think the single thing that you can arb the best is patience. Sometimes I look at Amazon through that lens that I have great, respect for them, but is all Amazon doing is just thinking long-term while feeding our short-term interest? You want a package? You want it two days? You want it same day? You want an hour? Check,

check, check, check, right? You want a book? You want it immediately. You want it on a Kindle. You want it on Audible. You want it in five seconds. You want to start reading it now, even though we're shipping it to you in 12 hours. Over and over and over, I feel they may have a long view about what makes sense for them, and no, we'll have a short view about what makes sense for us. And so the more they can feed us and serve our whims, the better. I think there are lots of other things, but patience is the one that is still the hardest. And then I think sometimes the more public scrutiny you get, the harder some of that patience is. There's a remarkable analog here between kind of what we do and what you were doing at all the teams, which is this kind of blend. Yes,

it's very data driven and data informed, but there's art to every single process. And you mentioned a phrase, which I think is the right phrase, which is the search for truth. What is actually the right thing to do? And data just is a fantastic way to get close. And data. And data just is a fantastic way to get closer to that, but it's just part of the thing. So I'm curious how you think then and maybe now about that balance, any nuance around where to stop looking at the data and not overdo it and let the people business, let the art component come into this. Yeah, I think you're in a never-ending search for better inputs. And so in an ideal world, you'll build tools that will work on autopilot and you don't have to put your hands on the knobs. The truth is, no one's near

there on much of anything. So often say to people, you flew to San Francisco a couple of days ago, do you want the pilot to fly on instruments or experience? I was like both. What are you talking about? Like as much as possible. And if autopilot's better for the middle 90% of the flight, autopilot. And if conditions are not, not. And so the truth is you have to put your hands on the knobs a lot to manually override any kind of automated decision system,

but you're trying to build a system that's good enough that you don't have to do it very often. Or that when you do it, you know how consciously you are. And you know the risk that comes with that, which is this set of data doesn't understand the whole, world. And so we need to be able to add context to it to make a good decision and know where its faults are. But we also need to know where our own faults are about the ways in which we have biases or the ways in which we're short-sighted on one thing or another. I don't care how to get to the best decision. I just want to get there about much of anything where our kids go to school, what we want to eat, where we want to go on vacation, whatever. You want to get the best info, what movie we watch on the weekends. You want to get the best info that's for you to make. make the best decision for the set of people that are involved. And whether that's a rotten tomatoes simple counting algorithm or a friend recommendation that you really trust that you resonate with and sees the world the way you do, you just want great outcomes. What are the benefits of and limits of a contrarian mindset? The returns to that are bigger in some spaces. In a place where

you have heavy constraints and there's scarcity around what you're able to do by a set of rules or a set of cap dollars or a set of roster spots or whatever in my particular role, thinking differently than others is one of the few ways you can do anything differently. And so once everyone agrees on something, then it's priced in. Exactly. And once it's priced in, now you're buying at market prices. You're buying at spot prices. And so there's just nothing left.

Now, you can get that player or do this transaction and try to do a little better from there. But the price you bought, you only have limited resources to do it. And it's not because anyone doesn't have deep pockets. It's just the way the game works. And so if you only have this many rolls of the dice and monopoly, what you pay for each of those properties matters. And if the price is already set by the market, then that makes it hard. So being contrarian in that sense makes a big difference. When you were leaving Stanford, you came in thinking about sports.

You're thinking about sports throughout and you're looking at sort of the options for applying this skill set or this mentality to different leagues or different types of sports. Maybe talk a little bit about that landscape, which sports maybe length. themselves to this kind of thinking more or less, where back then the most inefficiencies were and maybe where you think they might be today across the different types of sports. The more randomness there is and the less consensus there is around how the value is being created than the more opportunity there is. But you need some data to be able to do that. So places where you've had low samples, I mean, Michael Mavison's written about this, where you have low sample size events where the coach doesn't control who has the ball very much, where the difference in winning

and losing is very small. So soccer and hockey and those sorts of things are much more ripe in that sense. The downside is you have less data. And so a lot of people like the data we have, I don't trust. So like bring me a bunch of analytics on said sport. I'm an expert in the sport. I don't think that can really help. They're more or less right because the data's not been any good. The data is not high fidelity enough to be able to make great decisions on. You'd have to have to have a different set of data to be able to do that. You have to be able to either buy or generate or cobble together data sets that people haven't seen. If you're just if you're just looking at an a box score from 1970 and today, then, yeah, there's very little edge in that. You're going to need some other set of data. Some of that's a set of metrics, but even a new set of primary inputs to be able to generate insights out of that in a different way. Back to your earlier question, too, I should probably say this. Imagine in your world, you're like a fund, but everyone has the fund that's exactly the same size. It's like $100 million. And so it's exactly $100 million, and you can do as many transactions as you want, but whoever gets to the highest single dollar number at the end of the year gets a rank. In that world, being contrarian, turns out to be

important because if everyone agrees that Amazon's the stock to hold in your world, and you put 80 million of your 100 into it, and everyone else does too, there's no edge there. If everyone agrees, that's what it is. You're going to have to be willing to do something that others aren't, and you can do it in an intelligent way if you've got better inputs into it. How much do you think the world has changed from that point where there was probably a lot of low-hanging fruit, places old, analog ways of doing things that could be significantly improved through the use of data and analytics. That's a super popular idea now. We're talking a little bit about this, specifically through the lens of basketball, but just since you're looking at all sorts of

of the things across business and sports now, how much more level do you think that analytics and data-focused playing field is relative to when you were thinking about it back then? Obviously, has changed a lot in basketball in the NBA in particular has seen a real sea change and the level of investment in that space and the level of interest and maybe the level of media interest sometimes. Sometimes the media interest gets past the reality of some of these things. That's really a function in many ways of the change in the ownership profiles. And so as more owners come from newer industries where they made their mark and made the capital to be able to buy the team, they often want to employ a similar set of decision tools.

in a similar way in which they might manage the business to this new business that they run that is entirely new to them and a new industry, but many of them come from places where they're used to getting up the learning curve very fast on new industries. And this is yet another one. And so that's changed a lot. But I would say at some level, there's still a long way to go. I mean, actually, I've listened to your podcast, and I'm surprised all the time when you have people on and talk about the internal frictions they see even on Wall Street, where I think of Wall Street is not a decade ahead of. the NBA, but two or three decades ahead about how they might think about risk and return and how to gauge these things and how to be objective in your decisions and how to be top down and bottoms up and triangulate them both. And yet all the time you'll have guests on

that talk about the struggles that they would see internally between the art and the science. I think that's real in any environment and it's still real in basketball. All the job is about making decisions under uncertainty. That's a big part of what the tools are for is to help with that. And because they're uncertain, people don't agree what The right decision is for a while. And anytime you have longer feedback loops, it takes everyone longer to get down the curve of what the right answer is. So I always think about this perception reality gap, which is part of the source of mispricing in any market, whether it's for NBA talent or a stock or whatever. And there's kind of two ways to look at that.

There's be better at measuring the reality or be better at finding goofy perception. So as you think about as almost a value investor, and maybe we'll talk specifically about the NBA since that's where you spent your time. How do you think about that gap, about identifying that gap in a salary cap world and trying to find sort of mispriced assets, the equivalent of misprice assets in that world? Said another way you could think of it as buy price and sell price. And so what's it worth now and what will it be worth later? So what's it worth to me as a buyer and what might it be worth to the world if I were ever a seller? And so, I mean, the way that plays into, you know, in a sports environment often is in trade value, is in this thing you have today. What might it be

worth to others in the future. So you might be willing to pay a certain price for it. And then you have to think carefully about what other people would be willing to pay for it. One way we would often think about that is try to reverse them. To get rid of the endowment effect is try to reverse them and say, hey, if I had that other thing, if I already had this player, would I trade it for that? And if the answer is clear in one direction or not, there's real signal in that. Because sometimes it's harder to let go. If you told me you've got a position in this stock and you're proud you bought it, but if I said I could go out and get you this for it and you would reverse it, it's like maybe you should be holding cash instead of that. But it's being able to think through

both sides of that, which is a lot about kind of the market value of what it is. It's happening in your word, maybe the perception of the way the rest of the market thinks about it. You've also written about respect for tradition, and I'm always interested in institutional inertia, driven by tradition or other means that act as an impediment to adopting change in innovation. So there's the famous NFL study of you basically should trade away all your first round picks. And if you did that, that would be a good strategy. And everyone's presented with that information and just ignores it because it's so atypical. And there's like a career risk, if you will, attached to that problem.

Maybe talk about any experience you've had with that where tradition is good and where it becomes like a total pain to try to apply things that you've learned and innovation. It's true all of our lives, right? It's conventional wisdom because the consensus generally has settled there. That's not always right, but it's more or less right. And it's in lieu of better analysis. It should be a high bar. You should have some bulwark against progressivism or otherwise you would just flit around all the time. So it's true in our health.

It's true in all sorts of environments. So starting with where we are now might not be right, but it's the clearly best answer we have is fine. I remember early on when we would do a bunch of stuff. I think Darrell Morris talked about this before. We would do a bunch of analysis. and it would say, you should do this this way, right? The game, you should play the game this way. Our early check was we'd just check if the Spurs did it.

If the Spurs did it, we're like, okay, that might be right. Like the Spurs got to, the Spurs get to smart answers, and they have done a bunch of great stuff, and they do it that way. And now our analysis says, that's amazing to do it that way. And so we're like, ooh, we should maybe do that way. But if it says the Spurs did the opposite of that, we're like, I bet there's something wrong with our analysis. Let's go back again, because in the world that is the competition, the Spurs have gotten to this place, And they make generally really smart decisions and we have great respect for them. And so if our one or two days of analysis says something polar opposite to that,

guess who I'm going to trust for now. The burden of proof is on the newcomer to find something new. But let's go with the incumbent that's been working well today. What was the actual structure and process of the team that was doing the work at the various teams, meaning I'm always interested with what the kind of people you're looking for, emerging like machine. We'll talk a bit about machine learning in a bit and what that allows us to do. What was like the workflow of the culture and the team itself?

Like what did the structure itself look like? I mean, these companies are smaller than you might think. If you're asking about particular sports teams, 50-odd employees sort of on the front office side and another 150 or so in the overall operation. And so within that there's obviously lots of, there's increasingly software engineers that are building tools that really create lots of decision support areas. More and more you see people investing in machine learning and computer vision and various forms of AI to be able to create new. data sets out of that. Historically, there's been a fair number of statisticians. You see these people now increasingly need the ability to be able to code and to be able to manipulate their own data as well, but you'd have some sort of data scientists or statisticians in-house. But I would say generally what

you have are a lot of people that are deep in one area, but can moonlight and lots of others, too, because what you really need are intellectually curious, thoughtful people with all their own sets of experience to be able to bring to bear on this. And then to have a culture of trust, where you're looking for the truth, and you can kind of call BS if someone says it. Or you can say, like, no, actually it doesn't line up with our intel from the ground at all. Or that doesn't line up with this other data we have out of context or this other scouting information that we have out of context that it's totally different than that. And so we have to either resolve these two or we have to admit right now our confidence intervals should be very, very wide as it stands right now because we have two diverse sets of inputs and they differ widely. We trust both and we're interested in both and we're listening to both, but they don't coalesce in the same way.

And you need to either resolve that, you need to resolve that if you're going to have high conviction. And to make any kind of big contrarian bet, you're going to have to have high conviction. In our world, it's straightforward where if you talk about truth, a search for truth, the whole idea is finding observable variables now that are positively related to specific outcomes in the future. And for us, it's returns and maybe future earnings growth or cash flow growth or something like this. We know exactly what we're trying to correlate with. How do you think about what the measure of truth is in a sports context? Like, I wouldn't know what the answer to that is. I would say generally is very similar.

At some level, you're trying to predict how good a player will be in the future. But I guess measured how. Like what? So this is the same way. Just let me repeat my sentence with yours in it. You're trying to predict at what price a stock will trade in the future. It's sort of that. There's more nuance to it where within context, within context on our team,

within context on another team, that really matters. But doing the same way, like looking at how similar players that have performed as this player is performing now, what their curves have looked like and being able to make more and more accurate. You know, it's not a difficult prediction right now that we would all make just viscerally to say, hey, a very young player who just made an all-star team is likely to be very, very good over the next two or three years. Returns are going to be very similar to what we've seen in the past, even if he plays about the same. and he's young so he's reasonably unlikely to get hurt. Even just viscerally, you might say that's in a, the prior is big there.

But the truth is you're doing that in more and more sophisticated ways by using as much data as you can. But where everything goes over time and where it's going, not in sports, but everywhere, is you have less and less ability to point to the actual size of the coefficient of any one variable. Because these are not, you know, these are not simple linear regressions with five variables that here's the weight on these things that we all understand. that you can make intuitive sense out of. But instead, these are big ML algorithms that are driving what's happening. And often what you need is sort of side models to build around interpretability. But you're going to have to get comfortable with the fact that you don't actually deeply understand how it works.

That's how you built it. That's why you built it is because it's beyond a human's ability to actually comprehend how it was calculated. I'm amazed by how these things kind of operate in parallel. And again, using our world as an example, the people that were doing this original research, value and momentum, these simple kind of observable characteristics. And it's been sort of an arm's race since then, just like anything else. What were those kind of early observations or findings coming out of the data and analytical work, like in the very early days with the rockets? Like what were the things that you said, okay, wow, like this is an insight that's

maybe differentiated or different or will cause us to change our behavior in how we kind of build the team? So I'd say two things that are sound obvious, but it's the degree to which you're willing to trade on them or the degree to which you're willing to kind of like lean into them and resource them, defense is poorly measured and three is more than two. And I mean, we're 15 years later and that's still true. So defense is poorly measured, which means you'll see big skew and how big difference between perception and reality because all we collect are just a bunch of lossy variables, defensive rebounding and blocks and steals and just a couple things. And so there's, if you can find better and better ways to measure that with your eyes or your intel or, and

any kind of advanced analytics you might do, you can do that. And so you should expect more value to accrue from there. And threes more and two, by a lot, like by 50% more, right? And so I think the rest is history. I think, I think, I think, I think, I think, I think, Rayfer Alston set the all-time record for the Rockets for three points attempted, which has been broken not dozens of times, but many more than dozens of times since. But over and over and over, that three is more than two, and that that really impacts the game in a big, big way. I love to talk about your philosophy on a couple big areas, and maybe we'll start with hiring. We were talking before about the future and whether or not your decision-making

framework would be to find something really interesting or instead to just start with a fantastic group of people first as the priority. And I think the preference is for a fantastic group of people, which makes me think of hiring. I think that that can be an enormous source of advantage for anybody out there. And so I'd love to hear your philosophy on, we'll talk about culture as a separate topic, but hiring first and foremost, how you think that is best done. It's not clear I'm right, by the way. It's not clear I'm right about anything. I've said today. But I think I believe in the value of hiring and recruiting more than anyone I've ever met, which maybe you'd say, I overdo it. And it's not clear to me, I overdo it. It's not clear to me that so much of the value is about finding amazing people who can be additive and kind of a combinatorial system. So I tell people, if you've ever worked with somebody really amazing, that was a colleague, of yours or worked for you or you worked for.

This person just has this immense stature in your head. If you think that's fun, like work with a dozen or work with two dozen, it's amazing what you can do with the power law of incredible people. And I'm definitely imprinted early, aren't we all, by our early jobs. At Bain, the saying used to be A players hire A players and B players, hire C players, which I think is exactly right, that if you're a little insecure or a little nervous about somebody else being a little better than you, then there's a lot of incentive for you to hire someone clearly lesser than you, clearly lesser than you to work with you, to work under you because

they're less threatening and you end up with lots of C players. And I just think over time, it's more and more it feels like to me, this life is about spending time with amazing people and amazing people that challenge you and amazing people that when they make decisions that you're not in the room for and you hear about, you think to yourself, oh my gosh, that's so much better than what I would have come up with. I'm so happy to be on their team. I'm so happy to sort of let them pull the plow for me because they actually are better at this than I am and they push me in other ways. I think that's just incredible. I'm a big fan of increasingly of Naval Robin Khan. I just follow him on Twitter and, you know, he talks about, you know, do you want to

work this person for the rest of your life or not? And if you don't, what are you doing? Don't waste any time. Yeah, what are you doing? And the other way I like to sort of invert it and think about it is, would you work for this person. And if you would, hire them. And there's a bunch of people like that that used to work for me that if the world turns in a different way and someday I'm 55 and they're 50 and they have this amazing position and would like me to help them at whatever they're trying to. That would be a dream. It would be a dream to work with them again because you know how incredible they are. You don't care about the hierarchy so much of it was about being with amazing people. Netflix has this sort of famous deck about that and called stunning colleagues.

That's what it says on my computer and my Mac finder on all hiring. The folder is called stunning colleagues. You're looking for people that are absolutely stunning that you can't wait to tell your friends about and introduce to your family and meet other people with because they're so incredible. And you're so excited for other people to get to see them blossom and be their very best. So obviously we would all love to have these people. Getting them is a different question. So talk about the process there. You mentioned this idea of pinch points and very long recruiting cycles.

what was the actual practice of finding and recruiting these people? Yeah, very long recruiting cycles. Getting to know them over a long period of time, letting them get to know you, trying to sort of bring it to a point where it's obvious to you that you'd like to work with them and obvious to them that they should want to work with you. That's a big part of it.

Often people don't get very comfortable in a short period of time. So ways in which you can share more, share about yourself and share about what you care about and get them to share more. So, I mean, I even do it now. Like a lot of the people that I talk to now that maybe want to partner together on something, one conversation to dabble in something for an hour or two, sure. But if we really want to work together, like I would like to get to know them much better.

So like, send me a book to read. I'll send you a book to read. Introduce me some of your friends. I'll introduce you to some of my friends. And if you don't get a real sense of reciprocity there that they don't want to do that, and it's like, okay, great. You didn't want to be partners. And I learned that's what I was trying to do.

With learn you didn't want to be partners. You wanted one phone call and this would be done. And okay, then fine. I think you can't collect too much info on people. And I think increasingly, like the more, I'm a big believer in building systems where you work with people before you work with them. So work on a project and we'll pay you for it reasonably, even if we don't hire you, even if we don't offer you the job. Even if we offer you the job and you don't accept. We asked you for a real live project.

You did amazing. That was yet another data point, not the last, yet another data point that says you're our kind of person and the kind of person I'd love to work with again. And here's the pay. Thank you. Now, are we going on to step two or not? Let's talk about what that might look like. I'm going to take a weird step here, but just feels like the right time to do it. And I've been excited to talk about this, having the good fortune to meet your family and meet your wife. And I think a ton about marriage and parenting now. And I think it would be really fun to talk about, given the topic of spending time with great people and the impact that has on life, just kind of your thoughts on marriage, the institution of marriage, things that maybe people can do. Deep, we're deep. that can improve their own marriage. It should be the thing that we probably invest in more than anything, probably even before

parenting because it's sort of the fount from which good parenting then comes, I think. But I would love to hear any thoughts on that. Well, this is deep for an investing podcast. So I would say first, because my wife would require me to say this, I'm not the person to be giving advice on how to have a better marriage. So I shouldn't say that or shouldn't opine much on that. But I would say in a different way, maybe for like mindset, I tell people this a lot if they're in the right frame of mind to hear. hear it, if they ask, if they ask, I tell them this. I think it's the single biggest decision in your life, and I don't think it's close, who you choose to spend your life with.

I think it's the single biggest. So I think it's true for me. I think in hindsight, for me, and I had lots of advantages, grew up in a great family with lots of support and lots of people that poured themselves into me for decades on end when they didn't need to. Nothing changed my life, like the person I chose to marry and the outflows of all that and what that all means, not the least of which, because it's the longest duration. And it's the person you'll actually spend the single most amount of your time with and share 100% of your money with and share the biggest chunk of your time and all the deeply scarce and deeply precious things in your life. So I think it's, yeah, terribly important. And yes, I'm always that counseling people.

Get it right. Get it right. And then be thoughtful about not get it right for six months, right? Or get it right for what feels awesome now because that's a different answer than what real partnership looks like. I would say generally I lean on and I'm like a big Charlie. munger, nerd. I like him a lot. I'm not afraid to say it. I like him in part because he's pithy and he's pithy in ways I'm not because I'm too long-winded. He'll say, you want to be a good partner, deserve a good partner? Try. Do it first. Don't meet them halfway. Meet them more than halfway and be deserving of someone good and it will probably happen. I think that's reasonable, you know, sort of the golden rule spun another way, but it's like reasonable advice. I love the idea you had of

picking your kids' traits in your spouse. I did mention that. I was really influenced by this book that I recommend people sometimes called selfish reasons to have more kids. So the joke in our house is I wanted two kids and my wife wanted three and we compromised on four, which is about right. But one of the pieces of this book, this book is kind of freakonomics for parenting or freakonomics for kids is one way you could think about it. And this guy did a bunch of twins, looked at a bunch of twin studies, all he could get his hands on, almost 800 twin studies, and tried to find what was provably differentiated about parenting by finding. finding twins that were raised by different families. And so, of course, this is kind of call it the middle, 80% of the distribution, nothing crazy on either end, some abusive, silly, awful situation. And what he found is two things. Let's see if I get these right. One of the provable things over time that you could find is the religion your children chose when they were adults, the box they checked. Some level that makes sense. You don't grow up Jewish and one day become Muslim very often. And so that makes sense. And then secondly is when I mentioned childhood to you, what's the soundtrack in your? head. Is it metal or lullabies? And so the environment your parents created in your house might influence that a lot of like what's your own highlight reel of your childhood and how you think about that. And they're like, everything else is a mixture of your two parents. Your two parents. And so in that sense, one of the conclusions out of that is if you want particular traits in your children, like choose them in your spouse. And I definitely think that

in our house. So we have our oldest son is great. And when he was a baby, he was a baby, he was was like very, very happy. And all of our friends would say, like, oh, your son, he's so, he's so happy. And I used to say, if I was your mother, you'd be happy too. And I meant that. And the ones that knew her would all laugh and smile. And I meant that. And I meant that in the way it sounds, which is, that would be awesome because she's great and it's a good life. Now, I think it's different that she's like me. She has a natural bias towards optimism, too. She can sort of see the best in people. She can see past short-term fluctuations. She can be steady over the long-term. easily get herself to a place where she recognizes what really matters over the long term,

and she's really steady. And guess whose genes our kid has, mine and hers, in some measure. And so guess what? He's pretty happy, right? Most of the time, and he's pretty easygoing. And so I don't claim to know all the underlying forces that make that happen, but I do think about that a lot. And if they're corollary to that is if there are traits in your spouse that annoy you, right? And that's true with everyone. none of us perfect. Or, say, your potential spouse that, like, really annoy you that might be deal breakers, you're going to have a whole brood of them, right? That might have them in even a higher concentration if you're not careful.

So if that one thing is a potential deal breaker for you, it literally will appear in your progeny over and over if you're not careful. So we're sitting literally in the hotbed of innovation and interesting ideas. And we're talking about marriage and parenting. Which is that hopefully they're all listening. You've seen a ton of young thinkers, entrepreneurs. I think done some angel investing, et cetera. And you mentioned earlier this idea, you said, you know, not three or four years, no, no, no, no, like 30 or 40 years.

And this idea that good decisions, the impact of good decisions widens over time and compounds. As you think about identifying those asymmetric opportunities, what is your framework for thinking about that? So when you're talking to exploring, you know, investing in a startup or making any kind of investment, how in your mind you find markers of a potential for that sort of asymmetric outcome? I mean, I think as much as you can take a view where it lines up with a first principle's reasoning about the way the world is likely to go and what trends are that you believe in that are likely to be exploited over time and likely to persist, then I think that helps. I personally, for angel investing I do, I'd like to see a bunch of machine learning stuff and a bunch of computer vision stuff. and so I take almost all those meetings because that's in my wheelhouse and the kind of thing I care about and the kind of thing I love to learn alongside with them and the kind of thing I feel like I can often be helpful with. But at some other level, even if you're looking to be an advisor with a particular group or you're hanging out with a startup to help in these ways, you want to know if you can be helpful. So if you actually have something to add, that's one of my own criteria, is this in a space that I deeply want to learn about?

And I think is likely to be very big in the future and be influential for me. And then I'll be proud to have learned this. And then there's a lot of things like that, a lot of things I could learn from and ride sidecar and learn a ton from them. But in that sense, I would just be leaching off them, which one of the ultimate filters for me is, is there something about their stage or their experience or what they need as a key success factor that I might actually be able to help with? And if I can't, then sort of feel guilty sitting in the corner of the conference room once a week. And eventually you want to be able to get your hands dirty and help some way. This idea of machine learning comes up all the time now. Like any of these big buzzwords, like you kind of hear it and everyone somehow snuck it into their pitch sex because it's so popular.

Maybe you could give, since you've done a lot with it and thought about it a lot, maybe you could just describe what that means to you. Like, what is machine learning in the first place? And why do you continue to be so excited, meaning like what is changing about it or improving about it that's so interesting? To start, it's just a question of, do you believe you can make better predictions with data? And yes or no? And if yes, why? And does more data help? And we've long been in a place where a lot of our sort of early versions of analytics and then big data as it came along,

you were still reliant on a set of techniques that are time tested. So I like those in statistics to be able to select a set of variables and test against those and be able to explain it back to another human. And increasingly, there are ways with these new algorithms to be able to use wider sets of data. In the same way that when you open your phone, we did this morning, trying to figure out how warm it was going to be in Palo Alto today, there's not one data source that makes a prediction about the weather, but there's thousands of sensors and lots of information about the weather within 500 miles of here. And waiting in concentric circles more closely what's coming and the way the wind's blowing and everything. It's the same way of how do you take in lots and lots of data.

And machine learning allows you to do that. It allows you to take in more and more data and increasingly unstructured data in ways that you couldn't have before to be able to build prediction engines that are just better and that can do things that we can't easily describe and that we can't quickly calculate and that we can't do on our own. I mean, the classic example right now, which is still controversial, and I find it strange that it's controversial like a autonomous driving, which is you want two eyes in the front of your head or 100 and 360? Do you really think, you know, eyes in the back of your head, that's a basketball term. Like, you have eyes in the back of your head. You can see the floor everywhere. They actually exist in autonomous cars. Do you think that data is worth nothing? It might be worth very little to you because you're not used to it so you don't know how to process it, but this thing does. Or do you want a machine that focuses all of its efforts on driving the car?

Or do you want your machine that focuses a little bit of your efforts on driving the car? because you're on your own autopilot and you're listening to music or distracted or tired or blinded by the sun or looking at your phone or all the other things that can happen. And you're not putting all of your efforts on it where these other things are singularly focused on being able to take in information about and make predictions about is that a dog walking across the street? Is that a brake light? Is that a car? Are they coming towards me? How fast are they coming towards me? All those things.

Even now in Palo Alto, I'll drive my son to soccer practice. And on the way home, we'll be going down Middlefield, which is this normal little 25-mile-an-hour road. And we're all cruising along, a little four-lane road. And all of a sudden, you'll see there's something, must be something in the road. Like, everyone's like swerving around this thing. And you get up close and it's the Waymo car because it's going 24.8 miles per hour, right? And we're all going like 34. And so we're passing it like it's a mattress in the lane and just zooming around it left and right because it's following the law every time and doing what it's supposed to do.

What excites you more, the advances in the technology itself, you know, the machine learning AI, deep learning type stuff, or goofy unique applications of those things to gain an edge. So, you know, your career was, wow, look, we could build data sets that provide this incredible potential edge and how we make decisions and how we operate and build a team. Which of those two ideas is more interesting? We talked a little bit about this earlier, but I'd love to kind of get your take now. Yeah, so I spend some time reading papers about what's coming out and you're just, we're all just overwhelmed all the time with what's coming out of even the big firms around here from

Google to Facebook to the like. But that's not a place in which I have any real edge is, and nor am I contributing to fundamental research there. Yeah, it's mostly about the applications of ways in which you can use what's working wonderfully in other places. What's something like that that's exciting today? I just come from a worldview of probably everything we do sucks. Probably everything, all the decisions we make right now probably suck. And so it could help any of those. I don't really know which ones it can, it will hit first, but I'm anxious for it to come in one area or another. So maybe autonomous cars is a good one. I'll say it publicly. Looks like I might be wrong based on adoption, but I'm no expert on that. Like, I hope our children never drive. Never. I think one author

I like is, I guess he's written a book. Maybe that makes him an author is Gary Howlgan from International Justice Mission. And he talks about, we're all going to stand in front of the tribunals of our grandchildren and atone for what we did. And what has. And so we think about like, what is that for us? And we don't know. Obviously, they're blind spots. We don't know. We're not focused on them right now.

But I often think sometimes I might be driving. In the same way, like, maybe we look back at our parents about no car seats or, you know, smoking when they were pregnant or whatever things that just seem like silly. You know, kind of now. What did you guys do? Like, you drove these big death machines? And we're like, oh, yeah, we drove them all the time, right? What did you do? We're like, we just slaughtered people, just left and right.

Just like on the side of the road. There's just like little crosses everywhere. We just get drunk and hit a tree, right? And just like, these things would happen to ourselves and to each other. And we're just like, yeah, it's just what happened. Maybe in the way we look back at the turn of the century and, you know, industrial revolution, child labor and the machines that just maimed people left and right. It's just our, you know, Prius or our Lexus or something that did it. You teach two different topics here at Stanford.

I'd love to touch on each of those for a minute. So maybe first starting with negotiation. I was kind of surprised actually to see that as the thing that you were teaching. So maybe talk about how that got started. and maybe some of the finer points. Yeah, it's a new course last year that we put together here at Stanford, which has been fun, and they allowed me to be a part of that, which has been awesome.

And it's got a particular kind of like sports and entertainment bend to it. But a lot of it is even similar things like we're talking about today, is how do you take a bunch of lessons and a bunch of stories that come from a particular industry in sports, entertainment, and media, and the like, and apply generalizable lessons that you can use in all sorts of negotiations? And which ones really line up with theory, and which ones don't. And so how do you have a really critical mindset about what you store in your brain and what you'd like to use in the future? And so a lot of that is the basics of negotiations for in an NBA program. And then a lot of it is not the basics, but is actual sort of simulated practice of what will you say if this happens? What will you say if they say this? Try it. Try it in a low-risk environment here. Try it on me. Try it on the other people teaching the class. Try it with your classmate, that kind of thing. Are there any tactics or techniques that might surprise people that you think are effective ways of negotiating?

Prepare. That's it. Prepare. I mean, that's not, it's contrarian in how rarely it's practiced, like deeply prepare, deeply try to understand the other side in a really empathetic way. We talked about in our class just yesterday. I mean, one way is just to understand. One of Charlie Munger's things is I never try to have an opinion on something that I can't explain and I don't deeply understand the other side as well as they do. And if I don't, I might not win the argument unless I know all their points and know how they're waiting them and know why they think what they think. So it's the same in a negotiation. What does it they actually want? It might turn out when you figure out what they actually want, that there's just no overlap. What do you actually want? And being able to think about that ahead of time to know at what point you'd walk away and at what point it's better than your next alternative. You mentioned empathy. I'm curious maybe to hear a little bit more about that. Do you think of negotiation as this very contentious thing?

In an ideal world, it's not particularly in a repeat game that you need to understand where they're coming from. You need to understand the pressures they're under. And you need to be able to understand that to continue dealing with them, that you have to deal in a certain way. and that you have to, obviously, have respect and you have to, if your goal is to try to get a deal done or your goal is to try to get many, many deals done over years, you need to be able to empathize and help solve their problems because they've got problems too on their side. They got problems about their people to sell it to or their clients or their reputation that you have to be able to try to help with that as well. I'll come back to teaching in a second, but something you said triggered this book that we were talking about earlier, which is Will and Ariel Durant's Lessons of History. I'd be curious to hear your take on the things that most impacted you from that book. It's one of my all-time favorite books as well. Some of these great books are like a Rorschach test. So I'd be curious for that book in particular, what stands out when you think about it. Yeah, a lot of books are sort of like a shelling point for who you really resonate with in some way or another and who you want to spend a lot more time with if they have, not of they have similar views, but they found a similar honeypot. That one in particular, I mean, is my kind. I like. I like things that are dense. I like things that are distilled well. I like things that are translated really well. I think one of the things they pitch is it's 100 centuries and 100 pages. And they're like, this is an idiotic thing to try. And we proceed. And so here we go. And so great. I'm sure it's loss of you in some ways. I'm sure it's got its issues. But that's amazing. I mean, kind of the story behind them, which to me is captivating to me is they spent something like six decades of their life writing the whole civilization series. It used to be sold door to door, apparently,

as these big volumes that every, you know, upstanding, you know, middle America family should have. We didn't have. I'd never heard of. I haven't read them. And then when we finished, we put a 100-plus page summary of all that together. I was like, I'll start there. That sounds great. I like all the kind of, like, big history stuff these days.

So anything that zooms out and tries to take lessons from, or mostly when I read books, that's what I do anyway, is just zoom out. And what lessons can you take from that? And now the story makes it easier to remember. This one is like trying to force feed you the lessons on their own without the primary source material, which is a little dangerous, but mostly great. Lots of ebb and flow of history, how things change. Men have always been dishonest. Governments have always been corrupt.

Today, less so than in the past. You know, you could read two dozen books and come to that answer, or you could let him come to that answer for you and decide that's your new prior. All right, that's not bad. That's not bad at all, like, as a way to, like, think about things. There was one, I think we were talking about earlier, that I liked that would talk about even sort of sometimes the force of religion and that call it heaven or in the afterlife versus utopia on earth, that they were buckets in a well. As one goes down, the other goes up. And the more you find yourself in need of one, the less you find yourself in, you know, in need of the other. What's the time that you felt you took the biggest gamble or risk?

Gamble's the wrong word. where you were the most exposed to like uncertainty and risk in your career and it's something you did anyway. I'll tell you the first thing that comes to my head. Maybe I'll think of a better one as I'm saying this. The first thing that comes to my head is I remember when my wife and I went to the bank and wrote a personal check for like all of our school debt. We sort of had to pay back when I didn't go back to Bain. And instead I went to work for the Houston Rockets as their first analytics employee. And it was the biggest is a big chunk of money. I mean, it wasn't crazy, but it was I was 28 or 27.

it seemed crazy. More money than I'd ever seen and more money that I'd ever spent that we had, that we wrote a check out of our savings that borderline depleted us to be able to go do it because I went to work for 50 cents on the dollar of what I would have made, and I had to pay back all the school debt. That seemed terribly risky in the moment. We were comfortable doing it, but when you actually filled out the check, it was like, ooh, that one was tough. But I would say because I agreed to take over a team that was near the bottom and really had a long way to go to kind of be able to turn it around and have any chance of chasing titles down the line, I knew that would be big risk there when I went. And so even that decision was big for us to kind of uproot our family and move to Philadelphia the time. I knew that would be big risk.

And then most of the things we did while we were there, most of the big things were fairly big risk because they were all contrarian and people, not all, but most of them were contrarian. and a lot of people didn't agree with what you're doing. So a big trade here or there, you would, you knew, you know, if it didn't go well, it was very explicit what might happen. What were at the 76ers, maybe the ones that were perceived as the biggest risks or the biggest contrarian bets versus the ones that you felt were the biggest risks? Were those one of the same or were they different? I mean, probably one of the ones in hindsight that people would think of is I went there in 2013

and we had a rough season. We finished with second worst record in the league. And we ended up getting the third pick. But one of the kind of lights at the end of the tunnel all during the first. season, which was very hard, particularly down the stretch, was that we had two lottery picks coming because we had our pick and a pick from New Orleans. So two lottery picks coming, and we would get back another guy on our team who could help. And so that would be all great. And so everyone's very excited about that. And there's a bit of hype around that, even leading into the draft,

reasonably so for fans. And some of that came from the team, but it was all reasonable stuff about the Calvary's coming. And we got to draft night and we made a bunch of transactions. But the gist of, at least at the top of the draft, was we drafted one guy. that was structurally unavailable to play for the first year and another guy that was locked into play in Europe for the next two years and so that's in it's in beat in Daria's arch and so coming in everyone felt like we're going to get two great players tonight and it's going to be so much fun in summer league in a week and three hours later we have we don't have we what we don't have anyone right and so that was very hard I was like super proud of us for doing that and proud of ownership for having the

courage to be able to do that because we had talked about it days ahead of time that it might go that way and that there was a real possibility this could be our best option and if it was did we have the courage to do it and to their everlasting credit they did and we did it but it was hard it was hard because back to the amazon story you know we want what we want we want it now and i don't wait our kids want ice cream you know every night not tonight every night right and you know we have say no all the time we have to find systems that say no we have to find structural ways to I don't have any ice cream in the house. Sorry.

No, but yes on Saturday or whatever. And so that's hard. So the data sets have become super rich in the leagues themselves across sports, or at least it's going that direction kind of everywhere. But maybe, especially in basketball, the draft, talent coming out of Newspy high school and college is incredibly important to the future of any franchise. How did you think about that relative to probably the better analytics that were available for players as, let's say, trade assets or something like this or free agents relative to a dual. Embed that probably correct me if I'm wrong, but probably relied on more old school scouting methodology. I would say just regardless of any methods you use, the more uncertainty there is, the more inefficiency there is. And so, or the less data there is, the more inefficiency there is.

So I often say, just imagine there's an NBA draft, but you don't get in until you're 30. Well, it's like, it's pretty boring. Everybody takes the same dude at number one. We all agree who's the best of north of 30-year-old players in the league. And there's considerably more uncertainty if players are 19-ish or where one year in college or something, you know, in range of that. There's less uncertainty after their first year in the NBA. By the time they're turned 30, there's a lot less uncertainty. Go to the other extreme. What if you draft three-year-olds? And there's like tons of uncertainty, right? Well, anytime there's more uncertainty, there's more ability. Imagine some level you probably do it, I guess,

you know, public markets versus private market. Or, you know, imagine you don't have financials. Or imagine it's venture investing and you don't. There's no product, right? Or venture investing, it's two women in an idea. Yeah, that's the business. And so the less input you have, the more uncertainty you have. And so when you have that kind of uncertainty, that's, there's opportunity if you focus on making decisions under uncertainty. And if you come at it like on a leg uncertainty, well, okay, there's a different. You can play in the shallow end of the pool, but it's going to be harder. Am I interpreting it right that people probably miss or undervalue the claims on that asymmetric outcome as represented by draft picks, meaning they undervalue the value of draft picks relative to say someone that's been in a league four or five years and is getting towards that 30 mark where it's more of a known quantity? Generally, yes. I mean, I think a lot of studies would show that draft picks are terribly important, not the least which is, you know, imagine if Google could come across the street to Stanford

and recruit engineers, but they got to have them for nine years and no one else could have them. That would be pretty powerful, right? Getting Zuckerberg to come join you, not go start Facebook, might be amazing in its own way about what you could do with archetype, someone like him in the fold. So generally, yes. And then one thing that sports is weird is you have sort of asymmetric downside risk. So no one on earth right now can wake up tomorrow and be LeBron James except LeBron James. But everyone on earth can wake up tomorrow with a really bad injury and sadly never be able to play again. So you have...

Except LeBron apparently. Yeah. Today, he's defied everything. But you have more downside. risk than upside in that way. And so that's hard where standard in your field. Google's not going to be at zero tomorrow. Yeah. But at the same way, like yesterday's returns are not exactly indicative of today. Like something can happen to the downside. But yeah, you have a lot more comfort that it's turned out this kind of year after year, this kind of free cash flow year after year. It'll probably do about the same. And we're kind of like, it'll probably do about the same with a fatter tail on the left side of the distribution. I think a lot of how I think about the world is searching for fat tails.

and outsized outcomes. And so, and the difference in, call it the payout, the payout of a very low likelihood event and what that means. I could do some rough math. You got me talking about marriage earlier of the likelihood of my wife and I finding each other and that being all great. The payouts are enormous, enormous, and over a long, long period of time. That makes me think of our friend Josh Wolfe's randomness and optionality, right?

This is two great governing forces. So my favorite player growing up, I'm sad to have missed Jordan. And obviously I've seen a ton of Jordan just because I'm a huge basketball fan. But Alan Iverson for me was the guy that got me into the NBA. And I think for probably three years, I forced my parents to run like a second, whatever it was, cable line or modem line or something so that I can watch every game. I'm curious over the years who the players are that, again, we've talked a lot about like analytics and that can get a little cold and dark at the end of the day. This is like an incredibly fun thing that we do and watch as fans. if there are a couple of players that you've most enjoyed their game, most enjoyed watching them over the years.

I started working in the NBA in 2005, so since then I'm pretty partial to the ones I had direct experience with. I loved Chuck Hayes when we had him. He was the shortest starting center in the history of the NBA. He was an amazing defender that was wildly undervalued by the league for a very long period of time. It's just an awesome guy with this neat family. We acquired Shane Badiere after a while. He was amazing. Kyle Lowry was my kind of player forever. I saw him play in the Wells Fargo Center when Villanova was in a run and this amazing team Villanova had where Randy Foy played this power forward spot. And Kyle Lowry was this bulldog of a guy and then became a bulldog of an NBA player.

I like him. I like his kids. I think he's awesome. And then I have, you know, obviously a special place of heart for a lot of the players we had in Philly, some of which are still there and some of which have gone on. I mean, Joel and B. and I are still close. I love him and watch on pins and needles when he plays without his mask or something silly happens. T.J. McConnell, I love and sort of what he stands for. Robert Covington, I love and everywhere he's been.

There's a lot of guys like that that have been around. I hope we have a lot of players and both stops, so I'm leaving a bunch out. But I like people who, you know, have big dreams for themselves and whatever package they're in and whatever, and trying to outkick their coverage and overachieve. What overachieving for T.J. McConnell means is very different than what overachieving for Joel and Bede means because of the gifts that they have. But people who have a real grittiness but don't take themselves too seriously and are really realistic and are like everyday learners. I can't get enough for those people, obviously. What's it been like watching the Sixers in this amazing run? It's been fun. I'm a big fan. I love those guys and the more they play well, the more fun it is for me. That's been great. I feel great for the coach. And for Brett Brown, I'm a big fan of his. And a lot of the staff is the same. They've made plenty of changes too, but a lot of those people I just care deeply about, and we've got a lot of friends in Philly, and so that's, it's been fun. So last couple of closing questions, so the first is to ask why Robert Caro you think is your favorite author by a pretty decent margin, and maybe this is an excuse to talk about some of the best books ever. I just have deep respect for someone who devotes their whole adult life to help a whole bunch of people,

and I feel like that's what he's done all the way to the end. He's not. He's not. He's not done yet, but I mean, he's now spent whatever five decades or something writing about LBJ, at least four and more to come, writing about this, which is not about LBJ at all. And it's not about the salacious story of LBJ or any raunchy soundbites or anecdotes. It's about power and how it works and how it works in our country and how it's acquired and how aggressively some people think about it and how our U.S. government works and how systems work. And if you're a systems thinker and you want to see the machine get turned and see what comes out the other side. It's a great way to learn that lesson. I don't think it has anything to do with politics or anything to do

with exactly with LBJ. It has to do with here's what went into this machine and here's how this machine works and here's how some people gummed it up. And here's what came out. And what came out in some ways was really amazing during several stretches of his life that things that he was able to do. And the way it was made was really detestful. And seeing all that, I think, It's just amazing, but I probably come at it that, I mean, it's work reads like literature, and so I'm a big fan of that, and I like detail. But maybe I come at it that, like, tell me somebody spent five decades thinking about something, and I really want to listen to what they have to say, you know, A, if they have that kind of staying power,

and B, you know, I do think knowledge compounds over time. And so I'm deeply interested in learning about what they have to teach. So my closing question for everybody is to ask what the kindest thing that anyone's ever done for you is. I was 10 years old, and we had a. a real tragedy in our family that was hard. One of my best friends at the time was this guy named JJ Smith, and his father, his dad's name is John Smith. He came, a lot of people came to be supportive for our family, which was great. And he came in that moment, and he'd been there an hour or two, and he came back into this bedroom where I was. And he said, you want to come with me?

And I said, sure. And I went with him, and I went into his backyard, and his son was there. and he and his son and I played basketball for all the rest of the day and most of the night, and it got me out of a situation that no 10-year-old wants to be in, and it's something I've just never forgotten of what that took. I don't think he knew at the time how meaningful it would be, but even now it's meaningful to me because it was a real act of kindness, and it sort of got me out of a particular situation that was better for me to deal with in a different way, and allowed me to kind of find this refuge in a game I loved

at the time when I really needed it. Wonderful answer. I've had a good run of these lately. I love that question. I always love the answers. Thanks for such a wide-ranging, such an interesting conversation. So much fun today.

Awesome. Thanks so much. Hey, everyone. Patrick here again. To find more episodes of Invest like the best, go to Investorfieldguide.com forward slash podcast. If you're a book lover, you can also sign up for my book club

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