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Speaker 1: Fifteen days. That's how many days separate us from the

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Speaker 1: first preseason action of the new season, when the warriors

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Speaker 1: actually head off to a country that's very close to me, Japan,

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Speaker 1: to face the wizards. But we'll be talking about the

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Speaker 1: latest news and notes, as well as discussing some of

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Speaker 1: the concepts introducing the book the Mid Range Theory with

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Speaker 1: author and former Bucks Director of research, Seth Part. Now,

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Speaker 1: I'm miles gray and I'm Jack O'Brien and this is

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Speaker 1: miles Jack Got Mad madot nailed it. That was actually

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Speaker 1: one of the best ones. I think I sounded like

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Speaker 1: I was coming in over a radio. That was John Out. Yeah,

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Speaker 1: what's up, seth, seth part, now, welcome, welcome to the

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Speaker 1: most wonderful, hilarious show about basketball. It's part Um Fun

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Speaker 1: I had with your name. Look, I was just saying,

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Speaker 1: look at the freeze right. It's like right as you

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Speaker 1: introduced technology. Nailed it. So I gotta ask you what,

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Speaker 1: first of all, what was your favorite team? Uh So,

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Speaker 1: growing up I was a celtics fan. I grew up

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Speaker 1: in Alaska, but my dad is from Boston and so

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Speaker 1: came by. Came by honestly, but I went to my uh,

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Speaker 1: my saw, my freshman year of college was, I think, kg's.

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Speaker 1: I'm struggling to remember back now. It was either like

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Speaker 1: the year before Kg's rookie year or his second year.

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Speaker 1: Was My freshman year of college in Minnesota. Yeah, so, yeah, yeah,

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Speaker 1: my Freshman Year of college was his uh whatever. So

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Speaker 1: long ago, like I basically I was in I was

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Speaker 1: living in Minnesota for the start of the kg year.

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Speaker 1: So I became a temperwolves fan for basically until I

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Speaker 1: lost fandom by starting to do basketball professionally. That's funny.

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Speaker 1: We talked about that at like a lot of people

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Speaker 1: who are essentially at a certain point, the fandom does erode.

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Speaker 1: Like I'm actually just enjoying what I'm seeing. Yeah, I

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Speaker 1: like things that are good. Now it's hard for me

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Speaker 1: to be like you know that. It's growing up a

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Speaker 1: Celtics Fan. It's all be honest. It's hard for me

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Speaker 1: to not take some chat fraud over bad things happening

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Speaker 1: to the Lakers, like that's never gonna go away. But

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Speaker 1: the other fair, fair, fair, but that's what that's what

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Speaker 1: gets us out of bed, right. So, small victories, right.

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Speaker 1: And so now you're a pure basketball asteete. You just

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Speaker 1: sit back with a you know, in a white robe,

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Speaker 1: like what, like an ancient Greek philosopher, and just observe

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Speaker 1: the sport for the pure beauty of it. Yes, and

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Speaker 1: I never get into arguments or have strong opinions or

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Speaker 1: have play favorites or no, that's not that's not true.

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Speaker 1: It's not remotely true. But no, it's. But it's it's,

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Speaker 1: rather than being tied to a team, being tied to

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Speaker 1: concepts and and things like that. Right, and your book

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Speaker 1: is about analytics, but you kind approach it from an angle.

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Speaker 1: I this quote from the book, I think is a

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Speaker 1: good is a good way of saying what you think.

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Speaker 1: Analytics are a mode of thought hoping to reduce the

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Speaker 1: impact of the cognitive biases we all suffer from, um

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Speaker 1: which I that that's pretty cool. I I know that

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Speaker 1: makes it sound it's not like a catchy pool quote

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Speaker 1: that I've pulled out, but that that's the one that

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Speaker 1: really made it make sense to me. You know, I

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Speaker 1: I did not set out to be in basketball analytics.

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Speaker 1: I was a basketball fan and player first and sort

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Speaker 1: of by I don't know, by education and sort of

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Speaker 1: bent it kind of led me back to it in

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Speaker 1: sort of an economic slash numerican way and figuring out

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Speaker 1: a way to discuss the game in numbers and finding

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Speaker 1: out sort of what works rather than from a standpoint

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Speaker 1: pint of of I'm smarter because I'm doing it this way.

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Speaker 1: I mean, I hope that comes across in the book,

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Speaker 1: but that's that's really the approach to it is. It's just,

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Speaker 1: you know, I've I've kind of always thought of things

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Speaker 1: in that way. Um, all right, it's any any time

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Speaker 1: you do something on the court, you're not doing something else.

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Speaker 1: So figure out which is better. And you know some yeah,

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Speaker 1: and there's sometimes you can figure it out just by

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Speaker 1: sort of watching, and there's sometimes where you can't. I

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Speaker 1: mean the the classic example from like baseball analytics is

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Speaker 1: the difference between a three h hit and a two

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Speaker 1: seventy five hitter is one hit every two weeks. Now,

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Speaker 1: without without writing stuff down, watch two weeks of baseball

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Speaker 1: and tell me if you can tell one apart from

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Speaker 1: the other. Right and and so that's where you know that.

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Speaker 1: There there's stuff that and there's you know, there's shades

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Speaker 1: of gray as to win something is something you can

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Speaker 1: just you can tell purely from sort of observation when

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Speaker 1: something you need to collate more is uh, and everything

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Speaker 1: exists sort of along to continue on them. But there's

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Speaker 1: a lot of stuff that you you know, you can

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Speaker 1: watch a guy shoot a couple of jumpers, but you

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Speaker 1: probably get a better understanding if you can shoot in games,

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Speaker 1: by letting him shoot five jumpers and games right and then,

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Speaker 1: you know, seeing how many made it missed. I've always

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Speaker 1: said that the reason I didn't end up in the

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Speaker 1: NBA is because people just judge it too much on

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Speaker 1: watching me shoot a couple of jumpers here and there

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Speaker 1: and I didn't give me the chance to get up

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Speaker 1: five hundred shots in the N B A, like I

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Speaker 1: like I told them, hold, give me a run of

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Speaker 1: games real quick. You know what I means. The flawed

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Speaker 1: data set just because, okay, maybe I hurt my wrist

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Speaker 1: real bad shooting that free throw. I do like I

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Speaker 1: I do think there's a pretty of all of all

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Speaker 1: the like analytics, shifts, like big, big shifts, and the

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Speaker 1: understanding and how we approach a major sport. I feel

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Speaker 1: like what we saw with the N B A, I

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Speaker 1: guess it was like decades after the advent of the

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Speaker 1: three point shot, everyone just being like, oh, that is

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Speaker 1: a lot more points than a two pointer and just everyone,

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Speaker 1: you know, realizing how much of an advantage that was,

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Speaker 1: even though it kind of always been there, is pretty

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Speaker 1: like that's pretty wild and I do feel like people,

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Speaker 1: people now, when they think of analytics, are like so

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Speaker 1: they're gonna say something radical like that, and that's no

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Speaker 1: longer like that. There's not a hidden gem like threes

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Speaker 1: are actually worth a whole lot more than two's uh,

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Speaker 1: waiting to be discovered out there. It's more finding little

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Speaker 1: edges uh here and there. Right, yeah, there's, there's, you know, there's,

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Speaker 1: there might be more than one, but there aren't that

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Speaker 1: many big things. And I feel like the people who

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Speaker 1: are kind of anti the three to begin with maybe

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Speaker 1: get a little bit of a rod deal Um because,

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Speaker 1: if you think about it, we've only had like detailed

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Speaker 1: data on where shots come from in the NBA since

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Speaker 1: and that data didn't get like reliable till about eight so, okay,

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Speaker 1: I yes, you're gonna shoot better from eighteen feet than

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Speaker 1: you are from and you could be forgiven for thinking that. Well, no,

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Speaker 1: eighteen feet, I'm gonna make that half the time and

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Speaker 1: I'm gonna make the Otto One. Thirty percent of the

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Speaker 1: time and the math is still in my favor. Until

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Speaker 1: you can actually see the see it collated that way,

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Speaker 1: steither data pulled out, it's like actually, no, you shoot

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Speaker 1: a little bit better, but it's like thirty eight percent

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Speaker 1: if nobody's guarding you. And so, yeah, the extra point

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Speaker 1: is worth a ton more, but we'd have the information

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Speaker 1: needed to make it that obvious until like the late nineties. Uh,

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Speaker 1: and then, you know, combined that with some of the

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Speaker 1: rule changes in the early two thousands. I'm actually not surprised.

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Speaker 1: It kind of took until some of that happened and

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Speaker 1: then people had success, had success with it, for it

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Speaker 1: to start to accelerate. And then, you know, you start

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Speaker 1: to like then the Quantz came and ruin the game.

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Speaker 1: You are a former quant right, like you, you worked,

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Speaker 1: as we mentioned, does the Bucks Director of research. So

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Speaker 1: you've kind of you're you're saying that self effacing. Yeah, yes, no,

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Speaker 1: and I I yeah, and and you know I also

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Speaker 1: I say that it self effacing Lee in both directions,

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Speaker 1: both that know, the quants didn't ruin the game and

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Speaker 1: on the scale of those things, I am certainly on

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Speaker 1: the less quantity side than than than some people like

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Speaker 1: you know, they're the people who are coming in from

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Speaker 1: like an engineering computer science backgrounds. You know can go

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Speaker 1: much further in that direction, but you you do still

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Speaker 1: need kind of people in the middle that to to,

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Speaker 1: I mean translate, is a little bit strong of the language,

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Speaker 1: but you know, you do have to the language of

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Speaker 1: math and Statistics and the language of basketball are not

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Speaker 1: the same thing and you have to make them talk

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Speaker 1: to each other for for any of the information to

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Speaker 1: make any sense and, you know, affect decision making, whether it's,

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Speaker 1: you know, do we need a three here, or who

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Speaker 1: should we take in the draft right? What's kind of

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Speaker 1: like the tension between those two sides? You know, like

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Speaker 1: we're on one side, you're like it's in the numbers

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Speaker 1: right here, like this is something we need to address,

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Speaker 1: versus like you're saying these are different languages. What's that like?

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Speaker 1: Reconciliation Process, like when you're trying to like have the

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Speaker 1: proper acknowledgement of what what's born out of the statistics

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Speaker 1: sort of be properly processed and, like you're saying, end

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Speaker 1: up in a decision being made? I think there's a

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Speaker 1: little bit of a misunderstanding on both sides. If you

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Speaker 1: will as to what's actually happening. Uh, this is something

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Speaker 1: that I've you know, I've told the students, have told

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Speaker 1: the people who worked for me. Is the last thing

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Speaker 1: I want to hear you say is, well, the numbers

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Speaker 1: say this, like there's my maximus. The numbers don't say

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Speaker 1: you say if if it was self evident from the numbers,

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Speaker 1: you wouldn't need someone to work on the numbers. You're

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Speaker 1: making an argument about basketball based on sort of of

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Speaker 1: of numerical values and why this thing means basketball concepts

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Speaker 1: Um and I think from the from sort of the

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Speaker 1: traditional basketball side, I think there's a misunderstanding of that. Also,

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Speaker 1: there's there's a perception that, oh, this guy with his

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Speaker 1: numbers thinks he has all the answers, like no, here's

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Speaker 1: here's a possible answer, and here's why I think it's true.

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Speaker 1: Hear me out now. I think it's incumbent on the

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Speaker 1: the the analysts side to be able to speak the

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Speaker 1: language of basketball, because we're doing basketball here. So I like,

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Speaker 1: I'm not going to teach linear Algebra to you know,

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Speaker 1: assistant coach. So I have to like, okay, I just

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Speaker 1: here's maybe two sentences on what I'm doing. Trust me,

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Speaker 1: I know what I'm doing. Here's the implications, here's what

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Speaker 1: it means on the floor. Let's talk about it. Do

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Speaker 1: you think it helped your argument that you kept an

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Speaker 1: abbocus on the sideline and would say I'm actually at

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Speaker 1: half now. I'm just talking. I'll be honest. What helped

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Speaker 1: me more than anything else is the fact that I

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Speaker 1: had that I had played, like I played division three basketball.

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Speaker 1: So we could get on the court and I could

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Speaker 1: make a jumper and it's like then that rightly or wrongly, okay, well,

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Speaker 1: that's that's a well, all right, he's not a total

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Speaker 1: he's not a total tool about this stuff. Like numbers, numbers,

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Speaker 1: gotta go. Alright, numbers. There was. There was that like

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Speaker 1: my first year with the bucks, we were playing, like

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Speaker 1: the staff was playing a pickup game and one of

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Speaker 1: the coaches walked by. Then I hit a step back

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Speaker 1: three and the guy was like, okay, numbers. So I

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Speaker 1: already felt that. Yeah, yeah, exactly. It was always numbers, numbers.

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Speaker 1: So because I'm like really interested in I love like

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Speaker 1: data analysis and I love how much it offers people

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Speaker 1: like a glimpse into the game and how that can

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Speaker 1: affect decision making. But can you kind of give me

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Speaker 1: an example of just sort of like when you look

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Speaker 1: at when you're looking at the numbers and you're trying

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Speaker 1: to sort of find something, uh in the data that

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Speaker 1: you're looking at, like what are some what are some

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Speaker 1: trends that you're seeing, like in terms of underrated statistics

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Speaker 1: that you feel are starting to begin sort of taking

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Speaker 1: a little bit more, you're getting a little bit more attention,

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Speaker 1: whether it relates to like play style or even identifying

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Speaker 1: like effective players? Um, this is an interesting question. I

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Speaker 1: think that that I would I would rather than focus

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Speaker 1: on specific metrics. I think like the the thing that

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Speaker 1: is that is moving forward more is the idea of

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Speaker 1: starting with ways to talk about players skills, not just

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Speaker 1: saying this player is good, but this player does this

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Speaker 1: well and what. Okay, this, but they're like, okay, so

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Speaker 1: this player, you know, in in in these circumstances, this

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Speaker 1: like like this. You know, for example, with you know,

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Speaker 1: the tracking data that we have in the NBA, you

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Speaker 1: can say, all right, this player has made of done

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Speaker 1: contested threes. Ya, averages thirty eight. That's pretty good, so

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Speaker 1: he's he's good at this. Now you you you put

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Speaker 1: a contest on him, you put movement on him, you

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Speaker 1: make you make him shoot off the move. How does

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Speaker 1: that change? And you can start to describe, you know,

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Speaker 1: he you know, is he a stand still shooter or

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Speaker 1: movement shooter? Now we're getting into basketball concepts and and

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Speaker 1: you can you can quantify that in a way that

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Speaker 1: you know it's not perfect because there aren't enough. There

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Speaker 1: aren't that many players who actually shoot like two movement

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Speaker 1: threes in a season to give you a really good idea,

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Speaker 1: but against okay, if he's hit his of his catching shoot,

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Speaker 1: Dan still catching shoot, and of his off movements, even

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Speaker 1: if you only have a hundred shots of each, it's

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Speaker 1: not you know, it's not despositive, but it certainly gives

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Speaker 1: you an idea of like yeah, maybe he's better if

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Speaker 1: we just kind of stick him there and don't run

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Speaker 1: him off screen and stuff like that. Um. So that's

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Speaker 1: I think that the details, you know, kind of event

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Speaker 1: level data that we now have allows you to look

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Speaker 1: at things more that way. And it's not just shooting.

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Speaker 1: All of the shooting stats of being been the most

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Speaker 1: developed because, for whatever recent people care about buckets the

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Speaker 1: most Um and that's that's you know that that holds true. For,

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Speaker 1: you know, for for analysts as much as it does

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Speaker 1: for bad twitter. So there's that, there's rebounded, there's playmaking.

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Speaker 1: Defense is still hard. Um, there's a lot of kind

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Speaker 1: of bad attempts at quantifying defense out there because some

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Speaker 1: other you know, learning lessons from other sports. It's sometimes good,

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Speaker 1: but I think in many ways lessons from baseball in

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Speaker 1: particular have served as poorly when measuring defense. I mean

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Speaker 1: there's been a lot of stuff. You can tell, okay,

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Speaker 1: you can tell who the closest defender is to shoot

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Speaker 1: her and therefore you can say something about his defense

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Speaker 1: by what players shoot against him. Right. No, you can't.

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Speaker 1: I mean you can't, like there's there's places on the

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Speaker 1: floor you can. But there's a thing that's missing when

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Speaker 1: you're looking at that from a defensive perspective, which is,

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Speaker 1: what about all the times the guy doesn't shoot? You know,

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Speaker 1: baseball this analogy like these all the time baseball. You know,

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Speaker 1: great left handed hitter comes up, other team brings their

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Speaker 1: their left eat layer, left he headed, specialist receiver out

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Speaker 1: of the out of the bullpen. The guy can't be

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Speaker 1: like now, wait, yeah, where you get where you get

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Speaker 1: a kick out on the perimeter the guy closes out. Well,

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Speaker 1: it's like well, I got to shoot. There was a kickout,

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Speaker 1: like no, let me, let me do something else with it. So,

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Speaker 1: like from a defensive side, that's a pretty good outcome

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Speaker 1: and that shows up nowhere right. Yeah, so if you're

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Speaker 1: using like like, you know, jump shot defense is a

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Speaker 1: way to evaluate what a certain player is doing, you're

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Speaker 1: missing almost their best their best preps. Can you because that?

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Speaker 1: You know, the quote I mentioned up top was like

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Speaker 1: the ability of analytics to address cognitive biases. Can you

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Speaker 1: talk about like something like a journey you personally took

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Speaker 1: when you, you know, as somebody who played basketball and

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Speaker 1: presumably watched a lot of basketball, was a big basketball

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Speaker 1: fan coming into your career, a cognitive bias you had

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Speaker 1: that a lytics kind of helped you overcome? I think so.

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Speaker 1: I mean I you know, the player types when I

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Speaker 1: was when I was sort of a UH look, coming

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Speaker 1: out up more from a fan Ang or maybe play

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Speaker 1: some fantasy stuff like that. There was a player type

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Speaker 1: I kept missing on, which was, you know, coming in

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Speaker 1: the draft, I thought he's gonna be good like Randolph childers,

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Speaker 1: he's gonna be like, you know, it's kind of this

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Speaker 1: small scoring guard who you know, has a lot of

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Speaker 1: Moxie and makes big shots in college and stuff like that,

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Speaker 1: and it's like a you know, you get this now

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Speaker 1: is he's a winner. And so, like, I I was

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Speaker 1: with that. And then you start to study and he

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Speaker 1: realized actually that that's sort of mid range rocket guy

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Speaker 1: to sleep pull up. That's the province of just like

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Speaker 1: the very best players. So yeah, some guys are going

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Speaker 1: to do that, but you gotta be good to do that.

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Speaker 1: Like the guys who just do that, okay, right, are

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Speaker 1: actually pretty bad and being players. I mean they're bad

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Speaker 1: basketball players, but it means they're you know, you end

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Speaker 1: up being like Shane Larkin or something like that, who is,

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Speaker 1: you know, like a Euroleague all star but because of

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Speaker 1: his combination of size and skill set, isn't necessarily an

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Speaker 1: effective NB player. And so you know, kind of seeing

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Speaker 1: those weird things where a skill given depending on like

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Speaker 1: the context, then you need to be very valuable or

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Speaker 1: not valuable at all. That's wild. I hadn't thought about

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Speaker 1: Shane Larkins since he came into the League and you know,

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Speaker 1: he was very Barry Larkin's kid and I think he

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Speaker 1: was like a you know, definitely a first round draft

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Speaker 1: pick right and I yeah, I hadn't looked him up

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Speaker 1: since then, but that that makes sense. Very good, very

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Speaker 1: good European player. You know, the best, best player I

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Speaker 1: ever played pick up against was Kalid El Amy, and

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Speaker 1: he kind of fell into that, into that, into that

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Speaker 1: same kind of thing. That's same, like just not, you know,

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Speaker 1: not quite big enough, not quite good enough, but you

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Speaker 1: put him over in that, you know, in a slightly

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Speaker 1: different context of basketball, and that's pretty good. What skill

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Speaker 1: do you look for in a kind of smaller guard

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Speaker 1: that that yields a Chris Paul or someone like that?

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Speaker 1: I mean that's you look for them to be Chris Paul,

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Speaker 1: like measuring, like measuring decision making is is you know,

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Speaker 1: that's that's a little bit the holy grail right now. Um,

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Speaker 1: that's something that, uh, I think right now football actually

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Speaker 1: probably has the lead on because you can you can

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Speaker 1: actually you know, the quarterbacks past decision is actually a

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Speaker 1: little more straightforward then, because okay, I can throw to

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Speaker 1: this guy, I can throw that guy, I can wait

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Speaker 1: a little bit, maybe I can scramble around, but your

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Speaker 1: decisions are a little bit more cabin than they are,

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Speaker 1: whereas the guy's coming off a pick and roll. It's

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Speaker 1: like I can throw the ball here, I can take

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Speaker 1: another dribble, I can put the guy in jail. So

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Speaker 1: how do you even break down what the different decisions are?

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Speaker 1: And you can, you can start to look at some

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Speaker 1: sort of very abstract examples, but that's just like one

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Speaker 1: tiny aspect of it, and in the game of basketball

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Speaker 1: it's so many. Any you're making so many decisions that

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Speaker 1: you might not even realize you're like a players making

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Speaker 1: in a short span of time. And the player who

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Speaker 1: does that well, I was like, Oh, look at the

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Speaker 1: look at look at how he knows how to be

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Speaker 1: on the court, where to be on the court. Look

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Speaker 1: at just feel, look at his basketball. Like you whatever

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Speaker 1: you want to call it, that's the sum of like

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Speaker 1: you know, twenty decisions a second that the guy is

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Speaker 1: making better than the next player. So how do you

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Speaker 1: like so how do you even start to quantify that?

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Speaker 1: Like we have some things that proxy for that. Like

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Speaker 1: people wonder why, like college steel rate is such a

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Speaker 1: strong projector of NBA success, and I think that's it.

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Speaker 1: It's like, okay, you have a combination of physical ability

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Speaker 1: to get there and sort of mental recognition and like

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Speaker 1: speed of of translating thought to action that you can

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Speaker 1: then use whatever your physical abilities are. So, like, I

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Speaker 1: got the ball now, I saw it coming, I got

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Speaker 1: the ball and that and that, like that sort of

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Speaker 1: represents I can I know how to play amazing. All right.

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Speaker 1: Should we take a break and come back and keep

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Speaker 1: asking you questions, like who's your favorite player? Like, do

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Speaker 1: you think anyways that we'll be right back? And we're

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Speaker 1: back and yeah, just I'm having a great time sort

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Speaker 1: of hearing you kind of talk, talk about these things.

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Speaker 1: I was I was curious to hear if you have

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Speaker 1: a current player or a former player and or a

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Speaker 1: former player that you feel like contributed to more wins

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Speaker 1: than people realize because of cognitive biases or like just

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Speaker 1: kind of what what we look for on a basketball

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Speaker 1: court versus what's actually happening out there. So, I mean

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Speaker 1: the the the easy answer for the current players, the

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Speaker 1: player who seems like there's the biggest disconnect between how

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Speaker 1: they're perceived and how good they are is Rudy Gobert.

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Speaker 1: Like people just clown rudy so much. I can't the

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Speaker 1: I can't believe the timberwolves traded all this form is like, okay,

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Speaker 1: he's a walking top and defense and it's not sexy.

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Speaker 1: He's he's annoying, like people are annoyed by his personality,

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Speaker 1: which that's fine. There's players who annoy who's personally annoyed me.

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Speaker 1: But like, and maybe maybe his game isn't perfectly suited

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Speaker 1: to playoff basketball. That's all fine. He's still like like

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Speaker 1: a top three defensive player in the League and probably

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Speaker 1: a top twenty overall player who just it's it's because

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Speaker 1: he doesn't get buckets. It's like the stuff he does

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Speaker 1: is just like undervalued. And it's weird because, like there

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Speaker 1: are other players who, you know, similar like the other

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Speaker 1: kind of great, top level defenders who are, you know,

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Speaker 1: top twenty. You know, you know, maybe draymond has kind

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Speaker 1: of aged out of being a top twenty player, top

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Speaker 1: twenty player, but the other kind of defense first guys

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Speaker 1: who are up that high, like Draymond, Bam Ada, bio,

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Speaker 1: they don't seem to get the same maybe because they

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Speaker 1: have a little bit more at the ball, but neither

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Speaker 1: of them are really bucket getters. But it's rudy is

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Speaker 1: just like because his offensive game is like dive and

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Speaker 1: catch lobs and get foul and rebound. It's sort of well,

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Speaker 1: that doesn't matter. It's like, well, that's actually pretty impactful.

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Speaker 1: The draymond thing that you know, it took. It took

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Speaker 1: for him going out last year them being absolutely rolling

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Speaker 1: and dominant, uh for a while, him going out with

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Speaker 1: an injury and then being completely like seeming to be

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Speaker 1: completely average, him coming back and then winning the title.

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Speaker 1: I was like, all right, there might be something going

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Speaker 1: on here. Uh. That like, I mean I think I knew,

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Speaker 1: like you can kind of watch and be like, he

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Speaker 1: really does a lot of smart things out there, but

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Speaker 1: that that was pretty pretty clean case that that was

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Speaker 1: made last season with regards to Draymond and what he

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Speaker 1: does kind of off ball. Yeah, and I think that's

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Speaker 1: a that's a you know, not to go too deep

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Speaker 1: into that. I think the worry is is how much

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Speaker 1: longer you have sort of at that level because, like,

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Speaker 1: as you say, you saw healthy draymond the start of

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Speaker 1: the year, no Draymond, and then kind of hurt draymond

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Speaker 1: second half of the year, ramping into form, draymond over

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Speaker 1: the players, draymond forgetting how to play offensive basketball for

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Speaker 1: a few games to the finals and like from what

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Speaker 1: was it like? Late in game four of the finals

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Speaker 1: to the end? He was spectacular again, and she's like, Oh, yeah,

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Speaker 1: that's that team suddenly becomes you know, you can have

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Speaker 1: a you know, a third point guard on the floor

426
00:23:29,800 --> 00:23:33,560
Speaker 1: WHO's also your defensive center. Yeah, that's kind of Nice.

427
00:23:33,880 --> 00:23:35,760
Speaker 1: The other part of the question was a past player.

428
00:23:35,760 --> 00:23:38,360
Speaker 1: I don't know if I want to go impactful because frankly,

429
00:23:38,960 --> 00:23:41,359
Speaker 1: don't have the tools to measure this player's impact. But

430
00:23:41,400 --> 00:23:45,080
Speaker 1: my favorite player of all time is Reggie Lewis Um both,

431
00:23:45,240 --> 00:23:47,480
Speaker 1: you know, both, because he so I grew up in Alaska.

432
00:23:47,520 --> 00:23:49,359
Speaker 1: He came to the Great alska shootout, which is a

433
00:23:49,440 --> 00:23:52,960
Speaker 1: really good college tournament until it wasn't and doesn't exist anymore.

434
00:23:52,960 --> 00:23:55,359
Speaker 1: He was with North he was at northeastern, northeaster, and

435
00:23:55,400 --> 00:23:58,920
Speaker 1: he gave, he gave buckets and then and then he

436
00:23:58,920 --> 00:24:00,720
Speaker 1: went to the Celtics and was like the next Great

437
00:24:00,720 --> 00:24:03,679
Speaker 1: Celtic Star and like and also his game was so

438
00:24:03,760 --> 00:24:06,800
Speaker 1: interesting because he they still don't see a lot of

439
00:24:06,800 --> 00:24:09,320
Speaker 1: players like that. Jimmy Butler might almost be the closest.

440
00:24:09,480 --> 00:24:12,439
Speaker 1: Was He. You know, there's a certain rhythm that guys

441
00:24:12,480 --> 00:24:15,440
Speaker 1: play with. You know, you can kind of like pump, fake,

442
00:24:15,720 --> 00:24:19,760
Speaker 1: jab go, whereas you know, uh, he sort of he

443
00:24:19,840 --> 00:24:23,600
Speaker 1: sort of played the in between notes, like like pump,

444
00:24:23,640 --> 00:24:26,440
Speaker 1: fake and go like like and not bring it down

445
00:24:26,520 --> 00:24:29,359
Speaker 1: to like like playing, you know how like reggae is,

446
00:24:29,400 --> 00:24:32,520
Speaker 1: playing on the upstroke a little bit, a little bit

447
00:24:32,560 --> 00:24:35,160
Speaker 1: like that, and that was just super I was always

448
00:24:35,160 --> 00:24:37,040
Speaker 1: super intrigued by that. And also he was like one

449
00:24:37,040 --> 00:24:38,960
Speaker 1: of the guys who guarded him j the best, which

450
00:24:38,960 --> 00:24:42,320
Speaker 1: I which I thought was cool. That's right. Yeah, I'm curious,

451
00:24:42,400 --> 00:24:46,320
Speaker 1: like with the all the information that we have right sometimes,

452
00:24:46,600 --> 00:24:51,639
Speaker 1: in any industry or just job whatever, a lot of

453
00:24:51,680 --> 00:24:54,120
Speaker 1: information can be a good thing. Sometimes it can be

454
00:24:54,160 --> 00:24:57,120
Speaker 1: not a great thing because it can maybe complicate people's

455
00:24:57,200 --> 00:24:59,960
Speaker 1: thinking or whatever. They get too in focused on things

456
00:25:00,040 --> 00:25:03,159
Speaker 1: like that. How do you see like, because when I

457
00:25:03,160 --> 00:25:05,840
Speaker 1: look at just a lot of sports right and I'm

458
00:25:05,920 --> 00:25:10,000
Speaker 1: seeing just this general push to really embracing the analytics,

459
00:25:10,280 --> 00:25:13,879
Speaker 1: to really finding these margins to play more efficiently, whether

460
00:25:13,920 --> 00:25:16,840
Speaker 1: that's to get score more baskets, score more goals or

461
00:25:16,880 --> 00:25:20,000
Speaker 1: whatever in another sport, there's a lot of really like

462
00:25:20,040 --> 00:25:23,399
Speaker 1: I'm constantly blown away at like how people are looking

463
00:25:23,440 --> 00:25:26,560
Speaker 1: at a sport and how much it's influencing it is?

464
00:25:26,600 --> 00:25:28,960
Speaker 1: Do you see that like this sort of, you know,

465
00:25:29,080 --> 00:25:33,560
Speaker 1: influx of analytics and things like that is helping to

466
00:25:33,640 --> 00:25:36,760
Speaker 1: create like change even how people even set a team

467
00:25:36,840 --> 00:25:40,080
Speaker 1: up to play, like because they're really really focused or

468
00:25:40,119 --> 00:25:43,080
Speaker 1: really acknowledge that, like some of these things, they add

469
00:25:43,160 --> 00:25:45,720
Speaker 1: up over time, these margins and that's really something to

470
00:25:45,760 --> 00:25:48,879
Speaker 1: focus on? Or is it still kind of like, okay,

471
00:25:49,119 --> 00:25:51,359
Speaker 1: I like what you're saying, that's food for thought, but

472
00:25:51,400 --> 00:25:53,800
Speaker 1: I won't completely impact the strategy or the X is

473
00:25:53,840 --> 00:25:57,800
Speaker 1: and Os. I mean again, this is one of those

474
00:25:57,840 --> 00:26:00,560
Speaker 1: things where you you don't want to say that, uh,

475
00:26:00,800 --> 00:26:05,439
Speaker 1: you know, you can analytics, did this or that. I

476
00:26:05,440 --> 00:26:08,120
Speaker 1: think that that I've always find those comments pretty funny,

477
00:26:08,160 --> 00:26:09,960
Speaker 1: because that's like, I I wish I had that kind

478
00:26:09,960 --> 00:26:13,199
Speaker 1: of power. But I imagine on one level it's a

479
00:26:13,320 --> 00:26:16,760
Speaker 1: it catalyzes the process of something for sure, because we're

480
00:26:16,760 --> 00:26:19,600
Speaker 1: going from an information desert to one where it's very rich,

481
00:26:19,720 --> 00:26:22,320
Speaker 1: right like well, you know, Mike d'antoni didn't need me

482
00:26:22,680 --> 00:26:25,320
Speaker 1: or anyone else to tell him, Hey, let's put let's

483
00:26:25,359 --> 00:26:27,879
Speaker 1: put some shooters around and give give Steve Nash and

484
00:26:28,040 --> 00:26:32,080
Speaker 1: Mari Stdomier some space, like you know, you don't have to.

485
00:26:32,400 --> 00:26:34,080
Speaker 1: You don't have to come out of from a numerical

486
00:26:34,160 --> 00:26:37,440
Speaker 1: stands to see like yeah, that makes more sense. If

487
00:26:37,440 --> 00:26:40,040
Speaker 1: this guy standing at twenty four feet instead of eighteen feet,

488
00:26:40,240 --> 00:26:44,320
Speaker 1: there's more space for this thing to happen. Great, Um.

489
00:26:44,359 --> 00:26:47,440
Speaker 1: So I think that the that the analysis can can

490
00:26:47,480 --> 00:26:49,680
Speaker 1: work in candom with that. But again, I don't think

491
00:26:49,720 --> 00:26:52,600
Speaker 1: it's I don't think we should, we should force it

492
00:26:52,680 --> 00:26:56,879
Speaker 1: from strategy completely and we can't because it's like, okay,

493
00:26:56,920 --> 00:26:59,840
Speaker 1: that's a good thing to do. How he sient need

494
00:26:59,840 --> 00:27:01,840
Speaker 1: to so you do. They do need to work in hand,

495
00:27:01,920 --> 00:27:04,399
Speaker 1: hand in hand to implement. You can't just like, you know,

496
00:27:04,800 --> 00:27:08,320
Speaker 1: just shoot more threes. That's not what happens. You know,

497
00:27:08,359 --> 00:27:10,800
Speaker 1: it's not like you know, maybe now there are a

498
00:27:10,840 --> 00:27:13,119
Speaker 1: few players who can literally just lock the ball up

499
00:27:13,119 --> 00:27:16,440
Speaker 1: the floor and Jack Three. That's like there's three guys

500
00:27:16,440 --> 00:27:18,480
Speaker 1: in the League can do that. Two guys in the

501
00:27:18,560 --> 00:27:21,760
Speaker 1: League who should do that? Maybe one, depending on, you know,

502
00:27:22,200 --> 00:27:25,880
Speaker 1: if if if Dame's ABS or are are better or not,

503
00:27:25,920 --> 00:27:31,320
Speaker 1: like staff like trade as it probably shouldn't, um, but

504
00:27:31,320 --> 00:27:32,920
Speaker 1: but it was like, okay, so how do we get

505
00:27:32,960 --> 00:27:35,000
Speaker 1: more threes? Well, part of it is okay, instead of

506
00:27:35,200 --> 00:27:37,280
Speaker 1: having the guy on the opposite side of the court

507
00:27:37,359 --> 00:27:40,080
Speaker 1: stand at the in the short corner, have him stand

508
00:27:40,080 --> 00:27:42,720
Speaker 1: all the way in the corner. And and so that's

509
00:27:42,720 --> 00:27:45,399
Speaker 1: a that's that's a strategic thing. That allowed there to

510
00:27:45,440 --> 00:27:48,480
Speaker 1: be more threes. And then there's some additional benefits like well,

511
00:27:48,480 --> 00:27:50,760
Speaker 1: if he's actually that far out, all of a sudden

512
00:27:50,760 --> 00:27:53,399
Speaker 1: his defender has more ground to cover. That seems good.

513
00:27:54,240 --> 00:27:56,640
Speaker 1: And so it's it's so it's sort of builds on itself,

514
00:27:56,680 --> 00:27:59,920
Speaker 1: by the way. And so I again I'm I'm read

515
00:28:00,080 --> 00:28:03,320
Speaker 1: sent to say that it's all, or primarily part. I

516
00:28:03,320 --> 00:28:04,640
Speaker 1: don't mean part of. I don't and I don't even

517
00:28:04,680 --> 00:28:06,399
Speaker 1: mean to suggest that it's like. Well, what do you what?

518
00:28:06,400 --> 00:28:07,840
Speaker 1: Do you come in with your notebook and tell them?

519
00:28:07,920 --> 00:28:10,120
Speaker 1: But more just like what do you say when someone goes,

520
00:28:10,520 --> 00:28:14,960
Speaker 1: all right, the what I need you for? I knew that.

521
00:28:14,200 --> 00:28:18,280
Speaker 1: That's honestly, that's great, like that's that's sort of because, no,

522
00:28:18,400 --> 00:28:21,040
Speaker 1: that's that's sort of the almost some of the better

523
00:28:21,359 --> 00:28:24,760
Speaker 1: best way to get stuff implemented is you know this,

524
00:28:24,920 --> 00:28:29,240
Speaker 1: but right, you know, but but here's here. Let me

525
00:28:29,359 --> 00:28:31,639
Speaker 1: let me show you the degree to which this is true.

526
00:28:32,480 --> 00:28:34,680
Speaker 1: It's like, Oh, so, you know, you can, you can

527
00:28:34,720 --> 00:28:37,360
Speaker 1: look at like, you know, rebounding data and see, okay, yeah,

528
00:28:37,359 --> 00:28:39,760
Speaker 1: if I shoot from the right side, rebound is gonna

529
00:28:39,760 --> 00:28:42,440
Speaker 1: go over here to the left side and like, you know,

530
00:28:43,160 --> 00:28:45,520
Speaker 1: but it's but it's still it's useful to have that

531
00:28:45,600 --> 00:28:48,320
Speaker 1: reinforced and confirmed. Like I think that's one of the

532
00:28:48,360 --> 00:28:53,000
Speaker 1: things that's least understood, is like the stuff we study

533
00:28:53,160 --> 00:28:56,920
Speaker 1: probably confirmed sort of the conventional wisdom, because coaches have

534
00:28:57,000 --> 00:28:59,360
Speaker 1: been you know, it's not like it's not like there

535
00:28:59,400 --> 00:29:03,800
Speaker 1: hasn't been an off rewards to being a winner. Keep

536
00:29:04,000 --> 00:29:06,920
Speaker 1: keeping your job. Keeping your job is a pretty good

537
00:29:06,920 --> 00:29:10,320
Speaker 1: incentive to to do things better, and so a lot

538
00:29:10,360 --> 00:29:12,320
Speaker 1: of the things that are done are, you know, have

539
00:29:12,520 --> 00:29:15,280
Speaker 1: been tried and true and true and like no, you

540
00:29:15,320 --> 00:29:17,920
Speaker 1: win more games by doing it this way. And some

541
00:29:18,000 --> 00:29:19,840
Speaker 1: of that trial and error. And maybe we can speed

542
00:29:19,920 --> 00:29:21,840
Speaker 1: up that trial and error process a little bit by

543
00:29:21,880 --> 00:29:26,040
Speaker 1: suggesting a thing that seems likely to work. But I mean,

544
00:29:26,240 --> 00:29:29,840
Speaker 1: you know, some pretty some pretty dedicated people over sixty,

545
00:29:29,880 --> 00:29:32,200
Speaker 1: seventy years have been picking at this thing for a

546
00:29:32,200 --> 00:29:36,600
Speaker 1: while and you know, a lot of them do some stuff.

547
00:29:36,640 --> 00:29:39,360
Speaker 1: So it's not like you're gonna come in and say, Nope,

548
00:29:39,360 --> 00:29:41,400
Speaker 1: you're all wrong, throw it out from the book out.

549
00:29:42,040 --> 00:29:43,800
Speaker 1: Not True. I mean, you go like you go. As

550
00:29:44,160 --> 00:29:47,080
Speaker 1: you know, the foundation of like basketball analytics is the

551
00:29:47,080 --> 00:29:50,520
Speaker 1: four factors, and that's effective field goal percentage, turnover percentage,

552
00:29:50,520 --> 00:29:53,960
Speaker 1: free throw a rate, defensive rebound rate. What does every

553
00:29:53,960 --> 00:29:56,560
Speaker 1: coach you've ever had tell you? Get good shots, to

554
00:29:56,560 --> 00:29:58,880
Speaker 1: take care of the ball, gets the free throw line,

555
00:29:59,440 --> 00:30:04,480
Speaker 1: protect that's all it is. It's basically that for me. Yeah,

556
00:30:04,480 --> 00:30:06,800
Speaker 1: you know everything you ever heard, and it's just like

557
00:30:06,840 --> 00:30:09,680
Speaker 1: the basis of of you know, how you look at

558
00:30:09,680 --> 00:30:13,200
Speaker 1: a team from a statistical standpoint is those four things. Yeah,

559
00:30:13,480 --> 00:30:15,840
Speaker 1: but I guess that's where like, because everyone does have

560
00:30:15,960 --> 00:30:18,320
Speaker 1: the same sort of agreed upon way to do things.

561
00:30:18,400 --> 00:30:20,680
Speaker 1: I think that's where it's probably beneficial now to say,

562
00:30:21,200 --> 00:30:24,200
Speaker 1: given that we all believe these things, now, where are

563
00:30:24,240 --> 00:30:26,560
Speaker 1: these where can we find these other ways to be

564
00:30:26,600 --> 00:30:30,360
Speaker 1: more effective, and how important is each of them relative

565
00:30:30,400 --> 00:30:32,600
Speaker 1: to the other? Like, yeah, that's this is one of

566
00:30:32,600 --> 00:30:37,200
Speaker 1: those those areas where I found that sort of people

567
00:30:37,240 --> 00:30:41,160
Speaker 1: who come at things from a more observational are very

568
00:30:41,200 --> 00:30:45,920
Speaker 1: good at identifying things, not always as good at weighing

569
00:30:45,920 --> 00:30:49,120
Speaker 1: them against each other. M Hm. And so that's a

570
00:30:49,320 --> 00:30:51,680
Speaker 1: you know, and and those. And the interesting thing is

571
00:30:51,680 --> 00:30:54,800
Speaker 1: those things change, like the environment of the NBA. We

572
00:30:55,000 --> 00:30:57,520
Speaker 1: will change the degree to which the ability to hit

573
00:30:57,520 --> 00:31:00,720
Speaker 1: a corner three versus the ability stay in front of

574
00:31:00,720 --> 00:31:03,160
Speaker 1: a guy on a switch, like. Which of those things

575
00:31:03,200 --> 00:31:05,760
Speaker 1: is more, is more valuable, or how valuable it is

576
00:31:05,840 --> 00:31:07,400
Speaker 1: or who else is on your team makes it value.

577
00:31:08,120 --> 00:31:10,120
Speaker 1: So it's not like these are it's not this is

578
00:31:10,160 --> 00:31:12,880
Speaker 1: a set things. Do this and it's good. It's like,

579
00:31:12,960 --> 00:31:16,120
Speaker 1: in this environment this is more helpful than it's maybe

580
00:31:16,160 --> 00:31:21,680
Speaker 1: given credit for. Um, there's UH, there's a clip of

581
00:31:21,920 --> 00:31:27,000
Speaker 1: golden states gm Bob Meyers at the sports analytics conference

582
00:31:27,120 --> 00:31:30,880
Speaker 1: talking about like the difference between playoff basketball versus regular

583
00:31:30,920 --> 00:31:34,000
Speaker 1: season basketball. Like has that? Is that something that kind

584
00:31:34,000 --> 00:31:36,800
Speaker 1: of came up from an analytic standpoint in terms of

585
00:31:36,840 --> 00:31:40,880
Speaker 1: like the shift in Style and overall approach for playoff

586
00:31:41,000 --> 00:31:45,200
Speaker 1: teams versus regular season teams? Yeah, I mean I I

587
00:31:45,240 --> 00:31:47,400
Speaker 1: wrote about this in the in the playoff chapter in

588
00:31:47,600 --> 00:31:50,239
Speaker 1: the book. And there's so I have a among the

589
00:31:50,280 --> 00:31:53,520
Speaker 1: professions I've done before this, that was a professional poker player.

590
00:31:53,720 --> 00:31:58,920
Speaker 1: And there's there's Um, kind of two modes of play

591
00:31:59,040 --> 00:32:01,959
Speaker 1: Um and again, as with most things, it's not like

592
00:32:02,000 --> 00:32:04,480
Speaker 1: one or the other. It's somewhere in between. But there's

593
00:32:04,560 --> 00:32:07,200
Speaker 1: it's there's what's called Game Theory often, which is I

594
00:32:07,200 --> 00:32:10,680
Speaker 1: don't know what you are as a poker player, but

595
00:32:11,040 --> 00:32:13,320
Speaker 1: amongst the set of poker players I know about what

596
00:32:13,400 --> 00:32:16,560
Speaker 1: that means you do. So how do I play, knowing

597
00:32:16,600 --> 00:32:19,320
Speaker 1: you're from this group? First exploit of play is I

598
00:32:19,360 --> 00:32:21,920
Speaker 1: know exactly how you play. What can I do best

599
00:32:21,920 --> 00:32:24,400
Speaker 1: to beat you? Now tell me. The first bit doesn't

600
00:32:24,440 --> 00:32:28,600
Speaker 1: sound like regular season, where you're playing Sacramento Tonight, Houston Tomorrow,

601
00:32:29,120 --> 00:32:32,000
Speaker 1: Orlando the other the third night. And the exploit of

602
00:32:32,040 --> 00:32:34,880
Speaker 1: play is the playoffs. So it's a very different like

603
00:32:35,440 --> 00:32:37,880
Speaker 1: you know, design how to do best in each of

604
00:32:37,880 --> 00:32:42,200
Speaker 1: those things. It's it's not surprising. The different coaches, different players,

605
00:32:42,200 --> 00:32:46,480
Speaker 1: different skill sets function better in one environment versus together.

606
00:32:47,760 --> 00:32:50,360
Speaker 1: All right, a piece of news that we've been kind

607
00:32:50,400 --> 00:32:54,240
Speaker 1: of putting off, but I'm definitely eager to like kind

608
00:32:54,240 --> 00:32:56,560
Speaker 1: of bring it up and get any thoughts that you have.

609
00:32:57,040 --> 00:33:01,040
Speaker 1: Is The news about Donovan Mitchell going of the calves,

610
00:33:01,040 --> 00:33:05,320
Speaker 1: which that got me very excited. I feel like in

611
00:33:05,440 --> 00:33:09,600
Speaker 1: terms of fit for him. Continuing to have a great

612
00:33:09,680 --> 00:33:14,280
Speaker 1: defensive stopper behind him is great for his game. The

613
00:33:14,680 --> 00:33:17,480
Speaker 1: one thing that occurred to me is like usage. I

614
00:33:17,800 --> 00:33:20,360
Speaker 1: guess I hadn't even realized, but he's a top ten

615
00:33:20,520 --> 00:33:23,840
Speaker 1: usage guy in in the NBA in terms of how

616
00:33:23,920 --> 00:33:26,520
Speaker 1: much he dominates the ball, and then Garland is also

617
00:33:26,800 --> 00:33:29,880
Speaker 1: a big usage guy. But I mean, first of all,

618
00:33:29,920 --> 00:33:32,520
Speaker 1: it just overall, this makes me more excited to watch

619
00:33:32,680 --> 00:33:37,240
Speaker 1: NBA basketball next season having Donovan Mitchell on the calves.

620
00:33:37,840 --> 00:33:41,280
Speaker 1: So I'm excited. Yeah, I love that trade for both sides.

621
00:33:42,120 --> 00:33:44,440
Speaker 1: I really did. I thought I thought that jazz did

622
00:33:44,480 --> 00:33:47,000
Speaker 1: a you know in terms of pivoting off of their

623
00:33:47,360 --> 00:33:49,120
Speaker 1: looking at sort of their off season as a hole,

624
00:33:49,320 --> 00:33:51,600
Speaker 1: like all right, we knew that team was kind of

625
00:33:51,760 --> 00:33:56,000
Speaker 1: had reached it its conclusion. It's a pretty strong pivot

626
00:33:56,280 --> 00:33:59,360
Speaker 1: to what you know, going to whatever's next. And I

627
00:33:59,360 --> 00:34:02,000
Speaker 1: don't know how much you guys followed draft stuff, but

628
00:34:02,920 --> 00:34:04,800
Speaker 1: this would not be a bad year to end up

629
00:34:04,800 --> 00:34:09,600
Speaker 1: with the first pick. Yeah, yeah, you know, it's if

630
00:34:09,640 --> 00:34:14,319
Speaker 1: you you know, basically, if you had color film of

631
00:34:14,400 --> 00:34:17,359
Speaker 1: what Luell sender looked like in high school, I think

632
00:34:17,400 --> 00:34:20,080
Speaker 1: I look a lot like victor when Banyana Yama's use

633
00:34:20,160 --> 00:34:26,680
Speaker 1: film does so. Like, no pressure, young man, but but, um, yeah,

634
00:34:26,800 --> 00:34:28,719
Speaker 1: and then at the same time I like, like I

635
00:34:28,760 --> 00:34:32,520
Speaker 1: love the fit with Mitchell and Cleveland. I'm amused by

636
00:34:32,560 --> 00:34:35,120
Speaker 1: the fact that he's going to continue to be sort

637
00:34:35,120 --> 00:34:37,680
Speaker 1: of the face of the team and if they're going anywhere,

638
00:34:37,719 --> 00:34:40,600
Speaker 1: he won't be the best player on the team again. Um,

639
00:34:40,640 --> 00:34:43,680
Speaker 1: that's you know, that's essentially it's a pretty huge bet

640
00:34:43,719 --> 00:34:47,880
Speaker 1: that Evan mobiley is gonna be like that guy. Anybody's

641
00:34:47,880 --> 00:34:50,480
Speaker 1: gonna be that guy without having to be like throw

642
00:34:50,520 --> 00:34:52,040
Speaker 1: him the ball and get out of the way kind

643
00:34:52,040 --> 00:34:55,440
Speaker 1: of version of that guy, which I think is frankly,

644
00:34:55,520 --> 00:34:58,359
Speaker 1: is probably beneficial to him because maybe he develops that.

645
00:34:58,480 --> 00:35:02,400
Speaker 1: But they're actually isn't a lot in profile at this

646
00:35:02,480 --> 00:35:08,080
Speaker 1: point that suggests that's hugely likely. So, you know, turning

647
00:35:08,120 --> 00:35:13,160
Speaker 1: him into sort of the the the almost the next

648
00:35:13,200 --> 00:35:16,960
Speaker 1: iteration of Anthony Davis, rather than than having to be,

649
00:35:17,600 --> 00:35:20,040
Speaker 1: you know, having to be like Joel and bead or

650
00:35:20,080 --> 00:35:24,720
Speaker 1: something like that. Yeah, yeah, the Jabari in the comments

651
00:35:24,760 --> 00:35:26,839
Speaker 1: is like Kig and Boston is supposed to Gig many.

652
00:35:26,920 --> 00:35:29,400
Speaker 1: I think that's the reason to play and and frankly,

653
00:35:29,480 --> 00:35:32,279
Speaker 1: like kg and and many tried to play like I

654
00:35:32,320 --> 00:35:34,399
Speaker 1: think they like they were always trying to get him

655
00:35:34,440 --> 00:35:37,160
Speaker 1: to be more like that was the biggest difference between

656
00:35:37,239 --> 00:35:38,680
Speaker 1: him and Duncan. Was Dunk I was like, Oh, I

657
00:35:38,680 --> 00:35:40,880
Speaker 1: gotta get a bucket, I'm gonna get a bucket, whereas

658
00:35:40,960 --> 00:35:45,120
Speaker 1: kg always sort of was like it's moving around and

659
00:35:45,160 --> 00:35:47,160
Speaker 1: do some stuff, and I can, I can, I can

660
00:35:47,200 --> 00:35:50,400
Speaker 1: blend a little bit and and never quite got to

661
00:35:50,440 --> 00:35:53,600
Speaker 1: the point of wanting, of needing to just have the

662
00:35:53,640 --> 00:35:56,200
Speaker 1: ball in his hands at all the time. Uh, we got,

663
00:35:56,200 --> 00:35:58,160
Speaker 1: we got very off track. where, where am I going?

664
00:35:58,600 --> 00:36:00,800
Speaker 1: I think we were all gonna talk about the fact

665
00:36:00,800 --> 00:36:03,960
Speaker 1: that the real story, despite Donovan Mitchell, is that Montrase

666
00:36:04,040 --> 00:36:07,920
Speaker 1: harrold is with Philly and that makes Philly just undoubtedly

667
00:36:08,040 --> 00:36:19,759
Speaker 1: the championship rockets assemble the no, and I so to

668
00:36:19,840 --> 00:36:22,359
Speaker 1: your point about like Mitchell and Garland. I don't think

669
00:36:22,360 --> 00:36:24,239
Speaker 1: we've seen much in the last couple of years of

670
00:36:24,360 --> 00:36:28,680
Speaker 1: NBA basketball to suggests that it's bad thing to have

671
00:36:28,800 --> 00:36:31,040
Speaker 1: multiple guys who can make plays with the ball in

672
00:36:31,080 --> 00:36:34,160
Speaker 1: their hands. I think that that's sort of the and

673
00:36:34,280 --> 00:36:37,279
Speaker 1: as the person who unwittingly coined the term Um. I

674
00:36:37,320 --> 00:36:42,520
Speaker 1: don't think the heliocentric style is I don't think it's desirable.

675
00:36:42,560 --> 00:36:46,040
Speaker 1: I think certain teams have found expedient and necessary, but

676
00:36:46,640 --> 00:36:48,759
Speaker 1: having two dudes who can do that is probably better

677
00:36:48,800 --> 00:36:51,799
Speaker 1: than one, especially if they can, if if neither of

678
00:36:51,840 --> 00:36:54,240
Speaker 1: them actually needs to have the ball. But both Garland

679
00:36:54,280 --> 00:36:57,919
Speaker 1: and Mitchell are good enough shooters, that's Um and willing enough,

680
00:36:58,080 --> 00:37:01,520
Speaker 1: you know, catch and shoot players. That, uh yeah, you know,

681
00:37:02,200 --> 00:37:05,240
Speaker 1: from a pure basketball standpoint there's no reason it shouldn't

682
00:37:05,840 --> 00:37:09,239
Speaker 1: work offensively. But I'm very excited to see that team

683
00:37:09,239 --> 00:37:10,759
Speaker 1: because I think there could be a lot of fun too.

684
00:37:11,320 --> 00:37:14,200
Speaker 1: Speaking of having like multiple people who could who can

685
00:37:14,400 --> 00:37:17,200
Speaker 1: put it in like what? I'm just curious for your

686
00:37:17,239 --> 00:37:21,560
Speaker 1: thoughts on the nets. You know, when they were peak

687
00:37:21,600 --> 00:37:27,239
Speaker 1: form nets, they were so fun to watch, so unstoppable,

688
00:37:27,280 --> 00:37:30,200
Speaker 1: like even in that first round against Boston, and then,

689
00:37:30,320 --> 00:37:32,640
Speaker 1: you know, injuries got in the way. But like, I

690
00:37:32,680 --> 00:37:36,439
Speaker 1: think people are now like sort of settling into this

691
00:37:36,520 --> 00:37:40,239
Speaker 1: idea that like, well, that didn't work, and I don't

692
00:37:40,360 --> 00:37:43,560
Speaker 1: know that that's true because when it was working it

693
00:37:43,680 --> 00:37:48,560
Speaker 1: was wildly dominant, like on a game by game basis.

694
00:37:48,800 --> 00:37:52,120
Speaker 1: Even was that anomalous? I mean, like the way we

695
00:37:52,120 --> 00:37:55,440
Speaker 1: talked about that stuff is so jacked, like jnness couldn't

696
00:37:55,440 --> 00:37:57,520
Speaker 1: be the best player on a championship team until he was,

697
00:37:58,800 --> 00:38:01,000
Speaker 1: you know, and it's just like, okay, how many teams

698
00:38:01,160 --> 00:38:03,439
Speaker 1: work in a given a year, like last year? Would

699
00:38:03,440 --> 00:38:06,640
Speaker 1: you say one and a half, like the warriors in Boston,

700
00:38:06,719 --> 00:38:08,200
Speaker 1: but then they got to the finals and Oh no,

701
00:38:08,840 --> 00:38:11,560
Speaker 1: like's I think. I think that Boston worked. I think

702
00:38:11,600 --> 00:38:15,239
Speaker 1: that Cleveland worked last year when they were healthy. Like

703
00:38:15,239 --> 00:38:17,080
Speaker 1: like our definition of that is like, you know, you

704
00:38:17,080 --> 00:38:19,719
Speaker 1: think about the rockets teams that took the warriors, that

705
00:38:19,840 --> 00:38:23,359
Speaker 1: pushed the like the K D warriors in in the playoffs,

706
00:38:23,400 --> 00:38:25,920
Speaker 1: like this is one of the great teams ever assembled

707
00:38:25,960 --> 00:38:28,200
Speaker 1: and you took them to seven games in the conference

708
00:38:28,239 --> 00:38:31,399
Speaker 1: finals and that didn't work. Like what are we talking

709
00:38:31,440 --> 00:38:34,480
Speaker 1: about here? Yeah, like, I don't know. I so I

710
00:38:34,600 --> 00:38:37,600
Speaker 1: I you know the same. You know, those so like

711
00:38:37,640 --> 00:38:40,640
Speaker 1: the Jazz, the Mitchell Gobert jazz teams, like they were

712
00:38:40,719 --> 00:38:43,600
Speaker 1: plenty good and just you know, did they catch every break? No.

713
00:38:44,239 --> 00:38:47,080
Speaker 1: Were they the best team? No. Could they have made

714
00:38:47,080 --> 00:38:49,719
Speaker 1: the finals or win a championship if things broke their

715
00:38:49,719 --> 00:38:52,640
Speaker 1: way a little? I mean it's counterfactional. But yeah, I

716
00:38:52,640 --> 00:38:55,160
Speaker 1: think so. Like the hard rockets have won a champion.

717
00:38:55,200 --> 00:38:57,880
Speaker 1: I think so. Good. Last year's Celtics have what you know,

718
00:38:58,040 --> 00:39:02,400
Speaker 1: it's Lakers, you know. I mean that's know. I'm gonna,

719
00:39:02,400 --> 00:39:05,479
Speaker 1: I'm gonna, I'M gonna put my foot down there. But even, like, even,

720
00:39:05,480 --> 00:39:07,520
Speaker 1: like last year's bucks, like they could they have repeated

721
00:39:07,560 --> 00:39:11,480
Speaker 1: if Chris Middleton doesn't get hurt. Right like. Would I

722
00:39:11,520 --> 00:39:15,200
Speaker 1: have picked them over the weirds? Probably not, but like,

723
00:39:16,560 --> 00:39:19,839
Speaker 1: you can't dismiss it. Yeah, all right. Should we take

724
00:39:19,880 --> 00:39:23,640
Speaker 1: another break and come back firing rapidly with rapid fire?

725
00:39:24,000 --> 00:39:26,680
Speaker 1: Time for the rapid fire question. Oh, hold onto your

726
00:39:26,680 --> 00:39:41,920
Speaker 1: butts everyone, and we're back and it's time for the

727
00:39:42,000 --> 00:39:45,240
Speaker 1: Jack and miles or miles and Jack Got Mad boost

728
00:39:45,280 --> 00:39:49,000
Speaker 1: these rapid fire round of questions. They almost forgot what

729
00:39:49,040 --> 00:39:52,800
Speaker 1: the bit was, but I came back to it. Optimal podcast,

730
00:39:53,040 --> 00:39:57,840
Speaker 1: we're optimizing. Yeah, so you might listen to it and

731
00:39:57,920 --> 00:40:01,400
Speaker 1: be like wow, they're kind of dumb about basketball and

732
00:40:01,440 --> 00:40:04,200
Speaker 1: this is a basketball podcast, but then when you actually

733
00:40:04,320 --> 00:40:10,719
Speaker 1: dig into the numbers, it's pretty impressive. Wildly engagement per

734
00:40:10,760 --> 00:40:16,200
Speaker 1: point made its safe uh seth. You may or may

735
00:40:16,200 --> 00:40:18,520
Speaker 1: not have heard about this infamous segment on the show.

736
00:40:18,600 --> 00:40:20,920
Speaker 1: This is the rapid fire round of questions. We are

737
00:40:20,960 --> 00:40:24,000
Speaker 1: going to hit you with questions back to back at

738
00:40:24,080 --> 00:40:27,920
Speaker 1: lightning speed. Do not think, just respond with whatever your

739
00:40:27,960 --> 00:40:31,000
Speaker 1: brain or your heart is telling you. Don't ramble on.

740
00:40:31,239 --> 00:40:35,360
Speaker 1: This is rapid. Okay, here we go. Start the clock.

741
00:40:37,040 --> 00:40:39,719
Speaker 1: So angry to play the feud. Oh, I just have

742
00:40:39,880 --> 00:40:42,080
Speaker 1: to get you know what. I have to set a baseline.

743
00:40:42,200 --> 00:40:46,520
Speaker 1: You know, just here for what? where? This is going energetically? Okay, rapid.

744
00:40:46,560 --> 00:40:48,360
Speaker 1: Here we go. The clock is taking you want to go?

745
00:40:48,600 --> 00:40:52,799
Speaker 1: or WHO's got you? Um, I'll go for yeah, let

746
00:40:52,880 --> 00:40:56,279
Speaker 1: me just see where it yeah, let me go for you. Okay. Well,

747
00:40:56,320 --> 00:40:57,960
Speaker 1: I was gonna. I want to do the third, third,

748
00:40:57,960 --> 00:40:59,960
Speaker 1: but no, okay, you go, you go, you go. Okay,

749
00:41:00,160 --> 00:41:01,480
Speaker 1: you want to do a third, all right, let me

750
00:41:01,520 --> 00:41:05,319
Speaker 1: just yeah, just move that copy. You're putting in the

751
00:41:05,320 --> 00:41:12,359
Speaker 1: wrong doc. Now that's the tz dock. Alright, start the clock.

752
00:41:13,320 --> 00:41:18,920
Speaker 1: I'm actually using hot keys here. UH, cutting. Alright, cool, alright,

753
00:41:19,040 --> 00:41:22,680
Speaker 1: so Seth Uh Jack from Myles and Jack at Mat

754
00:41:22,680 --> 00:41:25,560
Speaker 1: boostie's here asking you a question. We're asking all of

755
00:41:25,560 --> 00:41:29,520
Speaker 1: our guests. Note a throwing one better chance of happening.

756
00:41:30,760 --> 00:41:36,560
Speaker 1: Building on last year's playoff birth, the timber wolves, the

757
00:41:36,680 --> 00:41:40,239
Speaker 1: NBA team from Minnesota, make the conference finals or we

758
00:41:40,320 --> 00:41:43,720
Speaker 1: get copious amounts of what that trick in the Western

759
00:41:43,719 --> 00:41:46,880
Speaker 1: Conference Finals says the Grizzlies take it to the next level.

760
00:41:48,239 --> 00:41:51,200
Speaker 1: That is a good one. Thank you. That is that is.

761
00:41:51,480 --> 00:41:58,080
Speaker 1: That is I think, man. I think I I trust

762
00:41:58,400 --> 00:42:00,480
Speaker 1: the roster up and down from mem it's a little

763
00:42:00,480 --> 00:42:03,120
Speaker 1: bit more so. I think that's where like the top

764
00:42:03,200 --> 00:42:05,480
Speaker 1: end talent on Minnesota, I think, is here. But I

765
00:42:05,560 --> 00:42:08,920
Speaker 1: just you know, there's some question marks about who's going

766
00:42:08,960 --> 00:42:12,279
Speaker 1: to be on the floor. Wind and there's and and,

767
00:42:12,360 --> 00:42:16,480
Speaker 1: whereas Memphis has some versatility, they can make choices, whereas

768
00:42:16,719 --> 00:42:21,840
Speaker 1: the timberwolves have questions, even though you yourself were saying

769
00:42:22,000 --> 00:42:25,560
Speaker 1: Rudy Gobert was the best player in the League mere

770
00:42:25,640 --> 00:42:29,080
Speaker 1: moments ago. No, that's not what you were saying, uh,

771
00:42:29,120 --> 00:42:32,680
Speaker 1: but that is probably how it would happen on first take.

772
00:42:32,840 --> 00:42:36,400
Speaker 1: Or what one of those shows where? Um, anyway, I

773
00:42:36,440 --> 00:42:38,640
Speaker 1: gotta I'm sorry, Seth, I gotta tell you gotta keep

774
00:42:38,640 --> 00:42:41,680
Speaker 1: the answers just a little bit quicker. Okay, let's keep

775
00:42:41,719 --> 00:42:45,920
Speaker 1: this moving, uh, seth, what are we underrating about the

776
00:42:46,000 --> 00:42:51,040
Speaker 1: upcoming season the number of teams that legitimately think they

777
00:42:51,080 --> 00:42:57,319
Speaker 1: have a chance, number of the teams that think so.

778
00:42:57,440 --> 00:42:59,759
Speaker 1: Usually you go into a season you think there's about

779
00:42:59,800 --> 00:43:03,440
Speaker 1: five teams that that really I think that number is

780
00:43:03,480 --> 00:43:07,760
Speaker 1: closer to ten this year. Okay, so you think, legit,

781
00:43:07,880 --> 00:43:10,200
Speaker 1: there are ten teams that could win it. I mean

782
00:43:10,320 --> 00:43:12,480
Speaker 1: I like, I have to go through, but you think about, no,

783
00:43:13,480 --> 00:43:19,560
Speaker 1: let me just start ripping them off a buck. Celtics, Warriors, sixers, nets,

784
00:43:20,560 --> 00:43:25,480
Speaker 1: Um Nuggets, clippers, you know, grizzlies, like you just you know,

785
00:43:25,920 --> 00:43:29,799
Speaker 1: you go like, yeah, there's a lot of people keep

786
00:43:29,880 --> 00:43:33,640
Speaker 1: getting a lot of the teams. Okay, now, a lot

787
00:43:33,640 --> 00:43:36,440
Speaker 1: of these teams. Yeah, it said Clippers, but a lot

788
00:43:36,480 --> 00:43:37,920
Speaker 1: of these teams like things are going to have to

789
00:43:37,960 --> 00:43:41,160
Speaker 1: go right for them. But like that's a lot more

790
00:43:41,200 --> 00:43:45,000
Speaker 1: teams than usually enter the season really thinking like that legitimate,

791
00:43:45,040 --> 00:43:47,200
Speaker 1: like a lot of teams like talk themselves into think,

792
00:43:47,800 --> 00:43:49,400
Speaker 1: but there are a lot of teams that over legit

793
00:43:49,880 --> 00:43:52,040
Speaker 1: like we're right there and like, okay, I'm I'm low

794
00:43:52,080 --> 00:43:54,520
Speaker 1: on Dallas, but you gotta Put Dallas on that list too, because,

795
00:43:54,600 --> 00:43:56,719
Speaker 1: like Luca could be the best player in the league

796
00:43:56,719 --> 00:44:02,000
Speaker 1: this year on that list. I completely forgot about the sun's. Yeah,

797
00:44:02,000 --> 00:44:05,080
Speaker 1: that is exciting and even at this time last year,

798
00:44:06,000 --> 00:44:09,080
Speaker 1: would most people have even put the warriors on that

799
00:44:09,200 --> 00:44:12,560
Speaker 1: list as one of the teams that it has a

800
00:44:12,640 --> 00:44:16,880
Speaker 1: legit shot? I went on Bay Area Radio on the

801
00:44:16,920 --> 00:44:20,640
Speaker 1: eve of last season and questioned whether they would be

802
00:44:20,640 --> 00:44:25,680
Speaker 1: a top six seat. Wow, so now I'm an idiot,

803
00:44:27,560 --> 00:44:29,879
Speaker 1: but I don't think a lot of people saw that coming, right,

804
00:44:30,239 --> 00:44:32,880
Speaker 1: I don't think. O. No, and then there's reasons to

805
00:44:33,080 --> 00:44:36,200
Speaker 1: you know, had been a little bit since since Steph

806
00:44:36,320 --> 00:44:38,680
Speaker 1: did that over a full season. We didn't know we're

807
00:44:38,680 --> 00:44:41,520
Speaker 1: going to get from draymond. They had a lot of

808
00:44:41,600 --> 00:44:45,640
Speaker 1: questions and and you know things. You know they're there.

809
00:44:46,480 --> 00:44:49,640
Speaker 1: Their support players all broke right. Clay came back better

810
00:44:49,680 --> 00:44:53,400
Speaker 1: than we could expected. Jordan's developed, Kevin Looney stayed healthy.

811
00:44:53,480 --> 00:44:55,160
Speaker 1: Like these are all things that were pretty important to

812
00:44:55,160 --> 00:44:58,600
Speaker 1: them last year. WE'RE gonna keep it moving. So I'm sorry,

813
00:44:59,000 --> 00:45:02,919
Speaker 1: I'm sorry, but we got to keep it moving. Next question. Well,

814
00:45:02,960 --> 00:45:06,560
Speaker 1: some of the Eurobasket success from guys like or Lori

815
00:45:06,760 --> 00:45:09,920
Speaker 1: Marken in translate into the upcoming regular season. What do

816
00:45:09,960 --> 00:45:12,879
Speaker 1: you think for the player that I'm prepared to take

817
00:45:12,960 --> 00:45:18,279
Speaker 1: my biggest hell on a long time Franz Wagner. Oh Yeah, yeah,

818
00:45:18,320 --> 00:45:23,000
Speaker 1: I did not think you good. Uh, can they play

819
00:45:23,080 --> 00:45:26,120
Speaker 1: that way in the NBA? It's always the question, I think,

820
00:45:26,200 --> 00:45:29,480
Speaker 1: you know, on those three guys, like from the scale

821
00:45:29,520 --> 00:45:32,800
Speaker 1: of Yes to no, it's probably like Wagner Marketing Chengoon

822
00:45:33,280 --> 00:45:35,080
Speaker 1: in terms of can they play the same way and

823
00:45:35,120 --> 00:45:40,799
Speaker 1: haven't work at the NBA level? So, yeah, yes, there

824
00:45:40,840 --> 00:45:42,719
Speaker 1: they are. They are good players, but I don't think

825
00:45:42,760 --> 00:45:45,960
Speaker 1: you're like you know as as like I said, Kaide

826
00:45:45,960 --> 00:45:48,320
Speaker 1: was a hipster guy, like Chengun is the hipsterist of

827
00:45:48,400 --> 00:45:52,080
Speaker 1: hipster players, and I'm still not completely convinced. Like, I

828
00:45:52,080 --> 00:45:53,440
Speaker 1: think he's a good player, is gonna be a good

829
00:45:53,440 --> 00:45:54,839
Speaker 1: player in the league for a long time. And then

830
00:45:54,880 --> 00:45:59,080
Speaker 1: we're like a legit star. I'm not sure. We will

831
00:45:59,120 --> 00:46:01,920
Speaker 1: ask you to just keep a little tighter. Um, if

832
00:46:01,960 --> 00:46:04,480
Speaker 1: you could change one rule to the game, to the

833
00:46:04,600 --> 00:46:07,480
Speaker 1: NBA game, that was like just for fun, just to

834
00:46:07,520 --> 00:46:10,200
Speaker 1: make it you know, throw throw things off balance, make

835
00:46:10,239 --> 00:46:13,920
Speaker 1: it fun, what would it be? UH, time limit on

836
00:46:14,000 --> 00:46:17,480
Speaker 1: replay review. Yeah, that would make it much more watchable.

837
00:46:18,080 --> 00:46:21,040
Speaker 1: Like what are we limiting it to? Three seconds, just

838
00:46:21,239 --> 00:46:25,720
Speaker 1: super quick first glance, the first thirty seconds or minute,

839
00:46:25,719 --> 00:46:27,399
Speaker 1: something like that. If you can't figure it out by then,

840
00:46:27,600 --> 00:46:30,680
Speaker 1: keep it. Movement like there's a little bit of there's

841
00:46:30,680 --> 00:46:32,839
Speaker 1: a little bit of what are we doing here? Two,

842
00:46:33,040 --> 00:46:36,520
Speaker 1: it's like like we're we're making a TV show here.

843
00:46:36,960 --> 00:46:43,120
Speaker 1: We're not. It's it's not a court case. So we're

844
00:46:43,120 --> 00:46:46,840
Speaker 1: looking at the refs have ordered pizza, so it seems

845
00:46:46,840 --> 00:46:52,200
Speaker 1: like they might have a decision soon or, conversely, extend

846
00:46:52,280 --> 00:46:54,680
Speaker 1: the amount of time to an infinite amount of time

847
00:46:54,760 --> 00:46:57,000
Speaker 1: for them to make a decision. The refs are upset.

848
00:46:57,160 --> 00:46:59,480
Speaker 1: The concessions have closed in the arena by now and

849
00:46:59,560 --> 00:47:03,240
Speaker 1: so they are testing by dragging this out. Those carvery

850
00:47:03,280 --> 00:47:07,720
Speaker 1: sandwiches are done. Well, I can't work under these conditions. Okay,

851
00:47:07,960 --> 00:47:10,520
Speaker 1: next question. Uh, this kind of goes along with how

852
00:47:10,560 --> 00:47:12,840
Speaker 1: you said. You know, we maybe have ten teams contending,

853
00:47:12,880 --> 00:47:15,200
Speaker 1: but what is to you? Maybe we're going to be

854
00:47:15,239 --> 00:47:18,160
Speaker 1: the biggest surprise contender that no one is considering. And

855
00:47:18,200 --> 00:47:19,759
Speaker 1: I get that you're not saying that they are going

856
00:47:19,800 --> 00:47:23,000
Speaker 1: to win, but someone if you said, Oh, they surprise people,

857
00:47:23,040 --> 00:47:27,680
Speaker 1: I'm not surprised about that. The Grizzlies from last year said, Um,

858
00:47:27,719 --> 00:47:30,919
Speaker 1: I think people haven't haven't been given the nuggets enough,

859
00:47:32,160 --> 00:47:34,359
Speaker 1: like I think, I think every like everything people are

860
00:47:34,360 --> 00:47:36,960
Speaker 1: saying about the clippers. Why aren't they also saying about

861
00:47:37,000 --> 00:47:43,000
Speaker 1: the nuggets? Right, okay, that's a that's a great question. Um.

862
00:47:43,040 --> 00:47:46,920
Speaker 1: And the final question, another great one. Is it Jack,

863
00:47:48,120 --> 00:47:51,719
Speaker 1: which is me, or miles? That guy over there has

864
00:47:51,760 --> 00:47:54,000
Speaker 1: been kind of rude like to you during this Jack,

865
00:47:54,800 --> 00:47:56,080
Speaker 1: I think he was actually kind of rude to me

866
00:47:57,960 --> 00:48:01,319
Speaker 1: talking about reality based analysis. I mean, I prefer I

867
00:48:01,360 --> 00:48:05,279
Speaker 1: prefer people a little spike here. So I'm gonna go, miles, go.

868
00:48:05,840 --> 00:48:12,720
Speaker 1: He's done it again. I haven't won a single one. Alright, wow,

869
00:48:13,160 --> 00:48:17,080
Speaker 1: that's my man. Sorry, but I knew where people when

870
00:48:17,080 --> 00:48:19,080
Speaker 1: he was talking about the reggae bubble on the on

871
00:48:19,120 --> 00:48:20,879
Speaker 1: the end, you know what I mean. I was like, okay,

872
00:48:20,920 --> 00:48:23,319
Speaker 1: now we're talking meter. Okay, I like this. I like

873
00:48:23,440 --> 00:48:27,640
Speaker 1: this sad part. Now you blessed guests. Thank you so

874
00:48:27,760 --> 00:48:31,319
Speaker 1: much for stopping by. Miles and Jack Got Mad boost these.

875
00:48:31,560 --> 00:48:35,359
Speaker 1: Where can people hear more from you? Read Your analysis

876
00:48:35,480 --> 00:48:37,960
Speaker 1: or read your book the Mid Range Theory, which, oh

877
00:48:38,000 --> 00:48:39,680
Speaker 1: by the way, we never get got to touch on it.

878
00:48:39,719 --> 00:48:42,319
Speaker 1: But that has something to do with tribe called quest

879
00:48:42,320 --> 00:48:43,600
Speaker 1: because we're like, all right, this got someone to do

880
00:48:43,680 --> 00:48:46,080
Speaker 1: low end theory. Was it inspired? It does so like

881
00:48:46,120 --> 00:48:47,840
Speaker 1: one of the I think that was probably the second

882
00:48:48,520 --> 00:48:50,160
Speaker 1: hip hop album I ever owned. We don't have to

883
00:48:50,160 --> 00:48:52,880
Speaker 1: talk about what the first one was. It might have

884
00:48:52,880 --> 00:48:58,000
Speaker 1: been policed hammer. Don't hurt him. See, you're good in

885
00:48:58,000 --> 00:49:03,040
Speaker 1: the bay as run up on hammer as no, and

886
00:49:03,120 --> 00:49:05,160
Speaker 1: so that was. So I've always been a big fan

887
00:49:05,239 --> 00:49:07,560
Speaker 1: of of of tribe, and then when we were starting

888
00:49:07,600 --> 00:49:10,959
Speaker 1: the blog that became nylon calculus, we were thinking of

889
00:49:11,080 --> 00:49:15,239
Speaker 1: like uh, thinking of names. So like because I had

890
00:49:15,640 --> 00:49:17,440
Speaker 1: I liked that album and just like the idea of

891
00:49:17,560 --> 00:49:21,319
Speaker 1: theory and how that works together with politics, like the

892
00:49:21,360 --> 00:49:23,840
Speaker 1: mid range theory. And that's so that we voted and

893
00:49:23,840 --> 00:49:26,440
Speaker 1: that finished second. So I always had in my back pocket,

894
00:49:26,560 --> 00:49:28,000
Speaker 1: or if I ever write a book I think I'll

895
00:49:28,000 --> 00:49:32,160
Speaker 1: call the mid range theory. And here we are. Alright,

896
00:49:32,200 --> 00:49:35,800
Speaker 1: respect that tribe question, by the way, or the tribe

897
00:49:35,840 --> 00:49:38,640
Speaker 1: observation that there might be a connection. There was me

898
00:49:38,760 --> 00:49:43,719
Speaker 1: actually it's pretty CLU didn't pick. So we let you know.

899
00:49:43,880 --> 00:49:48,120
Speaker 1: I think probably and the State Warriors, me and the

900
00:49:48,120 --> 00:49:52,760
Speaker 1: Golden State Warriors, looked like two people. Seth underestimated, Seth

901
00:49:52,960 --> 00:49:59,000
Speaker 1: didn't believe. said. Where can people find you? On twitter? Um,

902
00:49:59,040 --> 00:50:02,600
Speaker 1: at seth part now on twitter. Um, I'll I rate, uh,

903
00:50:02,640 --> 00:50:04,840
Speaker 1: you know. UH, once every couple of weeks at the

904
00:50:04,880 --> 00:50:07,920
Speaker 1: athletic I have a weekly podcast with David four and

905
00:50:08,040 --> 00:50:12,400
Speaker 1: modaquil on the athletics and gay show on Murdish, your wrote,

906
00:50:13,120 --> 00:50:17,759
Speaker 1: which is, you know, of course I. Um, I do

907
00:50:17,840 --> 00:50:21,400
Speaker 1: my own podcast on on the Colin App and Um,

908
00:50:21,480 --> 00:50:24,480
Speaker 1: and the book is available range. Theory. The paperback edition

909
00:50:24,520 --> 00:50:28,040
Speaker 1: comes out at the beginning of November and is available

910
00:50:28,080 --> 00:50:33,040
Speaker 1: for pre order now wherever books are sold. Gift for

911
00:50:33,120 --> 00:50:36,600
Speaker 1: your NBA and uh, you know, an NBA, NBA season

912
00:50:36,680 --> 00:50:41,120
Speaker 1: starting to say. Well, yeah, so happy NBA season, happy

913
00:50:41,160 --> 00:50:46,799
Speaker 1: NBA to you and and also to you. Uh. And

914
00:50:46,840 --> 00:50:49,120
Speaker 1: don't forget, guys, you can follow us on twitter at

915
00:50:49,160 --> 00:50:52,920
Speaker 1: mad boost these also instagram, m a D B O

916
00:50:52,920 --> 00:50:56,040
Speaker 1: o s t I e s. That says bout boosts,

917
00:50:56,400 --> 00:50:59,759
Speaker 1: so follow us there. Until next time, I'll be my

918
00:51:00,560 --> 00:51:03,560
Speaker 1: I'll be all right and we'll be mad boosey