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Speaker 1: Coming to you from fabulous Las Vegas. The right side

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Speaker 1: is the winning side. The late move is the correct move.

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Speaker 1: Sports betting capital of the world. We all know when

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Speaker 1: a sharp like me weighs in the lines move, it's

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Speaker 1: a party for your ears. This is the Buffet with

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Speaker 1: Chad and Scooch. I want to buy that guy at Buffet. Hello,

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Speaker 1: Welcome to the Buffet Podcast with Chad and Scooch from

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Speaker 1: the Action Network. Scooch is off this week. We are

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Speaker 1: dedicating this podcast to these Loan Sports Analytics Conference, which

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Speaker 1: we do it at least once a year. When I

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Speaker 1: say we, I mean I And in all the years

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Speaker 1: I was at ESPN UM and I was involved in

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Speaker 1: ESPN was involved with the Loan Sports Analytus Conference. We

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Speaker 1: were do a podcast every year highlighting the conference. Today's

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Speaker 1: guests are going to be later on in the show.

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Speaker 1: Jeff ma famed gambler from Bringing Down the House now

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Speaker 1: super big wig at Twitter, still very involved in the

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Speaker 1: sports betting community. He's going to be on a panel

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Speaker 1: with me at the conference uh this Friday. But first up,

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Speaker 1: the superstar of the conference, the GM of the best

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Speaker 1: team in the NBA. As we speak on a Tuesday, Morning,

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Speaker 1: subject of a Michael Lewis book, and most importantly, Daryl

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Speaker 1: Morey guest of the podcast UH creator with Jessica Gelman

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Speaker 1: of these Sloan Sports Analytics Conference. Welcome to the show,

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Speaker 1: my friend. Thanks Thanks man Chick. I think Obama is

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Speaker 1: the superstar of the conference this year. So I think

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Speaker 1: he is so um we um. You know. I I

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Speaker 1: joined this new company, the Action Network, which we started

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Speaker 1: by the Churning Group in October, and I joined in

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Speaker 1: September and we launched in January. And I had called

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Speaker 1: Jess in I think before we launched um and maybe

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Speaker 1: in December, and was like, you know, the folks at

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Speaker 1: Sloan who come are like our core audience, right, Like,

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Speaker 1: we are trying to reach people who want to use

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Speaker 1: analytics and data and reporting and research to make smarter

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Speaker 1: decisions about how they're gonna be sports investors for lack

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Speaker 1: of a better term, And so I want to get

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Speaker 1: in front of them. And you know, I know when

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Speaker 1: I was at ESPN and we ran the magazine and

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Speaker 1: we were involved in the conference, we'd always get the

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Speaker 1: magazines and the gift bags, and so I was like,

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Speaker 1: jess what do we do to get in the gift

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Speaker 1: bag and she's like to sponsorship and she tells me

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Speaker 1: what it costs. I'm like, all right, we can do that.

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Speaker 1: And she goes, by the way, you know, the main

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Speaker 1: stage sponsorship is still open, and she tells me what

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Speaker 1: it costs, and I'm like, wow, that's a lot for us,

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Speaker 1: where you know, we're still startup company. She's and she's like,

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Speaker 1: I'm telling you, I think you're gonna want to do it.

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Speaker 1: And this was a Friday, and Jessica is amazing and

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Speaker 1: we've been friends for a while and I'm like, all right,

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Speaker 1: let's just do it. Like I'm just gonna trust her.

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Speaker 1: And so, uh, later that day is when you announced

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Speaker 1: on Twitter and that Jessica calls to tell me that

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Speaker 1: President Obama is going to be the speaker on the

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Speaker 1: main stage, which is just fantastic because I love that.

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Speaker 1: Now we this small startup sports betting company is going

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Speaker 1: to be the main stage sponsor for the conference in

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Speaker 1: which he's speaking at under Promise over deliver, but a

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Speaker 1: good model. She certainly did that ad and so we're

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Speaker 1: appreciative of the opportunity. And by the way, I have

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Speaker 1: to remind people follow the buffet pot on Twitter, micro

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Speaker 1: Nell would be remiss if I didn't offer that. So, Darryl,

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Speaker 1: the premise for this is, um, the risks you've taken.

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Speaker 1: It's a segment we call you bet your life. And

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Speaker 1: so naturally, yesterday I saw, you know, I got my

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Speaker 1: alert that Simmons to put our new podcast and he's

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Speaker 1: got you on and he's to do your career path

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Speaker 1: and so I listened to it and it was anxious

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Speaker 1: little time because I'm like, oh my god, he's gonna

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Speaker 1: do all the stuff. Unfortunately, I don't think he got

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Speaker 1: to what you and I are going to talk about, um,

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Speaker 1: which was basically as simple as this, tell me about

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Speaker 1: the biggest risk you've taken and what happened next. Yeah,

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Speaker 1: So let me set the stage a little bit. I'm

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Speaker 1: in my first year officially as the GM of the

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Speaker 1: of the Houston Rockets, where we're having uh uh you know,

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Speaker 1: a very you know, very solid solid year on our

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Speaker 1: way to winning mid fifties with Rick Adelman as the coach.

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Speaker 1: And it's around the trade deadline and you know, we're

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Speaker 1: obviously we we are. Strategy at the trade deadline is

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Speaker 1: basically kick every tire. Now, this may not be a

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Speaker 1: good strategy. There some pluses and minuses to it, but

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Speaker 1: we've we've basically focused on deal flow and then so

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Speaker 1: you know, versus sort of maximizing every deal. And one

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Speaker 1: one deal that came up that trade deadline I was

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Speaker 1: extraordinarily nervous to do. Meaning essentially, our starting point guard

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Speaker 1: that year was Raper rayper Austin UH and obviously we're

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Speaker 1: playing extremely well. I think we were maybe the three year,

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Speaker 1: the four seed at the time, and a player we

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Speaker 1: had our ian we were hoping to draft the year.

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Speaker 1: The year before started maybe two years before UM was

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Speaker 1: Kyle Lowry. And one deal that we were working on

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Speaker 1: was essentially trading Rayper Austin ended up being a three

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Speaker 1: way where rayfer was going to Orlando, Orlando was going

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Speaker 1: to send the first round pick to Memphis, and then

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Speaker 1: Memphis would send us Kyle Lowry. Now now that sounds like, well,

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Speaker 1: of course that's a good deal, but at the time,

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Speaker 1: Kyle Lowry had never played for Memphis. He was the

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Speaker 1: third string point guard at the time, and we were

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Speaker 1: trading our starting point guard with thirty games to go

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Speaker 1: in the season. UH in a strong playoff season, H

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Speaker 1: four third screen point guard, and I distinctly remember sitting

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Speaker 1: with Rick Adelman in the office and basically him saying,

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Speaker 1: are you know, are you crazy We're going to trade

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Speaker 1: our starting point guard in the season when we're doing

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Speaker 1: this well. But you know, he basically like, look, you've

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Speaker 1: got the job. If you vouched for it, it's good.

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Speaker 1: You know. We had an assistant coach of the time

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Speaker 1: was with us now again, Brett Gunning, who actually coached

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Speaker 1: at Villanova, who was also instrumental and sort of stepping

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Speaker 1: up and making Rick comfortable with the move. But doing

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Speaker 1: a move like that and the middle of my first

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Speaker 1: year officially as a GM, it was probably the biggest

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Speaker 1: risk I took in my career. A lot of a

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Speaker 1: lot of folks didn't know what you know, what uh

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Speaker 1: what to think of me? Obviously was sort of an

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Speaker 1: unknown guy coming from Boston, uh And so all the

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Speaker 1: initial moves are extremely magnified when you get a job,

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Speaker 1: as you know, folks like Sam Hinky found out and

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Speaker 1: others like you know, you don't get as long as

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Speaker 1: a runway as you as you might want. So things

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Speaker 1: don't work out, I can go bad fast. So that

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Speaker 1: was probably the biggest risk I can remember in my

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Speaker 1: career and it hadn't gone well, I might not be

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Speaker 1: talking to you. Um, So that's uh, that's probably the

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Speaker 1: biggest one I can recall what made you comfortable making

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Speaker 1: the decision. So they're a bunch of factors. I would

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Speaker 1: say one was we really thought Kyle was gonna be better,

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Speaker 1: and even in his very limited time, I think he

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Speaker 1: had played for sure less than a thousand minutes I

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Speaker 1: want to say somewhere between three hundred and six hundred

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Speaker 1: minutes he had he had gotten in Memphis. He did

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Speaker 1: play very well in those minutes. And this was back

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Speaker 1: when basketball hadn't really still figured out what pioneers like

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Speaker 1: John Hollinger did, which is, you know, if you play

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Speaker 1: well even in in shorter minutes, you know often you

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Speaker 1: can expand those into a larger role. And so we

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Speaker 1: we were able to take advantage of arbitruge. So he

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Speaker 1: did play well in the time that he did get

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Speaker 1: a chance. Obviously we were able to watch all the

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Speaker 1: video of him playing, So I felt comfortable there. Second,

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Speaker 1: we felt a little bit comfortable because we had some

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Speaker 1: depth at the guard position that year. We felt like,

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Speaker 1: even though even though Raper was the starter, that if

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Speaker 1: Kyle didn't work out that Aaron Brooks could give us

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Speaker 1: solid minutes, So that was another thing that that heads

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Speaker 1: the risk. But to be fair, if you go back

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Speaker 1: and look at that deal, it was a head scratcher

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Speaker 1: for the local media. It's just generally not done where

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Speaker 1: you shade your starting point guard, your quarterback essentially of

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Speaker 1: your team in the middle of a fifty one season

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Speaker 1: where you know so much as magnified and and a

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Speaker 1: veteran coach who came here to win right away. So

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Speaker 1: in Rick Hattleman. So uh tho, I think the key

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Speaker 1: factors were that, yeah, both both we felt good about

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Speaker 1: our evaluation and also that we were a little bit

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Speaker 1: heads that we had some depth at the guard. So

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Speaker 1: what happened next? Yeah, so what happened now? Obviously Kyle

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Speaker 1: went on too, went on to be an All Star,

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Speaker 1: not with US but with Toronto. But Kyle was just

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Speaker 1: a major contributor to that team. Against the we took

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Speaker 1: the Lakers. We lost the Almang and Tracy McGrady that

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Speaker 1: year in the playoffs, first we lost Tracy, then we

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Speaker 1: lost Yeah, in the second round of the playoffs we

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Speaker 1: lost to Kimba Mattumbo. Are other backups center in the

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Speaker 1: first round of the playoffs to Portland. UH and um

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Speaker 1: we the Lakers won the title that year, actually won

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Speaker 1: the title against the Orlando team with rafer Alston as

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Speaker 1: the starting point guard of the Orlando team. So the

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Speaker 1: trade actually worked out for Orlando too, you could argue, um,

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Speaker 1: and Kyle was very instrumental in getting us. We're the

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Speaker 1: only team to take the Lakers to seven games that

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Speaker 1: are uh in the year they won the title, and

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Speaker 1: that was without Yeoman or Tracy McGrady. So um, you know,

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Speaker 1: it obviously worked out amazingly, which is maybe one reason

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Speaker 1: why I am talking about this story. I think folks

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Speaker 1: are biased and maybe talk about the ones that worked out. Um, yeah,

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Speaker 1: I can. I can definitely name ones that didn't work out,

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Speaker 1: you know, if we have more time. So we got

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Speaker 1: plenty of time, so tell me because it's actually interesting

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Speaker 1: a lot of people who come on and do the segment,

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Speaker 1: we'll talk about how things went egregiously and horribly wrong. Um.

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Speaker 1: What it taught them about the failure, but also it

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Speaker 1: led them into some place where they wouldn't be sort

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Speaker 1: of the massive excess they are if they hadn't taken

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Speaker 1: that completely wrong turn at one time or another, like

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Speaker 1: one guy came on and talked about how he quit

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Speaker 1: his job because he wanted to move to Florida and

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Speaker 1: didn't like what he was doing and took a job,

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Speaker 1: ended up taking a job as a paint salesman that

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Speaker 1: ultimately led him to selling paint to a prison in Florida.

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Speaker 1: And he was sitting in the room trying to sell

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Speaker 1: this special kind of paint that they were pitching, and

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Speaker 1: he thought to myself, what happened? Like what am I doing?

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Speaker 1: And like that day he quit his job and went

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Speaker 1: back to find himself in the career that he was

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Speaker 1: in before, but realizing how much he loved it. And

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Speaker 1: now he's flourished and become majorly successful and sort of

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Speaker 1: hiss built and incredible life only because he took a

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Speaker 1: risk to quit the job and then flame out considerably

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Speaker 1: at the new job that he took. What's a much

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Speaker 1: better story than mine? Well, it doesn't have Kyle Lowry. Yeah,

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Speaker 1: so the next keyboa and actually also relates to Kyle

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Speaker 1: Lowry and involves quite a bit of failure, So I

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Speaker 1: think so, I think maybe that's an interesting segue. So

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Speaker 1: that team that took the Lakers of seven games. I

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Speaker 1: believe it was an ah nine um was you know,

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Speaker 1: it was actually the peak of Yeaowming and Tracy mcgrady's

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Speaker 1: career here. Um, neither of them played significant minutes in

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Speaker 1: the NBA after that. Uh, Yeaomin came back one more

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Speaker 1: year and and played like five games. Tracy McGrady I

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Speaker 1: ended up playing a couple of places. But but you know,

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Speaker 1: obviously injuries derailed both of them. So we found ourselves

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Speaker 1: in the early you know, two thousand ten range, two

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Speaker 1: twos in twelve range, essentially trying to rebuild with a

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Speaker 1: mandate to not actually ever be bad, um and to

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Speaker 1: be fair even though being bad is the best way

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Speaker 1: to do it. Um. We had a team with just

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Speaker 1: just packed with very solid players like Louis Skola and

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Speaker 1: Shane Battier and Kyle Lowry that that you know, you know,

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Speaker 1: basically trying to take the path of getting a high

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Speaker 1: draft pick was going to be extraordinarily difficult. So we

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Speaker 1: were forced to say, how are we going to turn

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Speaker 1: our team around without, you know, without actually getting really

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Speaker 1: bad like losing you know, below thirty or winning below

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Speaker 1: thirty games, which is mostly what it requires. And so

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Speaker 1: we essentially decided to just take tons of very risky bets,

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Speaker 1: and we did that with mostly mostly really terrible failure. Frankly, UM,

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Speaker 1: so a lot of those moves in they're all all

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Speaker 1: didn't work out, with the exception of yet another Kyle

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Speaker 1: Lowry trade. But I'll first walk through the very risky

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Speaker 1: moves we did that didn't work out. So we traded

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Speaker 1: a first round pick to the Nets for Terence Williams.

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Speaker 1: Because Terence was picked I think twelve the year before, Uh,

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Speaker 1: sort of flamed out with the Nets, and we thought

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Speaker 1: he was much better than that, and we knew we

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Speaker 1: were going to never pick high. So trading our pick,

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Speaker 1: which I think was gonna land somewhere in the fifteen

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Speaker 1: to twenty range for a guy we thought was, you know,

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Speaker 1: maybe the fifth, sixth or seventh best player in the

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Speaker 1: draft the year prior. It seemed like a great idea.

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Speaker 1: Uh worked out really horribly. Uh never turned the corner.

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Speaker 1: And and frankly, was something you know, I should have

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Speaker 1: been smarter about. Anytime a team is willing to give

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Speaker 1: up on a first round pick, you know, one year in,

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Speaker 1: you know they're sitting on way more information than you are.

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Speaker 1: But the reality is we were a little bit desperate

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Speaker 1: to find high upside bets in a in a in

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Speaker 1: a strategy where we were gonna never pick high. So

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Speaker 1: he was he was one. We were able to trade

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Speaker 1: our way into three first round picks. One I think

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Speaker 1: this is eleven, and we we took no, might have

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Speaker 1: been twelve. We took three players. We took all higher risk,

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Speaker 1: sort of high return players, with the most famous one

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Speaker 1: being roy Swhite, who we thought again was a top

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Speaker 1: you know, five six seven talent in the league, but

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Speaker 1: had had had had you know, obviously had had some

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Speaker 1: off the court, um, you know, issues that we were

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Speaker 1: willing to bet on because you know, we again needed

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Speaker 1: high upside players. Uh, you know, we took roy Swhite,

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Speaker 1: We took Terrence Jones, both out of the league now

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Speaker 1: both high high risk character and guys. Both of them

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Speaker 1: did not pan out. We then signed Jeremy Lynn to

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Speaker 1: have to a very large contract, um, knowing that he

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Speaker 1: was playing in extremely high level, knowing that it was

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Speaker 1: in a very small sample size of obviously the very

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Speaker 1: famous insanity run. Uh, Jeremy actually worked out incredibly well. Um,

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Speaker 1: but it just wasn't it wasn't for us. Necessarily, he

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Speaker 1: obviously had the great one of the knicks, and he's

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Speaker 1: gone to be an extremely productive player in the league.

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Speaker 1: But you know here he Um he didn't work out

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Speaker 1: for reasons. I'll go into Um in a second. Really

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Speaker 1: weren't his fault, Um, And so those are all like

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Speaker 1: the high risk ky return moves we did over and

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Speaker 1: over trying to hit one and basically rolling Snake guys

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Speaker 1: every time. And then the one worked out was when

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Speaker 1: we traded Kyle Lowry out, and I'll go into that,

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Speaker 1: but I'll pause since I've just been rambling on in

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Speaker 1: case you had any questions. Chad, No, No, I want

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Speaker 1: to hear. I want to hear sort of the Kyle

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Speaker 1: Lowry and the Jeremy Lynn sort of resolutions. But I

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Speaker 1: also have so many questions about Royce White because I

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Speaker 1: remember when you guys did that, and I think when

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Speaker 1: I was in the magazine, we did a story about

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Speaker 1: Um when he was in college and fascinating player, so

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Speaker 1: open about sort of what was going on in his life.

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Speaker 1: What is the calculus in a decision like that? Again,

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Speaker 1: I wish there was more calculus to it than we

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Speaker 1: were desperately chasing any player who we thought had all

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Speaker 1: star potential and if you're not picking high you know

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Speaker 1: you're gonna take some you're buying some risk, like in

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Speaker 1: a big way UM. With with Terence Williams, it was,

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Speaker 1: you know, the risk that they're sitting on more information

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Speaker 1: and at the nets in terms of his ability, his

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Speaker 1: work ethic, how he interacts with teammates, things like that.

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Speaker 1: With Jeremy Lynne, we were buying big risk with sample

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Speaker 1: size with Royce why we were buying big risk with UM,

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Speaker 1: you know with you know, off the court potential issues

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Speaker 1: with UM. With Terrence Jones, we were buying, you know,

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Speaker 1: risk in terms of what you had heard from the

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Speaker 1: background and him from his coaching staff. So UM, there

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Speaker 1: was we were basically buying the risk. You're always buying

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Speaker 1: significant risk when you're chasing talent outside of where they're

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Speaker 1: normally picked the top ten in the draft. Uh. And

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Speaker 1: it's really which which problems are you're hoping are less

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Speaker 1: than other people anticipated. The market obviously has valued them

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Speaker 1: lower for a reason. Which one do you think has

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Speaker 1: the best chance of fixing itself? As that makes sense?

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Speaker 1: So what happened? What was the first with with Jeremy

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Speaker 1: Lynn and then second with Kyle Lowry. What resolutions. Yeah.

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Speaker 1: So our biggest hit, our biggest hit actually in this

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Speaker 1: whole period, was we trade. We traded Kyle Lowry out

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Speaker 1: to Toronto who needed a starting point guard. We knew

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Speaker 1: we were just sort of trading water as a five

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Speaker 1: hundred team, and they were willing to give us a

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Speaker 1: first round pick, but we we insisted to be structured

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Speaker 1: in a unique way that had never been done in

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Speaker 1: the NBA. Most of the time, first round picks are

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Speaker 1: traded and you say, hey, I'll give you this first

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Speaker 1: round pick for the player, but if the pick ends

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Speaker 1: up in the top ten, we get to keep it

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Speaker 1: um and you'll get our pick the next year until

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Speaker 1: we're not in the top ten. That's how picks have

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Speaker 1: been structured every year until this trade. When we talked

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Speaker 1: to Toronto, we were able to structure the pick because

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Speaker 1: they were very interested in Kyle, and correctly by the way,

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Speaker 1: he went on to be an All Star for multiple

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Speaker 1: years with them. And instead of it being if it's

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Speaker 1: in the top ten, you don't get to pick, it's

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Speaker 1: you only get the pick if it's in the top

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Speaker 1: fourteen essentially, so so it was basically a reverse protective

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Speaker 1: pick that was guaranteed to be a high pick in

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Speaker 1: the draft, and we thought that that would be a

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Speaker 1: really useful trade piece, either as a trade piece or

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Speaker 1: obviously we were looking for a high pick in the draft.

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Speaker 1: So that trade ended up being actually the key the

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Speaker 1: key piece in the in the James Harden deal. We

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Speaker 1: gave up a lot in the James Harden deal, but

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Speaker 1: the number one asset that Oklahoma City was interested in

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Speaker 1: was that pick that we traded Kyl for. So in anyways,

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Speaker 1: my career was essentially made on these two sort of

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Speaker 1: risky bets. One trading our starting point guard in the

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Speaker 1: middle of the playoff run for a guy who's third string,

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Speaker 1: and then two trading out Kyle Lowry are are unquestioned

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Speaker 1: best player at the time for a question mark draft

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Speaker 1: pick that then ended up being the key piece and

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Speaker 1: the hardened trade later. Haroller, Maury, that's what they call you.

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Speaker 1: What's up? Hell? Yeah, Well, as you know, it's a

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Speaker 1: we have a great team of people. You've met a

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Speaker 1: lot of them, Chad and uh and uh. It's it's

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Speaker 1: a whole system here is that I just happened to

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Speaker 1: be the one they get mad at when things feel

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Speaker 1: bad and sometimes get too much credit when it goes well.

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Speaker 1: I was surprised you didn't bring up the hardened deal.

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Speaker 1: That did not That was not a risk to us.

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Speaker 1: You know, we felt extraordinarily confident on our evaluation of

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Speaker 1: him that he was going to be a player we

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Speaker 1: could build around. We did not know he'd be this

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Speaker 1: good at all. I mean, he's he's gonna be a

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Speaker 1: Hall of Fame player and probably the best player in

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Speaker 1: the league right now. That you can't anticipate, but we

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Speaker 1: felt very confident he had a high probability to be

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Speaker 1: an All Star. They You mentioned something earlier about the

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Speaker 1: minutes that Kyle Lowry had played and that no one

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Speaker 1: had really discovered that you can sort of glean a

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Speaker 1: lot from a short a small sample size, how much

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Speaker 1: of when you're making any of these decisions because you

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Speaker 1: are so steeped in analytics, and like Simmons, I thought

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Speaker 1: the podcast that that Simmons here yesterday and you're going

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Speaker 1: back to sort of the Bill James conversation and you're

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Speaker 1: you're working at stats, and you know, I grew up

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Speaker 1: in Highland Park, you know, so I know all about

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Speaker 1: stats and what was happening in that area at that time. Um,

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Speaker 1: I thought that was fascinating. But as you've, as you've

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Speaker 1: your career has gone on, how much of the human

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Speaker 1: element takes over? You know, there's been a lot of

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Speaker 1: conversation from Billy Bean about sort of the way he's

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Speaker 1: transitioned and moved out of just thinking statistically, how much

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Speaker 1: is that playing into your risk factors when you're making

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Speaker 1: decisions on deals or draft picks or anything else. Well, yeah,

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Speaker 1: when you're a team of guards is very good. The

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Speaker 1: the locker room type elements, leadership elements, the type that

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Speaker 1: the media talks about a lot, So you don't you know,

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Speaker 1: as a team, you don't really need to talk about

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Speaker 1: it much because it's it's probably it's generally overvalued by

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Speaker 1: the marketplace and the media. But when you're a very

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Speaker 1: good team like we are, those those those variables become

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Speaker 1: fairly valued, I would say, I mean, they become big factors.

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Speaker 1: Were very careful when we were a good team to

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Speaker 1: not add any elements that some might sort of mess

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Speaker 1: up what we have going. So we didn't do any

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Speaker 1: trades of deadline. When we added Joe Johnson and Brandon Right,

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Speaker 1: it was after a lot of research that these are

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Speaker 1: two guys that will integrate wall with our team, are

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Speaker 1: coaching staff, our style of play, things like that. So

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Speaker 1: I think when you're very good, you take take less

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Speaker 1: risks on those things when you're when you're and people

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Speaker 1: perceive that we were like necessarily not sensitive to those

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Speaker 1: issues because during the period I was just talking about

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Speaker 1: from two thousand and ten through two thousand and thirteen

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Speaker 1: roughly in there, we were you know, I had to.

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Speaker 1: We were basically telling fans, look, don't buy a jersey.

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Speaker 1: If you buy a jersey, buy it with your name

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Speaker 1: on the back, because that's gonna be the only safe one. Uh.

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Speaker 1: And the reality is that's right if you're not a

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Speaker 1: good team, Like why have stability when your team isn't good? Like,

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Speaker 1: to me, that doesn't make any sense. In fact, the

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Speaker 1: risk is that you are too complacent in that period.

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Speaker 1: Um so yeah, so we you know, we uh, that's

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Speaker 1: sort of when because it was early in my career,

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Speaker 1: that was sort of when people sort of started to

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Speaker 1: say all the Rockets just shuffle players in and out,

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Speaker 1: And that was true during that period, but it was

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Speaker 1: the right It was the right plan at the time.

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Speaker 1: And now the right plan is to do what we did,

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Speaker 1: which is you know, not making any trades with the

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Speaker 1: trade dove on. Well, look, it's working out for you

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Speaker 1: so far. I'm glad for you guys. You know, I

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Speaker 1: hope it continues. Daryl, I'm gonna see you this week

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Speaker 1: the Sloan Conference uh at m I at the Sloan

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Speaker 1: supports an ALIS conference UM starts on Friday. It's Friday

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Speaker 1: and Saturday. People can follow it along on Twitter. People

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Speaker 1: will probably be able to stream a lot of the

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Speaker 1: UM stuff after the fact with some cool videos. Uh.

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Speaker 1: You know, it's a huge success story. Like in the

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Speaker 1: sports world. UM, it's become such an event. It's a

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Speaker 1: super bowlesque atmosphere in terms of people, you meet people,

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Speaker 1: you see the conversations that are had. Uh. What you

430
00:24:58,760 --> 00:25:01,639
Speaker 1: and Jessica have done pretty impressive. So congratulations and I'll

431
00:25:01,640 --> 00:25:03,679
Speaker 1: see you this week. Well, I gotta show at the

432
00:25:03,800 --> 00:25:06,280
Speaker 1: m I T students who uh who really make the

433
00:25:06,320 --> 00:25:08,960
Speaker 1: whole thing go and appreciate the kind of words and

434
00:25:08,960 --> 00:25:10,920
Speaker 1: and the fact that you help us every year. Is

435
00:25:11,200 --> 00:25:13,760
Speaker 1: is very appreciate it. So thanks for me. I appreciate it.

436
00:25:14,080 --> 00:25:17,520
Speaker 1: Joining me now on the buffet with Chat and Scooch,

437
00:25:18,320 --> 00:25:21,280
Speaker 1: one of my fellow panelists on the Sloan Sports and

438
00:25:21,400 --> 00:25:26,920
Speaker 1: It Looks Gambling panel this week at the conference. Jeff, Jeff,

439
00:25:26,960 --> 00:25:29,320
Speaker 1: how you doing, buddy. So that's what I've become to you,

440
00:25:29,480 --> 00:25:31,879
Speaker 1: just another one of your fellow panelists. I used to

441
00:25:31,880 --> 00:25:34,760
Speaker 1: be introduced as like one of your friends. What's going on? Well,

442
00:25:35,960 --> 00:25:38,439
Speaker 1: I was gonna get into more of it, like I

443
00:25:38,480 --> 00:25:40,560
Speaker 1: wanted to give people the top line here. I don't

444
00:25:40,560 --> 00:25:43,640
Speaker 1: know if they're all that interested in like personal relationships. Well,

445
00:25:43,680 --> 00:25:46,960
Speaker 1: I'm very sensitive these days yet, So it's it's it's

446
00:25:47,000 --> 00:25:49,480
Speaker 1: always good to know that we still we're still friends

447
00:25:49,480 --> 00:25:53,520
Speaker 1: in the industry. I would say, friends inside and outside

448
00:25:53,560 --> 00:25:57,440
Speaker 1: the industry. I appreciate that. I appreciate that. Um, well,

449
00:25:57,640 --> 00:25:59,840
Speaker 1: my friend, I'm going to see you this week, and

450
00:26:01,880 --> 00:26:05,000
Speaker 1: the panel is gonna be really good because it's gonna

451
00:26:05,080 --> 00:26:08,159
Speaker 1: have you. It's gonna have Ted Olsen, who is the

452
00:26:08,160 --> 00:26:11,560
Speaker 1: former Solicitor General of the United States who argued the

453
00:26:11,600 --> 00:26:15,400
Speaker 1: case for I believe it was George Bush over Al

454
00:26:15,480 --> 00:26:22,320
Speaker 1: Gore in UH two thousand UM that ultimately had George

455
00:26:22,320 --> 00:26:25,800
Speaker 1: Bush become president UH. And he most recently argued the

456
00:26:25,800 --> 00:26:30,080
Speaker 1: case in front of the Supreme Court to have UM

457
00:26:30,119 --> 00:26:32,800
Speaker 1: in favor of on the side of New Jersey. He

458
00:26:32,960 --> 00:26:36,119
Speaker 1: was the lawyer for the state of New Jersey arguing

459
00:26:36,160 --> 00:26:38,639
Speaker 1: that they should be allowed to have sports betting. Um,

460
00:26:38,680 --> 00:26:41,280
Speaker 1: so he's gonna talk about that. Uh, you're gonna be

461
00:26:41,280 --> 00:26:44,439
Speaker 1: on the panel. And I feel like this is a

462
00:26:44,440 --> 00:26:47,800
Speaker 1: good time to have this conversation because in Eurola, Twitter

463
00:26:47,960 --> 00:26:51,919
Speaker 1: as sort of puba of product. Uh, you are smacked

464
00:26:51,960 --> 00:26:55,480
Speaker 1: ab in the middle of what could be a monumentally

465
00:26:55,560 --> 00:27:00,600
Speaker 1: important tool once sports bet and becomes legalized. How do

466
00:27:00,640 --> 00:27:07,280
Speaker 1: you see Twitter activating against that? Well, well, one, I'm

467
00:27:07,280 --> 00:27:10,119
Speaker 1: not Poba product. UM. That's actually a guy by name

468
00:27:10,280 --> 00:27:12,480
Speaker 1: Keith Coleman, another guy name of Edo who kind of

469
00:27:12,560 --> 00:27:15,760
Speaker 1: run that. I'm more puba of data and data science

470
00:27:15,760 --> 00:27:19,800
Speaker 1: and analytics UM, which obviously is an important role, but

471
00:27:20,000 --> 00:27:22,760
Speaker 1: not quite the same as Pooba product. Now that being said,

472
00:27:23,080 --> 00:27:25,159
Speaker 1: obviously I spent a lot of time with those guys

473
00:27:25,200 --> 00:27:28,480
Speaker 1: and we talk a lot about the ramifications of different things.

474
00:27:28,560 --> 00:27:32,720
Speaker 1: I mean, as Twitter, um we are tagline or where

475
00:27:32,760 --> 00:27:35,360
Speaker 1: we've gone the last couple of years has really been

476
00:27:35,359 --> 00:27:38,920
Speaker 1: about to show you what's happening, or to be what's happening.

477
00:27:39,520 --> 00:27:42,000
Speaker 1: And what that means is that we've been focused a

478
00:27:42,080 --> 00:27:44,760
Speaker 1: lot on the real time aspect of things being the

479
00:27:44,840 --> 00:27:48,480
Speaker 1: first to get information. UM, it's sort of simplified the

480
00:27:48,600 --> 00:27:51,480
Speaker 1: use case around Twitter, and I think it dovetails really

481
00:27:51,520 --> 00:27:55,520
Speaker 1: well with the idea of legalized sports betting because so

482
00:27:55,640 --> 00:27:59,800
Speaker 1: much of sports information is being first to get that information,

483
00:28:00,000 --> 00:28:04,000
Speaker 1: whether it's injury information or you know, coaching changes or

484
00:28:04,200 --> 00:28:07,080
Speaker 1: personnel changes, anything like that. Um, you want to be

485
00:28:07,119 --> 00:28:09,600
Speaker 1: the first to get it. And the first used to

486
00:28:09,640 --> 00:28:12,440
Speaker 1: be for you know, like fantasy or just for the

487
00:28:12,920 --> 00:28:16,040
Speaker 1: pride or you know, the being able to say like,

488
00:28:16,040 --> 00:28:18,119
Speaker 1: oh I knew this first. UM. I don't know if

489
00:28:18,160 --> 00:28:20,879
Speaker 1: you remember the whole like Rick Riley thing that was

490
00:28:20,880 --> 00:28:23,919
Speaker 1: pretty embarrassing when he was on Monday Night Football and

491
00:28:23,960 --> 00:28:27,200
Speaker 1: they accidentally saying like yeah, yeah, like make sure everyone

492
00:28:27,240 --> 00:28:31,000
Speaker 1: knows I got that first. But in the Twitter parliamance,

493
00:28:31,240 --> 00:28:33,480
Speaker 1: and especially in the world of sports gambling, there's a

494
00:28:33,480 --> 00:28:36,919
Speaker 1: real financial reason to be first. Um. Even now, you

495
00:28:36,960 --> 00:28:41,280
Speaker 1: can see sports information and especially injuries breaking on Twitter

496
00:28:41,600 --> 00:28:45,320
Speaker 1: even before it's reflected in the line moves sometimes and UM,

497
00:28:45,720 --> 00:28:47,520
Speaker 1: I think one of the things that is interesting to

498
00:28:47,600 --> 00:28:51,160
Speaker 1: do is to watch how line moves, especially in the

499
00:28:51,280 --> 00:28:54,280
Speaker 1: n b A UM where they're all are all sorts

500
00:28:54,440 --> 00:28:59,600
Speaker 1: of new sort of trends around people sitting when you

501
00:28:59,640 --> 00:29:02,200
Speaker 1: don't effect them to sit UM, and just so much

502
00:29:02,280 --> 00:29:06,880
Speaker 1: uncertainty around who's playing on any given day. Sometimes you

503
00:29:06,920 --> 00:29:11,200
Speaker 1: see the market reflect the line move before the injury

504
00:29:11,280 --> 00:29:14,760
Speaker 1: is announced, and sometimes you see it happened twice where

505
00:29:14,760 --> 00:29:16,640
Speaker 1: you'll see the line move and then you'll see the

506
00:29:16,640 --> 00:29:19,080
Speaker 1: injury announcement go and you'll see the line move again,

507
00:29:19,560 --> 00:29:22,560
Speaker 1: which either means the line the market was pricing in

508
00:29:22,680 --> 00:29:25,920
Speaker 1: some level of uncertainty, and then when the certainty came

509
00:29:25,960 --> 00:29:28,800
Speaker 1: out that they weren't playing, it reacted even more. Sometimes

510
00:29:28,840 --> 00:29:31,640
Speaker 1: I believe it's really overreacting to a lot of the

511
00:29:31,720 --> 00:29:38,000
Speaker 1: century information. So Fantasy Labs, which is a company that's

512
00:29:38,080 --> 00:29:42,800
Speaker 1: in the Action Network, has an NBA Twitter handle UM

513
00:29:42,920 --> 00:29:45,719
Speaker 1: that is entirely built on what you're talking about. Like

514
00:29:45,800 --> 00:29:52,240
Speaker 1: they are phenomenal at tracking down d nps or expected

515
00:29:52,320 --> 00:29:54,960
Speaker 1: d n p s or injury news in the NBA,

516
00:29:55,160 --> 00:29:57,560
Speaker 1: because so much of daily fantasy obviously is driven by

517
00:29:58,000 --> 00:30:01,400
Speaker 1: you know who's not No, I follow them, and I

518
00:30:01,440 --> 00:30:05,600
Speaker 1: actually don't have UM notifications set for very many people. UM.

519
00:30:05,640 --> 00:30:07,760
Speaker 1: They are one of the accounts that I do have

520
00:30:07,840 --> 00:30:11,240
Speaker 1: notifications set for because I want to see who they're saying.

521
00:30:11,240 --> 00:30:13,320
Speaker 1: But that's but the interesting thing is, you know that

522
00:30:13,520 --> 00:30:16,840
Speaker 1: they're sort of limited by, you know, the information that

523
00:30:16,880 --> 00:30:18,560
Speaker 1: they're able to get through writers and things like that.

524
00:30:18,600 --> 00:30:21,080
Speaker 1: And sometimes I do believe the market knows things even

525
00:30:21,120 --> 00:30:24,400
Speaker 1: before they do, which which begs the question like how

526
00:30:24,440 --> 00:30:27,560
Speaker 1: do you continue to get even better information? Because I'm

527
00:30:27,560 --> 00:30:30,800
Speaker 1: sure they're working very hard to get the best information,

528
00:30:30,840 --> 00:30:33,480
Speaker 1: and as far as I know from like a definitive source,

529
00:30:33,960 --> 00:30:37,080
Speaker 1: they are the best information. But the market can often

530
00:30:37,160 --> 00:30:40,440
Speaker 1: reflect um things even before they do. Like you know,

531
00:30:40,520 --> 00:30:44,080
Speaker 1: you'll see things priced into the market, um, and they'll

532
00:30:44,120 --> 00:30:47,840
Speaker 1: say like oh and be still, um, you know, questionable

533
00:30:47,960 --> 00:30:50,680
Speaker 1: for tonight, and it's like clear from the market that

534
00:30:50,760 --> 00:30:52,920
Speaker 1: he is playing or that he isn't playing. Now, the

535
00:30:52,960 --> 00:30:55,440
Speaker 1: market isn't always right just like they aren't that they

536
00:30:55,440 --> 00:30:58,280
Speaker 1: wouldn't know as be right, um, But it is interesting

537
00:30:58,320 --> 00:31:00,640
Speaker 1: to know, like what are the source says that the

538
00:31:00,680 --> 00:31:04,280
Speaker 1: market is getting that even fantasy labs can't get. Well,

539
00:31:04,320 --> 00:31:08,720
Speaker 1: I wonder do you think that that is something that

540
00:31:08,760 --> 00:31:12,880
Speaker 1: will ever be revealed? Like is the market you know?

541
00:31:13,520 --> 00:31:16,120
Speaker 1: Betters are making decisions and they're not. The people who

542
00:31:16,120 --> 00:31:19,200
Speaker 1: are really moving the market aren't necessarily going to be

543
00:31:19,280 --> 00:31:26,680
Speaker 1: releasing on Twitter what they know before they're making their bet. Um.

544
00:31:26,720 --> 00:31:29,200
Speaker 1: I mean, yeah, obviously like the people that are moving them.

545
00:31:29,200 --> 00:31:31,520
Speaker 1: I mean, but but you'd be surprised about like the

546
00:31:31,560 --> 00:31:35,320
Speaker 1: people moving the markets because this day and age, a

547
00:31:35,320 --> 00:31:38,000
Speaker 1: lot of these sports books are really afraid of sharp

548
00:31:38,160 --> 00:31:40,680
Speaker 1: So there are people that will get labeled in certain

549
00:31:41,040 --> 00:31:43,080
Speaker 1: you know accounts, like a bet Chris or like a

550
00:31:43,080 --> 00:31:45,720
Speaker 1: Pinnacle the Sharper books. And if you get labeled that

551
00:31:45,800 --> 00:31:49,240
Speaker 1: way and you bet, um, you're automatically going to move

552
00:31:49,240 --> 00:31:51,640
Speaker 1: the market, even if you don't really know anything, and

553
00:31:51,720 --> 00:31:53,560
Speaker 1: they'll move the market a little bit against you. And

554
00:31:53,560 --> 00:31:56,800
Speaker 1: then if someone else that you might happen to trade uh,

555
00:31:56,880 --> 00:32:00,719
Speaker 1: you know, picks with also bets and that same account

556
00:32:00,800 --> 00:32:03,240
Speaker 1: or bets in that same sports book, that will move

557
00:32:03,240 --> 00:32:07,200
Speaker 1: the market also. So I think sometimes we give the

558
00:32:07,240 --> 00:32:11,600
Speaker 1: market too much credit, um, and sometimes sometimes people are

559
00:32:11,640 --> 00:32:14,880
Speaker 1: just you know, speculating and now I'll with the market.

560
00:32:14,960 --> 00:32:17,160
Speaker 1: So I think one of the things that will become

561
00:32:17,160 --> 00:32:20,920
Speaker 1: more interesting if sports gambling or when sports gamily becomes

562
00:32:21,000 --> 00:32:25,280
Speaker 1: legal is how much injury information is actually regulated in

563
00:32:25,400 --> 00:32:28,720
Speaker 1: terms of like the league's mandating that you have to

564
00:32:28,840 --> 00:32:32,479
Speaker 1: announce at a certain time who is and who isn't playing.

565
00:32:32,760 --> 00:32:34,640
Speaker 1: But I guess even in that case, people will still

566
00:32:34,680 --> 00:32:38,360
Speaker 1: speculate before that announcement. My guests is sports books will

567
00:32:38,360 --> 00:32:40,840
Speaker 1: try to like hold back limits until those announcements are

568
00:32:40,880 --> 00:32:45,120
Speaker 1: made because they're they're petrified of being beat by you know,

569
00:32:45,200 --> 00:32:48,840
Speaker 1: information asymmetry. So it'll be interesting to see how that

570
00:32:48,920 --> 00:32:53,040
Speaker 1: all plays out. What does that mean? Information asymmetry? It

571
00:32:53,160 --> 00:32:56,440
Speaker 1: just means that like they have information or someone has

572
00:32:56,480 --> 00:32:59,040
Speaker 1: information that they don't. There's like when when you have

573
00:32:59,080 --> 00:33:02,120
Speaker 1: information asymmetry, It just means that like the two sides,

574
00:33:02,200 --> 00:33:05,280
Speaker 1: like I have information that you don't, and therefore my

575
00:33:05,360 --> 00:33:07,920
Speaker 1: opinion on something is different than your opinion because I

576
00:33:08,000 --> 00:33:12,360
Speaker 1: have actual information that you don't. Like markets, and like

577
00:33:12,840 --> 00:33:16,160
Speaker 1: the stock market in the old days, information asymmetry would

578
00:33:16,200 --> 00:33:19,479
Speaker 1: cause you know, opportunities in the stock stock market because

579
00:33:19,920 --> 00:33:22,480
Speaker 1: like you know, certain types of news about companies was

580
00:33:22,520 --> 00:33:26,360
Speaker 1: not necessarily transparent, or certain like economic trends weren't transparent,

581
00:33:26,400 --> 00:33:29,880
Speaker 1: so that would create information asymmetry. You've had so many

582
00:33:29,920 --> 00:33:37,480
Speaker 1: experiences in studying markets. What would you say is from

583
00:33:37,520 --> 00:33:43,080
Speaker 1: from first to last, the most transparent of all the

584
00:33:43,160 --> 00:33:46,680
Speaker 1: markets that you've studied and been a part of, from

585
00:33:46,880 --> 00:33:53,800
Speaker 1: financial markets to uh sports betting markets too. I guess

586
00:33:53,800 --> 00:33:57,960
Speaker 1: you could frame you know, blackjack. You know you were

587
00:33:58,080 --> 00:34:00,560
Speaker 1: part of the bringing down the house rue in the

588
00:34:00,600 --> 00:34:02,400
Speaker 1: movie twenty one that was that was turned in the

589
00:34:02,400 --> 00:34:07,040
Speaker 1: movie one like to sort of. I guess card game

590
00:34:07,120 --> 00:34:10,000
Speaker 1: markets I don't know how to the market markets aren't

591
00:34:10,040 --> 00:34:11,960
Speaker 1: really markets, right, and those if you were going to

592
00:34:12,000 --> 00:34:14,440
Speaker 1: talk about what was the most transparent, they're certainly most

593
00:34:14,440 --> 00:34:18,800
Speaker 1: transparent because they're a closed system. Um. I think maybe

594
00:34:18,840 --> 00:34:22,080
Speaker 1: what you're asking about is studying systems versus market systems

595
00:34:22,080 --> 00:34:25,520
Speaker 1: that you can be and certainly blackjack is a closed system.

596
00:34:25,600 --> 00:34:30,080
Speaker 1: The rules are predetermined, Um. Everybody knows the rules. You

597
00:34:30,120 --> 00:34:32,200
Speaker 1: can't change things. And then if you move into something

598
00:34:32,239 --> 00:34:36,520
Speaker 1: like poker, where the rules are determined but you don't

599
00:34:36,600 --> 00:34:38,879
Speaker 1: necessarily know how the players are going to act at

600
00:34:38,880 --> 00:34:42,120
Speaker 1: certain times. Um. And then you go to like betting

601
00:34:42,160 --> 00:34:45,719
Speaker 1: markets and the stock market where you don't even know

602
00:34:45,760 --> 00:34:49,160
Speaker 1: the rules. You don't know how people are going to act. Um,

603
00:34:49,200 --> 00:34:51,279
Speaker 1: I guess, And I guess in betting markets, you know

604
00:34:51,400 --> 00:34:56,280
Speaker 1: the rules like theoretically of winning and losing. In stock markets,

605
00:34:56,320 --> 00:34:59,440
Speaker 1: you don't even know the rules right like you you know,

606
00:34:59,520 --> 00:35:02,160
Speaker 1: you watch stock and you watch their earnings come out,

607
00:35:02,200 --> 00:35:03,920
Speaker 1: and you would think like, oh, the market's going to

608
00:35:03,960 --> 00:35:06,360
Speaker 1: react a certain way, and they don't because that was

609
00:35:06,360 --> 00:35:09,040
Speaker 1: already priced in and that kind of thing. UM. I

610
00:35:09,080 --> 00:35:12,840
Speaker 1: think one of the reasons that the betting markets and this,

611
00:35:13,239 --> 00:35:15,560
Speaker 1: you know, I think you'd be hard pressed to argue

612
00:35:15,600 --> 00:35:18,480
Speaker 1: definitively one way or the other between the betting markets

613
00:35:18,480 --> 00:35:21,560
Speaker 1: and the stock markets. Which ones are more transparent or

614
00:35:21,600 --> 00:35:24,440
Speaker 1: which ones are more are easier to understand. You can

615
00:35:24,480 --> 00:35:27,080
Speaker 1: certainly make a case that like, you don't really know

616
00:35:27,120 --> 00:35:29,479
Speaker 1: what's going on in a publicly traded company. You don't

617
00:35:29,520 --> 00:35:32,400
Speaker 1: like see the the stuff played out on the field.

618
00:35:32,480 --> 00:35:34,919
Speaker 1: You don't get necessarily the same interviews in the same

619
00:35:34,960 --> 00:35:38,960
Speaker 1: inside look that you do UM at sports teams. But

620
00:35:39,120 --> 00:35:44,399
Speaker 1: in sports teams you have sort of ever changing UM landscape.

621
00:35:44,880 --> 00:35:47,640
Speaker 1: I think that that is even more it's even harder

622
00:35:47,680 --> 00:35:50,320
Speaker 1: to understand, like because there's all these new players that

623
00:35:50,360 --> 00:35:52,320
Speaker 1: have come into the But I guess that's true of

624
00:35:52,600 --> 00:35:56,560
Speaker 1: of stock market also with you know, high sprens trading

625
00:35:56,680 --> 00:35:59,600
Speaker 1: and all the things that have changed those markets, so

626
00:36:00,040 --> 00:36:02,279
Speaker 1: and like, I'm basically arguing between the two of them

627
00:36:02,280 --> 00:36:05,560
Speaker 1: and I can't come up with a real answer. So,

628
00:36:05,600 --> 00:36:09,800
Speaker 1: speaking of transparency, what do you do if sports betting

629
00:36:09,800 --> 00:36:13,480
Speaker 1: becomes legal or even if it doesn't. Listen, I love

630
00:36:13,520 --> 00:36:15,839
Speaker 1: Twitter and I'm honored all the time and it's an

631
00:36:15,840 --> 00:36:20,680
Speaker 1: invaluable tool for what I do. But from the information perspective,

632
00:36:20,760 --> 00:36:24,840
Speaker 1: you talk about information asymmetry, it's easy for someone to

633
00:36:24,880 --> 00:36:27,640
Speaker 1: start a rumor, do you know what I mean? Like,

634
00:36:27,680 --> 00:36:29,879
Speaker 1: how do you how do you manage that? Because like

635
00:36:30,120 --> 00:36:33,360
Speaker 1: on Twitter, that could spread like wildfire and all of

636
00:36:33,400 --> 00:36:38,359
Speaker 1: a sudden it's having a tremendous impact on a betting market. Yeah,

637
00:36:38,400 --> 00:36:40,600
Speaker 1: I mean that is that is the notion that is

638
00:36:40,600 --> 00:36:44,120
Speaker 1: a very challenging thing to face that both you know,

639
00:36:44,320 --> 00:36:49,240
Speaker 1: us and Facebook and YouTube to some degree are having

640
00:36:49,280 --> 00:36:52,640
Speaker 1: to deal with, which is like what role should we

641
00:36:52,760 --> 00:36:55,680
Speaker 1: play or do we do we have to play in

642
00:36:55,719 --> 00:36:58,120
Speaker 1: the world of this kind of concept of fake news

643
00:36:58,200 --> 00:37:01,879
Speaker 1: or real news or validating news. UM. What I think

644
00:37:01,920 --> 00:37:04,680
Speaker 1: that we have to do our best at is validating

645
00:37:04,719 --> 00:37:08,240
Speaker 1: the source of where that information is coming from. UM,

646
00:37:08,280 --> 00:37:10,560
Speaker 1: And we're spending a lot of time thinking about how

647
00:37:10,600 --> 00:37:14,560
Speaker 1: our verification process works and maybe even like different sort

648
00:37:14,600 --> 00:37:17,719
Speaker 1: of tiers of verification at some point. UM, But just

649
00:37:17,760 --> 00:37:22,040
Speaker 1: this idea that you know, verifying the sources the best way.

650
00:37:22,400 --> 00:37:25,520
Speaker 1: It's hard for us to verify each piece of individual

651
00:37:25,600 --> 00:37:29,359
Speaker 1: news because it's it's just it's just impossible the way

652
00:37:29,400 --> 00:37:32,479
Speaker 1: things go and how fast things go and whatnot. Um.

653
00:37:32,520 --> 00:37:34,880
Speaker 1: The other thing is like again like you have to

654
00:37:35,000 --> 00:37:37,759
Speaker 1: use leverage the market or leverage leverage the people in

655
00:37:37,840 --> 00:37:41,960
Speaker 1: the community to help you know, you validate some of

656
00:37:42,000 --> 00:37:45,760
Speaker 1: these things. Also, UM, there are there are machine ways

657
00:37:45,840 --> 00:37:48,560
Speaker 1: to try to do this, but generally it's it's definitely

658
00:37:48,600 --> 00:37:52,160
Speaker 1: an unsolved problem that, um, that we're all facing right

659
00:37:52,160 --> 00:37:54,719
Speaker 1: now together, and we're all facing this as a as

660
00:37:54,719 --> 00:37:57,799
Speaker 1: almost like as a world together because if you look

661
00:37:57,800 --> 00:37:59,760
Speaker 1: at what happened in the last selection, which has obviously

662
00:37:59,800 --> 00:38:03,080
Speaker 1: been publicized, there was a lot of things going on

663
00:38:03,200 --> 00:38:07,280
Speaker 1: that were sort of you know, manipulations of different platforms, um,

664
00:38:07,320 --> 00:38:10,799
Speaker 1: and it was it was a challenging thing for you know,

665
00:38:10,840 --> 00:38:14,200
Speaker 1: for platforms like ourselves on Facebook to deal with. You know,

666
00:38:15,320 --> 00:38:18,480
Speaker 1: Twitter was way ahead of the game on verification, Like

667
00:38:18,920 --> 00:38:23,279
Speaker 1: that blue check mark kind of meant everything, uh in

668
00:38:23,360 --> 00:38:26,040
Speaker 1: the beginning, and like getting the blue check mark, it

669
00:38:26,160 --> 00:38:31,680
Speaker 1: felt like you were validated in a very specific way. Um,

670
00:38:31,840 --> 00:38:34,320
Speaker 1: what work can you do to verify? Like? What is

671
00:38:34,360 --> 00:38:38,160
Speaker 1: another level of verification for someone like me? Do you

672
00:38:38,160 --> 00:38:39,560
Speaker 1: know what I mean? I've got a blue check mark?

673
00:38:39,680 --> 00:38:43,120
Speaker 1: What more is there to verify? Well, I think it's

674
00:38:43,239 --> 00:38:48,640
Speaker 1: more not necessary verification, but it's more just um, reputation

675
00:38:48,880 --> 00:38:52,319
Speaker 1: around what you're known for, what you're good at. Right,

676
00:38:52,320 --> 00:38:54,480
Speaker 1: So that idea would be like what you know, Like,

677
00:38:54,840 --> 00:38:58,239
Speaker 1: you're verified, But should I trust your opinion when it

678
00:38:58,280 --> 00:39:02,160
Speaker 1: comes to the best uh sushi restaurant in New York

679
00:39:02,960 --> 00:39:06,840
Speaker 1: or whether Hamilton's is the best musical ever? Um? I

680
00:39:06,880 --> 00:39:09,200
Speaker 1: don't know. I mean maybe maybe your your opinions. But

681
00:39:09,280 --> 00:39:12,520
Speaker 1: really what you're verified for, what you're known for is

682
00:39:12,880 --> 00:39:15,919
Speaker 1: sports and sports betting and that aspect of things. So

683
00:39:16,280 --> 00:39:18,840
Speaker 1: that would be the area, um, that we might be

684
00:39:18,880 --> 00:39:22,080
Speaker 1: able to push into, which is actually trying to help um,

685
00:39:22,120 --> 00:39:25,120
Speaker 1: you know, verify you or give you reputation or or

686
00:39:25,160 --> 00:39:29,080
Speaker 1: help you have more reputation around specifically what you are

687
00:39:29,160 --> 00:39:35,640
Speaker 1: known for. That's interesting. That will be really interesting to

688
00:39:35,680 --> 00:39:40,080
Speaker 1: bring it back to gambling, because that is a that

689
00:39:40,239 --> 00:39:45,000
Speaker 1: is an interesting that is reputationally challenged, like how do

690
00:39:45,080 --> 00:39:49,399
Speaker 1: you figure out if someone is someone that you want

691
00:39:49,440 --> 00:39:55,399
Speaker 1: to verify and someone who's reputation and information is good. Yeah,

692
00:39:55,400 --> 00:39:58,040
Speaker 1: I mean it's again like this isn't I'm not m

693
00:39:59,480 --> 00:40:01,799
Speaker 1: I don't want speak ahead of anything we're doing, like

694
00:40:01,840 --> 00:40:04,359
Speaker 1: this is not necessarily anything that we're planning on doing,

695
00:40:04,440 --> 00:40:06,560
Speaker 1: Like I'm not I don't want to give up any

696
00:40:06,600 --> 00:40:09,400
Speaker 1: of the thoughts around what we're doing before we actually

697
00:40:09,400 --> 00:40:11,960
Speaker 1: do them. But you know, when I when I envisioned

698
00:40:11,960 --> 00:40:16,320
Speaker 1: personally what some of these things could look like going forward, Um,

699
00:40:16,360 --> 00:40:19,279
Speaker 1: it really is around you know, just just what like

700
00:40:19,360 --> 00:40:21,480
Speaker 1: just like when you before we went on air, we

701
00:40:21,520 --> 00:40:23,880
Speaker 1: talked about gambling Twitter, right, and there are people that

702
00:40:23,920 --> 00:40:28,839
Speaker 1: are known for giving out good gambling information or there

703
00:40:28,880 --> 00:40:31,120
Speaker 1: are people that are known for you know, being the

704
00:40:31,160 --> 00:40:34,520
Speaker 1: contrarians on gambling or whatnot. And if you go on

705
00:40:34,560 --> 00:40:37,560
Speaker 1: to Twitter, you don't really know what who to trust.

706
00:40:37,760 --> 00:40:39,600
Speaker 1: You don't really know who's good and who's bad, and

707
00:40:39,640 --> 00:40:44,440
Speaker 1: who's a tow and who's whatnot. So certainly having some

708
00:40:44,600 --> 00:40:48,560
Speaker 1: aspect of understanding like who you can trust and who

709
00:40:48,600 --> 00:40:53,040
Speaker 1: you can't trust would be an interesting move um for

710
00:40:53,239 --> 00:40:56,560
Speaker 1: you know, like for for up for content generally, like

711
00:40:56,640 --> 00:40:59,160
Speaker 1: to understand what content you could trust and you know,

712
00:40:59,200 --> 00:41:01,320
Speaker 1: to bring it all back idea of like sports betting

713
00:41:01,320 --> 00:41:03,600
Speaker 1: become legal. It's it's just like the concept of having

714
00:41:04,080 --> 00:41:06,279
Speaker 1: you know, analysts in the stock market that you can

715
00:41:06,280 --> 00:41:10,040
Speaker 1: trust that are are giving you good information. Boy, that

716
00:41:10,040 --> 00:41:16,080
Speaker 1: would put Twitter at the white hot center of having

717
00:41:16,120 --> 00:41:20,759
Speaker 1: a tangible impact on a nascent legal industry. Right Like

718
00:41:21,280 --> 00:41:25,640
Speaker 1: if Twitter is bringing it upon itself to say, this

719
00:41:25,719 --> 00:41:28,960
Speaker 1: guy's it's out, this guy's not it's out. This guy

720
00:41:29,000 --> 00:41:31,920
Speaker 1: has good information, this guy doesn't have good information that

721
00:41:32,080 --> 00:41:35,280
Speaker 1: has a material impact on who people follow and who's

722
00:41:35,320 --> 00:41:39,160
Speaker 1: making money and what a particular person or a company

723
00:41:39,160 --> 00:41:41,120
Speaker 1: can do in the market and their value in the market.

724
00:41:41,960 --> 00:41:46,400
Speaker 1: Different the different level of verification. Then hey, I got

725
00:41:46,520 --> 00:41:50,080
Speaker 1: you know, twenty followers or a hundred thousand followers or

726
00:41:50,120 --> 00:41:54,200
Speaker 1: four million followers, um, And there's value in that. Yeah,

727
00:41:54,239 --> 00:41:58,440
Speaker 1: I mean, I think it's an interesting concept. Certainly, I

728
00:41:58,480 --> 00:42:01,920
Speaker 1: think that it's a challenge in concept um And it

729
00:42:02,000 --> 00:42:05,359
Speaker 1: comes back to this idea of you know, what's our

730
00:42:05,560 --> 00:42:10,400
Speaker 1: role in this world? Right? Are we this platform? Like

731
00:42:10,440 --> 00:42:12,879
Speaker 1: the thing about Twitter is it's a phenomenon, right, It's

732
00:42:12,960 --> 00:42:18,480
Speaker 1: it's this amazing platform for communicating from one too many. Um,

733
00:42:18,480 --> 00:42:20,839
Speaker 1: and all those things that you talked about, whether it's

734
00:42:20,960 --> 00:42:22,880
Speaker 1: you know, the number of followers you have, or that

735
00:42:23,080 --> 00:42:25,680
Speaker 1: the actual like the you know, the people that engage,

736
00:42:25,680 --> 00:42:27,920
Speaker 1: how much engagement you get worth of your tweets. All

737
00:42:27,960 --> 00:42:30,600
Speaker 1: that kind of stuff is stuff that like almost I

738
00:42:31,000 --> 00:42:34,640
Speaker 1: when I look at a someone new, that's what I do. Like,

739
00:42:34,680 --> 00:42:36,400
Speaker 1: what do you do? You click on their profile, you

740
00:42:36,400 --> 00:42:38,879
Speaker 1: see how many followers they have, You kind of look

741
00:42:38,920 --> 00:42:40,360
Speaker 1: at some of the things they tweeted, You look at

742
00:42:40,560 --> 00:42:43,040
Speaker 1: the people that follow them, and that's what you used

743
00:42:43,040 --> 00:42:45,759
Speaker 1: to decide what, you know, how much stock you want

744
00:42:45,760 --> 00:42:48,360
Speaker 1: to put into what this person says? Um, So I

745
00:42:48,360 --> 00:42:50,239
Speaker 1: would say to some of great Twitter already has that

746
00:42:50,360 --> 00:42:52,840
Speaker 1: it's just like a manual process that you do yourself

747
00:42:52,920 --> 00:42:55,680
Speaker 1: to figure out like whether this person is worth anything

748
00:42:55,719 --> 00:42:58,320
Speaker 1: to you. All right, So we're gonna do this panel

749
00:42:58,320 --> 00:43:02,279
Speaker 1: on Friday morning. It's at nine thirty at the Slow

750
00:43:02,320 --> 00:43:04,719
Speaker 1: and Sports ANALYTIS conference. You and I have been doing

751
00:43:04,719 --> 00:43:10,080
Speaker 1: it together for years. UM, what do you who do

752
00:43:10,160 --> 00:43:12,480
Speaker 1: you think will be the most interesting person on the

753
00:43:12,480 --> 00:43:17,920
Speaker 1: panel and why the panel is uh? You it's um

754
00:43:18,080 --> 00:43:22,120
Speaker 1: Daniel Wallack, who's a lawyer who's been studying what's happening

755
00:43:22,160 --> 00:43:26,920
Speaker 1: with UM, the case that's going through the Supreme Court. UH.

756
00:43:27,040 --> 00:43:30,960
Speaker 1: And the woman who runs sports radars domestic operations and

757
00:43:31,000 --> 00:43:34,600
Speaker 1: sports radars, you know, a data company that is the

758
00:43:34,600 --> 00:43:38,520
Speaker 1: official data provider for several leagues including the NFL, NBA,

759
00:43:38,560 --> 00:43:40,919
Speaker 1: and Major League Baseball, and then they sell that data

760
00:43:41,000 --> 00:43:43,680
Speaker 1: to people who wanted but also in Europe they have

761
00:43:43,719 --> 00:43:45,960
Speaker 1: a massive operation in which they are selling data to

762
00:43:46,719 --> 00:43:50,040
Speaker 1: bookmakers and part of their businesses about integrity. And then

763
00:43:50,080 --> 00:43:52,000
Speaker 1: Ted Olsen, who argued the case in front of the

764
00:43:52,000 --> 00:43:55,600
Speaker 1: Supreme Court on behalf of New Jersey, UH, handicapped for

765
00:43:55,680 --> 00:43:59,040
Speaker 1: me who you think will be the most interesting panelist

766
00:43:59,040 --> 00:44:02,120
Speaker 1: and why I can vote for myself. You are not

767
00:44:02,160 --> 00:44:03,920
Speaker 1: a lot of vote for yourself. Although I did have

768
00:44:03,960 --> 00:44:07,759
Speaker 1: a conversation UH with Ted Olson this morning and he

769
00:44:07,840 --> 00:44:11,080
Speaker 1: asked me like, how does this panel go? And I

770
00:44:11,160 --> 00:44:12,920
Speaker 1: was explaining to him, you know, some of the questions

771
00:44:12,920 --> 00:44:15,440
Speaker 1: I'll ask and the topics will discuss. And I was

772
00:44:15,440 --> 00:44:17,600
Speaker 1: giving a background and the people on the panel, and

773
00:44:17,640 --> 00:44:20,600
Speaker 1: I said, I promise you at some point on the panel,

774
00:44:21,120 --> 00:44:23,719
Speaker 1: Jeff will pose a challenging question that is going to

775
00:44:23,840 --> 00:44:26,560
Speaker 1: make everybody in the panel have to think quickly about

776
00:44:26,560 --> 00:44:29,640
Speaker 1: what their answer is going to be. So I did

777
00:44:29,719 --> 00:44:32,600
Speaker 1: give you, I did give you credit to ted Olsen

778
00:44:32,680 --> 00:44:36,120
Speaker 1: as being a guy who could essentially be the most

779
00:44:36,160 --> 00:44:39,320
Speaker 1: interesting or troublesome person on the panel. So you referenced

780
00:44:39,320 --> 00:44:42,800
Speaker 1: me both knowledgeable and troublesome, which is probably pretty accurate

781
00:44:43,600 --> 00:44:46,719
Speaker 1: in terms of like how the panel goes. You know,

782
00:44:47,560 --> 00:44:49,440
Speaker 1: I think it's gonna be really interesting just because the

783
00:44:49,920 --> 00:44:52,440
Speaker 1: those guys come from a very different background than I

784
00:44:52,520 --> 00:44:54,279
Speaker 1: come from, and and they're the ones that are sort

785
00:44:54,320 --> 00:44:57,440
Speaker 1: of like have pushed around a lot of the legal

786
00:44:58,160 --> 00:45:00,839
Speaker 1: rims cancers of this, I'm a lot of what I'm

787
00:45:00,960 --> 00:45:05,040
Speaker 1: sort of interested in is the commercial implications, i e. Like,

788
00:45:05,200 --> 00:45:09,680
Speaker 1: when this becomes um legal and you know, the the

789
00:45:09,800 --> 00:45:12,560
Speaker 1: NBA is out there talking about this this concept of

790
00:45:12,600 --> 00:45:15,480
Speaker 1: integrity see. So you know, the NBA has run out

791
00:45:15,480 --> 00:45:18,640
Speaker 1: this concept of the one percent integrity see and this

792
00:45:18,719 --> 00:45:23,200
Speaker 1: is either ignorant or disingenuous um. In both cases, I

793
00:45:23,239 --> 00:45:27,279
Speaker 1: think we're in trouble if it's if it's disingenuous, which

794
00:45:27,280 --> 00:45:29,040
Speaker 1: I hope it is. What I'm saying is that they

795
00:45:29,080 --> 00:45:31,920
Speaker 1: are actually you know, anchoring the rest of the world

796
00:45:32,120 --> 00:45:34,360
Speaker 1: in terms of thinking, like a one percent fee is

797
00:45:34,360 --> 00:45:36,919
Speaker 1: where they're starting, and they're gonna come back and ask

798
00:45:37,000 --> 00:45:39,880
Speaker 1: for like a point one percent fee, and everyone's say, oh,

799
00:45:39,880 --> 00:45:42,759
Speaker 1: well that's not reasonable. One percent. See, there's just no

800
00:45:42,840 --> 00:45:46,480
Speaker 1: way that sports book could be competitive or make money

801
00:45:46,880 --> 00:45:49,760
Speaker 1: in that case. And I've heard some like ridiculous things

802
00:45:49,800 --> 00:45:51,840
Speaker 1: like oh well maybe they should only do it off parlay.

803
00:45:52,000 --> 00:45:54,920
Speaker 1: It's just it's silly. Um. You know, I wrote an

804
00:45:55,000 --> 00:45:58,319
Speaker 1: article a while back for Chalk that basically talked about

805
00:45:58,360 --> 00:46:01,439
Speaker 1: this sort of top myths behind you know, what will

806
00:46:01,480 --> 00:46:03,680
Speaker 1: happen or the top sort of things that I predict

807
00:46:03,719 --> 00:46:07,759
Speaker 1: will happen when sports spending becomes legal. And I think

808
00:46:07,800 --> 00:46:09,480
Speaker 1: one of the things I said was just this idea

809
00:46:09,560 --> 00:46:11,640
Speaker 1: that the leagues, we're going to be greedy right away,

810
00:46:12,040 --> 00:46:14,000
Speaker 1: We're going to kind of ruin things, um and make

811
00:46:14,040 --> 00:46:16,520
Speaker 1: it really hard at least in the first path to

812
00:46:16,600 --> 00:46:19,880
Speaker 1: make this successful. UM. I mean, I believe they should

813
00:46:19,920 --> 00:46:21,719
Speaker 1: just sort of sit back and let this happen and

814
00:46:21,760 --> 00:46:25,600
Speaker 1: benefit from, you know, the increased engagement, increase marketing dollars,

815
00:46:25,640 --> 00:46:28,640
Speaker 1: and increase people spending money in this area. UM, And

816
00:46:28,680 --> 00:46:30,840
Speaker 1: maybe down the road there could be some other ancillary

817
00:46:30,880 --> 00:46:33,480
Speaker 1: revenue streams, but at least let this industry, you know,

818
00:46:33,560 --> 00:46:37,040
Speaker 1: become you know, a real a real industry, not just

819
00:46:37,080 --> 00:46:39,920
Speaker 1: like a Nathan struggling industry struggling to fight with the

820
00:46:39,960 --> 00:46:44,720
Speaker 1: illegal bookmakers. They are going to use some Trumpian negotiating tactics,

821
00:46:44,920 --> 00:46:49,759
Speaker 1: is what you're saying, Trumpian goodellion. I mean, I'm not

822
00:46:49,800 --> 00:46:52,359
Speaker 1: sure if there's a big difference at this point. This

823
00:46:52,440 --> 00:46:56,680
Speaker 1: is like a very um challenging thing to get right.

824
00:46:56,920 --> 00:46:59,360
Speaker 1: And I think again, like the thing that concerns me

825
00:46:59,440 --> 00:47:01,960
Speaker 1: the most is like this shows a general lack of

826
00:47:02,040 --> 00:47:05,920
Speaker 1: understanding of the sort of like industry itself and understanding

827
00:47:06,000 --> 00:47:10,440
Speaker 1: like what the you know, actual economics of bookmaking or

828
00:47:10,600 --> 00:47:15,200
Speaker 1: or you know, actually sports books are all right, Well,

829
00:47:16,000 --> 00:47:18,640
Speaker 1: we're gonna get into it on Friday. It's gonna be

830
00:47:18,640 --> 00:47:21,840
Speaker 1: a great panel. Yeah, it's on Friday afternoon, Friday morning.

831
00:47:21,840 --> 00:47:24,120
Speaker 1: I mean, it's gonna be a great panel. I have

832
00:47:24,160 --> 00:47:26,080
Speaker 1: no doubt you'll stir things up. I'm looking forward to

833
00:47:26,080 --> 00:47:28,680
Speaker 1: seeing you there. Um, I don't want us to get

834
00:47:28,680 --> 00:47:31,440
Speaker 1: off the phone without talking about BET the process, because

835
00:47:32,440 --> 00:47:34,320
Speaker 1: between you and Rufus it might be more i Q

836
00:47:34,520 --> 00:47:37,280
Speaker 1: points than is generated by the rest of the sports

837
00:47:37,320 --> 00:47:41,960
Speaker 1: betting community combined. And so please tell us that you're

838
00:47:41,960 --> 00:47:44,640
Speaker 1: not going to go on permanent hiatus now that the

839
00:47:44,760 --> 00:47:48,040
Speaker 1: NFL season is over and you have recapped the Super Bowl. No,

840
00:47:48,200 --> 00:47:50,200
Speaker 1: we actually are going to try to do one every

841
00:47:50,200 --> 00:47:53,239
Speaker 1: couple of weeks. We're gonna change the format a little bit. UM.

842
00:47:53,280 --> 00:47:56,960
Speaker 1: We actually launch or we released one of UM earlier

843
00:47:56,960 --> 00:47:59,359
Speaker 1: this week, and one of the topics that we talked

844
00:47:59,360 --> 00:48:02,400
Speaker 1: a lot about our sort of analytics around detecting fraud

845
00:48:02,840 --> 00:48:06,320
Speaker 1: UM or you know, match fixing and how you know,

846
00:48:06,400 --> 00:48:09,200
Speaker 1: RUFUS did some work early on that with La Vega

847
00:48:09,239 --> 00:48:12,680
Speaker 1: Sports Consultants. UM certainly talking about some of the work

848
00:48:12,719 --> 00:48:14,960
Speaker 1: that was done trying to figure out fraud and tennis

849
00:48:15,040 --> 00:48:17,719
Speaker 1: matches and things like that. This is certainly something that's

850
00:48:17,719 --> 00:48:21,480
Speaker 1: going to have to happen UM when sports betting becomes legal,

851
00:48:21,520 --> 00:48:23,600
Speaker 1: because there is gonna be a lot of scrutiny on

852
00:48:23,640 --> 00:48:26,080
Speaker 1: this and feeling like you you could have like we

853
00:48:26,120 --> 00:48:28,520
Speaker 1: have machine learning and all that kind of stuff UM

854
00:48:28,640 --> 00:48:30,879
Speaker 1: that allows us to identify this type of fraud will

855
00:48:30,920 --> 00:48:33,160
Speaker 1: be a really good marketing thing for people to talk about.

856
00:48:33,360 --> 00:48:35,440
Speaker 1: But now that's the process is gonna live like this

857
00:48:35,480 --> 00:48:37,960
Speaker 1: is a project that both of us are committed to. UM,

858
00:48:37,960 --> 00:48:40,160
Speaker 1: we'd like to have some guests on. We'd like to

859
00:48:40,640 --> 00:48:43,200
Speaker 1: also go outside of the world of sports and look

860
00:48:43,280 --> 00:48:46,160
Speaker 1: for people that can talk through this whole concept of

861
00:48:46,200 --> 00:48:49,720
Speaker 1: like process driven, UM, you know, process of being process

862
00:48:49,800 --> 00:48:54,120
Speaker 1: driven versus results driven. Okay, I'm looking forward to that.

863
00:48:54,320 --> 00:48:58,600
Speaker 1: I want that to succeed. People should go listen to it. Jeff,

864
00:48:58,600 --> 00:49:03,160
Speaker 1: I will see on Friday. Yeah, we'll see on Friday.

865
00:49:03,320 --> 00:49:05,400
Speaker 1: Looking forward to brother, I'll talk to you, super Bo