Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast.
Speaker 1: I'm Joe and I'm Tracy Halloway. Hey, Tracy, remember our
Speaker 1: episode from a few weeks ago with Phil helm Youth. Yes, yes,
Speaker 1: it seems like so long ago. Yeah, but I don't
Speaker 1: think it was. I think it was just like a
Speaker 1: month ago or something. Anyway, not to you know, pat
Speaker 1: ourselves on the shoulder, but you know, that was one
Speaker 1: of the most popular episode in Bloomberg podcast history. You're
Speaker 1: definitely patting yourself on the shoulder there, given that you're
Speaker 1: interested in poker, right, that's why we had him on. Yeah,
Speaker 1: but that's not why I'm not bringing that up to
Speaker 1: congratulate ourselves or anything like that. Um, there's actually a
Speaker 1: serious point behind that, behind its popularity, which is that
Speaker 1: a lot of the finance crowd that uh we ostensibly
Speaker 1: target and which we do target of Bloomberg is really
Speaker 1: into stuff about gambling and games and games of chance. Well,
Speaker 1: that's true, and I mean there's clearly an overlap there
Speaker 1: as well. Right, Yeah, there's definitely an overlap. Definitely lessons
Speaker 1: that could be applied from finance to gambling, Lessons from
Speaker 1: gambling and betting that could be applied back to finance,
Speaker 1: and I think from a personality standpoint, you get a
Speaker 1: lot of people obviously in the finance financial industry who
Speaker 1: just sort of also are very interested in the gambling side.
Speaker 1: And of course the history of finance and gambling are
Speaker 1: deeply intertwined. Are you telling me that we're going to
Speaker 1: talk about poker again? No, good news. We are not
Speaker 1: talking about poker on this episode, although actually I think
Speaker 1: maybe our guest likes poker, but I'm not sure. Um,
Speaker 1: but we are going to be talking about someone who
Speaker 1: has been active in both sides of the UH finance
Speaker 1: in gambling divide. Okay, Um, but gambling on what exactly
Speaker 1: this time? Gambling not on cards but on sports? Uh Joe, Joe.
Speaker 1: If it's not if it's not chess or poker, then
Speaker 1: it's sports. Yeah, it's sports. Um, so let's jump right
Speaker 1: into it. The guest on our episode today is UH
Speaker 1: Joe Peter. He was a trader at Lehman for many years,
Speaker 1: with involved in various things, including hedge funds. There he's
Speaker 1: at a hedge fund now. But in between those two things,
Speaker 1: he uh was also figured out a way to bet
Speaker 1: on baseball, and he wrote a book about betting on
Speaker 1: baseball called Trading Basses, So I think a perfect guest
Speaker 1: to talk about the two worlds. I'm going to caveat
Speaker 1: this with with my usual thing, which is I know
Speaker 1: nothing about baseball or sports betting, so I look forward
Speaker 1: to learn about it. Shoe. You know what, Tracy, it's
Speaker 1: never you know, you always say that, but it never
Speaker 1: proves to be a problem because you always ask fantastic questions,
Speaker 1: and so I'm confident that this discussion will be no different. Okay,
Speaker 1: that's very sweet. Let's let's have him on. Joe Peter,
Speaker 1: thank you very much for joining us. Joe and Tracy,
Speaker 1: it's a pleasure to be on. It's uh, you know,
Speaker 1: I'm on a lot of because of the topic of
Speaker 1: the book, I end up on a lot of you know,
Speaker 1: Vegas based podcast et cetera. And uh, it is a
Speaker 1: pleasure to be on a podcast that I know has
Speaker 1: such a high intellectual content. I will try my best
Speaker 1: to only lower it a little bit. No, no need,
Speaker 1: no need to uh flatter us, but we do appreciate
Speaker 1: was the would would you say that the intro is
Speaker 1: a fair characterization of your background from banking to betting
Speaker 1: and then now at a hedge fund. Absolutely the book
Speaker 1: and the book itself came about literally by accident. I
Speaker 1: was uh, still working on Wall Street in Lower Manhattan
Speaker 1: when I got run over by an ambulance in New
Speaker 1: York City. And while I was laid up and you know,
Speaker 1: wheelchair and I couldn't travel back to my home in
Speaker 1: San Francisco with my family. While I was laid up,
Speaker 1: I had this idea, uh to write a book that
Speaker 1: more or less um examined the critical reasoning overlap between
Speaker 1: asset management UM, the moneyballization of baseball UH and sports betting. UH.
Speaker 1: Those He was clearly a right what you know book
Speaker 1: for me, UM, but you definitely touched on those in
Speaker 1: the intro um. I can tell you when the book
Speaker 1: was up for was being passed around publishers houses. One
Speaker 1: of the editors that very much wanted to work on
Speaker 1: it was Phil Helmy was editor. He had written Play
Speaker 1: Poker Like the Pros. So yeah, there was definitely that
Speaker 1: overlap between the audiences. So before we get into the
Speaker 1: book itself, tell us what you were doing on Wall
Speaker 1: Street before you were hit by an ambulance and you said,
Speaker 1: you know the your you started focusing on whether some
Speaker 1: of these critical reasoning and data analytical tools could be
Speaker 1: applied to sports betting. I take it then, even before
Speaker 1: this endeavor, that this had already been an interest of yours. Yeah,
Speaker 1: I can tell you when it came up. I mean,
Speaker 1: certainly I had always been a baseball fan, and the
Speaker 1: book really is a lot about baseball. It isn't memoir,
Speaker 1: so it does really touch on, you know, sort of
Speaker 1: the role baseball has played in my family. My father
Speaker 1: was an immigrant, and one of the ways he was
Speaker 1: he wanted to adopt, you know, show his love for
Speaker 1: America because he was saddled with an Italian name or
Speaker 1: Minio that instantly announced him as, you know, an outsider. Um.
Speaker 1: One of the ways he wanted to show his love
Speaker 1: for America was he loved baseball, and he was going
Speaker 1: to become an English professor. That ultimately was you know,
Speaker 1: sort of his past. So baseball. I was always very
Speaker 1: interested in baseball. I had been introduced to Bill James
Speaker 1: and a lot of the saber metric theories in the
Speaker 1: nineties and early two thousand's, but it wasn't until I
Speaker 1: was with Lehman and moved from the cell side to
Speaker 1: the by side, which of course your audience understands. I
Speaker 1: I had moved to San Francisco to help launch a
Speaker 1: lehmand Uh funded hedge fund, and I was challenged with
Speaker 1: as running the trading desk there. What I found was
Speaker 1: I was working with analysts and portfolio managers who had
Speaker 1: different skills, and I was really sort of challenged with,
Speaker 1: how do we only have them do what they're good with?
Speaker 1: And and this is one of I know, one of
Speaker 1: your other guests. I know, Michael Mobison. I know you're
Speaker 1: a fan of his work, and you've had him on
Speaker 1: and this is something he talks about a lot too,
Speaker 1: and and and we've talked since, you know, we've read
Speaker 1: each other's books. And my goal was, how do I
Speaker 1: at these guys to only do what they're good at,
Speaker 1: and you know, not degrade the value that they that
Speaker 1: they bring to the table as either analysts or portfolio
Speaker 1: managers by doing something they're not good at. And I
Speaker 1: looked to and I knew to sort of convince them
Speaker 1: or try to change that behavior, it was going to
Speaker 1: have to be data driven. So I looked to the
Speaker 1: lessons of baseball. I'm like, well, baseball sta resolve this
Speaker 1: UM and so I started using that and so, and
Speaker 1: they were very crude tools and and but it was
Speaker 1: really trying to identify, you know, not results, but skill sets.
Speaker 1: And what really struck me, especially then after I got injured,
Speaker 1: when I started thinking about it, is you know, money
Speaker 1: Ball was such a huge hit as a as a
Speaker 1: book and as a theme, and it was really it
Speaker 1: was embraced by the business world, and I'm I was
Speaker 1: thinking to myself, the whole industry of Major League Baseball
Speaker 1: is worth you know, maybe thirty billion dollars. You know,
Speaker 1: it's probably an average of a billion dollars a team.
Speaker 1: There are single finance ancient institution is worth more than that.
Speaker 1: But why is baseball so much better at using its
Speaker 1: data to identify skills and not luck, you know, than
Speaker 1: than the financial industry is when there's so much more
Speaker 1: at stake. So that was sort of an underlying theme
Speaker 1: of the book. So, Joe, can we back up for
Speaker 1: a second, because whenever people talk about sports analytics, they
Speaker 1: always eventually start talking about baseball and um, moneyball and
Speaker 1: things like that. What is it about baseball specifically that
Speaker 1: seems to lend itself to analyzing facts and figures? And
Speaker 1: numbers and Tracy, that's a great question. There's there's really
Speaker 1: two parts of it. One, it's data rich. The history
Speaker 1: is data rich. It has been results in baseball beyond
Speaker 1: just the final score, but results of each play or
Speaker 1: really each pick, have been recorded for more than a
Speaker 1: hundred years. UM. So you've got this data rich environment.
Speaker 1: But most importantly, when you compare baseball to other sports,
Speaker 1: is ace ball is really a series of one on
Speaker 1: one matchup sixty or seventy one on one matchups a
Speaker 1: game disguised as a team sport. So I can, with
Speaker 1: very high confidence say something like you know, um, And
Speaker 1: I use this example in my book Randy Johnson, who
Speaker 1: was a prolific strikeout picture UM, played in both leagues,
Speaker 1: the American and National League. He played for five or
Speaker 1: six different teams, and he won Cy Young's I think
Speaker 1: for three different teams, and he pitched the different you
Speaker 1: know catchers. Despite that all, despite all those other variables,
Speaker 1: he struck out roughly one third of the batters he
Speaker 1: faced every year. And you could count on that, despite
Speaker 1: all those changing variables. There is no way we could
Speaker 1: look at Tom Brady and say so when we evaluate
Speaker 1: Tom Brady, we have to say things like Tom Brady
Speaker 1: running a Bill Belicheck offense, um play action, faking to
Speaker 1: these running backs, throwing who these receivers will complete six
Speaker 1: his passes, But you could not move him to another
Speaker 1: team and you know, and model his performance exactly the
Speaker 1: same way, because it's so much more there's so much
Speaker 1: more interdependence baseball, And that's why I found it the
Speaker 1: best to create models for betting. Baseball is very pure
Speaker 1: and that it really is one on one matchups, like
Speaker 1: I say, disguised as a team game. That is that
Speaker 1: that's a great explanation of why sports analytics uh so.
Speaker 1: So much of it comes back to baseball. So I
Speaker 1: read Moneyball, and you know, I want to get into
Speaker 1: where you get your edge because I read Moneyball and
Speaker 1: I you know, the whole ideas I took away, which
Speaker 1: is that a lot of these uh scouts of players
Speaker 1: and general managers had some sort of biases about what
Speaker 1: made a good player or not, and maybe they just
Speaker 1: had some rules of thumb and heuristics to look at
Speaker 1: a player and evaluate them, and they really weren't data driven.
Speaker 1: That when the nerds so to speak, took over and
Speaker 1: really started looking at the data that they found that
Speaker 1: these old some of this old baseball wisdom wasn't really
Speaker 1: matched by results. So there was a clear gap between
Speaker 1: what the data said and what the received wisdom said. Now,
Speaker 1: taking this over to the world of betting and going
Speaker 1: to a casino and placing a bet, and obviously the
Speaker 1: house and the casino, it's sort of data driven. Well,
Speaker 1: where where does the edge come from? Specifically when you
Speaker 1: sort of poured this over to the world of gambling,
Speaker 1: it's an evolving edged and it's certainly, uh has gotten
Speaker 1: smaller or it changes. It evolved, certainly, you know, just
Speaker 1: using sort of the money ball as an example. Back
Speaker 1: then in the book, on base percentage was undervalued, right, Uh,
Speaker 1: So there was value to picking up players that got
Speaker 1: on base a lot, even if they didn't have the
Speaker 1: other counting stats like home runs and urbis that were
Speaker 1: deemed important back then. Uh. And of course that's shifted.
Speaker 1: There is no across major league baseball now there is
Speaker 1: no um you know, there's on base percentage is not undervalued. However,
Speaker 1: defense may have been undervalued five or six years ago. Um.
Speaker 1: So there's always a pendulum and any time, and of
Speaker 1: course sports betting or or there's a price for each
Speaker 1: team every night, you know, and as you know from
Speaker 1: financial markets, while prices do incorporate a lot of known information,
Speaker 1: they also incorporate emotion um. And you can see that
Speaker 1: there is always when it comes to say, postseason batting
Speaker 1: or futures betting, there is always a I almost call
Speaker 1: it a A A and Er Mays type premium on
Speaker 1: the Yankees or the Cups. You know, there's a retail
Speaker 1: markup um. So you can find small edges there. But
Speaker 1: specifically when you are looking at single games um. As
Speaker 1: I talked about in the book five years ago there
Speaker 1: um and this really goes back to sort of what
Speaker 1: what Mobison says. There is still an element of looking
Speaker 1: at past results when pricing the current market. Uh and pictures,
Speaker 1: especially five years ago. Um, we're Verrey were subject to.
Speaker 1: You'd look at the pictures e r A, which is
Speaker 1: how many runs he gives up over a game, and
Speaker 1: that you know, his past d r A or his
Speaker 1: current e r A for the season had a lot
Speaker 1: to do with how he might be priced in July.
Speaker 1: But if you dig deeper, there were better ways to
Speaker 1: look at what his future e r A should be.
Speaker 1: And you do that by looking at his skill sets,
Speaker 1: not his results, because e r A is dependent on
Speaker 1: the defense behind him. UM, it's dependent on the luck
Speaker 1: of sequencing, which I call cluster luck UM in terms
Speaker 1: of of what what tends to be much more sticky
Speaker 1: is the skill set of what percentage of batters does
Speaker 1: he strike out, what percentage of batters does he walk?
Speaker 1: What percentage of of hit balls are groundballs? And at
Speaker 1: can't And you will find some pictures that you will
Speaker 1: look at those inputs and you'll say, oh, you know,
Speaker 1: a regression analysis tells me he should have an e
Speaker 1: r A of upper threes instead of upper twos. So
Speaker 1: you know, he might be overvalued on a single game.
Speaker 1: And the important thing with any UM, with any endeavor
Speaker 1: of of capital, of course, is you want to find
Speaker 1: a small egge and then you want to put a
Speaker 1: small amount of money on it. You know, it's that
Speaker 1: old idea that you never want to risk tomorrow's egge
Speaker 1: by overallocating today UM. And that's you know, that's just
Speaker 1: and that that's again a lot of the overlap. I
Speaker 1: find that the world of gamblers UM and even poker players,
Speaker 1: they they tend to overestimate their edge and I tried
Speaker 1: to talk about that in the book too, that Hey,
Speaker 1: one thing that's the financial industry is really good at.
Speaker 1: One thing hedge fund pros are really good at is
Speaker 1: understanding survival. Um. And then of course I drew the
Speaker 1: comparison to Dick Fold, who did not understand that, who
Speaker 1: had a wonderful franchise and risked it all, um, you know,
Speaker 1: by over betting on real estate. So, Joe, the subtitle
Speaker 1: of your book is how a Wall Street trader made
Speaker 1: a fortune betting on baseball. Walk us through exactly how
Speaker 1: you made your bets and how much you actually made.
Speaker 1: I will I will address that, but I will tell
Speaker 1: you we can get a little inside sort of publishing here. Um.
Speaker 1: And and you may know this, um, but don't tell
Speaker 1: me the exaggerated the title refus y right exactly. And
Speaker 1: you know this even from you know publication that you
Speaker 1: know bylines. The person who writes an article doesn't write
Speaker 1: the headline right, and they get trapped by the headlines sometimes. Well,
Speaker 1: I certainly learned from the publishing industry that the author
Speaker 1: owns everything between the covers, but the cover itself, including
Speaker 1: the title, belongs to the to the publisher. Now, fortunately
Speaker 1: the hard back cover. That was my title. Um. The
Speaker 1: title of the book was Irustrating basis a story about
Speaker 1: Wall Street gambling in baseball, not necessarily in that order.
Speaker 1: When these when the UH paperback rights were sold, I
Speaker 1: think to random House, they you know, they renamed it
Speaker 1: or they put the subtitle there. Um. I never wanted
Speaker 1: the focus to be on UM, you know that sort
Speaker 1: of thing like Calvin how somebody made a fortune. Because
Speaker 1: for one thing, a fortune is is UH is relative? Right? Um.
Speaker 1: And for another, one of the things I kind of
Speaker 1: talked about in the book is that professional UM investors
Speaker 1: they never talk about like you will never hear a
Speaker 1: hedge fund say, hey, we were up two million dollars yesterday.
Speaker 1: You will hear them say we were forty basis points right.
Speaker 1: And that is really what I tried to get across
Speaker 1: in the book is it doesn't matter how little or
Speaker 1: how much money you have, the the idea of capital
Speaker 1: allocation is the same for everyone. Well, Joe, let me
Speaker 1: let me rephrase my question then, because this is actually
Speaker 1: what I wanted to get into. So you're betting on
Speaker 1: a sport, you're betting on a particular outcome. Um, it
Speaker 1: seems to me like that outcome is probably going to be,
Speaker 1: you know, either win or lose. So how do you
Speaker 1: how do you risk adjust whatever return that you're actually
Speaker 1: making from sports spending. Fantastic question because I really did
Speaker 1: dive into this um. And to to to the story
Speaker 1: about you know, you know a fortune I did for
Speaker 1: the for the apologue of the book, I did raise
Speaker 1: a fund. It was a million dollar fund, and I
Speaker 1: went to Vegas for the summer of twelve and actually
Speaker 1: ran a baseball betting fund, you know, and legally in Vegas.
Speaker 1: And that you're the The idea of how much to
Speaker 1: bet on each game is very important because each game's
Speaker 1: binary event. It's it's you're either gonna you know, it's
Speaker 1: not double because the odds of the game might be,
Speaker 1: say two to one, but you're either going to lose
Speaker 1: everything you bet or you're going to you know, win
Speaker 1: you know, essentially what you bet, or a little more,
Speaker 1: maybe a little less, depending on the odds. So the
Speaker 1: idea was what I had. The idea was, Okay, let's
Speaker 1: look back to the financial industry. We know baseball has
Speaker 1: this great um, this idea of the replacement player, and
Speaker 1: it's it's kind of an amorphous concept. But um, and
Speaker 1: you hear that. You know, every player is essentially judged
Speaker 1: in the in the moneyball world by how much how
Speaker 1: many wins they create over the replacement player. And Tracy, wait,
Speaker 1: remember Tracy when we were when we were chatting, and
Speaker 1: Tracy's like, what's one thing I should know about baseball?
Speaker 1: And our colleague said, the one thing you should know
Speaker 1: is war, and uh, that was here you go wins
Speaker 1: above replacement. All right, So go on, I just wanted
Speaker 1: to sorry and point out that this was something that
Speaker 1: came up in Tracy's prep for the episode. Well, if
Speaker 1: you think about the and the the idea is the
Speaker 1: replacement player is readily available to anyone. You could pick
Speaker 1: up this player from either the minor leagues or on waivers,
Speaker 1: and any team has access to them for essentially a
Speaker 1: minimal contract. There's actually that concept actually applies perfectly in
Speaker 1: the financial world, and that is, you know, the sp
Speaker 1: that's the replaced player for every investor. That's the passive
Speaker 1: alternative that is readily available for any investor. So if
Speaker 1: you're gonna pay up um the you know, that's if
Speaker 1: you're gonna pay up for active management, right, they have
Speaker 1: to beat this passive benchmark. Well, the nice thing about
Speaker 1: the passive benchmark as well as it works as a
Speaker 1: investment tool in the sense that I know the standard
Speaker 1: deviation of returns of the SNP five dum and I
Speaker 1: know the expected return, so that if I'm really running
Speaker 1: a baseball fund that is truly in an alternative asset, well,
Speaker 1: I should have daily returns that either have this you know,
Speaker 1: hopefully have a higher return than the SNP five hundred,
Speaker 1: but the same amount of volatility. So to get back
Speaker 1: to your question, Tracy, that was really how I played
Speaker 1: with how much should I be betting on single games
Speaker 1: was to really find that amount that gave me returns
Speaker 1: without giving me excess volatility. And what it did turn
Speaker 1: out was, specifically, if you looked at a eight of
Speaker 1: games in a day, there's about fifteen games every day
Speaker 1: in Major League Baseball. If I identified five or six
Speaker 1: to bet on, it was rare that when I would
Speaker 1: even put one percent of the capital on one game,
Speaker 1: usually the bets were somewhere between a third of a
Speaker 1: percent to maybe three quarters of a percent based on
Speaker 1: how much I thought there was a perceived edge. So
Speaker 1: really it was almost like I always kind of likened
Speaker 1: it to owning a roulette wheel. Right, If you own
Speaker 1: a roulette wheel, if you're the house, you have a
Speaker 1: small egg, and you just want to spin that wheel
Speaker 1: as many times as possible in a day. And you
Speaker 1: don't want people place a million dollar bets, right, you
Speaker 1: want them place in a whole bunch of smaller bets,
Speaker 1: because that's how you know, that's where you have your egge,
Speaker 1: and that's where you extract your your games in the end,
Speaker 1: And that was the I. I applied that same capital
Speaker 1: application theory to a slate of baseball games each day.
Speaker 1: All right, Well, let's just talk results for a second, though.
Speaker 1: You you raised a million dollars for your fund. First
Speaker 1: of all, A, uh, who how did you raise the
Speaker 1: million dollars? And B how'd you do that? Well? The
Speaker 1: the the the A is fascinating. So I handed in
Speaker 1: my book. I handed in the manuscript for the book
Speaker 1: UM after the Season, which is what the bulk of
Speaker 1: the book is really about. It's sort of my my
Speaker 1: like I say, my memoir of being injured during UH.
Speaker 1: And I handed it in and the the publisher, Penguin
Speaker 1: UM came back to me. Once I handed the manuscript
Speaker 1: in March, and they they said, we love it, which
Speaker 1: was certainly satisfying to me because I was, you know,
Speaker 1: an unknown author with no works behind me. Uh. And
Speaker 1: they said, we're going to publish it in March, but
Speaker 1: we're gonna need something for and there. And they said,
Speaker 1: if we gave you the marketing budget for the book,
Speaker 1: would you go to Vegas and that on baseball games force.
Speaker 1: We kind of think that would be a cool pitch.
Speaker 1: And I of course, of course said absolutely that that
Speaker 1: really does sound like fun. And I knew if I
Speaker 1: did that that I really I had better talk to
Speaker 1: some of my degenerate family and friends too and see
Speaker 1: if they wanted to be involved as well. And that's
Speaker 1: how I raised a million dollars. It was essentially family
Speaker 1: and friends, UM and the publisher. And that was the
Speaker 1: the apologue of the book. UM. In In that year,
Speaker 1: I was up for the year in terms of of
Speaker 1: of the fund um, which is about what I think
Speaker 1: my egg would be. UM. The eleven season, which was
Speaker 1: just me and I was up. And that's not it
Speaker 1: was not repeatable. Just a lot went right. UM. You know,
Speaker 1: that's the old skill versu luck. The actual egg was
Speaker 1: much much slower, and I knew that, but it was
Speaker 1: a fun ride. UM, and I view Twelves as being
Speaker 1: much more indicative of what you could expect from a
Speaker 1: data driven model. You know that really tries to conquer
Speaker 1: baseball betting. So is sports betting? Is that a legitimate
Speaker 1: replacement for investing or trading more traditional financial assets? And
Speaker 1: if it is, then then what is actually the difference
Speaker 1: between trading and betting? Great to uh to quote George
Speaker 1: Washington and Hamilton's the musical, not yet because it's uh.
Speaker 1: The epilogue of the book, I really tried to write
Speaker 1: almost as a business school case study in this in
Speaker 1: sort of looking at it is is there actually a
Speaker 1: market as an alternative asset? Because one thing we do
Speaker 1: know is if you're betting on sports, or if you're
Speaker 1: investing with someone who's running a fund, we do know
Speaker 1: that it's not correlated to you know, stocks and bots.
Speaker 1: It is truly an uncorrelated asset, So it meets that requirement.
Speaker 1: What it doesn't meet, however, is there's not enough liquidity. Um.
Speaker 1: I estimated that I could have run two maybe three
Speaker 1: million dollar fund and that was it, because past that
Speaker 1: I couldn't have scaled up the bets. There just wasn't
Speaker 1: enough liquidity in the market to be making bets. But
Speaker 1: most importantly, and while I tried to give well, I
Speaker 1: tried to point out the ways that the financial industry
Speaker 1: can learn from baseball. In the book, I also tried
Speaker 1: to point out that Vegas and specifically the the industry
Speaker 1: of running sports markets could really learn a lot from
Speaker 1: Wall Street. And unfortunately they're not there yet. It is
Speaker 1: still an antagonistic relationship between the sports book and the
Speaker 1: better which is the way when I entered the Nasdaq market,
Speaker 1: That's the way NASTAC trading was. It was fragmented. We
Speaker 1: were suspicious of every customer who walked through the door,
Speaker 1: and every trade was us first them. Once NASDACK evolved
Speaker 1: in the more of an agency market, it became the
Speaker 1: business of asset collection and bringing together buyers and sellers.
Speaker 1: And what it's what poker does poker you know, as
Speaker 1: as you may know in the in in poker, the
Speaker 1: house doesn't bet against the players. The how simply collects
Speaker 1: rents by getting the players into the same room. And
Speaker 1: if sport betting ever evolved into that, it could be
Speaker 1: a huge market and it would turn into an asset
Speaker 1: gathering market as opposed to you know, sort of what
Speaker 1: it is now, which is us first then, um, I
Speaker 1: see that I cannot get some of the people in
Speaker 1: the industry to to see that, and I have tried
Speaker 1: Joe Peter, he's the author of Trading Basses and he's
Speaker 1: currently at Kingsford Capital. That was a great discussion. Loved
Speaker 1: that last bit about market structure that lesson there. Really
Speaker 1: appreciate you coming on. Odd Loves Joe and Tracy. Thank
Speaker 1: you so much for having me so Tracy, another episode
Speaker 1: about gambling and sports which you claim to not know
Speaker 1: anything about, but you didn't know stuff and you ask
Speaker 1: great questions. Well, I don't know that much about it,
Speaker 1: but what fascinates me is it's really that question of,
Speaker 1: you know, like what makes a market and what's the
Speaker 1: difference between betting and trading and investing and where's the overlap?
Speaker 1: And I thought Joe did a really really good job
Speaker 1: of identifying that. Yeah, I I did too. I mean,
Speaker 1: I really liked his answer about sort of how to
Speaker 1: a where he found the statistical edge from sports betting
Speaker 1: and you know, basically there's still a lot of emotion
Speaker 1: home team biases. Uh, streak biases, things like that that
Speaker 1: you can spot in the odds of the game. And
Speaker 1: then that last uh answer sort of about the difference
Speaker 1: between the sports book at a casino, which he described
Speaker 1: as antagonistic versus a poker room where they just want to,
Speaker 1: you know, sort of get liquidity and bring people together.
Speaker 1: Sort of very interesting lesson back to think about how uh,
Speaker 1: sort of more traditional financial markets are structured. Yeah, for sure.
Speaker 1: So Joe, when are we going to go on our
Speaker 1: crayfish eating poker playing baseball watching tour of the US.
Speaker 1: We gotta do that very soon. But until then, this
Speaker 1: has been another episode of the Odd Lots Podcast. I'm Joe,
Speaker 1: wi Isn't thal. You could follow me on Twitter at
Speaker 1: the Stalwart, and I'm Tracy Alloway. I'm on Twitter at
Speaker 1: Tracy Alloway. And you can find Joe on Twitter at
Speaker 1: at Magic rat SF and our producer Sarah Patterson on
Speaker 1: Twitter at Sarah pat With two Teas. Thanks for listening.