WEBVTT - Episode 13: How a Professor Won Gambling on an Obscure Sport

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<v Speaker 1>Hello, and welcome to another episode of Odd Lots. I'm

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<v Speaker 1>Joe Wisenthal, Managing editor of Bloomberg Markets, and I'm Tracy Alloway,

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<v Speaker 1>Executive editor of Bloomberg Markets. Hey, Tracy, do you know

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<v Speaker 1>what the fastest sport in the world is? Uh? Sailing?

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<v Speaker 1>Race car driving? Uh, those are pretty good guesses, but no,

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<v Speaker 1>Actually the answer is high lie high lie highlight. Highlight

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<v Speaker 1>high Okay, highlight. It's actually originally a game that originated

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<v Speaker 1>in the Spanish Basque country. It's kind of like racquetball,

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<v Speaker 1>except the players play on this gigantic court. The ball

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<v Speaker 1>goes nearly two miles the rackets of these gigantic crald

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<v Speaker 1>things that the players wear over their hands. The ball

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<v Speaker 1>is hard as a rock, and oh yeah, if it

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<v Speaker 1>were to hit you in the head, the ball could

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<v Speaker 1>kill you. This sounds like a made up sport. Um,

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<v Speaker 1>why are we talking about this? It's a good question because,

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<v Speaker 1>in addition to how crazy and intense the game is,

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<v Speaker 1>you know, as I said, two hundred mile an hour

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<v Speaker 1>ball is hard as a golf ball, potentially deadly. People

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<v Speaker 1>actually gamble on highlight, kind of like horse racing. Uh.

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<v Speaker 1>The people a bunch of players play in a tournament

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<v Speaker 1>of sorts and then people bet on whether the different

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<v Speaker 1>players will win place or shows. So there's this big

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<v Speaker 1>gambling element to it that's really interesting. Our guest today

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<v Speaker 1>that we'll be talking to is Steven Skina. He's a

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<v Speaker 1>professor at stony Brook University and he wrote a book

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<v Speaker 1>all about gambling on Highlight Online and how he cracked

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<v Speaker 1>the system and he made a bunch of money. He

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<v Speaker 1>cracked the system, so he beat the house. Yeah, it's

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<v Speaker 1>basically impossible theoretically, you know, gambling, the house is always

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<v Speaker 1>supposed to lose. But in these games we're sort of

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<v Speaker 1>betting against other people and the crowd sets the odds.

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<v Speaker 1>It's actually possible. And not only did Steven beat the

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<v Speaker 1>system and make a bunch of money, there's some interesting

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<v Speaker 1>lessons in terms of eating the stock market and odds

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<v Speaker 1>games in general. Okay, so I'm excited because not only

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<v Speaker 1>am I about to learn about a sport which I've

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<v Speaker 1>never heard of before, but I'm also interested in making money.

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<v Speaker 1>So this sounds too all right, I think you've seen

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<v Speaker 1>it all well, think fast, experience the wall to wall

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<v Speaker 1>action and NonStop excitement that is Miamie Highlive see world

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<v Speaker 1>class athletes climbed the walls to catch a rock hard

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<v Speaker 1>ball flying. It speeds over a hundreds of miles an hour.

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<v Speaker 1>Think excitement, Think Miami, Higlan, I think fast Steven, thank

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<v Speaker 1>you very much for joining us. Thank you, it's nice

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<v Speaker 1>to be here. Tracy has never heard of highlight before.

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<v Speaker 1>What is highlight and how did you get interested in? So?

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<v Speaker 1>Highlight is as as you said, a besque game. Originally,

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<v Speaker 1>it's sort of like a variation on handball. Um. The

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<v Speaker 1>reason people are exposed to it in the United States

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<v Speaker 1>is typically because in Florida it's a betting venue. Um.

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<v Speaker 1>There are these stadiums called front Times in Miami and

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<v Speaker 1>Dania near Fort Lauderdale where you can watch mostly basque

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<v Speaker 1>players play the sport. And that's actually exactly how I

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<v Speaker 1>discovered it. My family used to go down to Florida,

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<v Speaker 1>my North Miami beach every winter for a couple of weeks,

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<v Speaker 1>and we went occasionally to UH the Dania Highlight front Time.

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<v Speaker 1>That was exactly the way that we got involved. You know,

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<v Speaker 1>our family would every year take its vacation visiting the

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<v Speaker 1>relatives in Florida. We would drive down and we would

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<v Speaker 1>UH one night go to the Highlight front time. Is

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<v Speaker 1>it fun to watch these games? It sounds intense from

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<v Speaker 1>Joe's description, it's I think it's incredibly exciting. I mean,

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<v Speaker 1>first of all, it's fun to watch them make these

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<v Speaker 1>plays because the ball is moving very fast, they have

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<v Speaker 1>to make, you know, great catches and very difficult throws.

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<v Speaker 1>But also the scoring system involved in Highlight has some

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<v Speaker 1>infra sting mathematics instructor that makes it kind of fun

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<v Speaker 1>to watch. So depending up on how you bet and um,

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<v Speaker 1>the chances of winning change extremely rapidly with every point,

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<v Speaker 1>and so it's it's it's very exciting because the situation

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<v Speaker 1>is always changing. Yeah, so I said it was kind

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<v Speaker 1>of like horse race betting and that you could bet

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<v Speaker 1>on wind, place their show. But in a way it's

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<v Speaker 1>a little more complicated. Why don't you just sort of

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<v Speaker 1>describe how the similarities real quickly and the differences. So

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<v Speaker 1>it is like horse race betting and that you can

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<v Speaker 1>bet on win, place and show, and that's certainly what um,

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<v Speaker 1>what we're gonna be doing. The difference is the scoring system. UM. Basically,

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<v Speaker 1>in Highlight the winner is the first one to get

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<v Speaker 1>the seven points, and they have eight teams that are

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<v Speaker 1>playing in any given match, but because of the size

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<v Speaker 1>of the court, only two teams can play at once.

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<v Speaker 1>The teams wait in a line. Their their uniform numbers

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<v Speaker 1>are one through weight, corresponding to where they start in line.

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<v Speaker 1>And originally the first two players play each other. Then

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<v Speaker 1>the winner keeps playing, gets a point and keeps playing.

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<v Speaker 1>The loser goes to the end of the line, and

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<v Speaker 1>they keep playing until you get to seven points. And

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<v Speaker 1>if you think about that kind of a scoring system,

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<v Speaker 1>it gives an advantage to the people who start early

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<v Speaker 1>because um, obviously they get first cracks at getting points,

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<v Speaker 1>and even if they lose, they are more likely to

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<v Speaker 1>be they're gonna be the first player to come up

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<v Speaker 1>for a second time. So they make the scorches them

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<v Speaker 1>even more complicated. Whereafter every trip through the queue once

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<v Speaker 1>meaning every player has played its first point, now every

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<v Speaker 1>subsequent point counts for two. And this makes for a

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<v Speaker 1>very complicated scoring system. That means that even if you're

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<v Speaker 1>very very close to winning, if you suddenly lose that point,

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<v Speaker 1>you go to the end of the line and you

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<v Speaker 1>might not get another chance to play again. And the

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<v Speaker 1>betting system, it's a paramutual odds what exactly to that man.

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<v Speaker 1>So paramutual means that you're betting against the other players,

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<v Speaker 1>and it's not me betting against the house. If I

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<v Speaker 1>was betting against the house, the odds of me winning

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<v Speaker 1>are very small. That's why the house is usually big.

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<v Speaker 1>But in a para mutual system, what happens is the money,

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<v Speaker 1>all the money that is bet in the accompetition is

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<v Speaker 1>thrown into a pool. The house skims off a fee

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<v Speaker 1>something and the rest is divided among the winners. So

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<v Speaker 1>in order to you know, to have a successful betting system,

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<v Speaker 1>you have to be better than the other players. So

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<v Speaker 1>the other betters like basically there are a bunch of

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<v Speaker 1>dumb people like me and my family who used to

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<v Speaker 1>go there from time to time and bet and we

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<v Speaker 1>didn't know anything. And so theoretically, if you're really smart

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<v Speaker 1>and studied, we're the fish that you could take advantag.

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<v Speaker 1>That was the attraction. I mean again, it's it's a

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<v Speaker 1>very exciting sport. It's probably a hard sport to know

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<v Speaker 1>the players very well. You know, I don't think most fans.

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<v Speaker 1>Most fans are not that intense, but they go once

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<v Speaker 1>a year, twice a year. And before we get to

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<v Speaker 1>your sort of rigorous approach. A story in your book.

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<v Speaker 1>You won the first ever bet you placed on Highlights, right, So,

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<v Speaker 1>so the reason we got really hooked on this was

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<v Speaker 1>that when and our parents drove down the floor and

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<v Speaker 1>let us go to highlight one night, they also let

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<v Speaker 1>us make one bet. They gave us two dollars and

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<v Speaker 1>they said, you make one bet, and we followed the

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<v Speaker 1>bet that was listed in the local tow sheet, knowing nothing,

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<v Speaker 1>and astonishingly, it was a trifecta combination of first place

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<v Speaker 1>and show that astonishingly one. And so we won, you know,

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<v Speaker 1>a hundred four dollars. And this was an amazing amount

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<v Speaker 1>of money to a bunch of kids back in the seventies.

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<v Speaker 1>And uh, that's what I was, probably about twelve or

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<v Speaker 1>so at the time. Turning two hundred dollars into a

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<v Speaker 1>hundreds exciting any time, but but when you're a kid

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<v Speaker 1>in the seventies, it must be absolutely Uh. I could

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<v Speaker 1>see how you would then get hooked for life on

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<v Speaker 1>the game. Um, all right, let's fast forward a little bit,

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<v Speaker 1>and so you're a professor at a Stony Brook. Talk

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<v Speaker 1>us about how you started on your path to systematizing

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<v Speaker 1>a gambling system. For highlight and what you did. So

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<v Speaker 1>when you look at the scoring system again, a highlight

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<v Speaker 1>game is played in discreete points player one place player two.

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<v Speaker 1>One of them wins, the other goes to the end

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<v Speaker 1>of the line. You could imagine simulating the result of

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<v Speaker 1>a highlight match by flipping a coin for every particular

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<v Speaker 1>point player one. If player one is maybe better than

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<v Speaker 1>player two, maybe you'd say it as a six chance

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<v Speaker 1>of winning the first point. And if you could figure

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<v Speaker 1>out the odds that one player is going that every

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<v Speaker 1>player has against every other player in that they might

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<v Speaker 1>encounter in a match, you can build a simulation to

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<v Speaker 1>use random numbers to play through and simulate each match.

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<v Speaker 1>So is it like it sounds like a series of

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<v Speaker 1>tree charts almost right? You assigned probabilities for each outcome

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<v Speaker 1>and then you have them sort of branching across all

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<v Speaker 1>the possible outcomes. Right, so you you could view this

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<v Speaker 1>as a tree process. It's a it is a branching process.

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<v Speaker 1>It's a tree process where at every point in the

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<v Speaker 1>tree is every note in the tree is basically two

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<v Speaker 1>players playing each other with a certain score and a

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<v Speaker 1>certain status of players in the queue to come. Then

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<v Speaker 1>depending upon who wins it, you go to a different

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<v Speaker 1>state in the process, and this process ends when you

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<v Speaker 1>have identified who comes in for a second and third.

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<v Speaker 1>So this part sounds is where it seems to really

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<v Speaker 1>diverge from say horse racing, where you just have one event,

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<v Speaker 1>there's one, two, and three, not really all these different

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<v Speaker 1>permutations and sequences. So horse racing is not at the

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<v Speaker 1>scret event kind of a game. This is maybe a

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<v Speaker 1>little bit more akin to I would say, um baseball

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<v Speaker 1>than football. Baseball is a bunch of the scret events.

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<v Speaker 1>There's pitches and things happen. In basketball, things are very continuous.

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<v Speaker 1>In horse racing, things seem continue with So when you

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<v Speaker 1>started developing the system, when are we talking about how

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<v Speaker 1>long ago is this? This is something we started in

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<v Speaker 1>the I would say early nineties. If you have to

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<v Speaker 1>get back there, it's probably the story about in the

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<v Speaker 1>early nineties. Okay, so you broke highlight down into this

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<v Speaker 1>series of discrete events, then what's next in terms of

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<v Speaker 1>the creation of your gambling system. So again, once you

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<v Speaker 1>have the ability to view this as this tree process

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<v Speaker 1>or as this um you can simulate one game, you

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<v Speaker 1>can now simulate a million games or or or more

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<v Speaker 1>and see what this probability distribution is of outcomes, and

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<v Speaker 1>you can start to look at for every combination of first, second,

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<v Speaker 1>and third, how often did it come in? And from

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<v Speaker 1>this that gives you some insight into what things you

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<v Speaker 1>should bet on. But things get a little bit more complicated. First,

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<v Speaker 1>you have to accurately model how good the players are,

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<v Speaker 1>so you have a good guess as to how often

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<v Speaker 1>player one is gonna be player two. And more that

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<v Speaker 1>you have to get a build a model of how

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<v Speaker 1>the public is going to bet um. Again, it's a

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<v Speaker 1>paramutual system. I'm betting against the public. If everyone else

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<v Speaker 1>in the public was someone who programmed the computer and

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<v Speaker 1>did the analysis the way I did, I would have

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<v Speaker 1>no advantage. Are you looking for almost pricing descrepants? Ease

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<v Speaker 1>between where you think the outcome of the game is

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<v Speaker 1>going to come and where people are actually betting exactly

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<v Speaker 1>so that that that again there is a a based

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<v Speaker 1>on our simulation, basically an underlying real probability distribution which

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<v Speaker 1>would in some sense and for a price as to

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<v Speaker 1>what would be a fair return for a two dollar

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<v Speaker 1>bet on that outcome. And then you know we look

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<v Speaker 1>for pricing disreferences, so let's talk about them. Are there

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<v Speaker 1>some persistent biases that you learned about in how the

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<v Speaker 1>public bets? Like you know, I'm the public is silly

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<v Speaker 1>enough to mostly bet on by powerball tickets. People are irrational,

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<v Speaker 1>So what kind of irrationality is did you see that

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<v Speaker 1>you can take advantage of? What one interesting property is

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<v Speaker 1>that since it's very very hard for all the players

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<v Speaker 1>with high uniform numbers to do very well. If you

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<v Speaker 1>could imagine players six, seven, and eight, in order for six, seven,

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<v Speaker 1>and eight to do well well, six has to beat

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<v Speaker 1>seven in order to do well, but that puts seven

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<v Speaker 1>at the end of the line, and seven has to

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<v Speaker 1>eat eight, but that puts eight at the end of

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<v Speaker 1>the line. It's very very hard for there to be

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<v Speaker 1>combinations where all the big numbers come in and essentially

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<v Speaker 1>almost impossible. And yet you would always see people betting

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<v Speaker 1>on this because they didn't know that that way, they

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<v Speaker 1>were betting on essentially an outcome that essentially couldn't happen,

0:12:15.960 --> 0:12:18.640
<v Speaker 1>and the odds must look pretty juicy for those characters.

0:12:18.640 --> 0:12:20.440
<v Speaker 1>For those players, I mean, and it's up to the

0:12:20.440 --> 0:12:22.760
<v Speaker 1>payoff would be good if they want. Because there's only

0:12:22.760 --> 0:12:25.280
<v Speaker 1>one person betting on that during the course of any match,

0:12:25.600 --> 0:12:29.280
<v Speaker 1>but it's never gonna happen. So so so here's our

0:12:29.320 --> 0:12:31.880
<v Speaker 1>system would look for these disrepencies. The other thing that

0:12:31.920 --> 0:12:34.560
<v Speaker 1>we would look for that's sort of related to the

0:12:34.640 --> 0:12:36.720
<v Speaker 1>kind of models people build for trading, is we'd have

0:12:36.760 --> 0:12:41.080
<v Speaker 1>to look at what our impact on the betting pool is. So, um,

0:12:41.120 --> 0:12:44.800
<v Speaker 1>if we bet on something and we win, it doesn't

0:12:44.800 --> 0:12:46.480
<v Speaker 1>pay for us to bet a lot of money on

0:12:46.520 --> 0:12:49.880
<v Speaker 1>that outcome because we're just dividing the pool among all

0:12:49.880 --> 0:12:53.240
<v Speaker 1>the winning tickets, and so every subsequent ticket we would

0:12:53.280 --> 0:12:56.600
<v Speaker 1>buy would would have a lower and lower expected value.

0:12:56.760 --> 0:12:59.880
<v Speaker 1>All right, So you have the mathematics, all the trees,

0:13:00.160 --> 0:13:03.479
<v Speaker 1>you have the nature of how people bet, so the payoffs.

0:13:03.640 --> 0:13:06.640
<v Speaker 1>You also have this calculation about how your own bets

0:13:06.679 --> 0:13:09.600
<v Speaker 1>might affect the results. Now let's talk about putting it

0:13:09.679 --> 0:13:12.760
<v Speaker 1>into practice. What did you do then? So the question

0:13:12.800 --> 0:13:14.800
<v Speaker 1>now is how do we actually go bet on this thing?

0:13:14.800 --> 0:13:17.679
<v Speaker 1>We we couldn't have somebody stationed at the HIGHLFE front

0:13:17.760 --> 0:13:20.640
<v Speaker 1>on every day making our bets the front and we

0:13:20.640 --> 0:13:22.600
<v Speaker 1>were interested in betting in was in Connecticut, I was

0:13:22.640 --> 0:13:24.720
<v Speaker 1>in New York. This was not you know, I'm not

0:13:24.760 --> 0:13:28.160
<v Speaker 1>that crazy, but it turned out that Connecticut did have

0:13:28.200 --> 0:13:31.000
<v Speaker 1>an O TB and off track betting operation where they

0:13:31.040 --> 0:13:33.640
<v Speaker 1>had a phone system where you could dial it in

0:13:33.960 --> 0:13:37.040
<v Speaker 1>and dial in your bets. And so we programmed the

0:13:37.040 --> 0:13:40.760
<v Speaker 1>computer computers back then had these things called modems for

0:13:41.200 --> 0:13:44.080
<v Speaker 1>the kids listening, which I remember that I remember the

0:13:44.120 --> 0:13:46.760
<v Speaker 1>A O L dial up modem, these dial up modems,

0:13:46.760 --> 0:13:49.199
<v Speaker 1>and so you could in some sense, therefore, you had

0:13:49.200 --> 0:13:51.760
<v Speaker 1>a device that you could program that could make phone

0:13:51.800 --> 0:13:55.520
<v Speaker 1>calls and could likewise push buttons in some sense push

0:13:55.600 --> 0:13:58.400
<v Speaker 1>buttons on phones. And so we built a system that

0:13:58.440 --> 0:14:02.320
<v Speaker 1>would um take our bets and convert that into the

0:14:02.400 --> 0:14:05.640
<v Speaker 1>dial tone instructions that would be necessary to place this

0:14:05.720 --> 0:14:08.760
<v Speaker 1>bet at the Connecticut oft TACH betting operation. And so

0:14:08.800 --> 0:14:12.280
<v Speaker 1>we built essentially a complete, you know, programmed trading system

0:14:12.360 --> 0:14:15.680
<v Speaker 1>in highlight. Every day, it would identify go over the web,

0:14:15.720 --> 0:14:20.080
<v Speaker 1>identify what we're the game matches, and who was playing.

0:14:20.360 --> 0:14:23.120
<v Speaker 1>It would simulate each one a million times. It would

0:14:23.160 --> 0:14:26.040
<v Speaker 1>determine the most profitable betting outcomes, and then it would

0:14:26.040 --> 0:14:29.440
<v Speaker 1>phone it into O T B attempt to implement our trade.

0:14:29.520 --> 0:14:33.560
<v Speaker 1>This is amazing. This sounds like algorithmically driven high frequency

0:14:33.920 --> 0:14:36.640
<v Speaker 1>highlight trading essentially. What I love about that is that

0:14:36.680 --> 0:14:39.480
<v Speaker 1>you're exactly right, except there's this very old school part

0:14:39.560 --> 0:14:43.320
<v Speaker 1>because there's high frequency algorithmic highlight trading except for the

0:14:43.400 --> 0:14:46.160
<v Speaker 1>very last part, and it involves dial tones going through

0:14:46.160 --> 0:14:49.680
<v Speaker 1>a phone tree, and so it's this incredibly modern seeming

0:14:49.720 --> 0:14:52.480
<v Speaker 1>idea and then this very old school actually process of

0:14:52.520 --> 0:14:55.160
<v Speaker 1>placing the trades at the very end. I can't say

0:14:55.200 --> 0:14:57.280
<v Speaker 1>that the you know, the the We wrote a book

0:14:57.280 --> 0:15:01.480
<v Speaker 1>about this book called Calculated Bets and market for it

0:15:01.600 --> 0:15:04.200
<v Speaker 1>ended up being not high live fans because it's not

0:15:04.240 --> 0:15:06.440
<v Speaker 1>actually a big universe, but but if they're out to be,

0:15:06.680 --> 0:15:09.000
<v Speaker 1>yet a lot of play in people who were doing

0:15:09.320 --> 0:15:12.760
<v Speaker 1>trading and building these program trading operations, because it is

0:15:13.240 --> 0:15:15.640
<v Speaker 1>essentially the same idea that people are using in the

0:15:15.680 --> 0:15:19.160
<v Speaker 1>same technologies, and it kind of explains basically how these

0:15:19.200 --> 0:15:21.520
<v Speaker 1>things work. I want to just step back and ask

0:15:21.600 --> 0:15:24.440
<v Speaker 1>the dumb question, how would you do? What's an algorithm?

0:15:24.600 --> 0:15:27.480
<v Speaker 1>We hear it all the time, algorithmic trading, and people

0:15:27.520 --> 0:15:29.920
<v Speaker 1>have some idea that it means computers in math, But

0:15:30.000 --> 0:15:32.600
<v Speaker 1>what does this actually mean for someone in plain English?

0:15:32.640 --> 0:15:34.760
<v Speaker 1>As it as it is said in in the world

0:15:34.800 --> 0:15:38.440
<v Speaker 1>of algorithmic trading. It is typically a it's really I guess,

0:15:38.440 --> 0:15:42.680
<v Speaker 1>a programmed procedure from making decisions so that uh, you know,

0:15:42.720 --> 0:15:46.160
<v Speaker 1>there is a decision in a program trading system. There

0:15:46.200 --> 0:15:49.240
<v Speaker 1>has to be somebody making a decision to buy or

0:15:49.280 --> 0:15:52.040
<v Speaker 1>sell this particular stock at this particular time. So a

0:15:52.080 --> 0:15:54.160
<v Speaker 1>set of rules basically, so it can be sort of

0:15:54.200 --> 0:15:56.640
<v Speaker 1>a set of rules as usually some level of input.

0:15:56.840 --> 0:15:59.080
<v Speaker 1>I would say, it could be a simulation, it could

0:15:59.120 --> 0:16:02.480
<v Speaker 1>be sets of rules. It's some kind of a procedure

0:16:02.920 --> 0:16:05.400
<v Speaker 1>that that the sides that this bet or this series

0:16:05.440 --> 0:16:09.160
<v Speaker 1>of bets are profitable, and goes and executes them without

0:16:09.320 --> 0:16:13.320
<v Speaker 1>human involvement. And now you set up the trading system,

0:16:13.400 --> 0:16:16.960
<v Speaker 1>they're dialed in. How much money did you make? Well,

0:16:16.840 --> 0:16:20.400
<v Speaker 1>we may we made okay by percentage wise. Again, recognize

0:16:20.440 --> 0:16:23.120
<v Speaker 1>that the betting pool and highlight is very small. So

0:16:23.200 --> 0:16:26.000
<v Speaker 1>that I told you that a you know, making too

0:16:26.080 --> 0:16:29.080
<v Speaker 1>many bets on any particular match would would rapidly saturate

0:16:29.120 --> 0:16:31.080
<v Speaker 1>the pool and none of the bets would be profitable.

0:16:31.680 --> 0:16:33.480
<v Speaker 1>But but over the course of our trade, we made

0:16:33.480 --> 0:16:38.680
<v Speaker 1>over return on our investment. It was sizeable enough that

0:16:38.720 --> 0:16:41.800
<v Speaker 1>it got to be a little scary to UM run

0:16:41.840 --> 0:16:44.760
<v Speaker 1>it on university research machines, and so we eventually ended.

0:16:44.840 --> 0:16:47.360
<v Speaker 1>It wasn't so much that I have deep regrets about

0:16:47.360 --> 0:16:50.840
<v Speaker 1>turning the system off. What time frame we we had

0:16:50.840 --> 0:16:54.760
<v Speaker 1>it running for about a six month period. Six months, Yes,

0:16:55.000 --> 0:16:57.320
<v Speaker 1>would you would you have gotten to the point if

0:16:57.320 --> 0:16:59.560
<v Speaker 1>you had continued with it where you would just be

0:16:59.640 --> 0:17:03.080
<v Speaker 1>the entire your market UM if we kept um again,

0:17:03.080 --> 0:17:04.960
<v Speaker 1>if if we made our bets bigger and bigger than

0:17:05.000 --> 0:17:07.119
<v Speaker 1>we could have easily become the entire market you know.

0:17:07.440 --> 0:17:09.600
<v Speaker 1>In fact, one of the one of the things that

0:17:09.720 --> 0:17:11.960
<v Speaker 1>was key to our systems again we our system bet

0:17:12.000 --> 0:17:16.640
<v Speaker 1>on Trifecta's combinations of wind, Place and show, which are

0:17:17.040 --> 0:17:20.560
<v Speaker 1>rare events and but but really didn't make it payoff

0:17:20.640 --> 0:17:22.440
<v Speaker 1>was they had a special type of bet called to

0:17:22.480 --> 0:17:26.040
<v Speaker 1>Trifecta box where we could buy a particular set of

0:17:26.080 --> 0:17:30.720
<v Speaker 1>all combinations of three numbers UM cheaper than we could

0:17:30.760 --> 0:17:32.840
<v Speaker 1>the corresponding tickets. And our goal was really to have

0:17:32.880 --> 0:17:35.600
<v Speaker 1>as little impact on the pool as possible, and that

0:17:35.640 --> 0:17:37.920
<v Speaker 1>was really what was necessary. It really wasn't a big

0:17:37.960 --> 0:17:40.560
<v Speaker 1>margin here. If someone wants to do this, I mean,

0:17:40.600 --> 0:17:42.840
<v Speaker 1>I know that it's harder, but what are the key

0:17:42.920 --> 0:17:46.240
<v Speaker 1>areas of mathematics to study. So again I am a

0:17:46.240 --> 0:17:49.679
<v Speaker 1>computer scientist, and so in this case there was um,

0:17:49.720 --> 0:17:52.840
<v Speaker 1>you know, to understand things about Monte Carlo simulations. Again

0:17:52.840 --> 0:17:55.560
<v Speaker 1>we were talking about this tree process. Um, you could

0:17:55.640 --> 0:17:59.280
<v Speaker 1>view this as building a tree that you exhaustively analyze

0:17:59.280 --> 0:18:01.480
<v Speaker 1>where you could simul late at using something called Monte

0:18:01.560 --> 0:18:04.639
<v Speaker 1>Carlo simulation, where you really did use random numbers to

0:18:04.760 --> 0:18:07.960
<v Speaker 1>describe the path down the trade. So knowing computer science

0:18:08.040 --> 0:18:11.600
<v Speaker 1>is a good thing. Um, you know, knowing something about statistics.

0:18:11.920 --> 0:18:14.240
<v Speaker 1>You know, data science is a new field called data science,

0:18:14.240 --> 0:18:16.520
<v Speaker 1>which is the kind of area where my my lab works.

0:18:16.760 --> 0:18:18.960
<v Speaker 1>And uh then this kind of field that this is

0:18:18.960 --> 0:18:20.680
<v Speaker 1>the kind of stuff that I think is good learning

0:18:20.680 --> 0:18:23.600
<v Speaker 1>how to build models, this kind of thing. So you

0:18:23.640 --> 0:18:26.239
<v Speaker 1>mentioned you came out of this book Calculated Beds, and

0:18:26.280 --> 0:18:28.800
<v Speaker 1>it wasn't a huge hit among high life fans because

0:18:28.840 --> 0:18:31.359
<v Speaker 1>there aren't that many high life fans, but it got

0:18:31.400 --> 0:18:34.240
<v Speaker 1>a lot of followers among people who play the market,

0:18:34.359 --> 0:18:37.280
<v Speaker 1>people in banks. What are some of the key lessons

0:18:37.320 --> 0:18:39.239
<v Speaker 1>in terms of what you did and how else they

0:18:39.280 --> 0:18:42.359
<v Speaker 1>apply to someone wanting to play the markets and setting

0:18:42.400 --> 0:18:45.320
<v Speaker 1>up a trading system. So the first thing that I

0:18:45.359 --> 0:18:50.560
<v Speaker 1>would say is that that markets are in general relatively efficient,

0:18:50.720 --> 0:18:54.040
<v Speaker 1>even in high Lie where we had you know, these

0:18:54.119 --> 0:18:57.320
<v Speaker 1>crazy you know, the people who were watching embedding workers only,

0:18:57.359 --> 0:18:59.760
<v Speaker 1>these people who went once a year and didn't know anything,

0:18:59.840 --> 0:19:02.600
<v Speaker 1>the pools of dumb money. I was surprised how hard

0:19:02.640 --> 0:19:04.880
<v Speaker 1>it was for us to build a system that actually

0:19:04.960 --> 0:19:07.960
<v Speaker 1>did um, did have a positive return. I thought it

0:19:08.000 --> 0:19:10.479
<v Speaker 1>was gonna be a lot easier than that than it

0:19:10.480 --> 0:19:14.000
<v Speaker 1>turned out to be. And that's that's probably a lesson

0:19:14.040 --> 0:19:17.440
<v Speaker 1>that most markets are are more efficient than you would think.

0:19:17.680 --> 0:19:19.520
<v Speaker 1>You know, even in horse ray. They've been done studies

0:19:19.560 --> 0:19:23.000
<v Speaker 1>in horse racing, and the markets there are relatively efficient.

0:19:23.040 --> 0:19:26.119
<v Speaker 1>You know, the fact is that there's a large um

0:19:26.280 --> 0:19:30.800
<v Speaker 1>transaction cost essentially of the house keeps, and that's a

0:19:30.880 --> 0:19:34.600
<v Speaker 1>large transaction cost, and that's white people lose. So certain

0:19:34.640 --> 0:19:36.840
<v Speaker 1>things are models are harder to build than than you

0:19:36.840 --> 0:19:39.720
<v Speaker 1>would think. That's I guess one lesson here. The other

0:19:39.920 --> 0:19:42.720
<v Speaker 1>is that if you're careful and you're you're thinking hard enough,

0:19:42.760 --> 0:19:44.560
<v Speaker 1>and you beat on it and maybe there's something there.

0:19:44.960 --> 0:19:49.040
<v Speaker 1>Does the lesson of not saturating the market apply to

0:19:49.280 --> 0:19:52.639
<v Speaker 1>broader financial markets because obviously we hear a lot today

0:19:53.200 --> 0:19:57.400
<v Speaker 1>about um high frequency trading, algorithmically driven trading, and it's

0:19:57.440 --> 0:20:00.200
<v Speaker 1>impact on markets. What do you think, Yeah, so it's

0:20:00.200 --> 0:20:03.240
<v Speaker 1>certainly the case that in any market, you if you

0:20:03.320 --> 0:20:07.600
<v Speaker 1>bet enough, you're eventually betting against yourself. And uh, the

0:20:07.600 --> 0:20:10.800
<v Speaker 1>the advantage of the financial markets that they're generally large

0:20:10.920 --> 0:20:13.760
<v Speaker 1>enough that you can put in a tremendous amount of

0:20:13.760 --> 0:20:16.680
<v Speaker 1>capital and play before it, you know, you really start

0:20:16.720 --> 0:20:19.720
<v Speaker 1>betting against yourself. But again, you know many in many

0:20:19.800 --> 0:20:24.639
<v Speaker 1>hedge funds, in some sense there's they will occasionally occasionally

0:20:24.640 --> 0:20:26.920
<v Speaker 1>a hedge funds will return capital if they can't think

0:20:26.920 --> 0:20:29.960
<v Speaker 1>they can invest it efficiently enough. And that's basically because

0:20:30.000 --> 0:20:32.360
<v Speaker 1>of these saturation effects. But what about if you get

0:20:32.359 --> 0:20:36.880
<v Speaker 1>a market that becomes dominated by algorithmic trading and they

0:20:36.920 --> 0:20:39.280
<v Speaker 1>all kind of feed on each other. Does that end

0:20:39.320 --> 0:20:43.040
<v Speaker 1>up having the same effect. It's an interesting question. Um,

0:20:43.080 --> 0:20:47.440
<v Speaker 1>if everybody was doing the exact same thing in a market,

0:20:47.840 --> 0:20:51.159
<v Speaker 1>then there wouldn't be an interesting market going on. And

0:20:51.200 --> 0:20:54.720
<v Speaker 1>so the question of whether whether algorithmic trading is going

0:20:54.760 --> 0:20:58.600
<v Speaker 1>to eventually get into a world where you know, sometimes

0:20:58.800 --> 0:21:01.240
<v Speaker 1>people are betting against them where nothing is happening, It

0:21:01.320 --> 0:21:04.639
<v Speaker 1>depends upon the traders doing different things. I guess markets

0:21:04.640 --> 0:21:08.160
<v Speaker 1>would only get into trouble if all the different traders

0:21:08.160 --> 0:21:10.320
<v Speaker 1>were doing the exact same things. That's I guess when

0:21:10.320 --> 0:21:14.040
<v Speaker 1>you get into bubbles and when you get into into crashes,

0:21:14.080 --> 0:21:17.840
<v Speaker 1>there any sort of final key lessons for markets from

0:21:17.880 --> 0:21:20.719
<v Speaker 1>what you did. Has anyone has anyone written to you

0:21:20.760 --> 0:21:23.000
<v Speaker 1>and said they've used your book and made a fortune?

0:21:23.040 --> 0:21:26.159
<v Speaker 1>And I feel I have heard from every person who

0:21:26.240 --> 0:21:28.080
<v Speaker 1>has read the book the people the book is and

0:21:28.119 --> 0:21:31.000
<v Speaker 1>I acted books that did reasonably well, but it's still

0:21:31.000 --> 0:21:34.440
<v Speaker 1>the case that uh, a lot of people felt very

0:21:34.520 --> 0:21:37.240
<v Speaker 1>very close to this book because it does tell a

0:21:37.320 --> 0:21:39.560
<v Speaker 1>story that's that's a kiss akin to what a lot

0:21:39.640 --> 0:21:42.760
<v Speaker 1>of traders basically do. And UM, you know, I hear

0:21:42.800 --> 0:21:45.160
<v Speaker 1>from people. I occasionally hear from people who want to

0:21:45.200 --> 0:21:47.240
<v Speaker 1>want me to get involved in their betting scheme. I

0:21:47.280 --> 0:21:51.200
<v Speaker 1>heard from a Russian syndicate recently that wanted UH to

0:21:51.240 --> 0:21:55.520
<v Speaker 1>do trading in um soccer pools and uh. And I've

0:21:55.640 --> 0:21:59.320
<v Speaker 1>I've hung around a gambling syndicate in Macau where they've

0:21:59.320 --> 0:22:02.240
<v Speaker 1>been in horse racing and uh. And so you know,

0:22:02.280 --> 0:22:04.920
<v Speaker 1>so there are these these and I've also spoken through

0:22:04.960 --> 0:22:07.320
<v Speaker 1>a lot of traders, and uh again we met at

0:22:07.320 --> 0:22:11.760
<v Speaker 1>a financial conference. So it's been an interesting leading into

0:22:11.760 --> 0:22:14.480
<v Speaker 1>a world that's quite different from me as a computer scientist.

0:22:14.880 --> 0:22:18.920
<v Speaker 1>Your system was ultimately based on pure mathematics. Does that

0:22:18.960 --> 0:22:22.120
<v Speaker 1>take the emotion out of winning and making the bet?

0:22:22.400 --> 0:22:24.479
<v Speaker 1>It was true that that that there was sort of

0:22:24.560 --> 0:22:27.119
<v Speaker 1>the fact that there was a real event happening, that

0:22:27.200 --> 0:22:30.240
<v Speaker 1>they were really these besques tossing a ball around was

0:22:30.280 --> 0:22:32.640
<v Speaker 1>really an abstraction. You know, every day I would get

0:22:32.720 --> 0:22:36.359
<v Speaker 1>email from my machine about how we did, and every

0:22:36.440 --> 0:22:39.800
<v Speaker 1>night the computer would play a million simulations of this

0:22:39.800 --> 0:22:42.640
<v Speaker 1>game that was going to happen tomorrow. But and and

0:22:42.680 --> 0:22:45.280
<v Speaker 1>somehow we were divorced from the real aspect of it.

0:22:45.640 --> 0:22:48.119
<v Speaker 1>And that may be true in certain certain aspects of

0:22:48.160 --> 0:22:51.080
<v Speaker 1>the markets. I mean, people are busy trading stocks around

0:22:51.240 --> 0:22:53.800
<v Speaker 1>in some ways, quite independent of whether or not these

0:22:53.800 --> 0:22:56.320
<v Speaker 1>are You know that there are companies there, and that

0:22:56.359 --> 0:22:58.720
<v Speaker 1>people are working and people are building things, and there's

0:22:58.760 --> 0:23:01.320
<v Speaker 1>things happening. So there's a certain sense in which this

0:23:01.359 --> 0:23:04.040
<v Speaker 1>was an abstraction of the world that that may may

0:23:04.040 --> 0:23:05.919
<v Speaker 1>have felt a little bit funny when you think about it.

0:23:06.000 --> 0:23:09.120
<v Speaker 1>And last question, are you doing any betting on anything?

0:23:09.280 --> 0:23:11.920
<v Speaker 1>Or these days or back to hip pure academic stuff.

0:23:11.960 --> 0:23:14.480
<v Speaker 1>I am, I am up again. I am a professor.

0:23:14.640 --> 0:23:17.560
<v Speaker 1>I I you know, I live a clean life. But

0:23:17.560 --> 0:23:20.040
<v Speaker 1>but again, my my research area these days is related

0:23:20.080 --> 0:23:22.199
<v Speaker 1>to data science data analysis. We do a lot of

0:23:22.240 --> 0:23:25.960
<v Speaker 1>projects related to data modeling and uh things like this,

0:23:26.080 --> 0:23:28.200
<v Speaker 1>and so every once in a while my work touches

0:23:28.240 --> 0:23:30.960
<v Speaker 1>on some kind of a model related to that does

0:23:31.000 --> 0:23:34.159
<v Speaker 1>have relations with financial markets and other things. Thank you

0:23:34.280 --> 0:23:36.760
<v Speaker 1>very much for joining. Its fascinating and just talking with

0:23:36.840 --> 0:23:42.160
<v Speaker 1>Thank you, there's a lot of fun. So Tracy, are

0:23:42.200 --> 0:23:44.320
<v Speaker 1>you Are you a high life fan? Though? I kind

0:23:44.320 --> 0:23:45.760
<v Speaker 1>of want to go watch a game. I want to

0:23:45.800 --> 0:23:47.000
<v Speaker 1>I want to bet on a game. I want to

0:23:47.040 --> 0:23:49.760
<v Speaker 1>be the dumb money on the sidelines of the game. Um.

0:23:49.600 --> 0:23:52.399
<v Speaker 1>I love that conversation. I thought that was like, I

0:23:52.400 --> 0:23:54.600
<v Speaker 1>don't know, I just I thought it was fascinating. So

0:23:54.680 --> 0:23:57.439
<v Speaker 1>I learned a lot about the sport. And it also

0:23:57.560 --> 0:24:00.640
<v Speaker 1>is really helpful in bringing to life some the concepts

0:24:00.640 --> 0:24:03.360
<v Speaker 1>that we talk about all the time and markets like algorithms.

0:24:03.760 --> 0:24:09.080
<v Speaker 1>Absolutely like all these things in terms of simulations, decision trees, algorithms,

0:24:09.440 --> 0:24:12.520
<v Speaker 1>and particularly that part about how you know the end

0:24:12.560 --> 0:24:14.399
<v Speaker 1>we were talking about about how if you get too

0:24:14.400 --> 0:24:16.760
<v Speaker 1>big at a market, or if everybody's chasing the same

0:24:16.800 --> 0:24:20.560
<v Speaker 1>algorithmic strategy, how can all break down? Yeah, exactly, And

0:24:20.680 --> 0:24:23.480
<v Speaker 1>my absolute favorite part was when he described the betting

0:24:23.520 --> 0:24:25.760
<v Speaker 1>system and you have like all the math and then

0:24:25.760 --> 0:24:28.720
<v Speaker 1>you have the algorithm. But how in the nineties and

0:24:28.880 --> 0:24:30.679
<v Speaker 1>finally ended up where you had to like have your

0:24:30.720 --> 0:24:34.200
<v Speaker 1>computer make dial tones to enter in the debates. There's

0:24:34.240 --> 0:24:37.760
<v Speaker 1>a marriage of old style and modern trading techniques. I

0:24:37.760 --> 0:24:41.280
<v Speaker 1>thought was hilarious to imagine. Yeah, that was great. All right,

0:24:41.400 --> 0:24:43.920
<v Speaker 1>that is all for Odd Lodge. Thank you for listening.

0:24:44.080 --> 0:24:48.320
<v Speaker 1>I'm Joe Wisenthal, Managing Editor Bloomberg Markets, and you can

0:24:48.400 --> 0:24:50.480
<v Speaker 1>follow me on Twitter at the stall War. And I'm

0:24:50.520 --> 0:24:53.600
<v Speaker 1>Tracy Alloway, Executive editor of Bloomberg Markets and I'm on

0:24:53.600 --> 0:25:06.760
<v Speaker 1>Twitter at Tracy Alloway. Thanks again. Joe and I are

0:25:06.960 --> 0:25:09.560
<v Speaker 1>very proud of our new podcast, Odd Lots, but we

0:25:09.600 --> 0:25:13.359
<v Speaker 1>are also very proud of Bloomberg's other growing suite of

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<v Speaker 1>original podcast all designed to help you navigate the complexities

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<v Speaker 1>addition to our own podcast, please don't miss Benchmark with

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<v Speaker 1>free look at the inner workings of the global economy.

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