Odd Lots - Episode 13: How a Professor Won Gambling on an Obscure Sport

Episode Date: February 1, 2016

Episode 13: Everyone dreams of being able to win almost every time when gambling. Of course, whether it's blackjack, horse betting, poker or the stock market, it's really hard to consistently win. But one professor, armed with advanced mathematical knowledge and computers, was able to beat the system while gambling on the obscure sport of Jai Alai. In this week's Odd Lots podcast, Steven Skiena, who teaches computer science at Stony Brook University in New York, tells the story of how he made 500 percent on his money in six months by gambling on Jai Alai. Skiena also explains how his approach applies to much bigger arenas, including algorithmic trading on the stock market.See omnystudio.com/listener for privacy information.

Thanks for listening to OddLots. Follow the show on Amazon Music for more future episodes or just ask Alexa play the podcast OddLots on Amazon Music. Hello and welcome to another episode of OddLots. I'm Joe Wisenthal, managing editor of Bloomberg Markets. And I'm Tracy Allaway, executive editor of Bloomberg Markets. Hey Tracy, do you know what the fastest sport in the world is? Uh, sailing, race car driving? Uh, those are pretty good guesses. But no, actually, actually, Actually, the answer is High-Ly. High-Ly? High-Ly. High-Ly.

Okay. High-Ly. It's actually originally a game that originated in the Spanish Basque country. It's kind of like racquetball, except the players play on this gigantic court. The ball goes nearly 200 miles an hour. The rackets are these gigantic, curled things that the players wear over their hands. The ball is hard as a rock. And, oh yeah, if it were to hit you in the head, the ball could kill you.

This sounds like a made-up sport. Why are we talking about this? It's a good question. Because in addition to how crazy and intense the game is, you know, as I said, 200-mile-an-hour ball as hard as a golf ball, potentially deadly, people actually gamble on Highline, kind of like horse racing. There's people, a bunch of players play in a tournament of sorts, and then people bet on whether the different players will win, place, or show.

So there's this big gambling element to it. Huh, that's really interesting. Our guest today that we'll be talking to is Steven Skeena. He's a professor at Stony Brook University, and he wrote a book all about gambling on High Lie Online and how he cracked the system and he made a bunch of money. Wait, he cracked the system, so he beat the house? Yeah, it's basically impossible, theoretically.

You know, gambling, the house is always supposed to lose. Right. But in these games where you're sort of betting against other people and the crowd sets the odds, it's actually possible. And not only did Stephen beat the system and make a bunch of money, there are some interesting lessons in terms of beating the stock market and odds games in general. Okay, so I'm excited because not only am I about to learn about a sport, which I've never heard of before, but I'm also interested in making money.

So this sounds good. Me too. All right. Well, think fast. Experience the wall-to-wall action and not. non-stop excitement that is Miami Highline. See world-class athletes climb the walls to catch a rock-hard ball, flying at speeds over 150 miles an hour.

Think excitement. Think fun. Miami Highline. Think that's good. Stephen, thank you very much for joining us. Thank you. It's nice to be here. Tracy has never heard of High Lie before. What is High Lie and how did you get interested in?

So High Lie is, as you said, a Basque game originally. it's sort of like a variation on handball. The reason people are exposed to it in the United States is typically because in Florida, it's a betting venue. There are these stadiums called Frantons in Miami and Dania near Fort Lauderdale, where you can watch mostly Basque players play this sport. And that's actually exactly how I discovered it. My family used to go down to Florida, North Miami Beach, every winter for a couple of weeks. and we went occasionally to the Dania High Live fronton.

That was exactly the way that we got involved. Our family would every year take its vacation visiting the relatives in Florida. We would drive down and we would one night go to the High Life Fronton. Is it fun to watch these games? It sounds intense from Joe's description. I think it's incredibly exciting. I mean, first of all, it's fun to watch them make these plays because the ball is moving very fast.

They have to make great catches and very difficult throws. but also the scoring system involved in Highline has some interesting mathematics instructor that makes it kind of fun to watch. So depending upon how you bet and the chances of winning change extremely rapidly with every point. And so it's very exciting because the situation is always changing. Yeah, so I said it was kind of like horse race betting and that you could bet on win, place or show. But in a way, it's a little more complicated. and what do you just sort of describe how the similarities real quickly and the difference is? So it is like horse race betting in that you can bet on win place and show,

and that's certainly what we're going to be doing. The difference is the scoring system. Basically, in High-Lai, the winner is the first one to get the seven points. And they have eight teams that are playing in any given match. But because of the size of the court, only two teams can play at once. to the teams wait in a line, their uniform numbers are one through eight, corresponding to where they start in line, and originally the first two players play each other, then the winner keeps playing, gets a point and keeps playing, the loser goes to the end of the line, and they keep playing until you get to seven points.

And if you think about that kind of a scoring system, it gives an advantage to the people who start early. because obviously they get first cracks at getting points, and even if they lose, they are more likely they're going to be the first player to come up for a second time. So they make the scoring system even more complicated, where after every trip through the queue once, meaning every player has played its first point, now every subsequent point counts for two.

And this makes for a very complicated scoring system that means that even if you're very, very close to winning, if you suddenly lose that point, you go to the end of the line and you might not get another chance to play again. And the betting system, it's a paramutual odds. What exactly does that mean? So paramutual means that you're betting against the other players

and it's not me betting against the house. If I was betting against the house, the odds of me winning are very small. That's why the house is usually big. But in a paramutual system, what happens is all the money that is bet in the competition is thrown into a pool, the house skims off a fee, something like 20%,

and the rest is divided among the winners. So in order to have a successful betting system, you have to be better than the other players. Other betters, I guess. Basically, there are a bunch of dumb people like me and my family who used to go there from time to time and bet, and we didn't know anything. And so theoretically, if you're really smart and studied,

we're the fish that you could take advantage. That was the attraction. I mean, again, it's a very exciting sport. It's probably a hard sport to know. know the players very well. I don't think most fans are, most fans are not that intense, but they go once a year, twice a year. And before we get to your sort of rigorous approach, a story in your book, you won the first ever bet you placed on Highland. Isn't that correct? So the reason we got really hooked on this was that when our parents drove down to Florida and let us

go to High Lie one night, they also let us make one bet. They gave us $2 and they said, you can make one bet. And we followed the bet that was listed, in the local tout sheet, knowing nothing. And astonishingly, it was a trifecta, a combination of first place in show, that astonishingly won. And so we won, you know, $124, and this was an amazing amount of money to a bunch of kids back in the 70s. And that's what God of thought of us hoped. I was probably about 12 or so at the time.

Turning $200 into $124 is exciting anytime. But when you're a $2 into $174. Oh, yeah, that's what I meant. But when you're a kid in the 70s, that must be absolutely. I could see how you would then get hooked for life on the game. Yes. All right, let's fast forward a little bit. And so you're a professor at Stony Brook.

Talk us about how you started on your path to system a gambling system for High Lie and what you did. So when you look at the scoring system, again, a Highlight game is played in discrete points. Player 1, place, player 2. One of them wins. The other goes to the end of the line. you could imagine simulating the result of a high line match by flipping a coin for every particular point

if player one is maybe better than player two maybe you'd say it has a 60% chance of winning the first point and if you could figure out the odds that one player is going that every player has against every other player in that they might encounter in a match you can build a term a simulation to use random numbers to play through and simulate each match. So is it like, it sounds like a series of tree charts almost, right? You assign probabilities for each outcome, and then you have them sort of branching across all the possible outcomes. Right. So you could view this as a tree process.

It is a branching process. It's a tree process where at every point in the tree is, every node in the tree is basically two players playing each other with a certain score and a certain status of players in the queue to come, then depending upon who wins it, you go to a different state in the process. And this process ends when you have identified who comes in first, second, and third.

So this part sounds, is where it seems to really diverge from, say, horse racing, where you just have one event, there's one, two, and three, not really all these different permutations and sequences. So horse racing is not a discrete event kind of a game. This is maybe a little bit more akin to, I would say, Baseball than football. Baseball is a bunch of discrete events. There's pitches and things happen. In basketball, things are very continuous. And horse racing, things seem continuous. So when you started developing the system, when are we talking about? How long ago was this?

This is something we started in the, I would say, early 90s. If I have to get back there. It's probably the story about in the early 90s. Okay, so you broke highlight down into the series of discrete events. Then what's next in terms of the creation of your gambling system? So again, once you have the ability to view this as this tree process or as this, you can simulate one game, you can now simulate a million games or more and see what the probability distribution is of outcomes. And you can start to look at for every combination of first, second, and third, how often did it come in? And from this, that gives you some insight into what things you should bet on. But things get a little bit more complicated. you have to accurately model how good the players are. So you have a good guess as to how often player one is going to be player two.

And more than that, you have to build a model of how the public is going to bet. Again, it's a paramutual system. I'm betting against the public. If everyone else in the public was someone who programmed the computer and did the analysis the way I did, I would have no advantage. Are you looking for almost pricing discrepancies between where you think the outcome of the game is going to come and where people are actually betting. Exactly. So that, again, there is a, based on our simulation, basically,

an underlying real probability distribution, which would, in some sense, infer a price as to what would be a fair return for a $2 bet on that outcome. And then, you know, we'd look for pricing disrepancy. So let's talk about them. Are there some persistent biases that you learned about in how the public bets, like, you know, the public is silly enough to mostly bet on,

by powerball tickets. People are irrational. So what kind of irrationalities did you see that you could take advantage of? One interesting property is that since it's very, very hard for all the players with high uniform numbers to do very well. If you could imagine players six, seven and eight, in order for six, seven and eight to do well, well, six has to beat seven in order to do well, but that puts seven at the end of the line. And seven has to beat eight, but that puts eight at the end of the line. It's very, very hard for there to be combinations where all the big numbers come in and essentially almost impossible. And yet you would always see people betting on this because they didn't know that they were betting on essentially an outcome that essentially couldn't happen.

And the odds must look pretty juicy for those players. I mean, it's up that the payoff would be good if they won because presumably there's only one person betting on that during the course of any match, but it's never going to happen. It doesn't compensate. So, so, you know, so our system would look for these disrepancies. The other thing, thing that we would look for that's sort of related to the kind of models people build for trading, if we'd have to look at what our impact on the betting pool is. So if we bet on something and we win, it doesn't pay for us to bet a lot of money on that outcome because we're just dividing the pool among all the winning tickets. And so every subsequent ticket we would buy would have a lower and

lower expected value. All right. So you have the mathematics, all the trees, you have the nature of how people bet, so the payoff. You also have this calculation about how your own bets might affect the results. Now, let's talk about putting it into practice. What did you do then? So the question now is, how do we actually go bet on this thing? We couldn't have somebody stationed at the High Life Fronton every day making our bets. The fronton we were interested in betting in was in Connecticut. I was in New York. This was not, you know, I'm not that crazy. But it turned out that Connecticut did have an OTB, an off-track betting operation where they had a phone system where you could dial it in and dial in your bets. And so we programmed the computer.

Computers back then had these things called modems, which were... For the kids listening. I remember that. I remember the AOL dial-up modem very well. Right, so these dial-up modems. And so you could, in some sense, therefore you had a device that you could program that could make phone calls and could likewise push buttons, in some sense, push buttons on phones. And so we built a system that would take our bets and kind of convert that into the dial tone instructions that would be necessary to place this bet at the Connecticut off-tack betting operation. And so we built essentially a complete, you know, program trading system in High Lie. Every day it would identify, go over the web, identify what were

the game matches and who was playing. It would simulate each one a million times. It would determine the most profitable betting outcomes. And then it would phone it into OTB to implement our trade. This is amazing. This sounds like algorithms. driven high-frequency-frequency high-lie trading, essentially. What I love about that is that you're exactly right, except there's this very old-school part because there's high-frequency algorithmic high-lie trading except for the very last part, and it involves dial-tones going through a phone tree. And so it's this incredibly modern-seeming idea, and then this very old-school actually process of placing the trades at the very end.

I can't say that we wrote a book about this book called Calculated Betts. And the market for it ended up being not High Live fans because it's not actually a big universe. But it turned out to get a lot of play in people who were doing trading and building these program trading operations because it is essentially the same idea that people are using and the same technologies. And it kind of explains basically how these things work. I want to just step back and ask the dumb question. How would you do?

What's an algorithm? We hear it all the time algorithmic trading and people have some idea that it means computers and math. But what does this actually mean for someone in plain English? As it is said in the word of algorithmic trading, it is typically a, it is really, I guess, a programmed procedure for making decisions. So that, you know, there is a decision in a program trading system. There has to be somebody making a decision to buy or sell this particular stock at this

particular time. So a set of rules, basically. So it can be sort of a set of rules. There's usually some level of input. I would say, you know, it could be a simulation. it could be sets of rules. It's some kind of a procedure that decides that this bet or this series of bets are profitable and goes and executes them without human involvement. And now you set up the trading system. They're dialed in. How much money did you make?

Well, we may, okay, by percentage-wise, again, recognize that the betting pool in High-Eye is very small. So that I told you that a, you know, making too many bets on any particular match would would rapidly saturate the pool and none of the bets would be profitable. But over the course of our trade, we made over a 500% return on our investment. It was sizable enough that it got to be a little scary to run it on university research machines. And so we eventually ended it. It wasn't so much that I have deep regrets about turning the system off. Over what time frame? We had it running for about a six-month period. 500% in six months? Yes. Wow. Would you have gotten to the point if you had

continued with that where you would just be the entire market? If we kept, again, if we made our bets bigger and bigger than we could have easily become the entire market. In fact, one of the things that was key to our systems, again, our system bet on trifectives, combinations of wind place and show, which are rare events. But really to make it pay off was they had a special type of bet called the trifecta box where we could buy a particular set of all combinations of, three numbers, cheaper than we go to the corresponding tickets.

And our goal was really to have as little impact on the pool as possible. And that was really what was necessary because it really wasn't a big margin here. If someone wants to do this, I mean, I know that it's harder, but what are the key areas of mathematics to study? So again, I am a computer scientist. And so in this case, there was, you know, to understand things about Monte Carlo simulations. Again, we were talking about this tree process. you could view this as building a tree that you exhaustively analyze, or you could simulate it using something called Monte Carlo simulation,

where you really did use random numbers to describe the path down the tree. So knowing computer science is a good thing. You know, knowing something about statistics, you know, data science, there's a new field called data science, which is the kind of area where my lab works. And then this kind of field, this is the kind of stuff that I think is good, learning how to build models, this kind of thing. So you mentioned you came up.

Oh, this book Calculated Bets, and it wasn't a huge hit among High Life fans because there aren't that many High Lie fans, but it got a lot of followers among people who play the market, people in banks. What are some of the key lessons in terms of what you did and how else they apply to someone wanting to play the markets and setting up a trading system? So the first thing that I would say is that markets are, in general, relatively efficient, even in Highline where we had, you know, these crazy, you know, the people who were watching and betting were presumably these people who went once a year and didn't know anything. The pools of dumb money. I was surprised how hard it was for us to build a system that actually did have a positive return.

I thought it was going to be a lot easier than it turned out to be. And that's probably a lesson that most markets are more efficient than you would think. You know, even in horse race, they've done studies in horse racing. And the markets there are relatively efficient. You know, the fact is that there's a large transaction cost, essentially 20% of the house keeps 20%. And that's a large transaction cost. And that's why people lose. So certain things are models are harder to build than you would think.

That's, I guess, one lesson here. The other is that if you're careful and you're thinking hard enough and you beat on it, maybe there's something there. Does the lesson of not saturating the market apply to broader financial? markets, because obviously we hear a lot today about high-frequency-driven trading, algorithmically driven trading, and its impact on markets. What do you think? Yeah, so it's certainly the case that in any market, if you bet enough, you're eventually betting against yourself. And the advantage of the financial markets is they're generally large enough that you can put a tremendous amount of capital in play before you really start betting against yourself. But again, you know, in many hedge funds,

in some sense, they will occasionally, occasionally a hedge fund will return capital if they can't think they can invest it efficiently enough. And that's basically because of the saturation effect. But what about if you get a market that becomes dominated by algorithmic trading and they all kind of feed on each other? Does that end up having the same effect? It's an interesting question. If everybody was doing the exact same thing in a market, then there wouldn't be an interesting market going on. And so the question of whether algorithmic trading is going to

eventually get into a world where sometimes people are betting against themselves or nothing is happening. It depends upon the traders doing different things. I guess markets would presumably get into trouble if all the different traders were doing the exact same things. That's, I guess, when you get into bubbles and when you get into

crashes there. Any sort of final key lessons for markets from what you did? Has anyone written you and said they've used your book and made a fortune in the market? I feel I have heard from every person who has read the book. The book is, and the books did reasonably well, but it's still the case that a lot of people felt very, very close to this book because it does tell a story that's akin to what a lot

of traders basically do. And, you know, I hear from people, I occasionally hear from people who want me to get involved in their betting scheme. I heard from a Russian syndicate recently that wanted a. to do trading in soccer pools. And I've hung around a gambling syndicate in Macau where they bet in horse racing. And so there are these, and I've also spoken to a lot of traders. And again, we met at a financial conference.

So it's been an interesting lead-in into a world that's quite different for me as a computer scientist. Your system was ultimately based on pure mathematics. Does that take the emotion out of winning? out of making the bet? It was true that there was sort of, the fact that there was a real event happening, that they were really these basks tossing a ball around, was really an abstraction.

You know, every day I would get email from my machine about how we did, and every night the computer would play a million simulations of this game that was going to happen tomorrow, and somehow we were divorced from the real aspect of it. And that may be true in certain aspects of the markets.

I mean, people are busy trading stock. around in some ways quite independent of whether or not these are, you know, that they are companies there are working and people are building things and there's things happening. And so there's a certain sense in which this was an abstraction of the world that may have felt a little bit funny when you think about it. And last question, are you doing any betting on anything or these days or back to pure academic study? I am a, again, I am a professor.

I, you know, I live a clean life. But again, my research area these days is related to data. science, data analysis. We do a lot of projects related to data modeling and things like this. And so every once in a while, my work touches on some kind of a model related to, they does have relations with financial markets and other things. Well, thank you very much for joining us. It's fascinating just talking with you. Thank you. It was a lot of fun. So, Tracy, are you a, are you a high-life fan now? I kind of want to go watch a game. I want to bet on a game. I want to be the dumb money on the

sidelines of a game. I love that conversation. I thought that was like, I don't know, I just I thought it was fascinating. So I learned a lot about the sport, and it also was really helpful in bringing to life some of the concepts that we talk about all the time in markets, like algorithms. Absolutely. Like all these things in terms of simulations, decision trees, algorithms, and particularly that part about how, you know, the end we were talking about, about how if you get too big at a market or if everybody's chasing the same algorithmic strategy, how can all break down? Yeah, exactly. And my absolute favorite part was when he described the betting system and you have like all the math and then you have the algorithm. But how in the 90s, it finally ended up where you had to like have your computer make dial tones to enter in the debates.

There's a marriage of old style and modern trading techniques I thought was hilarious to imagine. Yeah, that was great. All right. That is all for odd lots. Thank you for listening. I'm Joe Wisenthall, managing editor of Bloomberg Markets. And you can follow me on Twitter at the stalwart. And I'm Tracy Alloway, executive editor of Bloomberg Markets, and I'm on Twitter at Tracy Allaway.

Thanks again. Joe and I are very proud of our new podcast, Oddlots, but we are also very proud of Bloomberg's other growing suite of original podcast, all designed to help you navigate the complexities of business, financial markets, and the global economy. So in addition to our own podcast, please don't miss Benchmark with Dan Moss, Tori Stillwell, and Aki-Ito, an informative, jargon-free look at the global economy. the inner workings of the global economy. Then there's deal of the week with our M&A reporter, Alex Sherman, which is a breakdown of the biggest M&A deals and gives you an inside peak at corporate boardrooms. All three shows are available on iTunes, SoundCloud, Pocketcast for Android, Bloomberg.com, and of course the Bloomberg Terminal. The news doesn't stop on the weekends. Context changes constantly. And now Bloomberg is the

place to stay on top of it all. Hi, I'm David Gurra. Join us every Saturday and Sunday for the new Bloomberg this weekend. I'm Christina Ruffini. We'll bring you the latest headlines, in-depth analysis, and big interviews, all the stories that hit home on your days off. And I'm Lisa Mateo. Watch and listen to Bloomberg this weekend for thoughtful, enlightening conversations about business, lifestyle, people, and culture. On Saturday mornings, we put the past week's events into context, examining what happened in the markets and the world. That on Sundays, we speak with journalists, columnists, and key political figures to prepare you for the week ahead. Join us, as soon as you wake up and bring us with you wherever your weekend plans take you.

Watch us on Bloomberg Television. Listen on Bloomberg Radio, stream the show live on the Bloomberg business app, or listen to the podcast. That's Bloomberg this weekend. Saturdays and Sundays starting at 7 a.m. Eastern. Make us part of your weekend routine on Bloomberg Television, radio, and wherever you get your podcasts. What separates good leaders from transformational ones? I'm Jessica Chen and in season two of leading by example, we'll sit down with executives like Grace Chen of Bertie Gray to find out. It's important to understand where you spike, but also really acknowledge where you don't and find people who can fill those gaps.

Listen to leading by example, executives making an impact on the IHeart radio app, Apple podcast, or wherever you get your podcasts.