1 00:00:04,880 --> 00:00:08,119 Speaker 1: Hello, and welcome to another episode of Odd Lots. I'm 2 00:00:08,200 --> 00:00:11,920 Speaker 1: Joe Wisenthal, Managing editor of Bloomberg Markets, and I'm Tracy Alloway, 3 00:00:11,960 --> 00:00:15,280 Speaker 1: Executive editor of Bloomberg Markets. Hey, Tracy, do you know 4 00:00:15,400 --> 00:00:19,320 Speaker 1: what the fastest sport in the world is? Uh? Sailing? 5 00:00:19,720 --> 00:00:23,439 Speaker 1: Race car driving? Uh, those are pretty good guesses, but no, 6 00:00:23,760 --> 00:00:28,400 Speaker 1: Actually the answer is high lie high lie highlight. Highlight 7 00:00:28,600 --> 00:00:34,080 Speaker 1: high Okay, highlight. It's actually originally a game that originated 8 00:00:34,159 --> 00:00:38,040 Speaker 1: in the Spanish Basque country. It's kind of like racquetball, 9 00:00:38,159 --> 00:00:41,600 Speaker 1: except the players play on this gigantic court. The ball 10 00:00:41,720 --> 00:00:46,240 Speaker 1: goes nearly two miles the rackets of these gigantic crald 11 00:00:46,280 --> 00:00:50,000 Speaker 1: things that the players wear over their hands. The ball 12 00:00:50,120 --> 00:00:52,640 Speaker 1: is hard as a rock, and oh yeah, if it 13 00:00:52,680 --> 00:00:54,440 Speaker 1: were to hit you in the head, the ball could 14 00:00:54,520 --> 00:01:00,000 Speaker 1: kill you. This sounds like a made up sport. Um, 15 00:01:00,080 --> 00:01:02,480 Speaker 1: why are we talking about this? It's a good question because, 16 00:01:02,520 --> 00:01:05,920 Speaker 1: in addition to how crazy and intense the game is, 17 00:01:06,440 --> 00:01:08,280 Speaker 1: you know, as I said, two hundred mile an hour 18 00:01:08,920 --> 00:01:12,520 Speaker 1: ball is hard as a golf ball, potentially deadly. People 19 00:01:12,560 --> 00:01:16,280 Speaker 1: actually gamble on highlight, kind of like horse racing. Uh. 20 00:01:16,560 --> 00:01:19,120 Speaker 1: The people a bunch of players play in a tournament 21 00:01:19,200 --> 00:01:22,080 Speaker 1: of sorts and then people bet on whether the different 22 00:01:22,080 --> 00:01:25,119 Speaker 1: players will win place or shows. So there's this big 23 00:01:25,160 --> 00:01:28,920 Speaker 1: gambling element to it that's really interesting. Our guest today 24 00:01:28,920 --> 00:01:31,360 Speaker 1: that we'll be talking to is Steven Skina. He's a 25 00:01:31,400 --> 00:01:34,400 Speaker 1: professor at stony Brook University and he wrote a book 26 00:01:34,440 --> 00:01:38,440 Speaker 1: all about gambling on Highlight Online and how he cracked 27 00:01:38,480 --> 00:01:40,680 Speaker 1: the system and he made a bunch of money. He 28 00:01:40,680 --> 00:01:43,000 Speaker 1: cracked the system, so he beat the house. Yeah, it's 29 00:01:43,000 --> 00:01:47,080 Speaker 1: basically impossible theoretically, you know, gambling, the house is always 30 00:01:47,120 --> 00:01:49,920 Speaker 1: supposed to lose. But in these games we're sort of 31 00:01:49,920 --> 00:01:52,920 Speaker 1: betting against other people and the crowd sets the odds. 32 00:01:53,040 --> 00:01:56,400 Speaker 1: It's actually possible. And not only did Steven beat the 33 00:01:56,400 --> 00:01:58,880 Speaker 1: system and make a bunch of money, there's some interesting 34 00:01:58,920 --> 00:02:01,760 Speaker 1: lessons in terms of eating the stock market and odds 35 00:02:01,800 --> 00:02:04,960 Speaker 1: games in general. Okay, so I'm excited because not only 36 00:02:05,000 --> 00:02:07,840 Speaker 1: am I about to learn about a sport which I've 37 00:02:07,880 --> 00:02:10,960 Speaker 1: never heard of before, but I'm also interested in making money. 38 00:02:11,040 --> 00:02:19,520 Speaker 1: So this sounds too all right, I think you've seen 39 00:02:19,560 --> 00:02:25,040 Speaker 1: it all well, think fast, experience the wall to wall 40 00:02:25,080 --> 00:02:29,400 Speaker 1: action and NonStop excitement that is Miamie Highlive see world 41 00:02:29,440 --> 00:02:32,160 Speaker 1: class athletes climbed the walls to catch a rock hard 42 00:02:32,200 --> 00:02:35,600 Speaker 1: ball flying. It speeds over a hundreds of miles an hour. 43 00:02:36,240 --> 00:02:44,200 Speaker 1: Think excitement, Think Miami, Higlan, I think fast Steven, thank 44 00:02:44,240 --> 00:02:46,000 Speaker 1: you very much for joining us. Thank you, it's nice 45 00:02:46,040 --> 00:02:49,079 Speaker 1: to be here. Tracy has never heard of highlight before. 46 00:02:49,600 --> 00:02:52,360 Speaker 1: What is highlight and how did you get interested in? So? 47 00:02:52,480 --> 00:02:55,880 Speaker 1: Highlight is as as you said, a besque game. Originally, 48 00:02:55,960 --> 00:02:59,120 Speaker 1: it's sort of like a variation on handball. Um. The 49 00:02:59,160 --> 00:03:01,600 Speaker 1: reason people are exposed to it in the United States 50 00:03:01,639 --> 00:03:06,160 Speaker 1: is typically because in Florida it's a betting venue. Um. 51 00:03:06,200 --> 00:03:09,960 Speaker 1: There are these stadiums called front Times in Miami and 52 00:03:10,080 --> 00:03:15,240 Speaker 1: Dania near Fort Lauderdale where you can watch mostly basque 53 00:03:15,840 --> 00:03:19,440 Speaker 1: players play the sport. And that's actually exactly how I 54 00:03:19,520 --> 00:03:21,960 Speaker 1: discovered it. My family used to go down to Florida, 55 00:03:22,200 --> 00:03:25,800 Speaker 1: my North Miami beach every winter for a couple of weeks, 56 00:03:26,120 --> 00:03:29,639 Speaker 1: and we went occasionally to UH the Dania Highlight front Time. 57 00:03:29,800 --> 00:03:32,040 Speaker 1: That was exactly the way that we got involved. You know, 58 00:03:32,040 --> 00:03:34,679 Speaker 1: our family would every year take its vacation visiting the 59 00:03:34,760 --> 00:03:38,040 Speaker 1: relatives in Florida. We would drive down and we would 60 00:03:38,200 --> 00:03:41,040 Speaker 1: UH one night go to the Highlight front time. Is 61 00:03:41,040 --> 00:03:44,280 Speaker 1: it fun to watch these games? It sounds intense from 62 00:03:44,320 --> 00:03:47,960 Speaker 1: Joe's description, it's I think it's incredibly exciting. I mean, 63 00:03:48,000 --> 00:03:50,200 Speaker 1: first of all, it's fun to watch them make these 64 00:03:50,200 --> 00:03:52,800 Speaker 1: plays because the ball is moving very fast, they have 65 00:03:52,920 --> 00:03:56,320 Speaker 1: to make, you know, great catches and very difficult throws. 66 00:03:56,360 --> 00:03:59,560 Speaker 1: But also the scoring system involved in Highlight has some 67 00:03:59,680 --> 00:04:03,040 Speaker 1: infra sting mathematics instructor that makes it kind of fun 68 00:04:03,080 --> 00:04:07,080 Speaker 1: to watch. So depending up on how you bet and um, 69 00:04:07,200 --> 00:04:10,760 Speaker 1: the chances of winning change extremely rapidly with every point, 70 00:04:11,080 --> 00:04:13,480 Speaker 1: and so it's it's it's very exciting because the situation 71 00:04:13,560 --> 00:04:16,240 Speaker 1: is always changing. Yeah, so I said it was kind 72 00:04:16,320 --> 00:04:19,120 Speaker 1: of like horse race betting and that you could bet 73 00:04:19,160 --> 00:04:20,840 Speaker 1: on wind, place their show. But in a way it's 74 00:04:20,839 --> 00:04:23,159 Speaker 1: a little more complicated. Why don't you just sort of 75 00:04:23,200 --> 00:04:26,640 Speaker 1: describe how the similarities real quickly and the differences. So 76 00:04:26,720 --> 00:04:28,920 Speaker 1: it is like horse race betting and that you can 77 00:04:28,960 --> 00:04:32,040 Speaker 1: bet on win, place and show, and that's certainly what um, 78 00:04:32,080 --> 00:04:36,719 Speaker 1: what we're gonna be doing. The difference is the scoring system. UM. Basically, 79 00:04:37,320 --> 00:04:39,840 Speaker 1: in Highlight the winner is the first one to get 80 00:04:39,880 --> 00:04:42,640 Speaker 1: the seven points, and they have eight teams that are 81 00:04:42,640 --> 00:04:45,640 Speaker 1: playing in any given match, but because of the size 82 00:04:45,680 --> 00:04:47,880 Speaker 1: of the court, only two teams can play at once. 83 00:04:48,520 --> 00:04:51,640 Speaker 1: The teams wait in a line. Their their uniform numbers 84 00:04:51,640 --> 00:04:54,760 Speaker 1: are one through weight, corresponding to where they start in line. 85 00:04:55,440 --> 00:04:59,320 Speaker 1: And originally the first two players play each other. Then 86 00:04:59,360 --> 00:05:02,520 Speaker 1: the winner keeps playing, gets a point and keeps playing. 87 00:05:02,520 --> 00:05:05,240 Speaker 1: The loser goes to the end of the line, and 88 00:05:06,120 --> 00:05:09,159 Speaker 1: they keep playing until you get to seven points. And 89 00:05:09,200 --> 00:05:11,600 Speaker 1: if you think about that kind of a scoring system, 90 00:05:11,640 --> 00:05:14,160 Speaker 1: it gives an advantage to the people who start early 91 00:05:14,920 --> 00:05:18,640 Speaker 1: because um, obviously they get first cracks at getting points, 92 00:05:19,200 --> 00:05:21,680 Speaker 1: and even if they lose, they are more likely to 93 00:05:21,720 --> 00:05:23,800 Speaker 1: be they're gonna be the first player to come up 94 00:05:23,839 --> 00:05:27,320 Speaker 1: for a second time. So they make the scorches them 95 00:05:27,360 --> 00:05:31,240 Speaker 1: even more complicated. Whereafter every trip through the queue once 96 00:05:31,480 --> 00:05:34,080 Speaker 1: meaning every player has played its first point, now every 97 00:05:34,120 --> 00:05:37,640 Speaker 1: subsequent point counts for two. And this makes for a 98 00:05:37,720 --> 00:05:40,880 Speaker 1: very complicated scoring system. That means that even if you're 99 00:05:40,960 --> 00:05:44,080 Speaker 1: very very close to winning, if you suddenly lose that point, 100 00:05:44,720 --> 00:05:46,080 Speaker 1: you go to the end of the line and you 101 00:05:46,160 --> 00:05:49,359 Speaker 1: might not get another chance to play again. And the 102 00:05:49,480 --> 00:05:53,280 Speaker 1: betting system, it's a paramutual odds what exactly to that man. 103 00:05:53,360 --> 00:05:56,560 Speaker 1: So paramutual means that you're betting against the other players, 104 00:05:56,600 --> 00:05:58,880 Speaker 1: and it's not me betting against the house. If I 105 00:05:58,920 --> 00:06:01,080 Speaker 1: was betting against the house, the odds of me winning 106 00:06:01,120 --> 00:06:03,920 Speaker 1: are very small. That's why the house is usually big. 107 00:06:04,279 --> 00:06:07,680 Speaker 1: But in a para mutual system, what happens is the money, 108 00:06:07,839 --> 00:06:10,120 Speaker 1: all the money that is bet in the accompetition is 109 00:06:11,160 --> 00:06:14,119 Speaker 1: thrown into a pool. The house skims off a fee 110 00:06:14,240 --> 00:06:18,640 Speaker 1: something and the rest is divided among the winners. So 111 00:06:18,680 --> 00:06:21,280 Speaker 1: in order to you know, to have a successful betting system, 112 00:06:21,480 --> 00:06:24,039 Speaker 1: you have to be better than the other players. So 113 00:06:24,160 --> 00:06:26,200 Speaker 1: the other betters like basically there are a bunch of 114 00:06:26,320 --> 00:06:28,960 Speaker 1: dumb people like me and my family who used to 115 00:06:29,000 --> 00:06:30,760 Speaker 1: go there from time to time and bet and we 116 00:06:30,800 --> 00:06:34,039 Speaker 1: didn't know anything. And so theoretically, if you're really smart 117 00:06:34,120 --> 00:06:37,200 Speaker 1: and studied, we're the fish that you could take advantag. 118 00:06:37,200 --> 00:06:38,840 Speaker 1: That was the attraction. I mean again, it's it's a 119 00:06:39,080 --> 00:06:41,920 Speaker 1: very exciting sport. It's probably a hard sport to know 120 00:06:42,600 --> 00:06:45,200 Speaker 1: the players very well. You know, I don't think most fans. 121 00:06:45,560 --> 00:06:48,279 Speaker 1: Most fans are not that intense, but they go once 122 00:06:48,320 --> 00:06:50,480 Speaker 1: a year, twice a year. And before we get to 123 00:06:50,880 --> 00:06:54,160 Speaker 1: your sort of rigorous approach. A story in your book. 124 00:06:54,279 --> 00:06:57,480 Speaker 1: You won the first ever bet you placed on Highlights, right, So, 125 00:06:57,480 --> 00:06:59,479 Speaker 1: so the reason we got really hooked on this was 126 00:06:59,520 --> 00:07:02,160 Speaker 1: that when and our parents drove down the floor and 127 00:07:02,240 --> 00:07:04,840 Speaker 1: let us go to highlight one night, they also let 128 00:07:04,920 --> 00:07:06,919 Speaker 1: us make one bet. They gave us two dollars and 129 00:07:06,960 --> 00:07:11,040 Speaker 1: they said, you make one bet, and we followed the 130 00:07:11,080 --> 00:07:14,080 Speaker 1: bet that was listed in the local tow sheet, knowing nothing, 131 00:07:14,640 --> 00:07:18,119 Speaker 1: and astonishingly, it was a trifecta combination of first place 132 00:07:18,120 --> 00:07:21,400 Speaker 1: and show that astonishingly one. And so we won, you know, 133 00:07:21,400 --> 00:07:24,160 Speaker 1: a hundred four dollars. And this was an amazing amount 134 00:07:24,200 --> 00:07:26,800 Speaker 1: of money to a bunch of kids back in the seventies. 135 00:07:27,200 --> 00:07:30,960 Speaker 1: And uh, that's what I was, probably about twelve or 136 00:07:31,000 --> 00:07:33,480 Speaker 1: so at the time. Turning two hundred dollars into a 137 00:07:33,520 --> 00:07:39,240 Speaker 1: hundreds exciting any time, but but when you're a kid 138 00:07:39,280 --> 00:07:42,160 Speaker 1: in the seventies, it must be absolutely Uh. I could 139 00:07:42,200 --> 00:07:44,000 Speaker 1: see how you would then get hooked for life on 140 00:07:44,000 --> 00:07:47,560 Speaker 1: the game. Um, all right, let's fast forward a little bit, 141 00:07:47,960 --> 00:07:51,880 Speaker 1: and so you're a professor at a Stony Brook. Talk 142 00:07:52,000 --> 00:07:56,280 Speaker 1: us about how you started on your path to systematizing 143 00:07:56,640 --> 00:07:59,720 Speaker 1: a gambling system. For highlight and what you did. So 144 00:08:00,200 --> 00:08:03,080 Speaker 1: when you look at the scoring system again, a highlight 145 00:08:03,120 --> 00:08:07,200 Speaker 1: game is played in discreete points player one place player two. 146 00:08:07,280 --> 00:08:09,560 Speaker 1: One of them wins, the other goes to the end 147 00:08:09,560 --> 00:08:13,080 Speaker 1: of the line. You could imagine simulating the result of 148 00:08:13,120 --> 00:08:16,920 Speaker 1: a highlight match by flipping a coin for every particular 149 00:08:17,000 --> 00:08:20,680 Speaker 1: point player one. If player one is maybe better than 150 00:08:20,720 --> 00:08:23,240 Speaker 1: player two, maybe you'd say it as a six chance 151 00:08:23,280 --> 00:08:26,600 Speaker 1: of winning the first point. And if you could figure 152 00:08:26,600 --> 00:08:29,640 Speaker 1: out the odds that one player is going that every 153 00:08:29,640 --> 00:08:33,240 Speaker 1: player has against every other player in that they might 154 00:08:33,360 --> 00:08:37,400 Speaker 1: encounter in a match, you can build a simulation to 155 00:08:37,720 --> 00:08:41,280 Speaker 1: use random numbers to play through and simulate each match. 156 00:08:41,720 --> 00:08:44,080 Speaker 1: So is it like it sounds like a series of 157 00:08:44,160 --> 00:08:48,000 Speaker 1: tree charts almost right? You assigned probabilities for each outcome 158 00:08:48,040 --> 00:08:50,680 Speaker 1: and then you have them sort of branching across all 159 00:08:50,720 --> 00:08:53,440 Speaker 1: the possible outcomes. Right, so you you could view this 160 00:08:53,480 --> 00:08:56,000 Speaker 1: as a tree process. It's a it is a branching process. 161 00:08:56,000 --> 00:08:59,640 Speaker 1: It's a tree process where at every point in the 162 00:08:59,640 --> 00:09:02,040 Speaker 1: tree is every note in the tree is basically two 163 00:09:02,120 --> 00:09:04,760 Speaker 1: players playing each other with a certain score and a 164 00:09:04,880 --> 00:09:09,200 Speaker 1: certain status of players in the queue to come. Then 165 00:09:09,520 --> 00:09:11,640 Speaker 1: depending upon who wins it, you go to a different 166 00:09:11,640 --> 00:09:14,040 Speaker 1: state in the process, and this process ends when you 167 00:09:14,120 --> 00:09:16,679 Speaker 1: have identified who comes in for a second and third. 168 00:09:16,960 --> 00:09:19,680 Speaker 1: So this part sounds is where it seems to really 169 00:09:19,720 --> 00:09:22,640 Speaker 1: diverge from say horse racing, where you just have one event, 170 00:09:22,920 --> 00:09:25,880 Speaker 1: there's one, two, and three, not really all these different 171 00:09:25,880 --> 00:09:29,040 Speaker 1: permutations and sequences. So horse racing is not at the 172 00:09:29,080 --> 00:09:31,200 Speaker 1: scret event kind of a game. This is maybe a 173 00:09:31,200 --> 00:09:35,079 Speaker 1: little bit more akin to I would say, um baseball 174 00:09:35,120 --> 00:09:37,920 Speaker 1: than football. Baseball is a bunch of the scret events. 175 00:09:37,960 --> 00:09:41,840 Speaker 1: There's pitches and things happen. In basketball, things are very continuous. 176 00:09:41,840 --> 00:09:44,840 Speaker 1: In horse racing, things seem continue with So when you 177 00:09:44,880 --> 00:09:48,079 Speaker 1: started developing the system, when are we talking about how 178 00:09:48,120 --> 00:09:50,360 Speaker 1: long ago is this? This is something we started in 179 00:09:50,400 --> 00:09:52,439 Speaker 1: the I would say early nineties. If you have to 180 00:09:52,520 --> 00:09:54,520 Speaker 1: get back there, it's probably the story about in the 181 00:09:54,559 --> 00:09:57,920 Speaker 1: early nineties. Okay, so you broke highlight down into this 182 00:09:58,080 --> 00:10:01,600 Speaker 1: series of discrete events, then what's next in terms of 183 00:10:01,640 --> 00:10:04,240 Speaker 1: the creation of your gambling system. So again, once you 184 00:10:04,320 --> 00:10:07,319 Speaker 1: have the ability to view this as this tree process 185 00:10:07,440 --> 00:10:10,520 Speaker 1: or as this um you can simulate one game, you 186 00:10:10,559 --> 00:10:13,560 Speaker 1: can now simulate a million games or or or more 187 00:10:14,000 --> 00:10:17,640 Speaker 1: and see what this probability distribution is of outcomes, and 188 00:10:17,720 --> 00:10:22,800 Speaker 1: you can start to look at for every combination of first, second, 189 00:10:22,800 --> 00:10:26,560 Speaker 1: and third, how often did it come in? And from 190 00:10:26,559 --> 00:10:30,040 Speaker 1: this that gives you some insight into what things you 191 00:10:30,080 --> 00:10:33,040 Speaker 1: should bet on. But things get a little bit more complicated. First, 192 00:10:33,120 --> 00:10:36,400 Speaker 1: you have to accurately model how good the players are, 193 00:10:36,679 --> 00:10:38,520 Speaker 1: so you have a good guess as to how often 194 00:10:38,880 --> 00:10:41,920 Speaker 1: player one is gonna be player two. And more that 195 00:10:42,040 --> 00:10:44,040 Speaker 1: you have to get a build a model of how 196 00:10:44,080 --> 00:10:46,360 Speaker 1: the public is going to bet um. Again, it's a 197 00:10:46,360 --> 00:10:51,080 Speaker 1: paramutual system. I'm betting against the public. If everyone else 198 00:10:51,080 --> 00:10:54,120 Speaker 1: in the public was someone who programmed the computer and 199 00:10:54,160 --> 00:10:55,960 Speaker 1: did the analysis the way I did, I would have 200 00:10:56,040 --> 00:11:00,240 Speaker 1: no advantage. Are you looking for almost pricing descrepants? Ease 201 00:11:00,280 --> 00:11:02,560 Speaker 1: between where you think the outcome of the game is 202 00:11:02,559 --> 00:11:05,199 Speaker 1: going to come and where people are actually betting exactly 203 00:11:05,320 --> 00:11:08,400 Speaker 1: so that that that again there is a a based 204 00:11:08,400 --> 00:11:13,680 Speaker 1: on our simulation, basically an underlying real probability distribution which 205 00:11:13,679 --> 00:11:15,960 Speaker 1: would in some sense and for a price as to 206 00:11:16,000 --> 00:11:19,199 Speaker 1: what would be a fair return for a two dollar 207 00:11:19,320 --> 00:11:22,400 Speaker 1: bet on that outcome. And then you know we look 208 00:11:22,440 --> 00:11:25,160 Speaker 1: for pricing disreferences, so let's talk about them. Are there 209 00:11:25,200 --> 00:11:28,640 Speaker 1: some persistent biases that you learned about in how the 210 00:11:28,679 --> 00:11:31,240 Speaker 1: public bets? Like you know, I'm the public is silly 211 00:11:31,360 --> 00:11:35,079 Speaker 1: enough to mostly bet on by powerball tickets. People are irrational, 212 00:11:35,360 --> 00:11:37,600 Speaker 1: So what kind of irrationality is did you see that 213 00:11:37,640 --> 00:11:40,440 Speaker 1: you can take advantage of? What one interesting property is 214 00:11:40,480 --> 00:11:44,880 Speaker 1: that since it's very very hard for all the players 215 00:11:45,000 --> 00:11:47,920 Speaker 1: with high uniform numbers to do very well. If you 216 00:11:47,920 --> 00:11:52,160 Speaker 1: could imagine players six, seven, and eight, in order for six, seven, 217 00:11:52,200 --> 00:11:54,760 Speaker 1: and eight to do well well, six has to beat 218 00:11:55,280 --> 00:11:57,439 Speaker 1: seven in order to do well, but that puts seven 219 00:11:57,440 --> 00:11:59,880 Speaker 1: at the end of the line, and seven has to 220 00:12:00,040 --> 00:12:01,840 Speaker 1: eat eight, but that puts eight at the end of 221 00:12:01,880 --> 00:12:04,120 Speaker 1: the line. It's very very hard for there to be 222 00:12:04,200 --> 00:12:08,000 Speaker 1: combinations where all the big numbers come in and essentially 223 00:12:08,040 --> 00:12:11,120 Speaker 1: almost impossible. And yet you would always see people betting 224 00:12:11,120 --> 00:12:12,800 Speaker 1: on this because they didn't know that that way, they 225 00:12:12,800 --> 00:12:15,880 Speaker 1: were betting on essentially an outcome that essentially couldn't happen, 226 00:12:15,960 --> 00:12:18,640 Speaker 1: and the odds must look pretty juicy for those characters. 227 00:12:18,640 --> 00:12:20,440 Speaker 1: For those players, I mean, and it's up to the 228 00:12:20,440 --> 00:12:22,760 Speaker 1: payoff would be good if they want. Because there's only 229 00:12:22,760 --> 00:12:25,280 Speaker 1: one person betting on that during the course of any match, 230 00:12:25,600 --> 00:12:29,280 Speaker 1: but it's never gonna happen. So so so here's our 231 00:12:29,320 --> 00:12:31,880 Speaker 1: system would look for these disrepencies. The other thing that 232 00:12:31,920 --> 00:12:34,560 Speaker 1: we would look for that's sort of related to the 233 00:12:34,640 --> 00:12:36,720 Speaker 1: kind of models people build for trading, is we'd have 234 00:12:36,760 --> 00:12:41,080 Speaker 1: to look at what our impact on the betting pool is. So, um, 235 00:12:41,120 --> 00:12:44,800 Speaker 1: if we bet on something and we win, it doesn't 236 00:12:44,800 --> 00:12:46,480 Speaker 1: pay for us to bet a lot of money on 237 00:12:46,520 --> 00:12:49,880 Speaker 1: that outcome because we're just dividing the pool among all 238 00:12:49,880 --> 00:12:53,240 Speaker 1: the winning tickets, and so every subsequent ticket we would 239 00:12:53,280 --> 00:12:56,600 Speaker 1: buy would would have a lower and lower expected value. 240 00:12:56,760 --> 00:12:59,880 Speaker 1: All right, So you have the mathematics, all the trees, 241 00:13:00,160 --> 00:13:03,479 Speaker 1: you have the nature of how people bet, so the payoffs. 242 00:13:03,640 --> 00:13:06,640 Speaker 1: You also have this calculation about how your own bets 243 00:13:06,679 --> 00:13:09,600 Speaker 1: might affect the results. Now let's talk about putting it 244 00:13:09,679 --> 00:13:12,760 Speaker 1: into practice. What did you do then? So the question 245 00:13:12,800 --> 00:13:14,800 Speaker 1: now is how do we actually go bet on this thing? 246 00:13:14,800 --> 00:13:17,679 Speaker 1: We we couldn't have somebody stationed at the HIGHLFE front 247 00:13:17,760 --> 00:13:20,640 Speaker 1: on every day making our bets the front and we 248 00:13:20,640 --> 00:13:22,600 Speaker 1: were interested in betting in was in Connecticut, I was 249 00:13:22,640 --> 00:13:24,720 Speaker 1: in New York. This was not you know, I'm not 250 00:13:24,760 --> 00:13:28,160 Speaker 1: that crazy, but it turned out that Connecticut did have 251 00:13:28,200 --> 00:13:31,000 Speaker 1: an O TB and off track betting operation where they 252 00:13:31,040 --> 00:13:33,640 Speaker 1: had a phone system where you could dial it in 253 00:13:33,960 --> 00:13:37,040 Speaker 1: and dial in your bets. And so we programmed the 254 00:13:37,040 --> 00:13:40,760 Speaker 1: computer computers back then had these things called modems for 255 00:13:41,200 --> 00:13:44,080 Speaker 1: the kids listening, which I remember that I remember the 256 00:13:44,120 --> 00:13:46,760 Speaker 1: A O L dial up modem, these dial up modems, 257 00:13:46,760 --> 00:13:49,199 Speaker 1: and so you could in some sense, therefore, you had 258 00:13:49,200 --> 00:13:51,760 Speaker 1: a device that you could program that could make phone 259 00:13:51,800 --> 00:13:55,520 Speaker 1: calls and could likewise push buttons in some sense push 260 00:13:55,600 --> 00:13:58,400 Speaker 1: buttons on phones. And so we built a system that 261 00:13:58,440 --> 00:14:02,320 Speaker 1: would um take our bets and convert that into the 262 00:14:02,400 --> 00:14:05,640 Speaker 1: dial tone instructions that would be necessary to place this 263 00:14:05,720 --> 00:14:08,760 Speaker 1: bet at the Connecticut oft TACH betting operation. And so 264 00:14:08,800 --> 00:14:12,280 Speaker 1: we built essentially a complete, you know, programmed trading system 265 00:14:12,360 --> 00:14:15,680 Speaker 1: in highlight. Every day, it would identify go over the web, 266 00:14:15,720 --> 00:14:20,080 Speaker 1: identify what we're the game matches, and who was playing. 267 00:14:20,360 --> 00:14:23,120 Speaker 1: It would simulate each one a million times. It would 268 00:14:23,160 --> 00:14:26,040 Speaker 1: determine the most profitable betting outcomes, and then it would 269 00:14:26,040 --> 00:14:29,440 Speaker 1: phone it into O T B attempt to implement our trade. 270 00:14:29,520 --> 00:14:33,560 Speaker 1: This is amazing. This sounds like algorithmically driven high frequency 271 00:14:33,920 --> 00:14:36,640 Speaker 1: highlight trading essentially. What I love about that is that 272 00:14:36,680 --> 00:14:39,480 Speaker 1: you're exactly right, except there's this very old school part 273 00:14:39,560 --> 00:14:43,320 Speaker 1: because there's high frequency algorithmic highlight trading except for the 274 00:14:43,400 --> 00:14:46,160 Speaker 1: very last part, and it involves dial tones going through 275 00:14:46,160 --> 00:14:49,680 Speaker 1: a phone tree, and so it's this incredibly modern seeming 276 00:14:49,720 --> 00:14:52,480 Speaker 1: idea and then this very old school actually process of 277 00:14:52,520 --> 00:14:55,160 Speaker 1: placing the trades at the very end. I can't say 278 00:14:55,200 --> 00:14:57,280 Speaker 1: that the you know, the the We wrote a book 279 00:14:57,280 --> 00:15:01,480 Speaker 1: about this book called Calculated Bets and market for it 280 00:15:01,600 --> 00:15:04,200 Speaker 1: ended up being not high live fans because it's not 281 00:15:04,240 --> 00:15:06,440 Speaker 1: actually a big universe, but but if they're out to be, 282 00:15:06,680 --> 00:15:09,000 Speaker 1: yet a lot of play in people who were doing 283 00:15:09,320 --> 00:15:12,760 Speaker 1: trading and building these program trading operations, because it is 284 00:15:13,240 --> 00:15:15,640 Speaker 1: essentially the same idea that people are using in the 285 00:15:15,680 --> 00:15:19,160 Speaker 1: same technologies, and it kind of explains basically how these 286 00:15:19,200 --> 00:15:21,520 Speaker 1: things work. I want to just step back and ask 287 00:15:21,600 --> 00:15:24,440 Speaker 1: the dumb question, how would you do? What's an algorithm? 288 00:15:24,600 --> 00:15:27,480 Speaker 1: We hear it all the time, algorithmic trading, and people 289 00:15:27,520 --> 00:15:29,920 Speaker 1: have some idea that it means computers in math, But 290 00:15:30,000 --> 00:15:32,600 Speaker 1: what does this actually mean for someone in plain English? 291 00:15:32,640 --> 00:15:34,760 Speaker 1: As it as it is said in in the world 292 00:15:34,800 --> 00:15:38,440 Speaker 1: of algorithmic trading. It is typically a it's really I guess, 293 00:15:38,440 --> 00:15:42,680 Speaker 1: a programmed procedure from making decisions so that uh, you know, 294 00:15:42,720 --> 00:15:46,160 Speaker 1: there is a decision in a program trading system. There 295 00:15:46,200 --> 00:15:49,240 Speaker 1: has to be somebody making a decision to buy or 296 00:15:49,280 --> 00:15:52,040 Speaker 1: sell this particular stock at this particular time. So a 297 00:15:52,080 --> 00:15:54,160 Speaker 1: set of rules basically, so it can be sort of 298 00:15:54,200 --> 00:15:56,640 Speaker 1: a set of rules as usually some level of input. 299 00:15:56,840 --> 00:15:59,080 Speaker 1: I would say, it could be a simulation, it could 300 00:15:59,120 --> 00:16:02,480 Speaker 1: be sets of rules. It's some kind of a procedure 301 00:16:02,920 --> 00:16:05,400 Speaker 1: that that the sides that this bet or this series 302 00:16:05,440 --> 00:16:09,160 Speaker 1: of bets are profitable, and goes and executes them without 303 00:16:09,320 --> 00:16:13,320 Speaker 1: human involvement. And now you set up the trading system, 304 00:16:13,400 --> 00:16:16,960 Speaker 1: they're dialed in. How much money did you make? Well, 305 00:16:16,840 --> 00:16:20,400 Speaker 1: we may we made okay by percentage wise. Again, recognize 306 00:16:20,440 --> 00:16:23,120 Speaker 1: that the betting pool and highlight is very small. So 307 00:16:23,200 --> 00:16:26,000 Speaker 1: that I told you that a you know, making too 308 00:16:26,080 --> 00:16:29,080 Speaker 1: many bets on any particular match would would rapidly saturate 309 00:16:29,120 --> 00:16:31,080 Speaker 1: the pool and none of the bets would be profitable. 310 00:16:31,680 --> 00:16:33,480 Speaker 1: But but over the course of our trade, we made 311 00:16:33,480 --> 00:16:38,680 Speaker 1: over return on our investment. It was sizeable enough that 312 00:16:38,720 --> 00:16:41,800 Speaker 1: it got to be a little scary to UM run 313 00:16:41,840 --> 00:16:44,760 Speaker 1: it on university research machines, and so we eventually ended. 314 00:16:44,840 --> 00:16:47,360 Speaker 1: It wasn't so much that I have deep regrets about 315 00:16:47,360 --> 00:16:50,840 Speaker 1: turning the system off. What time frame we we had 316 00:16:50,840 --> 00:16:54,760 Speaker 1: it running for about a six month period. Six months, Yes, 317 00:16:55,000 --> 00:16:57,320 Speaker 1: would you would you have gotten to the point if 318 00:16:57,320 --> 00:16:59,560 Speaker 1: you had continued with it where you would just be 319 00:16:59,640 --> 00:17:03,080 Speaker 1: the entire your market UM if we kept um again, 320 00:17:03,080 --> 00:17:04,960 Speaker 1: if if we made our bets bigger and bigger than 321 00:17:05,000 --> 00:17:07,119 Speaker 1: we could have easily become the entire market you know. 322 00:17:07,440 --> 00:17:09,600 Speaker 1: In fact, one of the one of the things that 323 00:17:09,720 --> 00:17:11,960 Speaker 1: was key to our systems again we our system bet 324 00:17:12,000 --> 00:17:16,640 Speaker 1: on Trifecta's combinations of wind, Place and show, which are 325 00:17:17,040 --> 00:17:20,560 Speaker 1: rare events and but but really didn't make it payoff 326 00:17:20,640 --> 00:17:22,440 Speaker 1: was they had a special type of bet called to 327 00:17:22,480 --> 00:17:26,040 Speaker 1: Trifecta box where we could buy a particular set of 328 00:17:26,080 --> 00:17:30,720 Speaker 1: all combinations of three numbers UM cheaper than we could 329 00:17:30,760 --> 00:17:32,840 Speaker 1: the corresponding tickets. And our goal was really to have 330 00:17:32,880 --> 00:17:35,600 Speaker 1: as little impact on the pool as possible, and that 331 00:17:35,640 --> 00:17:37,920 Speaker 1: was really what was necessary. It really wasn't a big 332 00:17:37,960 --> 00:17:40,560 Speaker 1: margin here. If someone wants to do this, I mean, 333 00:17:40,600 --> 00:17:42,840 Speaker 1: I know that it's harder, but what are the key 334 00:17:42,920 --> 00:17:46,240 Speaker 1: areas of mathematics to study. So again I am a 335 00:17:46,240 --> 00:17:49,679 Speaker 1: computer scientist, and so in this case there was um, 336 00:17:49,720 --> 00:17:52,840 Speaker 1: you know, to understand things about Monte Carlo simulations. Again 337 00:17:52,840 --> 00:17:55,560 Speaker 1: we were talking about this tree process. Um, you could 338 00:17:55,640 --> 00:17:59,280 Speaker 1: view this as building a tree that you exhaustively analyze 339 00:17:59,280 --> 00:18:01,480 Speaker 1: where you could simul late at using something called Monte 340 00:18:01,560 --> 00:18:04,639 Speaker 1: Carlo simulation, where you really did use random numbers to 341 00:18:04,760 --> 00:18:07,960 Speaker 1: describe the path down the trade. So knowing computer science 342 00:18:08,040 --> 00:18:11,600 Speaker 1: is a good thing. Um, you know, knowing something about statistics. 343 00:18:11,920 --> 00:18:14,240 Speaker 1: You know, data science is a new field called data science, 344 00:18:14,240 --> 00:18:16,520 Speaker 1: which is the kind of area where my my lab works. 345 00:18:16,760 --> 00:18:18,960 Speaker 1: And uh then this kind of field that this is 346 00:18:18,960 --> 00:18:20,680 Speaker 1: the kind of stuff that I think is good learning 347 00:18:20,680 --> 00:18:23,600 Speaker 1: how to build models, this kind of thing. So you 348 00:18:23,640 --> 00:18:26,239 Speaker 1: mentioned you came out of this book Calculated Beds, and 349 00:18:26,280 --> 00:18:28,800 Speaker 1: it wasn't a huge hit among high life fans because 350 00:18:28,840 --> 00:18:31,359 Speaker 1: there aren't that many high life fans, but it got 351 00:18:31,400 --> 00:18:34,240 Speaker 1: a lot of followers among people who play the market, 352 00:18:34,359 --> 00:18:37,280 Speaker 1: people in banks. What are some of the key lessons 353 00:18:37,320 --> 00:18:39,239 Speaker 1: in terms of what you did and how else they 354 00:18:39,280 --> 00:18:42,359 Speaker 1: apply to someone wanting to play the markets and setting 355 00:18:42,400 --> 00:18:45,320 Speaker 1: up a trading system. So the first thing that I 356 00:18:45,359 --> 00:18:50,560 Speaker 1: would say is that that markets are in general relatively efficient, 357 00:18:50,720 --> 00:18:54,040 Speaker 1: even in high Lie where we had you know, these 358 00:18:54,119 --> 00:18:57,320 Speaker 1: crazy you know, the people who were watching embedding workers only, 359 00:18:57,359 --> 00:18:59,760 Speaker 1: these people who went once a year and didn't know anything, 360 00:18:59,840 --> 00:19:02,600 Speaker 1: the pools of dumb money. I was surprised how hard 361 00:19:02,640 --> 00:19:04,880 Speaker 1: it was for us to build a system that actually 362 00:19:04,960 --> 00:19:07,960 Speaker 1: did um, did have a positive return. I thought it 363 00:19:08,000 --> 00:19:10,479 Speaker 1: was gonna be a lot easier than that than it 364 00:19:10,480 --> 00:19:14,000 Speaker 1: turned out to be. And that's that's probably a lesson 365 00:19:14,040 --> 00:19:17,440 Speaker 1: that most markets are are more efficient than you would think. 366 00:19:17,680 --> 00:19:19,520 Speaker 1: You know, even in horse ray. They've been done studies 367 00:19:19,560 --> 00:19:23,000 Speaker 1: in horse racing, and the markets there are relatively efficient. 368 00:19:23,040 --> 00:19:26,119 Speaker 1: You know, the fact is that there's a large um 369 00:19:26,280 --> 00:19:30,800 Speaker 1: transaction cost essentially of the house keeps, and that's a 370 00:19:30,880 --> 00:19:34,600 Speaker 1: large transaction cost, and that's white people lose. So certain 371 00:19:34,640 --> 00:19:36,840 Speaker 1: things are models are harder to build than than you 372 00:19:36,840 --> 00:19:39,720 Speaker 1: would think. That's I guess one lesson here. The other 373 00:19:39,920 --> 00:19:42,720 Speaker 1: is that if you're careful and you're you're thinking hard enough, 374 00:19:42,760 --> 00:19:44,560 Speaker 1: and you beat on it and maybe there's something there. 375 00:19:44,960 --> 00:19:49,040 Speaker 1: Does the lesson of not saturating the market apply to 376 00:19:49,280 --> 00:19:52,639 Speaker 1: broader financial markets because obviously we hear a lot today 377 00:19:53,200 --> 00:19:57,400 Speaker 1: about um high frequency trading, algorithmically driven trading, and it's 378 00:19:57,440 --> 00:20:00,200 Speaker 1: impact on markets. What do you think, Yeah, so it's 379 00:20:00,200 --> 00:20:03,240 Speaker 1: certainly the case that in any market, you if you 380 00:20:03,320 --> 00:20:07,600 Speaker 1: bet enough, you're eventually betting against yourself. And uh, the 381 00:20:07,600 --> 00:20:10,800 Speaker 1: the advantage of the financial markets that they're generally large 382 00:20:10,920 --> 00:20:13,760 Speaker 1: enough that you can put in a tremendous amount of 383 00:20:13,760 --> 00:20:16,680 Speaker 1: capital and play before it, you know, you really start 384 00:20:16,720 --> 00:20:19,720 Speaker 1: betting against yourself. But again, you know many in many 385 00:20:19,800 --> 00:20:24,639 Speaker 1: hedge funds, in some sense there's they will occasionally occasionally 386 00:20:24,640 --> 00:20:26,920 Speaker 1: a hedge funds will return capital if they can't think 387 00:20:26,920 --> 00:20:29,960 Speaker 1: they can invest it efficiently enough. And that's basically because 388 00:20:30,000 --> 00:20:32,360 Speaker 1: of these saturation effects. But what about if you get 389 00:20:32,359 --> 00:20:36,880 Speaker 1: a market that becomes dominated by algorithmic trading and they 390 00:20:36,920 --> 00:20:39,280 Speaker 1: all kind of feed on each other. Does that end 391 00:20:39,320 --> 00:20:43,040 Speaker 1: up having the same effect. It's an interesting question. Um, 392 00:20:43,080 --> 00:20:47,440 Speaker 1: if everybody was doing the exact same thing in a market, 393 00:20:47,840 --> 00:20:51,159 Speaker 1: then there wouldn't be an interesting market going on. And 394 00:20:51,200 --> 00:20:54,720 Speaker 1: so the question of whether whether algorithmic trading is going 395 00:20:54,760 --> 00:20:58,600 Speaker 1: to eventually get into a world where you know, sometimes 396 00:20:58,800 --> 00:21:01,240 Speaker 1: people are betting against them where nothing is happening, It 397 00:21:01,320 --> 00:21:04,639 Speaker 1: depends upon the traders doing different things. I guess markets 398 00:21:04,640 --> 00:21:08,160 Speaker 1: would only get into trouble if all the different traders 399 00:21:08,160 --> 00:21:10,320 Speaker 1: were doing the exact same things. That's I guess when 400 00:21:10,320 --> 00:21:14,040 Speaker 1: you get into bubbles and when you get into into crashes, 401 00:21:14,080 --> 00:21:17,840 Speaker 1: there any sort of final key lessons for markets from 402 00:21:17,880 --> 00:21:20,719 Speaker 1: what you did. Has anyone has anyone written to you 403 00:21:20,760 --> 00:21:23,000 Speaker 1: and said they've used your book and made a fortune? 404 00:21:23,040 --> 00:21:26,159 Speaker 1: And I feel I have heard from every person who 405 00:21:26,240 --> 00:21:28,080 Speaker 1: has read the book the people the book is and 406 00:21:28,119 --> 00:21:31,000 Speaker 1: I acted books that did reasonably well, but it's still 407 00:21:31,000 --> 00:21:34,440 Speaker 1: the case that uh, a lot of people felt very 408 00:21:34,520 --> 00:21:37,240 Speaker 1: very close to this book because it does tell a 409 00:21:37,320 --> 00:21:39,560 Speaker 1: story that's that's a kiss akin to what a lot 410 00:21:39,640 --> 00:21:42,760 Speaker 1: of traders basically do. And UM, you know, I hear 411 00:21:42,800 --> 00:21:45,160 Speaker 1: from people. I occasionally hear from people who want to 412 00:21:45,200 --> 00:21:47,240 Speaker 1: want me to get involved in their betting scheme. I 413 00:21:47,280 --> 00:21:51,200 Speaker 1: heard from a Russian syndicate recently that wanted UH to 414 00:21:51,240 --> 00:21:55,520 Speaker 1: do trading in um soccer pools and uh. And I've 415 00:21:55,640 --> 00:21:59,320 Speaker 1: I've hung around a gambling syndicate in Macau where they've 416 00:21:59,320 --> 00:22:02,240 Speaker 1: been in horse racing and uh. And so you know, 417 00:22:02,280 --> 00:22:04,920 Speaker 1: so there are these these and I've also spoken through 418 00:22:04,960 --> 00:22:07,320 Speaker 1: a lot of traders, and uh again we met at 419 00:22:07,320 --> 00:22:11,760 Speaker 1: a financial conference. So it's been an interesting leading into 420 00:22:11,760 --> 00:22:14,480 Speaker 1: a world that's quite different from me as a computer scientist. 421 00:22:14,880 --> 00:22:18,920 Speaker 1: Your system was ultimately based on pure mathematics. Does that 422 00:22:18,960 --> 00:22:22,120 Speaker 1: take the emotion out of winning and making the bet? 423 00:22:22,400 --> 00:22:24,479 Speaker 1: It was true that that that there was sort of 424 00:22:24,560 --> 00:22:27,119 Speaker 1: the fact that there was a real event happening, that 425 00:22:27,200 --> 00:22:30,240 Speaker 1: they were really these besques tossing a ball around was 426 00:22:30,280 --> 00:22:32,640 Speaker 1: really an abstraction. You know, every day I would get 427 00:22:32,720 --> 00:22:36,359 Speaker 1: email from my machine about how we did, and every 428 00:22:36,440 --> 00:22:39,800 Speaker 1: night the computer would play a million simulations of this 429 00:22:39,800 --> 00:22:42,640 Speaker 1: game that was going to happen tomorrow. But and and 430 00:22:42,680 --> 00:22:45,280 Speaker 1: somehow we were divorced from the real aspect of it. 431 00:22:45,640 --> 00:22:48,119 Speaker 1: And that may be true in certain certain aspects of 432 00:22:48,160 --> 00:22:51,080 Speaker 1: the markets. I mean, people are busy trading stocks around 433 00:22:51,240 --> 00:22:53,800 Speaker 1: in some ways, quite independent of whether or not these 434 00:22:53,800 --> 00:22:56,320 Speaker 1: are You know that there are companies there, and that 435 00:22:56,359 --> 00:22:58,720 Speaker 1: people are working and people are building things, and there's 436 00:22:58,760 --> 00:23:01,320 Speaker 1: things happening. So there's a certain sense in which this 437 00:23:01,359 --> 00:23:04,040 Speaker 1: was an abstraction of the world that that may may 438 00:23:04,040 --> 00:23:05,919 Speaker 1: have felt a little bit funny when you think about it. 439 00:23:06,000 --> 00:23:09,120 Speaker 1: And last question, are you doing any betting on anything? 440 00:23:09,280 --> 00:23:11,920 Speaker 1: Or these days or back to hip pure academic stuff. 441 00:23:11,960 --> 00:23:14,480 Speaker 1: I am, I am up again. I am a professor. 442 00:23:14,640 --> 00:23:17,560 Speaker 1: I I you know, I live a clean life. But 443 00:23:17,560 --> 00:23:20,040 Speaker 1: but again, my my research area these days is related 444 00:23:20,080 --> 00:23:22,199 Speaker 1: to data science data analysis. We do a lot of 445 00:23:22,240 --> 00:23:25,960 Speaker 1: projects related to data modeling and uh things like this, 446 00:23:26,080 --> 00:23:28,200 Speaker 1: and so every once in a while my work touches 447 00:23:28,240 --> 00:23:30,960 Speaker 1: on some kind of a model related to that does 448 00:23:31,000 --> 00:23:34,159 Speaker 1: have relations with financial markets and other things. Thank you 449 00:23:34,280 --> 00:23:36,760 Speaker 1: very much for joining. Its fascinating and just talking with 450 00:23:36,840 --> 00:23:42,160 Speaker 1: Thank you, there's a lot of fun. So Tracy, are 451 00:23:42,200 --> 00:23:44,320 Speaker 1: you Are you a high life fan? Though? I kind 452 00:23:44,320 --> 00:23:45,760 Speaker 1: of want to go watch a game. I want to 453 00:23:45,800 --> 00:23:47,000 Speaker 1: I want to bet on a game. I want to 454 00:23:47,040 --> 00:23:49,760 Speaker 1: be the dumb money on the sidelines of the game. Um. 455 00:23:49,600 --> 00:23:52,399 Speaker 1: I love that conversation. I thought that was like, I 456 00:23:52,400 --> 00:23:54,600 Speaker 1: don't know, I just I thought it was fascinating. So 457 00:23:54,680 --> 00:23:57,439 Speaker 1: I learned a lot about the sport. And it also 458 00:23:57,560 --> 00:24:00,640 Speaker 1: is really helpful in bringing to life some the concepts 459 00:24:00,640 --> 00:24:03,360 Speaker 1: that we talk about all the time and markets like algorithms. 460 00:24:03,760 --> 00:24:09,080 Speaker 1: Absolutely like all these things in terms of simulations, decision trees, algorithms, 461 00:24:09,440 --> 00:24:12,520 Speaker 1: and particularly that part about how you know the end 462 00:24:12,560 --> 00:24:14,399 Speaker 1: we were talking about about how if you get too 463 00:24:14,400 --> 00:24:16,760 Speaker 1: big at a market, or if everybody's chasing the same 464 00:24:16,800 --> 00:24:20,560 Speaker 1: algorithmic strategy, how can all break down? Yeah, exactly, And 465 00:24:20,680 --> 00:24:23,480 Speaker 1: my absolute favorite part was when he described the betting 466 00:24:23,520 --> 00:24:25,760 Speaker 1: system and you have like all the math and then 467 00:24:25,760 --> 00:24:28,720 Speaker 1: you have the algorithm. But how in the nineties and 468 00:24:28,880 --> 00:24:30,679 Speaker 1: finally ended up where you had to like have your 469 00:24:30,720 --> 00:24:34,200 Speaker 1: computer make dial tones to enter in the debates. There's 470 00:24:34,240 --> 00:24:37,760 Speaker 1: a marriage of old style and modern trading techniques. I 471 00:24:37,760 --> 00:24:41,280 Speaker 1: thought was hilarious to imagine. Yeah, that was great. All right, 472 00:24:41,400 --> 00:24:43,920 Speaker 1: that is all for Odd Lodge. Thank you for listening. 473 00:24:44,080 --> 00:24:48,320 Speaker 1: I'm Joe Wisenthal, Managing Editor Bloomberg Markets, and you can 474 00:24:48,400 --> 00:24:50,480 Speaker 1: follow me on Twitter at the stall War. And I'm 475 00:24:50,520 --> 00:24:53,600 Speaker 1: Tracy Alloway, Executive editor of Bloomberg Markets and I'm on 476 00:24:53,600 --> 00:25:06,760 Speaker 1: Twitter at Tracy Alloway. Thanks again. Joe and I are 477 00:25:06,960 --> 00:25:09,560 Speaker 1: very proud of our new podcast, Odd Lots, but we 478 00:25:09,600 --> 00:25:13,359 Speaker 1: are also very proud of Bloomberg's other growing suite of 479 00:25:13,359 --> 00:25:16,960 Speaker 1: original podcast all designed to help you navigate the complexities 480 00:25:17,000 --> 00:25:20,879 Speaker 1: of business, financial markets, and the global economy. So in 481 00:25:20,880 --> 00:25:24,359 Speaker 1: addition to our own podcast, please don't miss Benchmark with 482 00:25:24,480 --> 00:25:28,679 Speaker 1: Dan Moss Tory Stillwell and Aki Edo and informative, jargon 483 00:25:28,760 --> 00:25:31,320 Speaker 1: free look at the inner workings of the global economy. 484 00:25:31,960 --> 00:25:33,920 Speaker 1: Then there's Deal of the Week with our M and 485 00:25:33,960 --> 00:25:36,480 Speaker 1: A reporter Alec Sherman, which is a breakdown of the 486 00:25:36,520 --> 00:25:38,800 Speaker 1: biggest M and A deals and gives you an inside 487 00:25:38,840 --> 00:25:44,160 Speaker 1: peak at corporate boardrooms. All three shows are available on iTunes, SoundCloud, 488 00:25:44,280 --> 00:25:47,520 Speaker 1: pocket Cast for Android, Bloomberg dot Com, and of course, 489 00:25:47,600 --> 00:25:48,560 Speaker 1: the Bloomberg Terminal.