1 00:00:08,519 --> 00:00:12,160 Speaker 1: Good afternoon. You're listening to Gambling with an Edge. 2 00:00:12,359 --> 00:00:16,280 Speaker 2: Now Here are your hosts, Bob Dancer and Richard Munchkin. 3 00:00:16,839 --> 00:00:19,839 Good afternoon, Welcome to Gambling with an Edge. Bob Dancer 4 00:00:20,280 --> 00:00:24,719 and I'm Richard Munchkin. Today we're speaking with tit Schllelle, 5 00:00:25,079 --> 00:00:30,039 a senior writer for Bloomberg, about his new book Lucky Devils, 6 00:00:30,600 --> 00:00:34,359 which is a biography of Bill Bentter, Rob Wrightson, and 7 00:00:34,439 --> 00:00:38,039 Bill Nelson. Kit Schallelle, Welcome to Gambling with an Edge. 8 00:00:38,560 --> 00:00:39,439 Speaker 1: Thank you for having me. 9 00:00:40,039 --> 00:00:42,759 Speaker 2: Before we get into the book itself, tell us a 10 00:00:42,840 --> 00:00:45,200 bit about your background. What kind of writing do you 11 00:00:45,280 --> 00:00:46,960 usually do and where are you based. 12 00:00:47,679 --> 00:00:50,399 Speaker 1: Well, I'm a journalist by Trey. I've been a journalist 13 00:00:50,439 --> 00:00:55,039 for twenty years doing various different jobs, latterly and investigative 14 00:00:55,119 --> 00:00:59,600 journalist looking at you know, big crime, war, corruption stories. 15 00:01:00,000 --> 00:01:02,880 And then the last few years I write solely long 16 00:01:02,920 --> 00:01:06,480 magazine features and books, and I tend to sort of 17 00:01:07,000 --> 00:01:10,959 hone in on subjects that are poorly understood, hidden behind 18 00:01:11,079 --> 00:01:14,200 layers of secrecy. Kind of secret world is what is 19 00:01:14,200 --> 00:01:15,000 what draws me in? 20 00:01:15,640 --> 00:01:18,560 Speaker 3: Could you give us an example of some other than gambling? 21 00:01:19,200 --> 00:01:21,959 Speaker 1: Oh, there's plenty yeah. So I wrote a story a 22 00:01:22,000 --> 00:01:26,599 couple of years ago about deep sea treasure hunters, which 23 00:01:26,640 --> 00:01:30,560 is this kind of unique subculture of tech minded normally 24 00:01:30,560 --> 00:01:34,239 people who work in Wall Street and in finance, who decide, 25 00:01:34,480 --> 00:01:37,719 you know, just as a hobby to spend their millions 26 00:01:37,920 --> 00:01:41,680 using undersea robots to find sunken Spanish galleons and warships 27 00:01:41,920 --> 00:01:45,239 carrying bars of gold. And like a lot of things 28 00:01:45,319 --> 00:01:47,280 I write about, they were quite keen for the wider 29 00:01:47,319 --> 00:01:49,959 world not to know what they were doing. But once 30 00:01:50,000 --> 00:01:51,640 I heard about them doing it, I kind of I 31 00:01:51,959 --> 00:01:56,640 had to know more, and when I get I must 32 00:01:56,680 --> 00:01:58,760 know more about this than I know. It's a story. 33 00:01:59,359 --> 00:02:02,519 Speaker 3: Well, part of the part of the problem, don't they 34 00:02:02,560 --> 00:02:06,519 have If people know they've found things, are governments of 35 00:02:06,599 --> 00:02:09,400 various countries trying to say that belongs to us and 36 00:02:09,439 --> 00:02:10,240 you can't have it. 37 00:02:10,840 --> 00:02:13,039 Speaker 1: Yeah, you got it. Finding the treasures the easy bit. 38 00:02:13,680 --> 00:02:15,319 It's keeping it that's the challenge. 39 00:02:15,879 --> 00:02:19,639 Speaker 3: It's just like gambling. Learning learning how to get an 40 00:02:19,759 --> 00:02:21,840 edge it's easy, but learning how to get the money 41 00:02:21,840 --> 00:02:23,360 out of a casino is difficult. 42 00:02:24,039 --> 00:02:27,360 Speaker 1: Once you find Once you find a Spanish galleon loaded 43 00:02:27,400 --> 00:02:30,919 with the goldaballoons suddenly everyone starts claiming it. You'll have 44 00:02:30,960 --> 00:02:34,240 ten or fifteen different people coming out of the woodwork. 45 00:02:34,759 --> 00:02:37,439 The Spanish government will claim it, you know, some Peruvian 46 00:02:37,520 --> 00:02:41,360 tribe will claim it. Various other shipwreck hunters will say 47 00:02:41,360 --> 00:02:44,680 we found it first, and yeah, that was a fascinating one. 48 00:02:44,719 --> 00:02:48,080 But lots of things like that, also, computer hacking, corporate spying, 49 00:02:48,800 --> 00:02:51,400 sort of organized crime. It's those sorts of things that 50 00:02:50,919 --> 00:02:53,159 I tend to lure me in. 51 00:02:53,960 --> 00:02:55,520 Speaker 3: And you're based in London. 52 00:02:56,120 --> 00:02:58,080 Speaker 1: Yeah, I'm based in London, which is a good place 53 00:02:58,080 --> 00:03:00,560 to explore the world. It's also of a good place 54 00:03:00,599 --> 00:03:03,879 to write about gambling. I think the gambling revolution that 55 00:03:05,039 --> 00:03:08,240 happened in the US more recently, the UK was a 56 00:03:08,240 --> 00:03:13,080 few years ahead. We embraced universal gambling quite early on, 57 00:03:13,240 --> 00:03:14,719 and as a result, you know, we've got one of 58 00:03:14,759 --> 00:03:17,719 the more developed betting markets in the world. So lots 59 00:03:17,759 --> 00:03:21,319 of big online companies are based in the UK and 60 00:03:21,360 --> 00:03:23,159 there's a big betting scene here and that's kind of 61 00:03:23,199 --> 00:03:24,639 what got my attention to start with. 62 00:03:25,400 --> 00:03:28,840 Speaker 2: So how did this particular book al about and how 63 00:03:28,879 --> 00:03:30,319 did you get these guys. 64 00:03:30,000 --> 00:03:33,400 Speaker 1: To talk to you? Well, I by a complete accident. 65 00:03:33,439 --> 00:03:36,639 I stumbled across the world of advantage gambling. A few 66 00:03:36,680 --> 00:03:40,000 years back when I heard about Bill Benter, who of 67 00:03:40,039 --> 00:03:44,759 course is the godfather of computerized besting on horses. He 68 00:03:44,879 --> 00:03:48,000 was doing it long before anyone else, really, he sort 69 00:03:48,000 --> 00:03:50,800 of pioneered the scene. I heard about him in a 70 00:03:50,840 --> 00:03:53,520 conversation with a friend who works in sort of venture 71 00:03:53,520 --> 00:03:57,080 capital in London, over a couple of beers, and he said, 72 00:03:57,520 --> 00:04:00,159 we were talking about horse racing, and he said, you know, 73 00:04:00,159 --> 00:04:02,520 there are these guys in Hong Kong who who do 74 00:04:02,599 --> 00:04:05,439 it like a hedge fund. They use algorithms and high 75 00:04:05,439 --> 00:04:08,400 power of computers and they made a billion dollars. When 76 00:04:08,439 --> 00:04:11,639 I heard that number, it sounded fantastical. But I went 77 00:04:11,639 --> 00:04:14,360 away and did my thing and spent a few months 78 00:04:14,560 --> 00:04:17,000 calling people and trying to find out. And I found 79 00:04:17,040 --> 00:04:19,319 out about these computer syndicates, and it turned out the 80 00:04:19,399 --> 00:04:22,680 rumor was largely true. You know, there was this American 81 00:04:22,720 --> 00:04:25,040 team in Hong Kong who had done really well. I 82 00:04:25,079 --> 00:04:27,879 got the name Bill Bentter, and once I met him, 83 00:04:27,879 --> 00:04:29,439 I was I was hurked really at. 84 00:04:30,040 --> 00:04:31,720 Speaker 3: How did you mean him? 85 00:04:32,000 --> 00:04:36,720 Speaker 1: I just booked a flight to Pittsburg. Yeah, yeah, yeah. 86 00:04:36,879 --> 00:04:39,519 He wasn't very keen to talk to me. At first, 87 00:04:39,720 --> 00:04:45,079 and maybe he still isn't you know, as you know, 88 00:04:45,199 --> 00:04:47,839 being an advantage gambler, you know you have to have 89 00:04:47,920 --> 00:04:50,759 a level of discretion, you have to be it helps 90 00:04:50,800 --> 00:04:53,720 to be hidden from view, and actually publicity can be 91 00:04:53,759 --> 00:04:56,920 a big hindrance. I got to build towards the end 92 00:04:56,959 --> 00:04:59,360 of his career, or he'd been doing it a long time. 93 00:05:00,199 --> 00:05:02,560 And you know, I think once he realized that I 94 00:05:02,600 --> 00:05:05,240 wasn't trying to steal his formulas or compete with him. 95 00:05:05,639 --> 00:05:08,319 I was interested in his job. I was interested in 96 00:05:08,319 --> 00:05:09,839 the sort of the work that he had done to 97 00:05:09,920 --> 00:05:12,959 build his model. I think, like like anyone else, you know, 98 00:05:13,000 --> 00:05:15,399 this was his life's passion, and you know, someone who 99 00:05:15,480 --> 00:05:18,439 was genuinely interested in it, and someone who showed up 100 00:05:18,560 --> 00:05:21,319 largely uninvited in his hometown. I think I think he 101 00:05:21,399 --> 00:05:23,439 just thought it'd be easier just to just to talk. 102 00:05:23,480 --> 00:05:26,519 And Bill was my gateway into the community. He's been 103 00:05:26,519 --> 00:05:29,240 really helpful introducing me to other people. It was through 104 00:05:29,319 --> 00:05:32,480 him that I learned about roulette prediction and I got 105 00:05:32,560 --> 00:05:36,199 into poker bots on you Once you realize there are 106 00:05:36,240 --> 00:05:39,399 people who do this for a living, you start seeing 107 00:05:39,399 --> 00:05:41,720 it everywhere. And it opened up this whole world that 108 00:05:41,720 --> 00:05:42,639 I didn't know existed. 109 00:05:43,319 --> 00:05:47,199 Speaker 3: So Bill introduced you to Bill Nelson and to Rob Rightson. 110 00:05:47,480 --> 00:05:48,560 Is that how you met them? 111 00:05:49,079 --> 00:05:52,160 Speaker 1: I can't remember if Bill introduced me to Bill Nelson. 112 00:05:53,040 --> 00:05:55,839 That may been, that may have happened indirectly. But he 113 00:05:55,959 --> 00:05:58,600 made me, he made me aware of Rob Rysmon's existence. 114 00:05:58,920 --> 00:06:00,959 And then I had a whole other challenge with Rob 115 00:06:01,600 --> 00:06:03,680 persuading him that it would be a good idea to 116 00:06:03,720 --> 00:06:06,480 spend some time with me, which is maybe even more 117 00:06:06,480 --> 00:06:10,879 difficult than Bill Benter because Rob didn't love the idea 118 00:06:10,920 --> 00:06:12,040 of being in the media. 119 00:06:12,120 --> 00:06:15,720 Speaker 3: Again, well, yeah, you know, I think he has a 120 00:06:15,759 --> 00:06:21,680 great ambivalence. You know, he likes it, but he realizes 121 00:06:21,879 --> 00:06:23,720 that maybe it's not the best idea. 122 00:06:23,920 --> 00:06:28,879 Speaker 1: But yeah, like I said, there's benefit to getting people 123 00:06:28,920 --> 00:06:31,319 towards the end of their careers. I mean, look, when 124 00:06:31,360 --> 00:06:33,040 you're in your twenties and you're starting out and you're 125 00:06:33,040 --> 00:06:35,920 desperately seeking an edge, you know, I think you're very 126 00:06:36,000 --> 00:06:38,600 unlikely to sit down with a journalist or a writer 127 00:06:38,759 --> 00:06:42,480 and talk about it. But once you've been gambling for decades, 128 00:06:42,560 --> 00:06:45,199 you know, anyone as they get towards the end of 129 00:06:45,240 --> 00:06:48,120 their career, they become reflective, they start thinking about what 130 00:06:48,160 --> 00:06:50,839 it means to do the job they do. It's natural. 131 00:06:51,079 --> 00:06:53,199 They also have a kind of unique perspective that I 132 00:06:53,240 --> 00:06:57,800 found really interesting. They've been betting at an elite level 133 00:06:57,879 --> 00:07:02,800 since the some cases the late seventies, but early eighties onwards, 134 00:07:03,360 --> 00:07:07,240 you know, four decades worth. They know this gambling industry 135 00:07:07,639 --> 00:07:09,680 kind of inside and out. They have a perspective that 136 00:07:09,720 --> 00:07:13,399 almost no one else in the world has. And look 137 00:07:13,519 --> 00:07:16,319 right now, we have an American president who's a former 138 00:07:16,360 --> 00:07:19,480 casino owner. We have people betting on the second coming 139 00:07:19,519 --> 00:07:22,959 of Jesus. It's the gambling world has changed so much. 140 00:07:23,000 --> 00:07:26,560 It's spread into popular culture and the economy and sports 141 00:07:27,040 --> 00:07:29,560 in a kind of an unprecedented way. So speaking to 142 00:07:29,600 --> 00:07:32,959 people who have done this for a whole lifetime was fascinating. 143 00:07:33,560 --> 00:07:36,800 Speaker 3: You know. I wrote a book that was a collection 144 00:07:36,839 --> 00:07:41,439 of interviews with professional gamblers called Gambling Wizards, and I 145 00:07:41,480 --> 00:07:43,920 got the idea. I was on a plane to Hong 146 00:07:44,040 --> 00:07:47,720 Kong to do some work for Bill and you talk 147 00:07:47,759 --> 00:07:51,480 about it in the book, where the jockey club in 148 00:07:51,519 --> 00:07:55,759 Hong Kong had stopped accepting phone bets, and so he 149 00:07:55,879 --> 00:07:59,120 needed people to run to the bet shops. We had 150 00:07:59,120 --> 00:08:02,360 these in ormous printers and we would print the tickets 151 00:08:02,399 --> 00:08:06,000 and run to the bed shops. Anyway, when that happened, 152 00:08:06,160 --> 00:08:08,319 I was reading a book on the plane going over 153 00:08:08,360 --> 00:08:11,639 to Hong Kong called Market Wizards. That's a collection of 154 00:08:11,720 --> 00:08:15,160 interviews with commodities and futures traders, and I was like, 155 00:08:15,279 --> 00:08:18,560 you know, gamblers are way more interesting than these people. 156 00:08:19,399 --> 00:08:22,759 And so when I got there, I said to Bill, hey, 157 00:08:22,800 --> 00:08:25,519 I have this idea. He had a copy of Market 158 00:08:25,519 --> 00:08:28,000 Wizards there in the office in all Koch and I said, 159 00:08:28,360 --> 00:08:30,720 I want to do this with professional gamblers. Will you 160 00:08:30,759 --> 00:08:33,159 do an interview? And he was like I can't. You know, 161 00:08:33,279 --> 00:08:37,159 I can't. I can't handle that kind of you know, exposure. 162 00:08:37,200 --> 00:08:39,480 He goes, but you know, Alan'll do it. And I 163 00:08:39,919 --> 00:08:43,279 was like, oh, okay, So I called Alan. He was like, yeah, okay, sure, 164 00:08:43,360 --> 00:08:44,080 no problem. 165 00:08:44,960 --> 00:08:45,879 Speaker 2: And yeah. 166 00:08:45,919 --> 00:08:49,120 Speaker 3: So see, I just was twenty five years too early. 167 00:08:49,840 --> 00:08:54,120 Speaker 1: Yeah. I do think that's the gambling community I think 168 00:08:55,039 --> 00:08:58,960 is not It's not treated as seriously as it might 169 00:08:59,000 --> 00:09:02,039 be by the by the wider media. You know, gambling 170 00:09:02,080 --> 00:09:05,279 is often dismissed as being a vice or disease or 171 00:09:05,360 --> 00:09:09,600 societal problem. And I keep you I see it time 172 00:09:09,679 --> 00:09:13,600 time again, the way the mainstream media cover this subject. 173 00:09:14,000 --> 00:09:19,240 Speaker 3: And also they just get so much wrong. They just 174 00:09:19,960 --> 00:09:24,200 you know, and often they will equate professional advantage players 175 00:09:24,240 --> 00:09:28,840 with cheats or there's just no end of the mistakes 176 00:09:28,879 --> 00:09:31,360 they make. It's like they don't really do their homework 177 00:09:31,399 --> 00:09:33,000 when they read these articles. 178 00:09:33,320 --> 00:09:38,039 Speaker 1: Well, it's I think the tendency is to view it 179 00:09:38,039 --> 00:09:41,759 as a problem, and I just disagree with that approach fundamentally. 180 00:09:41,840 --> 00:09:45,000 I always have, you know, I think gambling is anibly 181 00:09:45,120 --> 00:09:48,720 important part of human nature. We've been doing it since 182 00:09:48,759 --> 00:09:52,399 we lived in caves. It is ingrained in us. Actually, 183 00:09:52,480 --> 00:09:54,879 you can argue it's one of the reasons that as 184 00:09:54,879 --> 00:09:58,720 a species we've become so successful. Our ability to imagine 185 00:09:58,759 --> 00:10:05,200 complex risks and you know, calculate probability. That's why we 186 00:10:05,919 --> 00:10:09,399 got in boats and traveled across oceans to populate new land. 187 00:10:09,519 --> 00:10:12,200 That's you know, that's why we developed technology. It's the 188 00:10:12,240 --> 00:10:14,279 reason that we traveled into space. A lot of the 189 00:10:14,279 --> 00:10:16,639 good things that we do as human beings come from 190 00:10:16,679 --> 00:10:21,159 our ability to tolerate risk and and and and do 191 00:10:21,279 --> 00:10:24,519 those formulas in our heads or using a computer. None 192 00:10:24,519 --> 00:10:28,879 other species can do that, So it isn't inherently negative. 193 00:10:28,919 --> 00:10:31,519 It's just like anything else. It's part of us. And 194 00:10:31,720 --> 00:10:33,120 you know, I wanted to I wanted to write a 195 00:10:33,120 --> 00:10:36,480 book the treat that paid attention to gambling as a 196 00:10:36,759 --> 00:10:40,759 very human trait and and developed that and interrogated that 197 00:10:40,879 --> 00:10:44,039 rather than dismissing it as like a cancer on society. 198 00:10:44,679 --> 00:10:47,519 Speaker 3: Well, your book also seems to be about this moment 199 00:10:47,679 --> 00:10:53,919 in time when gambling went through this massive shift because 200 00:10:54,039 --> 00:10:58,960 of computers. Right the way that people approached it trying 201 00:10:59,000 --> 00:11:01,159 to get an advantage. I think people had always tried 202 00:11:01,200 --> 00:11:05,799 to get an advantage gambling, but suddenly, with the access 203 00:11:05,919 --> 00:11:11,600 to computers, everything changed, and it changed. And in your book, 204 00:11:12,519 --> 00:11:16,279 you know, you show the way it changed in horse racing, 205 00:11:16,559 --> 00:11:19,720 in sports betting, in roulette, and in poker. 206 00:11:20,399 --> 00:11:23,240 Speaker 1: Yeah. I think this was a genuinely amazing moment in 207 00:11:23,279 --> 00:11:25,720 the history of technology. I really believe that. You know, 208 00:11:25,759 --> 00:11:30,200 you've got thousands of young, highly intelligent, mostly men, it 209 00:11:30,279 --> 00:11:34,720 must be said, descending on Las Vegas. They're all mathematically minded, 210 00:11:34,720 --> 00:11:37,240 they've all read ed Thorpe's Beat the Dealer, they're all 211 00:11:37,279 --> 00:11:42,159 card counting. And that moment happened just as the home 212 00:11:42,159 --> 00:11:47,639 computer became a household object. It was this technology was 213 00:11:48,159 --> 00:11:53,240 just starting to creep into the regular consumer economy, and 214 00:11:53,320 --> 00:11:55,679 so you had those two things coming together at the 215 00:11:55,720 --> 00:11:59,279 same time, and the result was some really remarkable technological innovation. 216 00:11:59,720 --> 00:12:01,200 I mean, and if you look at the devices that 217 00:12:01,240 --> 00:12:03,960 were being made back then for the purposes of beating 218 00:12:04,000 --> 00:12:06,759 casino games, they're amazing. You know. You can go to 219 00:12:06,799 --> 00:12:09,720 the Barona casino outside San Diego and San Diego and 220 00:12:09,759 --> 00:12:13,879 you can see these these ancient wearable computers and the thing, 221 00:12:14,000 --> 00:12:16,320 you know, made in the nineteen seventies or eighties. The 222 00:12:16,399 --> 00:12:19,000 surprising thing about them is how good they look. They 223 00:12:19,039 --> 00:12:22,559 look they don't look dissimilar to Apple devices from the 224 00:12:22,600 --> 00:12:26,399 nineteen nineties. They're small, you know, they're functional, and they 225 00:12:26,399 --> 00:12:29,240 were made in people's garages and apartments, you know, using 226 00:12:29,279 --> 00:12:32,639 bits and pieces that they cobbled together, so you know it. 227 00:12:32,960 --> 00:12:36,360 It was this flourishing of a certain type of talent. 228 00:12:37,159 --> 00:12:40,559 And one of the reasons that advantage gambling is a 229 00:12:40,639 --> 00:12:43,000 small communities. I think a lot of the senior members 230 00:12:43,000 --> 00:12:46,519 of the community can trace the lineage back to this 231 00:12:46,919 --> 00:12:50,919 time and place, and they spread across the world, and 232 00:12:50,960 --> 00:12:53,759 their ideas spread across the world. That's what I think 233 00:12:54,039 --> 00:12:57,120 made me pay attention. This didn't just stay limited to 234 00:12:57,159 --> 00:13:00,000 Las Vegas. Bill Benter went to Hong Kong bill Nell's 235 00:13:00,080 --> 00:13:04,279 and took his roulette technology around Europe. Rob Wrightson his 236 00:13:04,399 --> 00:13:08,759 pokerbot empire, you know, spanned the globe and these ideas 237 00:13:08,840 --> 00:13:13,039 permeated the gambling industry from quite a small starting point. 238 00:13:13,879 --> 00:13:19,080 Speaker 2: So we mentioned Bill and Alan Woods in Hong Kong. 239 00:13:19,919 --> 00:13:24,960 So for our listeners who aren't generally familiar with that 240 00:13:25,559 --> 00:13:30,360 what they did. They started out as partners, had falling out, 241 00:13:30,759 --> 00:13:34,000 and your book describes a falling out from Bill Benter's 242 00:13:34,039 --> 00:13:37,320 point of view, said she couldn't talk to Alan because 243 00:13:37,320 --> 00:13:41,879 he's dead. And so what exactly did they do in 244 00:13:41,919 --> 00:13:42,440 Hong Kong. 245 00:13:43,080 --> 00:13:46,399 Speaker 1: Well, their approach was to look at horse racing as 246 00:13:46,399 --> 00:13:52,000 a statistical problem two, instead of betting using instinct or 247 00:13:52,519 --> 00:13:55,759 a sort of vague assessment of the form guide or tips. 248 00:13:56,200 --> 00:13:58,200 They you know, they said, you know, this is this 249 00:13:58,279 --> 00:14:00,480 is a collection of data points that we can using 250 00:14:00,519 --> 00:14:02,759 a computer, and if we do that, we will have 251 00:14:02,799 --> 00:14:05,559 an advantage over anyone else who's gambling. And back then 252 00:14:05,600 --> 00:14:08,000 that was true, you know, in the early nineteen eighties, 253 00:14:08,399 --> 00:14:13,480 they just weren't people using computers to analyze horse racing data. 254 00:14:13,480 --> 00:14:15,600 They were the only ones doing it. And of course, 255 00:14:15,679 --> 00:14:19,399 Hong Kong was and remains one of the biggest horse 256 00:14:19,519 --> 00:14:22,919 racing markets in the world. They chose it purely because 257 00:14:22,960 --> 00:14:25,240 of its size, because they wanted somewhere where they could 258 00:14:25,240 --> 00:14:28,720 operate and not be noticed, not attrapped assension. So yeah, 259 00:14:28,720 --> 00:14:32,639 they flew to Hong Kong with three bulky k Pro 260 00:14:32,840 --> 00:14:36,480 computers in their luggage. They hold these machines over there. 261 00:14:36,519 --> 00:14:38,480 They spent a couple of years collecting data from the 262 00:14:38,559 --> 00:14:41,279 racing books and then they built this thing from scratch. 263 00:14:41,360 --> 00:14:44,159 And you know, they both Alan Woods and Bill Benter 264 00:14:44,320 --> 00:14:48,879 benefited hugely from being first. It's the same in markets. 265 00:14:49,159 --> 00:14:52,080 You know, if you're the first options trader to use 266 00:14:52,120 --> 00:14:54,480 the Black Skulls model, you've got a huge advantage over 267 00:14:54,480 --> 00:14:56,600 everyone else and you get a golden period of a 268 00:14:56,600 --> 00:14:59,399 few years before the rest of the world catches up 269 00:14:59,440 --> 00:15:02,519 with you. Because Bill bent and Allen Woods were so early, 270 00:15:03,200 --> 00:15:07,120 they once they got the model working, they had some really, 271 00:15:07,159 --> 00:15:11,000 really profitable years. It still do I believe, Well, it's 272 00:15:11,600 --> 00:15:14,519 Bill Benter's still this is still his job. He's still 273 00:15:14,519 --> 00:15:18,559 tweaking his model. These days, he will be using artificial 274 00:15:18,559 --> 00:15:22,000 intelligence models rather than his you know, his guest work 275 00:15:22,039 --> 00:15:24,919 con trial and error. But fundamentally he's doing the same 276 00:15:24,960 --> 00:15:27,960 thing he's always done, which I find fascinating. I mean, 277 00:15:28,000 --> 00:15:31,480 to have the focus and the dedication to spend your 278 00:15:31,639 --> 00:15:34,799 entire working life on an equation for horse racing is 279 00:15:34,799 --> 00:15:35,639 a remarkable thing. 280 00:15:36,360 --> 00:15:39,759 Speaker 2: Well, one of the reasons they selected Hong Kong, I 281 00:15:39,840 --> 00:15:44,399 believe is the horses didn't come and go. The horses 282 00:15:44,440 --> 00:15:47,000 stayed there. You didn't ship in a lot of horses 283 00:15:47,000 --> 00:15:49,399 to Hong Kong to go to the races, and so 284 00:15:49,480 --> 00:15:53,799 you could build up data about all the horses in 285 00:15:53,840 --> 00:15:58,679 the market more easily. Later when Bill was in Pittsburgh, 286 00:15:59,639 --> 00:16:02,559 this is not the case. It is very possible to 287 00:16:02,639 --> 00:16:07,840 truck in horses to and from Pittsburgh, and so there's 288 00:16:08,759 --> 00:16:12,840 not the same database of horses that you have. There's 289 00:16:12,879 --> 00:16:15,480 always new ones that you don't know about, and some 290 00:16:15,519 --> 00:16:17,799 of the ones you have all those data on have 291 00:16:17,879 --> 00:16:19,759 moved away. Well. 292 00:16:20,200 --> 00:16:23,080 Speaker 3: It's actually also that in Hong Kong there are only 293 00:16:23,120 --> 00:16:28,279 two race tracks and far fewer horses than there are 294 00:16:29,200 --> 00:16:33,200 here in the US. You know, many many more tracks 295 00:16:33,240 --> 00:16:35,720 here and many many more horses to keep track of. 296 00:16:35,919 --> 00:16:39,639 But if you have a small pool of horses to 297 00:16:39,720 --> 00:16:40,840 deal with, then. 298 00:16:41,440 --> 00:16:44,080 Speaker 1: It makes that job easier. What they were doing was 299 00:16:44,120 --> 00:16:46,879 experimental science. You have to remember back then this just 300 00:16:46,919 --> 00:16:50,320 simply didn't exist. There was one academic paper, but it 301 00:16:50,360 --> 00:16:52,639 was kind of half asked. I mean, they did it 302 00:16:52,720 --> 00:16:55,399 as an intellectual exercise. They didn't do it to make 303 00:16:55,440 --> 00:16:58,000 a profitable business. They did it to see if it 304 00:16:58,039 --> 00:17:00,759 could be done. You know what mathematical lessons they could learn. 305 00:17:00,840 --> 00:17:03,919 So this was something new to the world. You can 306 00:17:04,079 --> 00:17:07,200 imagine the appeal of having a contained environment to do 307 00:17:07,279 --> 00:17:10,240 your experiment that. You know, what you're talking about is 308 00:17:10,279 --> 00:17:13,680 a data problem and once you have once your method 309 00:17:13,799 --> 00:17:16,920 is proven, you can apply anywhere where where there's horse racing. 310 00:17:16,920 --> 00:17:19,839 And of course now you know, any racetrack in the 311 00:17:19,880 --> 00:17:23,839 world will have advantage players using computers to calculate odds 312 00:17:23,880 --> 00:17:24,799 and try and beat the market. 313 00:17:25,880 --> 00:17:31,200 Speaker 2: Hey, it's always spoken about Bill rob Wrightson started out 314 00:17:31,240 --> 00:17:36,039 in blackjack and then move to other games. So how 315 00:17:36,039 --> 00:17:38,839 did he use computers to help him in blackjack? 316 00:17:39,799 --> 00:17:45,079 Speaker 1: Rob was part of that advantage playing generation that was 317 00:17:45,160 --> 00:17:49,279 quite quick to adopt wearable technology. So he and his 318 00:17:49,440 --> 00:17:54,640 friends would use the AFT wearable computer, which was a 319 00:17:54,680 --> 00:17:58,920 sort of homemade gadget made by an enthusiastic but quite 320 00:17:59,000 --> 00:18:02,720 religious fella who sort of soldered them in his attic 321 00:18:03,440 --> 00:18:05,440 and sold them to gamblers who wanted to use them. 322 00:18:05,599 --> 00:18:09,200 And they were just it was car counting. Plus it 323 00:18:09,279 --> 00:18:12,359 was just a way to be extremely accurate and efficient 324 00:18:12,400 --> 00:18:15,359 in the way you counted cards, inputting data using a 325 00:18:15,400 --> 00:18:19,880 toa switch, receiving data back through an earpiece or through 326 00:18:19,920 --> 00:18:23,359 a buzzing patch somewhere in your body. And I think 327 00:18:23,799 --> 00:18:27,880 once Rights and his friends realized just how good that 328 00:18:27,920 --> 00:18:30,880 advantage was, you know, it wasn't a big leap to say, well, 329 00:18:30,920 --> 00:18:32,680 if this works in blackjack, we can do it in 330 00:18:32,720 --> 00:18:36,599 other games as well. And of course Rob took the 331 00:18:36,640 --> 00:18:40,799 technology that he had used to beat blackjack and started 332 00:18:40,799 --> 00:18:43,839 thinking about poker. Poker had been his passion since he 333 00:18:43,920 --> 00:18:48,279 was young anyway. You know, poker is fundamentally a sort 334 00:18:48,279 --> 00:18:52,400 of mathematical problem at its heart, and I think he 335 00:18:52,559 --> 00:18:57,599 recognized very early before many people that the way poker 336 00:18:57,680 --> 00:19:01,880 was played back then was inefficient and suboptimal. You know, 337 00:19:01,920 --> 00:19:04,240 he could early on, he could see the way the 338 00:19:04,319 --> 00:19:08,599 pros even the pros played the game had flaws, and 339 00:19:09,680 --> 00:19:12,599 so he spent many, many years and enormous sums of 340 00:19:12,599 --> 00:19:16,240 money building a building some of the world's first poker 341 00:19:16,279 --> 00:19:20,319 bots functionally. You know, he had MIT graduates and math 342 00:19:20,359 --> 00:19:23,759 professors and programmers, and he had a whole team dedicated 343 00:19:23,759 --> 00:19:25,160 to doing this, and it took a long time to 344 00:19:25,160 --> 00:19:29,319 pay off. Poker is an enormous challenge, much harder than blackjack. 345 00:19:29,839 --> 00:19:31,759 But he got there, and he ended up making a 346 00:19:31,799 --> 00:19:34,440 significant amount of his career earnings in poker. 347 00:19:34,759 --> 00:19:37,880 Speaker 3: When he and again he got there first. You know, 348 00:19:38,279 --> 00:19:41,799 later we heard all about game theory optimal and these 349 00:19:42,440 --> 00:19:48,480 various other people who were working on programs to solve 350 00:19:49,279 --> 00:19:53,000 but again he got there first, and rather than write 351 00:19:53,000 --> 00:19:55,880 an academic paper about it, he used it to make 352 00:19:56,200 --> 00:19:57,559 a tremendous amount of money. 353 00:19:58,119 --> 00:20:01,759 Speaker 1: Well, he had the incentive the academics didn't have, which 354 00:20:01,839 --> 00:20:04,640 was that, you know, this the stakes for him were real. 355 00:20:05,240 --> 00:20:10,839 This wasn't an intellectual exercise, This wasn't some arcane challenge 356 00:20:10,880 --> 00:20:13,039 that he took on. This was his way to make 357 00:20:13,160 --> 00:20:16,880 to make his fortune. So you know, his incentives were 358 00:20:17,799 --> 00:20:20,480 were make or break, and he had a gift for it, 359 00:20:20,519 --> 00:20:23,640 and you know, he's very proud even now to talk 360 00:20:23,720 --> 00:20:26,880 about how his early poker bots would beat the academic 361 00:20:26,960 --> 00:20:31,000 models out of Carnegie Mellon University and Alberta in Canada. 362 00:20:31,000 --> 00:20:34,759 Those are the two universities that were at the forefront 363 00:20:34,400 --> 00:20:38,039 of poker AI and Rob used to test his model 364 00:20:38,079 --> 00:20:40,440 against them, and his were better. And you know, you 365 00:20:40,440 --> 00:20:44,559 can imagine why he His testing ground was real life. 366 00:20:45,039 --> 00:20:48,640 You know, the academics tended to do a lot of 367 00:20:48,640 --> 00:20:53,279 their experimental work in the university. Rob would take his 368 00:20:53,440 --> 00:20:57,799 theorems out into the poker rooms in LA and around California. 369 00:20:58,559 --> 00:21:01,000 You know, he knew the game better, and he had 370 00:21:01,000 --> 00:21:03,480 this gift for mathematics, and he had a particular talent 371 00:21:03,880 --> 00:21:07,799 for attracting the right people to his operation. I think 372 00:21:07,799 --> 00:21:11,319 one of his gifts was recognizing that he needed expertise 373 00:21:11,359 --> 00:21:14,240 in certain areas, so he assembled this team. He had 374 00:21:14,279 --> 00:21:17,759 real world poker experience, and his early bots were really good. 375 00:21:18,000 --> 00:21:19,720 I mean they were better than anything else out there. 376 00:21:20,279 --> 00:21:24,960 Speaker 2: Well, he was part of a company that did on 377 00:21:26,200 --> 00:21:31,519 the providing games on the Internet, and he would put 378 00:21:31,559 --> 00:21:34,680 a lot of bots out there, and at the time 379 00:21:34,880 --> 00:21:41,119 there was no law against it. Later on, there a 380 00:21:41,160 --> 00:21:44,279 lot of sites have tried to stop that, but at 381 00:21:44,319 --> 00:21:47,640 the time it was a new idea and very successful 382 00:21:47,720 --> 00:21:49,400 and profitable to him. 383 00:21:49,759 --> 00:21:53,920 Speaker 1: Yeah, Rob was there at the turning point. This technology 384 00:21:54,160 --> 00:21:59,480 was a quirky curio. Throughout the eighties and nineties, there 385 00:21:59,519 --> 00:22:01,880 was sort of a vague awareness that computers were trying 386 00:22:01,920 --> 00:22:05,160 to play poker, but honestly, they played it badly. The 387 00:22:05,200 --> 00:22:10,000 wider gambling industry didn't take it that seriously, so Rob 388 00:22:10,039 --> 00:22:12,799 got in before the penny dropped. I think that this 389 00:22:13,039 --> 00:22:16,480 was an effective way to play poker and could potentially 390 00:22:17,799 --> 00:22:20,720 lead to a superior software that could beat almost any 391 00:22:20,759 --> 00:22:24,920 human opponent. But then I think it was the late 392 00:22:25,039 --> 00:22:29,119 nineties in early two thousands was the moment when the 393 00:22:29,160 --> 00:22:32,400 bots got good enough that people started noticing their existence. 394 00:22:32,759 --> 00:22:35,839 People who played poker online would notice that they were 395 00:22:35,839 --> 00:22:38,599 playing against opponents who didn't seem to be human. They 396 00:22:38,599 --> 00:22:42,240 didn't play like human beings, they played differently, they didn't chat. 397 00:22:42,480 --> 00:22:46,200 The platform started noticing it, and this backlash happened quite 398 00:22:46,279 --> 00:22:50,559 quickly against the idea of competing against the bop and 399 00:22:50,599 --> 00:22:53,319 you can you can imagine why people were upset by it. 400 00:22:53,400 --> 00:22:55,960 You know, if I finished work after a long day 401 00:22:55,960 --> 00:22:58,279 and I log onto my computer to play online poker. 402 00:22:58,720 --> 00:23:00,799 I want to play other human beings. Proved my metal 403 00:23:00,799 --> 00:23:04,640 against other players. If I'm playing against software, it's no 404 00:23:04,799 --> 00:23:09,440 fun and it feels unfair. So that Rob was right 405 00:23:09,480 --> 00:23:13,079 there at the forefront when that backlash happened, and it 406 00:23:13,079 --> 00:23:15,599 was a really interesting moment in poker history because the 407 00:23:15,640 --> 00:23:17,160 game hasn't really been the same sense. 408 00:23:17,759 --> 00:23:20,960 Speaker 3: Yeah, I know it. At that point, he trained a 409 00:23:20,960 --> 00:23:26,799 bunch of women how to play, and people the online 410 00:23:26,839 --> 00:23:30,920 poker sites thought the women were bots. And I remember 411 00:23:30,960 --> 00:23:33,519 at the time there was one in particular who set 412 00:23:33,559 --> 00:23:39,000 up a webcam showing her playing because one of the sites, 413 00:23:39,079 --> 00:23:41,559 I think it was Full Tilt, had stolen a bunch 414 00:23:41,559 --> 00:23:46,480 of money of hers, claiming she was a bot. And yeah, 415 00:23:46,559 --> 00:23:51,759 so he could teach the humans to play as well well. 416 00:23:51,799 --> 00:23:53,640 Speaker 1: He taught them to play like machines. Though. That was 417 00:23:54,319 --> 00:23:58,920 the difference. The woman you're talking about, she was a 418 00:23:58,920 --> 00:24:02,200 poker machine. I mean she she didn't have much poker 419 00:24:02,240 --> 00:24:06,960 experience before she met Rob Rightson, but she trained hour 420 00:24:07,160 --> 00:24:10,960 after hour after hour on his software that taught her 421 00:24:11,079 --> 00:24:15,480 game theory, optimal poker. She was a good sponge for 422 00:24:15,519 --> 00:24:17,960 that knowledge. She learned to play very much like a bot, 423 00:24:18,200 --> 00:24:20,880 and it made her an extremely effective player. But she 424 00:24:20,960 --> 00:24:24,079 played just like a computer, and she thinks about poker 425 00:24:24,160 --> 00:24:26,039 like a computer. She thinks of the game in terms 426 00:24:26,119 --> 00:24:28,319 of am I over five hundred games? 427 00:24:28,319 --> 00:24:28,400 Speaker 2: Not? 428 00:24:28,440 --> 00:24:30,519 Speaker 1: Am I going to win this hand? Okay? So you 429 00:24:30,559 --> 00:24:33,240 know she's got this very mathematical approach to the game. 430 00:24:33,839 --> 00:24:36,799 It was ironic that, you know, at that moment, Rob 431 00:24:36,839 --> 00:24:40,000 had this empire of poker bots, thousands of them deployed 432 00:24:40,039 --> 00:24:44,200 on all the main poker platforms, using various software tricks 433 00:24:44,200 --> 00:24:46,240 to hide the fact there were bots from the operators. 434 00:24:46,559 --> 00:24:48,799 But the ones who caught heat, the ones who had 435 00:24:48,839 --> 00:24:51,799 their money sees, were the humans that he had trained 436 00:24:51,839 --> 00:24:53,119 to play like computers. 437 00:24:53,480 --> 00:24:55,759 Speaker 3: Do you know if she's still playing, She. 438 00:24:55,799 --> 00:24:56,440 Speaker 1: Is still playing. 439 00:24:57,160 --> 00:24:57,480 Speaker 2: She is. 440 00:24:57,839 --> 00:24:59,799 Speaker 1: Yeah, I met as far as I know. I met 441 00:24:59,799 --> 00:25:00,680 her couple of years ago. 442 00:25:01,400 --> 00:25:01,559 Speaker 2: Yeah. 443 00:25:02,079 --> 00:25:05,039 Speaker 1: She likes to go to you know, she's this tiny 444 00:25:05,559 --> 00:25:07,839 Chinese lady. She likes to go to poker rooms full 445 00:25:07,880 --> 00:25:09,240 of men and take all that money. 446 00:25:09,920 --> 00:25:11,119 Speaker 2: Yeah. 447 00:25:11,160 --> 00:25:15,920 Speaker 3: Did you talk much about Core, the banking operation in California. 448 00:25:16,559 --> 00:25:18,960 Speaker 1: Yeah, that's part of the book, but it's I mean, 449 00:25:18,960 --> 00:25:22,519 the focus of the book is advantage gambling and the 450 00:25:22,559 --> 00:25:25,519 player banking thing was a source of income for Robert 451 00:25:25,519 --> 00:25:28,880 good One, but I wouldn't categorize it as advantage play. 452 00:25:28,920 --> 00:25:32,039 It was just smart business, right yep. 453 00:25:32,759 --> 00:25:38,200 Speaker 2: The third subject of your book is Bill Nelson coming in. 454 00:25:38,240 --> 00:25:41,000 When I started reading the book, I had not heard 455 00:25:41,079 --> 00:25:46,319 of Bill, although we very possibly came close to each 456 00:25:46,319 --> 00:25:49,839 other when I was doing graduate work and economics at UCLA. 457 00:25:49,920 --> 00:25:52,920 He was in the business school at UCLA with their 458 00:25:52,960 --> 00:25:55,319 next door to each other, so we very likely passed 459 00:25:55,319 --> 00:25:58,640 by each other, But I did not know who he was. 460 00:25:59,200 --> 00:26:01,160 So how does he fit into your story? 461 00:26:01,839 --> 00:26:04,000 Speaker 1: Yeah, he's He's not as well known to the others, 462 00:26:04,000 --> 00:26:06,799 but he he has sort of separated himself from the 463 00:26:06,839 --> 00:26:10,759 community of professional gamblers. He's become disenchanted with the life 464 00:26:10,799 --> 00:26:13,160 over the years, and so he doesn't go to blackjack ball. 465 00:26:13,440 --> 00:26:16,160 He doesn't really move in those circles now. He lives 466 00:26:16,200 --> 00:26:20,160 in relative peace in Poland. But you know, the reason 467 00:26:20,200 --> 00:26:23,240 I wanted to write about him was that he did 468 00:26:23,279 --> 00:26:28,720 remarkable things in roulette. Back when this was an experimental technology. 469 00:26:29,240 --> 00:26:32,359 He made it work. He spent most of his career 470 00:26:32,440 --> 00:26:37,359 as a roulette specialist, predicting roulette first with wearable computers, 471 00:26:37,599 --> 00:26:40,000 and then he learned to just do basically the same 472 00:26:40,039 --> 00:26:42,559 thing in his head, which was far easier and far 473 00:26:42,599 --> 00:26:45,039 more profitable. And then later on his career sort of 474 00:26:45,039 --> 00:26:49,359 transitioned into bias wheel play, going right up into twenty 475 00:26:49,440 --> 00:26:52,480 ten when he had a big night in Caesars Casino 476 00:26:53,200 --> 00:26:55,640 just finding a wheel that was a bit too predictable 477 00:26:55,680 --> 00:26:58,319 and hammering it for eighteen hours straight. So he's had 478 00:26:58,319 --> 00:27:01,240 a fascinating roulette career. I thought I had to include Roulette. 479 00:27:01,319 --> 00:27:05,079 Roulette is the game that most people struggle to understand 480 00:27:05,079 --> 00:27:07,680 how it could be beaten. You walk into a casino 481 00:27:07,759 --> 00:27:10,720 and you find your average gambler and you say, there 482 00:27:10,759 --> 00:27:12,359 is a way to beat Roulette. There was a way 483 00:27:12,400 --> 00:27:14,839 to reliably make money from Roulette. They won't know what 484 00:27:14,880 --> 00:27:16,960 you're talking about, most of them. So that was the 485 00:27:16,960 --> 00:27:19,920 one that had the big surprise factor. Bill also happened 486 00:27:19,920 --> 00:27:22,920 to be part of the computer team which was maybe 487 00:27:22,920 --> 00:27:27,440 the world's first data driven sports betting operation out of 488 00:27:27,519 --> 00:27:30,519 Las Vegas, So he was part of that operation as well, 489 00:27:30,559 --> 00:27:33,480 So he was away into different areas of the gambling 490 00:27:33,480 --> 00:27:35,759 world that I wouldn't otherwise have got to see. 491 00:27:36,279 --> 00:27:42,279 Speaker 3: He was actually instrumental in the whole group. My understanding 492 00:27:42,319 --> 00:27:47,000 is he's the one who introduced Mike Kent to a Middland, 493 00:27:47,519 --> 00:27:53,119 which Mike Kent was the one who developed the software 494 00:27:53,279 --> 00:27:56,680 to analyze and beat sports, and Middland was the one 495 00:27:56,680 --> 00:28:00,640 who put together the group of gamblers were betting all 496 00:28:00,680 --> 00:28:02,319 across the United States. 497 00:28:02,839 --> 00:28:05,559 Speaker 1: Yeah, he was arguably a founder member. I mean at 498 00:28:05,559 --> 00:28:09,160 the time. He the funny thing was that he was 499 00:28:09,200 --> 00:28:11,279 fixated on roulette. You know, you know how it is 500 00:28:11,279 --> 00:28:15,119 the professional gamblers, they they they find a challenge and 501 00:28:15,160 --> 00:28:19,720 then they you know, they will dedicate their entire life 502 00:28:19,799 --> 00:28:22,039 for two years to solving the problem with roulette. So 503 00:28:22,079 --> 00:28:25,200 he was fixated on roulette at the time. But he 504 00:28:25,279 --> 00:28:27,319 met Mike Kent in Vegas in the in the late 505 00:28:27,400 --> 00:28:30,279 nineteen seventies, very early on when he first arrived and 506 00:28:30,359 --> 00:28:32,319 introduced him to the people that would become part of 507 00:28:32,359 --> 00:28:36,799 the computer team. He was also prosecuted by the FBI 508 00:28:37,240 --> 00:28:39,359 along with the other members, and went through that whole 509 00:28:39,400 --> 00:28:45,279 experience of feeling the might of the American federal government 510 00:28:45,319 --> 00:28:48,279 bar down on him for betting on sports, and that 511 00:28:48,480 --> 00:28:51,119 that was also a fascinating moment in the history of gambling. 512 00:28:51,519 --> 00:28:55,319 You know, it's easy to forget now that these days, 513 00:28:55,319 --> 00:28:57,559 when you can bet on any game in any way 514 00:28:57,559 --> 00:29:00,920 you want, a thousand different ways, it's so so widespread, 515 00:29:00,960 --> 00:29:04,119 it's so available. But back in the late seventies and eighties, 516 00:29:04,440 --> 00:29:07,640 you know, sports betting was niche and mostly illegal. If 517 00:29:07,680 --> 00:29:09,960 you wanted to do it, you had to know basically 518 00:29:09,960 --> 00:29:12,640 a crook, or if you were in Nevada, you had 519 00:29:12,640 --> 00:29:15,559 to go to one of the sports books, which were 520 00:29:15,559 --> 00:29:18,200 not glamorous places to go and put money down on 521 00:29:18,240 --> 00:29:21,079 your game. So it was very different back then, and 522 00:29:22,160 --> 00:29:25,240 you know, the attempt to prosecute the computer team was 523 00:29:25,319 --> 00:29:28,920 indicative of a moment in time when America was still 524 00:29:29,519 --> 00:29:31,880 railing against the idea that people might want to bet 525 00:29:31,880 --> 00:29:35,279 on sports for leisure. There was still this pervasive attitude 526 00:29:35,279 --> 00:29:38,240 that it was somehow sinful or that it might damage 527 00:29:38,240 --> 00:29:41,920 the sporting events themselves. And of course that's gone away 528 00:29:42,440 --> 00:29:44,440 completely in the last few years in the States, but 529 00:29:44,480 --> 00:29:47,079 back then it was it was a real issue. It 530 00:29:47,119 --> 00:29:47,920 was criminalized. 531 00:29:48,640 --> 00:29:54,759 Speaker 3: Well so, actually, my understanding is that the FBI could 532 00:29:54,799 --> 00:29:59,079 not believe that any group of people could be making 533 00:29:59,200 --> 00:30:03,200 that much money just by betting, and assumed that they 534 00:30:03,359 --> 00:30:08,200 must be book makers. And that was really the problem, 535 00:30:08,279 --> 00:30:11,519 because the person making the bet, I don't think is 536 00:30:12,160 --> 00:30:16,759 created is committing a crime. It's the person accepting the bet. 537 00:30:17,559 --> 00:30:21,160 Speaker 1: Well, that was an open question back in the nineteen eighties. 538 00:30:21,200 --> 00:30:24,279 To be fair, even the FBI didn't have a clear 539 00:30:24,319 --> 00:30:27,119 answer as to whether the act of making a bet 540 00:30:27,440 --> 00:30:31,759 was illegal. But you know, I think another issue that 541 00:30:31,799 --> 00:30:35,039 the Computer team had was that because they operated in 542 00:30:35,079 --> 00:30:37,279 Las Vegas, and because this was the tail end of 543 00:30:37,319 --> 00:30:40,880 the Mafia era of Las Vegas, some quite senior members 544 00:30:40,880 --> 00:30:44,000 of the mob became aware of what the Computer Team 545 00:30:44,039 --> 00:30:47,200 were doing, and of course they wanted a slice, And 546 00:30:47,400 --> 00:30:51,519 so the FBI had wiretapped everyone more affiliated in the 547 00:30:51,599 --> 00:30:55,640 Las Vegas area, and they heard these guys, these wise guys, 548 00:30:55,680 --> 00:30:59,160 talking about the computer. What's the computer doing? I need 549 00:30:59,160 --> 00:31:02,200 the computer's tip. And of course, if you're an FBI 550 00:31:02,240 --> 00:31:05,480 agent and you hear that, it sounds it sounds bad. 551 00:31:05,720 --> 00:31:09,400 It sounds like you know the Matthi' into this. You 552 00:31:09,480 --> 00:31:12,039 don't know who's behind it. It's it's betting on sports. 553 00:31:12,079 --> 00:31:14,680 It sounds criminal. So I think that started them down 554 00:31:14,720 --> 00:31:18,039 a path that the FEI probably should have turned back from. 555 00:31:18,039 --> 00:31:21,160 But once they once, once there was that mafia that 556 00:31:21,240 --> 00:31:24,400 the width of sort of organized crime. They couldn't seem 557 00:31:24,440 --> 00:31:26,160 to stop themselves, and they and they carried on this 558 00:31:26,240 --> 00:31:29,559 prosecution that turned out to be a huge embarrassment of 559 00:31:29,640 --> 00:31:30,119 the agency. 560 00:31:30,160 --> 00:31:34,720 Speaker 3: I think, Yeah, you know, I I I got exposed 561 00:31:34,960 --> 00:31:38,480 to the world of roulette a number of years ago 562 00:31:38,799 --> 00:31:43,839 and was really shocked because the world of professional blackjack 563 00:31:43,920 --> 00:31:49,160 players it's it's it's not that big, and people all 564 00:31:49,240 --> 00:31:52,359 tend to know each other. And then I found out 565 00:31:52,400 --> 00:31:56,839 there was this whole world of roulette players out there 566 00:31:57,759 --> 00:32:01,559 that the ven diagram of the intersection of those two 567 00:32:01,680 --> 00:32:05,839 is tiny. They're just is very little overlap. I mean, 568 00:32:05,920 --> 00:32:09,720 Bill was in the blackjack world fifty years ago or whatever. 569 00:32:09,839 --> 00:32:15,319 But yeah, it's really interesting that there's still a big 570 00:32:15,359 --> 00:32:18,319 subculture of people who were out there trying to beat Roulette. 571 00:32:18,839 --> 00:32:22,319 Speaker 1: Well, I mean, Broulette goes back to ed Thorpe. Yeah, no, 572 00:32:23,319 --> 00:32:27,720 as he was a sort of developing card counting as 573 00:32:27,720 --> 00:32:31,480 a systemic method, using his computers to wage the shifting 574 00:32:31,519 --> 00:32:34,799 odds in blackjack. At the same time he was doing that, 575 00:32:34,839 --> 00:32:37,160 he spent a long he spent many years working on 576 00:32:37,200 --> 00:32:41,000 a roulette computer that he said was effective and worked, 577 00:32:41,039 --> 00:32:42,960 but he couldn't quite get it to work in the casino. 578 00:32:43,759 --> 00:32:45,599 But he wrote about it in the book that everyone 579 00:32:45,680 --> 00:32:49,480 read Find Me An Advantage. Gambloo hasn't read To Beat 580 00:32:49,519 --> 00:32:52,240 the Dealer. Do they still read it? I'm sure they must, 581 00:32:52,400 --> 00:32:53,559 but anyway back in the day. 582 00:32:54,240 --> 00:32:57,000 Speaker 3: Yeah, he touch about it more in his recent book 583 00:32:57,079 --> 00:32:58,480 Man for All Markets. 584 00:32:58,759 --> 00:33:01,000 Speaker 1: Yeah, he goes into more detail well about who he 585 00:33:01,119 --> 00:33:03,000 was working with. It turned out to be Claude Shannon, 586 00:33:03,359 --> 00:33:07,279 who is the father of information theory and an incredibly 587 00:33:07,319 --> 00:33:09,920 influential scientist. So it tells you something that these two 588 00:33:10,000 --> 00:33:12,759 men were so taken with the idea of roulette that 589 00:33:12,759 --> 00:33:16,559 they dedicated years of their life and considerable resources for 590 00:33:16,680 --> 00:33:19,680 making a computer. I think it comes from the same mindset, 591 00:33:20,920 --> 00:33:22,720 but yeah, there was a diversience at some point. I 592 00:33:22,759 --> 00:33:26,279 think Roulette is a big game in Europe, so at 593 00:33:26,359 --> 00:33:29,079 some point the knowledge that Roulette could be beaten spread 594 00:33:29,759 --> 00:33:34,640 And when I was looking at professional roulette specialists, my 595 00:33:34,720 --> 00:33:38,119 search led me to know all over Europe, to Germany 596 00:33:38,240 --> 00:33:41,599 to Croatia. I heard there were a group of Chinese 597 00:33:41,640 --> 00:33:44,960 players in Macau who were predicting roulette using a computer. 598 00:33:46,519 --> 00:33:51,200 This knowledge sort of disseminates itself through the very small 599 00:33:51,799 --> 00:33:55,759 and closed off worlds of professional gamblers, and if it 600 00:33:55,799 --> 00:33:59,119 works its spreads and roulette prediction. That definitely happened. 601 00:34:00,240 --> 00:34:04,400 Speaker 3: I've quoted Bill Benter. Bill Bender said back in about 602 00:34:04,480 --> 00:34:09,480 nineteen eighty sooner or later everyone has a Roulette project, 603 00:34:09,639 --> 00:34:12,360 because Bill had been working on roulette as well before 604 00:34:12,360 --> 00:34:16,480 he ever, you know, got to got to horses. 605 00:34:17,119 --> 00:34:20,920 Speaker 1: Yeah, Bill, Bill tried Roulette. It's you can see the appeal. 606 00:34:20,960 --> 00:34:23,840 I mean, it's Roulette is an amazing puzzle. And the 607 00:34:24,159 --> 00:34:27,280 idea of predicting Roulette feels like a superpower. The idea 608 00:34:27,280 --> 00:34:29,440 that you could have a computer that could tell you 609 00:34:29,679 --> 00:34:32,760 a section of the wheel that the ball will land in. 610 00:34:32,920 --> 00:34:37,880 You know, it's it feels superhuman. But actually writing this book, 611 00:34:37,920 --> 00:34:40,519 I learned some of the most effective Roulette methods a 612 00:34:40,960 --> 00:34:44,440 low tech essentially low tech you know, bias will play 613 00:34:45,239 --> 00:34:47,599 I believe has diminished in recent years because the wheels 614 00:34:47,599 --> 00:34:51,039 have gotten much better and then much better at spotting discrepancies. 615 00:34:51,079 --> 00:34:53,760 Speaker 3: But well, Bill Nelson had a lot to do with that. 616 00:34:54,400 --> 00:34:57,599 He did know. They had those old wheels all over Vegas, 617 00:34:57,840 --> 00:35:02,199 and you know, they smacked a number of casinos, and 618 00:35:02,440 --> 00:35:05,039 so they came out with this wheel called the starburst 619 00:35:05,639 --> 00:35:08,840 where the ball just scatters all over the all over 620 00:35:08,880 --> 00:35:09,360 the place. 621 00:35:09,400 --> 00:35:13,000 Speaker 1: And him and a couple of others, a couple of 622 00:35:13,039 --> 00:35:17,760 other very good roulette predictors, led to a change in 623 00:35:17,800 --> 00:35:19,679 the design of the roulette whel that we can still 624 00:35:19,719 --> 00:35:22,239 see today. Go into any casino anywhere in the world. 625 00:35:22,440 --> 00:35:24,519 Look at the shape of the wheel. The shape of 626 00:35:24,559 --> 00:35:26,840 that wheel is different to how it was forty years ago. 627 00:35:27,039 --> 00:35:29,599 And the only reason for that is because people have 628 00:35:29,719 --> 00:35:33,400 predicted roulette. So you know, the industry had spent a 629 00:35:33,480 --> 00:35:36,159 lot of money replacing old wheels. I mean, these things 630 00:35:36,159 --> 00:35:39,400 aren't cheap. And I think that's that's proof if anyone 631 00:35:39,440 --> 00:35:43,039 needed that, you can roulette is beatable. But it speaks 632 00:35:43,079 --> 00:35:46,639 to this arms race that's that's still going on between 633 00:35:47,280 --> 00:35:49,920 the gamblers who want to make a living from gambling 634 00:35:50,480 --> 00:35:53,440 and the casinos and the bookmakers and the operators who 635 00:35:53,480 --> 00:35:56,559 don't want them to make a living. And you know, 636 00:35:56,639 --> 00:35:59,960 I feel like the gamblers I write about and then 637 00:36:00,400 --> 00:36:03,960 and their sort of modern equivalents, they tend to have 638 00:36:04,039 --> 00:36:07,880 an advantage because it's all they do. They will spend 639 00:36:07,960 --> 00:36:10,920 years of their life developing a system for a five 640 00:36:10,920 --> 00:36:14,239 percent advantage in the casino game or in a sports 641 00:36:14,239 --> 00:36:17,920 book or or whatever it might be. And there are 642 00:36:17,920 --> 00:36:20,079 thousands of them, maybe millions. I mean, no one knows 643 00:36:20,119 --> 00:36:23,559 the numbers, but they're all very smart. And I always 644 00:36:23,599 --> 00:36:25,719 feel like the industry is always a couple of years behind. 645 00:36:25,760 --> 00:36:28,400 But there is always a moment when they realize they're 646 00:36:28,440 --> 00:36:31,039 losing so much to advantage players that they need to adapt. 647 00:36:31,280 --> 00:36:34,840 It happened in Roulette, happened in Blackjack, it happened in 648 00:36:35,599 --> 00:36:38,360 you know, fan duels and DraftKings and those sports books. 649 00:36:39,239 --> 00:36:42,320 There's always an industry pushback and they tend to change 650 00:36:42,360 --> 00:36:46,239 the technology to prevent the advantage that's been paying out 651 00:36:46,239 --> 00:36:49,599 for people, and then the gamming community has to find 652 00:36:49,599 --> 00:36:51,559 a new edge, but they always seem to you know, 653 00:36:51,599 --> 00:36:54,000 this goes on and on. It'll never end right. 654 00:36:54,079 --> 00:36:58,960 Speaker 3: And I think the reason that the casino is always 655 00:36:59,039 --> 00:37:03,320 so far behind is because the people running the casinos 656 00:37:03,440 --> 00:37:07,639 now really don't understand gambling. Much like we were talking 657 00:37:07,679 --> 00:37:12,280 about the journalists earlier. Now it's the people running the 658 00:37:12,320 --> 00:37:16,400 casinos are people coming out of business school who really 659 00:37:16,400 --> 00:37:21,320 don't understand the games. And it isn't until they really 660 00:37:21,400 --> 00:37:24,360 take a beating that they finally go, well, maybe we 661 00:37:24,360 --> 00:37:26,760 should hire somebody to tell us what our problem is. 662 00:37:27,719 --> 00:37:30,440 Speaker 1: I also think that there's truth in the idea that 663 00:37:31,159 --> 00:37:36,960 card counting was really good for casinos. Oh yeah, the 664 00:37:37,039 --> 00:37:40,280 idea that is possible to beat a game that should 665 00:37:40,320 --> 00:37:44,119 be waiting against you is very compelling and it inspires 666 00:37:44,159 --> 00:37:46,559 a lot of people to try their luck. And now 667 00:37:46,719 --> 00:37:49,920 the reality of card counting and any advantage gambling system 668 00:37:50,039 --> 00:37:54,840 is it's extremely difficult to make it work. It takes dedication, intelligence, 669 00:37:55,239 --> 00:38:00,280 bank raw management, mathematical ability, all sorts of They have 670 00:38:00,280 --> 00:38:02,199 to be in your favor to make it pay. Lots 671 00:38:02,239 --> 00:38:04,800 of people burn out. But I don't know what the 672 00:38:04,800 --> 00:38:07,599 precise numbers are, But you know, what's the number of 673 00:38:07,599 --> 00:38:10,719 failed card counters for every one card counter that makes 674 00:38:10,719 --> 00:38:13,400 a living out of it? You know, the casinos make 675 00:38:13,480 --> 00:38:16,400 more money from failed car counters than they than they 676 00:38:16,400 --> 00:38:18,960 lose to successful ones. I think that's true for any 677 00:38:18,960 --> 00:38:22,360 advantage system. So you know, if you're a marketing professional 678 00:38:22,360 --> 00:38:25,320 for the for the gambling industry, the existence of an 679 00:38:25,320 --> 00:38:28,840 elite zero point one percent who can make a living 680 00:38:28,880 --> 00:38:31,880 betting is a boon for them because it sets an 681 00:38:31,920 --> 00:38:35,400 example for the something to aim for. It's aspirational for 682 00:38:35,440 --> 00:38:38,039 the rest of the world's gamblers to come and try, 683 00:38:38,119 --> 00:38:40,800 and of course most of them fail, and the casinos 684 00:38:40,800 --> 00:38:43,519 make money they you know, yeah, it's it's like a 685 00:38:43,559 --> 00:38:45,400 cost of business. It's like it's a price they're willing 686 00:38:45,440 --> 00:38:48,599 to pay to hoover up billions of dollars that they 687 00:38:48,639 --> 00:38:50,400 make from their regular business. 688 00:38:51,039 --> 00:38:52,440 Speaker 2: That's so willing sometimes. 689 00:38:53,199 --> 00:38:56,239 Speaker 3: Yeah, right, your. 690 00:38:56,079 --> 00:39:00,320 Speaker 2: Book is called Lucky Devils. What is the genesis. 691 00:39:01,480 --> 00:39:04,199 Speaker 1: Lucky Devils? I wanted the book to be about people, 692 00:39:04,280 --> 00:39:08,559 not systems, And there's kind of a religious theme in 693 00:39:08,599 --> 00:39:11,920 the book, or at least a moral theme in the book. 694 00:39:12,559 --> 00:39:15,159 Gambling is evolution from being a sin and a vice 695 00:39:15,960 --> 00:39:21,000 and something undesirable to being entirely mainstream and corporate the 696 00:39:21,039 --> 00:39:23,800 way it is now. You know. That was that's a 697 00:39:23,840 --> 00:39:27,840 sort of cultural ethical revolution that's happened in America and 698 00:39:27,880 --> 00:39:30,360 around the world that I wanted to sort of reflect 699 00:39:30,360 --> 00:39:34,519 from the title of the book, and also, you know, 700 00:39:35,440 --> 00:39:39,119 the idea of luck. Of course, the Lucky Devils they're 701 00:39:39,119 --> 00:39:40,800 not lucky. They make their own luck, you know, luck, 702 00:39:40,840 --> 00:39:43,199 Lucky is the opposite of what they do. Luck is 703 00:39:43,199 --> 00:39:44,880 what I do. When I go into a casino and 704 00:39:45,000 --> 00:39:46,960 I put one hundred dollars on black at the rolette 705 00:39:46,960 --> 00:39:49,159 table and win or lose. That's my only bed of 706 00:39:49,159 --> 00:39:53,159 the evening. That's you know what what advantage gamblers do 707 00:39:53,480 --> 00:39:57,679 is is large numbers. It is more akin to a 708 00:39:57,760 --> 00:40:01,559 hedge fund or a market operator than it is to 709 00:40:01,639 --> 00:40:04,599 a true gambler who relies on luck. So there's a 710 00:40:04,599 --> 00:40:07,960 bit of irony in that title too. Yeah. 711 00:40:08,000 --> 00:40:10,559 Speaker 2: So there are a number of other gamblers out there 712 00:40:10,840 --> 00:40:16,440 in that are successful different games than we're done by 713 00:40:16,480 --> 00:40:20,039 Bill and Rob and Bill, do you plan to write 714 00:40:20,119 --> 00:40:22,719 one or more additional biographies about other gamblers? 715 00:40:23,599 --> 00:40:27,920 Speaker 1: I don't know. I was interested in the culture of 716 00:40:27,960 --> 00:40:31,079 advantage play and I felt like there was something to 717 00:40:31,199 --> 00:40:35,360 learn from these people and the way they work, the method, 718 00:40:35,400 --> 00:40:39,320 the mindset, you know, the way they approach risk and uncertainty, 719 00:40:39,400 --> 00:40:42,920 I think is interesting. The way they can separate their 720 00:40:42,960 --> 00:40:46,599 emotional half of the brain from the rational part of 721 00:40:46,639 --> 00:40:49,760 the brain. They don't celebrate when they win a bet. 722 00:40:50,320 --> 00:40:54,320 You know, when when when Bill Bner's computers are betting 723 00:40:54,320 --> 00:40:56,840 in Hong Kong and he hits a long shot bet, 724 00:40:56,840 --> 00:40:59,719 he doesn't whoop and cheer. You know, it's it's it's 725 00:40:59,719 --> 00:41:02,119 a very different way of thinking about the world. It's 726 00:41:02,480 --> 00:41:06,079 cold calculus. So I was fascinated by that. Look, the 727 00:41:06,679 --> 00:41:09,880 gambling world is changing so rapidly. I mean the latest 728 00:41:09,880 --> 00:41:13,519 thing is prediction markets, which of course claim they're not gambling, 729 00:41:13,559 --> 00:41:17,280 but of course are. And that's that's a whole other 730 00:41:17,480 --> 00:41:20,599 other phenomenon, and you could write, you could write several 731 00:41:20,599 --> 00:41:23,440 books about what's happening now and prediction markets. People betting 732 00:41:23,519 --> 00:41:26,360 on wars when they have inside knowledge of military strikes 733 00:41:26,360 --> 00:41:29,159 that are about to happen. The idea of people betting 734 00:41:29,239 --> 00:41:31,440 on the second coming of Jesus or against the second 735 00:41:31,440 --> 00:41:33,960 coming of Jesus and earning a five to ten percent 736 00:41:34,000 --> 00:41:38,400 return every year, the idea of sort of securitizing the future. 737 00:41:39,000 --> 00:41:42,639 It's all very strange and unfamiliar. So yeah, maybe maybe. 738 00:41:42,760 --> 00:41:45,559 Speaker 2: If any of our listeners wanted to get in touch 739 00:41:45,639 --> 00:41:47,559 with you, how could they do that. 740 00:41:48,480 --> 00:41:52,800 Speaker 1: I'm on x formerly Twitter, I'm on LinkedIn. You can 741 00:41:52,800 --> 00:41:56,199 find me on the Bloomberg website or my details on there. 742 00:41:56,880 --> 00:42:00,480 I would just discourage anyone to ask me for gambling advice. 743 00:42:03,079 --> 00:42:06,000 I always I always think that some of the worst 744 00:42:06,079 --> 00:42:09,320 qualified people in the world to dish out financial advice 745 00:42:09,320 --> 00:42:12,920 a journalists Stephen, financial journalists never ask a financial journalist 746 00:42:12,920 --> 00:42:15,159 for financial advice. We don't know any better than you. 747 00:42:15,480 --> 00:42:18,000 But that's particularly true when it comes to gambling. I 748 00:42:18,039 --> 00:42:21,239 write about it. I like the stories. I'm a terrible gambler. 749 00:42:21,239 --> 00:42:23,679 I'm not good at it, so I'm happy to share 750 00:42:23,679 --> 00:42:26,199 stories with people. But I don't have the secret sauce. 751 00:42:26,840 --> 00:42:29,960 Speaker 3: Yeah, I don't think you have that worry with this 752 00:42:30,320 --> 00:42:35,000 our audience. I think I think our audience many of 753 00:42:35,000 --> 00:42:39,079 them are knowledgeable gamblers, and if they had questions, they 754 00:42:39,079 --> 00:42:42,239 would probably direct them at either Bob or me and not. 755 00:42:42,840 --> 00:42:44,760 Speaker 1: And that would be that would be a wise approach. 756 00:42:47,320 --> 00:42:51,199 Speaker 2: Richard usually ask people their favorite restaurant in Las Vegas? 757 00:42:51,239 --> 00:42:54,039 Have you been here enough to have one? 758 00:42:53,639 --> 00:42:55,800 Speaker 1: I do have one about. 759 00:42:55,559 --> 00:42:59,000 Speaker 3: It's not usually I qualify that as one that's not 760 00:42:59,159 --> 00:43:03,159 in a casino. But but since you're not here very often. 761 00:43:03,280 --> 00:43:06,960 Speaker 1: No, Well, I've spent I've spent a lot of time 762 00:43:07,000 --> 00:43:08,840 in Las Vegas, and I'm just having to look it 763 00:43:08,920 --> 00:43:10,880 up because i haven't been there in a while. It 764 00:43:10,960 --> 00:43:14,039 is the original Lindo mitchak. 765 00:43:14,079 --> 00:43:16,159 Speaker 3: Oh, that's a good road. 766 00:43:16,480 --> 00:43:23,519 Speaker 1: Lindourepan that's the one. Yeah, I've I've come to Vegas 767 00:43:23,559 --> 00:43:26,280 a bunch of times in the last few years researching 768 00:43:26,280 --> 00:43:28,840 this book. And also I wanted to know the town better. 769 00:43:28,880 --> 00:43:31,000 I mean, it is it is the place where this 770 00:43:31,000 --> 00:43:33,840 this whole world sprang from, and I didn't know it well. 771 00:43:34,079 --> 00:43:35,800 And I wanted to see the sides of Vegas that 772 00:43:35,840 --> 00:43:38,239 the tourists don't go to. So I wanted to. I 773 00:43:38,280 --> 00:43:39,920 wanted to, you know, I wanted to go spend some 774 00:43:39,960 --> 00:43:43,159 more time downtown. I wanted to spend some more time, 775 00:43:44,159 --> 00:43:46,559 you know, seeing how how people actually live. But yeah, 776 00:43:46,559 --> 00:43:50,920 that that that Mexican restaurant phenomenally good, great fun and 777 00:43:50,960 --> 00:43:52,599 a real scene. That's that's my spot. 778 00:43:53,159 --> 00:43:53,400 Speaker 2: Yep. 779 00:43:53,679 --> 00:43:56,280 Speaker 3: I often when I'm meeting people, I often meet them 780 00:43:56,320 --> 00:44:00,079 at that restaurant. So yeah, it's a good one, all right. 781 00:44:00,079 --> 00:44:06,599 Speaker 2: Right, very good, Thank you, Kit, Thank you Richard. Go 782 00:44:06,639 --> 00:44:11,039 out and hit lots of royal flushes, everybody. Good day. 783 00:44:11,400 --> 00:44:16,239 Speaker 1: Free audio post production Byophonic dot Com.