1 00:00:10,560 --> 00:00:15,160 Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast. 2 00:00:15,280 --> 00:00:19,360 Speaker 1: I'm Joe and I'm Tracy Halloway. Hey, Tracy, remember our 3 00:00:19,440 --> 00:00:25,760 Speaker 1: episode from a few weeks ago with Phil helm Youth. Yes, yes, 4 00:00:26,960 --> 00:00:29,360 Speaker 1: it seems like so long ago. Yeah, but I don't 5 00:00:29,360 --> 00:00:30,440 Speaker 1: think it was. I think it was just like a 6 00:00:30,440 --> 00:00:33,760 Speaker 1: month ago or something. Anyway, not to you know, pat 7 00:00:33,800 --> 00:00:35,440 Speaker 1: ourselves on the shoulder, but you know, that was one 8 00:00:35,440 --> 00:00:41,040 Speaker 1: of the most popular episode in Bloomberg podcast history. You're 9 00:00:41,120 --> 00:00:45,240 Speaker 1: definitely patting yourself on the shoulder there, given that you're 10 00:00:45,320 --> 00:00:48,000 Speaker 1: interested in poker, right, that's why we had him on. Yeah, 11 00:00:48,040 --> 00:00:50,479 Speaker 1: but that's not why I'm not bringing that up to 12 00:00:50,640 --> 00:00:55,240 Speaker 1: congratulate ourselves or anything like that. Um, there's actually a 13 00:00:55,280 --> 00:00:58,640 Speaker 1: serious point behind that, behind its popularity, which is that 14 00:00:58,840 --> 00:01:04,120 Speaker 1: a lot of the finance crowd that uh we ostensibly 15 00:01:04,319 --> 00:01:07,279 Speaker 1: target and which we do target of Bloomberg is really 16 00:01:07,319 --> 00:01:11,440 Speaker 1: into stuff about gambling and games and games of chance. Well, 17 00:01:11,480 --> 00:01:14,720 Speaker 1: that's true, and I mean there's clearly an overlap there 18 00:01:14,760 --> 00:01:19,680 Speaker 1: as well. Right, Yeah, there's definitely an overlap. Definitely lessons 19 00:01:19,760 --> 00:01:23,559 Speaker 1: that could be applied from finance to gambling, Lessons from 20 00:01:23,760 --> 00:01:27,440 Speaker 1: gambling and betting that could be applied back to finance, 21 00:01:27,760 --> 00:01:30,800 Speaker 1: and I think from a personality standpoint, you get a 22 00:01:30,800 --> 00:01:34,120 Speaker 1: lot of people obviously in the finance financial industry who 23 00:01:34,200 --> 00:01:37,040 Speaker 1: just sort of also are very interested in the gambling side. 24 00:01:37,240 --> 00:01:39,959 Speaker 1: And of course the history of finance and gambling are 25 00:01:40,000 --> 00:01:42,760 Speaker 1: deeply intertwined. Are you telling me that we're going to 26 00:01:42,840 --> 00:01:46,840 Speaker 1: talk about poker again? No, good news. We are not 27 00:01:46,920 --> 00:01:50,720 Speaker 1: talking about poker on this episode, although actually I think 28 00:01:50,760 --> 00:01:53,760 Speaker 1: maybe our guest likes poker, but I'm not sure. Um, 29 00:01:53,800 --> 00:01:55,880 Speaker 1: but we are going to be talking about someone who 30 00:01:55,920 --> 00:02:01,280 Speaker 1: has been active in both sides of the UH finance 31 00:02:01,400 --> 00:02:06,440 Speaker 1: in gambling divide. Okay, Um, but gambling on what exactly 32 00:02:07,320 --> 00:02:15,360 Speaker 1: this time? Gambling not on cards but on sports? Uh Joe, Joe. 33 00:02:15,400 --> 00:02:18,239 Speaker 1: If it's not if it's not chess or poker, then 34 00:02:18,240 --> 00:02:23,400 Speaker 1: it's sports. Yeah, it's sports. Um, so let's jump right 35 00:02:23,400 --> 00:02:26,040 Speaker 1: into it. The guest on our episode today is UH 36 00:02:26,400 --> 00:02:30,880 Speaker 1: Joe Peter. He was a trader at Lehman for many years, 37 00:02:30,919 --> 00:02:34,920 Speaker 1: with involved in various things, including hedge funds. There he's 38 00:02:34,960 --> 00:02:38,520 Speaker 1: at a hedge fund now. But in between those two things, 39 00:02:38,919 --> 00:02:42,680 Speaker 1: he uh was also figured out a way to bet 40 00:02:42,680 --> 00:02:46,040 Speaker 1: on baseball, and he wrote a book about betting on 41 00:02:46,160 --> 00:02:49,840 Speaker 1: baseball called Trading Basses, So I think a perfect guest 42 00:02:49,919 --> 00:02:52,760 Speaker 1: to talk about the two worlds. I'm going to caveat 43 00:02:52,800 --> 00:02:54,920 Speaker 1: this with with my usual thing, which is I know 44 00:02:55,000 --> 00:02:59,560 Speaker 1: nothing about baseball or sports betting, so I look forward 45 00:02:59,600 --> 00:03:02,000 Speaker 1: to learn about it. Shoe. You know what, Tracy, it's 46 00:03:02,040 --> 00:03:03,960 Speaker 1: never you know, you always say that, but it never 47 00:03:04,000 --> 00:03:07,080 Speaker 1: proves to be a problem because you always ask fantastic questions, 48 00:03:07,240 --> 00:03:12,760 Speaker 1: and so I'm confident that this discussion will be no different. Okay, 49 00:03:12,840 --> 00:03:23,760 Speaker 1: that's very sweet. Let's let's have him on. Joe Peter, 50 00:03:23,960 --> 00:03:26,800 Speaker 1: thank you very much for joining us. Joe and Tracy, 51 00:03:26,840 --> 00:03:29,000 Speaker 1: it's a pleasure to be on. It's uh, you know, 52 00:03:29,160 --> 00:03:31,840 Speaker 1: I'm on a lot of because of the topic of 53 00:03:31,840 --> 00:03:33,800 Speaker 1: the book, I end up on a lot of you know, 54 00:03:33,919 --> 00:03:37,480 Speaker 1: Vegas based podcast et cetera. And uh, it is a 55 00:03:37,520 --> 00:03:40,160 Speaker 1: pleasure to be on a podcast that I know has 56 00:03:40,200 --> 00:03:45,080 Speaker 1: such a high intellectual content. I will try my best 57 00:03:45,640 --> 00:03:47,960 Speaker 1: to only lower it a little bit. No, no need, 58 00:03:48,240 --> 00:03:51,560 Speaker 1: no need to uh flatter us, but we do appreciate 59 00:03:51,760 --> 00:03:54,440 Speaker 1: was the would would you say that the intro is 60 00:03:54,480 --> 00:03:59,840 Speaker 1: a fair characterization of your background from banking to betting 61 00:04:00,120 --> 00:04:03,520 Speaker 1: and then now at a hedge fund. Absolutely the book 62 00:04:03,600 --> 00:04:06,720 Speaker 1: and the book itself came about literally by accident. I 63 00:04:06,840 --> 00:04:10,800 Speaker 1: was uh, still working on Wall Street in Lower Manhattan 64 00:04:10,800 --> 00:04:13,200 Speaker 1: when I got run over by an ambulance in New 65 00:04:13,240 --> 00:04:17,240 Speaker 1: York City. And while I was laid up and you know, 66 00:04:17,279 --> 00:04:19,719 Speaker 1: wheelchair and I couldn't travel back to my home in 67 00:04:19,760 --> 00:04:23,720 Speaker 1: San Francisco with my family. While I was laid up, 68 00:04:23,880 --> 00:04:26,880 Speaker 1: I had this idea, uh to write a book that 69 00:04:27,120 --> 00:04:31,560 Speaker 1: more or less um examined the critical reasoning overlap between 70 00:04:32,120 --> 00:04:39,160 Speaker 1: asset management UM, the moneyballization of baseball UH and sports betting. UH. 71 00:04:39,160 --> 00:04:41,200 Speaker 1: Those He was clearly a right what you know book 72 00:04:41,279 --> 00:04:43,920 Speaker 1: for me, UM, but you definitely touched on those in 73 00:04:43,960 --> 00:04:46,400 Speaker 1: the intro um. I can tell you when the book 74 00:04:46,520 --> 00:04:50,000 Speaker 1: was up for was being passed around publishers houses. One 75 00:04:50,040 --> 00:04:52,120 Speaker 1: of the editors that very much wanted to work on 76 00:04:52,160 --> 00:04:55,720 Speaker 1: it was Phil Helmy was editor. He had written Play 77 00:04:55,760 --> 00:04:59,719 Speaker 1: Poker Like the Pros. So yeah, there was definitely that 78 00:05:00,000 --> 00:05:05,560 Speaker 1: overlap between the audiences. So before we get into the 79 00:05:05,600 --> 00:05:09,200 Speaker 1: book itself, tell us what you were doing on Wall 80 00:05:09,240 --> 00:05:12,720 Speaker 1: Street before you were hit by an ambulance and you said, 81 00:05:12,920 --> 00:05:16,000 Speaker 1: you know the your you started focusing on whether some 82 00:05:16,080 --> 00:05:19,440 Speaker 1: of these critical reasoning and data analytical tools could be 83 00:05:19,600 --> 00:05:23,560 Speaker 1: applied to sports betting. I take it then, even before 84 00:05:23,720 --> 00:05:27,960 Speaker 1: this endeavor, that this had already been an interest of yours. Yeah, 85 00:05:28,000 --> 00:05:29,640 Speaker 1: I can tell you when it came up. I mean, 86 00:05:29,680 --> 00:05:31,640 Speaker 1: certainly I had always been a baseball fan, and the 87 00:05:31,640 --> 00:05:35,040 Speaker 1: book really is a lot about baseball. It isn't memoir, 88 00:05:35,160 --> 00:05:37,359 Speaker 1: so it does really touch on, you know, sort of 89 00:05:37,360 --> 00:05:39,480 Speaker 1: the role baseball has played in my family. My father 90 00:05:39,520 --> 00:05:42,040 Speaker 1: was an immigrant, and one of the ways he was 91 00:05:42,279 --> 00:05:45,360 Speaker 1: he wanted to adopt, you know, show his love for 92 00:05:45,400 --> 00:05:49,000 Speaker 1: America because he was saddled with an Italian name or 93 00:05:49,120 --> 00:05:53,560 Speaker 1: Minio that instantly announced him as, you know, an outsider. Um. 94 00:05:53,600 --> 00:05:55,560 Speaker 1: One of the ways he wanted to show his love 95 00:05:55,720 --> 00:05:57,919 Speaker 1: for America was he loved baseball, and he was going 96 00:05:58,000 --> 00:06:01,680 Speaker 1: to become an English professor. That ultimately was you know, 97 00:06:01,720 --> 00:06:04,160 Speaker 1: sort of his past. So baseball. I was always very 98 00:06:04,160 --> 00:06:07,160 Speaker 1: interested in baseball. I had been introduced to Bill James 99 00:06:07,160 --> 00:06:10,160 Speaker 1: and a lot of the saber metric theories in the 100 00:06:10,240 --> 00:06:13,120 Speaker 1: nineties and early two thousand's, but it wasn't until I 101 00:06:13,160 --> 00:06:16,479 Speaker 1: was with Lehman and moved from the cell side to 102 00:06:16,520 --> 00:06:19,680 Speaker 1: the by side, which of course your audience understands. I 103 00:06:19,680 --> 00:06:22,359 Speaker 1: I had moved to San Francisco to help launch a 104 00:06:22,520 --> 00:06:27,400 Speaker 1: lehmand Uh funded hedge fund, and I was challenged with 105 00:06:28,279 --> 00:06:31,200 Speaker 1: as running the trading desk there. What I found was 106 00:06:31,240 --> 00:06:37,560 Speaker 1: I was working with analysts and portfolio managers who had 107 00:06:37,760 --> 00:06:42,120 Speaker 1: different skills, and I was really sort of challenged with, 108 00:06:42,240 --> 00:06:45,560 Speaker 1: how do we only have them do what they're good with? 109 00:06:45,760 --> 00:06:47,520 Speaker 1: And and this is one of I know, one of 110 00:06:47,560 --> 00:06:49,600 Speaker 1: your other guests. I know, Michael Mobison. I know you're 111 00:06:49,600 --> 00:06:51,200 Speaker 1: a fan of his work, and you've had him on 112 00:06:51,480 --> 00:06:53,760 Speaker 1: and this is something he talks about a lot too, 113 00:06:53,800 --> 00:06:56,320 Speaker 1: and and and we've talked since, you know, we've read 114 00:06:56,320 --> 00:06:59,640 Speaker 1: each other's books. And my goal was, how do I 115 00:07:00,040 --> 00:07:02,280 Speaker 1: at these guys to only do what they're good at, 116 00:07:02,640 --> 00:07:07,080 Speaker 1: and you know, not degrade the value that they that 117 00:07:07,200 --> 00:07:10,080 Speaker 1: they bring to the table as either analysts or portfolio 118 00:07:10,120 --> 00:07:13,480 Speaker 1: managers by doing something they're not good at. And I 119 00:07:13,600 --> 00:07:16,480 Speaker 1: looked to and I knew to sort of convince them 120 00:07:16,560 --> 00:07:18,160 Speaker 1: or try to change that behavior, it was going to 121 00:07:18,240 --> 00:07:20,080 Speaker 1: have to be data driven. So I looked to the 122 00:07:20,120 --> 00:07:22,360 Speaker 1: lessons of baseball. I'm like, well, baseball sta resolve this 123 00:07:22,960 --> 00:07:27,880 Speaker 1: UM and so I started using that and so, and 124 00:07:28,120 --> 00:07:30,520 Speaker 1: they were very crude tools and and but it was 125 00:07:30,560 --> 00:07:33,960 Speaker 1: really trying to identify, you know, not results, but skill sets. 126 00:07:34,560 --> 00:07:37,520 Speaker 1: And what really struck me, especially then after I got injured, 127 00:07:37,520 --> 00:07:40,440 Speaker 1: when I started thinking about it, is you know, money 128 00:07:40,440 --> 00:07:43,280 Speaker 1: Ball was such a huge hit as a as a 129 00:07:43,440 --> 00:07:46,360 Speaker 1: book and as a theme, and it was really it 130 00:07:46,440 --> 00:07:48,800 Speaker 1: was embraced by the business world, and I'm I was 131 00:07:48,840 --> 00:07:52,160 Speaker 1: thinking to myself, the whole industry of Major League Baseball 132 00:07:52,840 --> 00:07:55,600 Speaker 1: is worth you know, maybe thirty billion dollars. You know, 133 00:07:55,840 --> 00:07:58,680 Speaker 1: it's probably an average of a billion dollars a team. 134 00:07:58,720 --> 00:08:01,760 Speaker 1: There are single finance ancient institution is worth more than that. 135 00:08:02,320 --> 00:08:06,440 Speaker 1: But why is baseball so much better at using its 136 00:08:06,560 --> 00:08:10,480 Speaker 1: data to identify skills and not luck, you know, than 137 00:08:11,000 --> 00:08:13,200 Speaker 1: than the financial industry is when there's so much more 138 00:08:13,200 --> 00:08:15,400 Speaker 1: at stake. So that was sort of an underlying theme 139 00:08:15,400 --> 00:08:18,480 Speaker 1: of the book. So, Joe, can we back up for 140 00:08:18,480 --> 00:08:21,600 Speaker 1: a second, because whenever people talk about sports analytics, they 141 00:08:21,640 --> 00:08:25,600 Speaker 1: always eventually start talking about baseball and um, moneyball and 142 00:08:25,640 --> 00:08:28,880 Speaker 1: things like that. What is it about baseball specifically that 143 00:08:28,960 --> 00:08:33,199 Speaker 1: seems to lend itself to analyzing facts and figures? And 144 00:08:33,320 --> 00:08:36,680 Speaker 1: numbers and Tracy, that's a great question. There's there's really 145 00:08:36,720 --> 00:08:40,280 Speaker 1: two parts of it. One, it's data rich. The history 146 00:08:40,400 --> 00:08:44,200 Speaker 1: is data rich. It has been results in baseball beyond 147 00:08:44,240 --> 00:08:47,640 Speaker 1: just the final score, but results of each play or 148 00:08:47,720 --> 00:08:50,480 Speaker 1: really each pick, have been recorded for more than a 149 00:08:50,520 --> 00:08:53,960 Speaker 1: hundred years. UM. So you've got this data rich environment. 150 00:08:54,040 --> 00:08:58,800 Speaker 1: But most importantly, when you compare baseball to other sports, 151 00:08:59,760 --> 00:09:03,679 Speaker 1: is ace ball is really a series of one on 152 00:09:03,679 --> 00:09:07,280 Speaker 1: one matchup sixty or seventy one on one matchups a 153 00:09:07,480 --> 00:09:11,600 Speaker 1: game disguised as a team sport. So I can, with 154 00:09:12,040 --> 00:09:17,400 Speaker 1: very high confidence say something like you know, um, And 155 00:09:17,440 --> 00:09:21,800 Speaker 1: I use this example in my book Randy Johnson, who 156 00:09:21,840 --> 00:09:26,040 Speaker 1: was a prolific strikeout picture UM, played in both leagues, 157 00:09:26,080 --> 00:09:28,880 Speaker 1: the American and National League. He played for five or 158 00:09:28,880 --> 00:09:31,320 Speaker 1: six different teams, and he won Cy Young's I think 159 00:09:31,320 --> 00:09:34,920 Speaker 1: for three different teams, and he pitched the different you 160 00:09:34,960 --> 00:09:39,000 Speaker 1: know catchers. Despite that all, despite all those other variables, 161 00:09:39,040 --> 00:09:41,600 Speaker 1: he struck out roughly one third of the batters he 162 00:09:41,640 --> 00:09:44,320 Speaker 1: faced every year. And you could count on that, despite 163 00:09:44,320 --> 00:09:47,280 Speaker 1: all those changing variables. There is no way we could 164 00:09:47,280 --> 00:09:50,520 Speaker 1: look at Tom Brady and say so when we evaluate 165 00:09:50,559 --> 00:09:53,360 Speaker 1: Tom Brady, we have to say things like Tom Brady 166 00:09:53,760 --> 00:09:57,880 Speaker 1: running a Bill Belicheck offense, um play action, faking to 167 00:09:58,160 --> 00:10:02,040 Speaker 1: these running backs, throwing who these receivers will complete six 168 00:10:02,600 --> 00:10:05,600 Speaker 1: his passes, But you could not move him to another 169 00:10:05,679 --> 00:10:09,800 Speaker 1: team and you know, and model his performance exactly the 170 00:10:09,840 --> 00:10:11,959 Speaker 1: same way, because it's so much more there's so much 171 00:10:12,000 --> 00:10:16,040 Speaker 1: more interdependence baseball, And that's why I found it the 172 00:10:16,080 --> 00:10:19,760 Speaker 1: best to create models for betting. Baseball is very pure 173 00:10:19,800 --> 00:10:21,960 Speaker 1: and that it really is one on one matchups, like 174 00:10:22,000 --> 00:10:24,439 Speaker 1: I say, disguised as a team game. That is that 175 00:10:24,440 --> 00:10:30,520 Speaker 1: that's a great explanation of why sports analytics uh so. 176 00:10:30,520 --> 00:10:32,800 Speaker 1: So much of it comes back to baseball. So I 177 00:10:32,840 --> 00:10:36,160 Speaker 1: read Moneyball, and you know, I want to get into 178 00:10:36,640 --> 00:10:39,600 Speaker 1: where you get your edge because I read Moneyball and 179 00:10:39,679 --> 00:10:42,680 Speaker 1: I you know, the whole ideas I took away, which 180 00:10:42,720 --> 00:10:45,760 Speaker 1: is that a lot of these uh scouts of players 181 00:10:45,840 --> 00:10:50,400 Speaker 1: and general managers had some sort of biases about what 182 00:10:50,520 --> 00:10:53,280 Speaker 1: made a good player or not, and maybe they just 183 00:10:53,360 --> 00:10:56,360 Speaker 1: had some rules of thumb and heuristics to look at 184 00:10:56,360 --> 00:10:59,960 Speaker 1: a player and evaluate them, and they really weren't data driven. 185 00:11:00,080 --> 00:11:03,520 Speaker 1: That when the nerds so to speak, took over and 186 00:11:03,520 --> 00:11:06,480 Speaker 1: really started looking at the data that they found that 187 00:11:06,600 --> 00:11:10,199 Speaker 1: these old some of this old baseball wisdom wasn't really 188 00:11:10,240 --> 00:11:13,480 Speaker 1: matched by results. So there was a clear gap between 189 00:11:13,480 --> 00:11:18,040 Speaker 1: what the data said and what the received wisdom said. Now, 190 00:11:18,160 --> 00:11:20,800 Speaker 1: taking this over to the world of betting and going 191 00:11:20,880 --> 00:11:24,560 Speaker 1: to a casino and placing a bet, and obviously the 192 00:11:24,640 --> 00:11:28,840 Speaker 1: house and the casino, it's sort of data driven. Well, 193 00:11:28,880 --> 00:11:32,240 Speaker 1: where where does the edge come from? Specifically when you 194 00:11:32,280 --> 00:11:35,040 Speaker 1: sort of poured this over to the world of gambling, 195 00:11:35,240 --> 00:11:39,319 Speaker 1: it's an evolving edged and it's certainly, uh has gotten 196 00:11:39,360 --> 00:11:42,120 Speaker 1: smaller or it changes. It evolved, certainly, you know, just 197 00:11:42,240 --> 00:11:45,319 Speaker 1: using sort of the money ball as an example. Back 198 00:11:45,360 --> 00:11:49,000 Speaker 1: then in the book, on base percentage was undervalued, right, Uh, 199 00:11:49,040 --> 00:11:52,760 Speaker 1: So there was value to picking up players that got 200 00:11:52,840 --> 00:11:55,160 Speaker 1: on base a lot, even if they didn't have the 201 00:11:55,240 --> 00:11:59,120 Speaker 1: other counting stats like home runs and urbis that were 202 00:11:59,120 --> 00:12:01,920 Speaker 1: deemed important back then. Uh. And of course that's shifted. 203 00:12:02,000 --> 00:12:04,520 Speaker 1: There is no across major league baseball now there is 204 00:12:04,559 --> 00:12:09,600 Speaker 1: no um you know, there's on base percentage is not undervalued. However, 205 00:12:09,960 --> 00:12:13,240 Speaker 1: defense may have been undervalued five or six years ago. Um. 206 00:12:13,280 --> 00:12:16,200 Speaker 1: So there's always a pendulum and any time, and of 207 00:12:16,240 --> 00:12:19,200 Speaker 1: course sports betting or or there's a price for each 208 00:12:19,280 --> 00:12:22,200 Speaker 1: team every night, you know, and as you know from 209 00:12:22,559 --> 00:12:28,120 Speaker 1: financial markets, while prices do incorporate a lot of known information, 210 00:12:28,600 --> 00:12:32,280 Speaker 1: they also incorporate emotion um. And you can see that 211 00:12:32,320 --> 00:12:36,920 Speaker 1: there is always when it comes to say, postseason batting 212 00:12:37,000 --> 00:12:41,200 Speaker 1: or futures betting, there is always a I almost call 213 00:12:41,280 --> 00:12:44,480 Speaker 1: it a A A and Er Mays type premium on 214 00:12:44,559 --> 00:12:47,120 Speaker 1: the Yankees or the Cups. You know, there's a retail 215 00:12:47,160 --> 00:12:50,000 Speaker 1: markup um. So you can find small edges there. But 216 00:12:50,080 --> 00:12:54,400 Speaker 1: specifically when you are looking at single games um. As 217 00:12:54,440 --> 00:12:56,840 Speaker 1: I talked about in the book five years ago there 218 00:12:57,280 --> 00:12:59,040 Speaker 1: um and this really goes back to sort of what 219 00:12:59,040 --> 00:13:03,480 Speaker 1: what Mobison says. There is still an element of looking 220 00:13:03,520 --> 00:13:10,439 Speaker 1: at past results when pricing the current market. Uh and pictures, 221 00:13:10,640 --> 00:13:14,800 Speaker 1: especially five years ago. Um, we're Verrey were subject to. 222 00:13:15,640 --> 00:13:17,360 Speaker 1: You'd look at the pictures e r A, which is 223 00:13:17,360 --> 00:13:20,920 Speaker 1: how many runs he gives up over a game, and 224 00:13:21,040 --> 00:13:22,800 Speaker 1: that you know, his past d r A or his 225 00:13:22,840 --> 00:13:24,600 Speaker 1: current e r A for the season had a lot 226 00:13:24,640 --> 00:13:26,400 Speaker 1: to do with how he might be priced in July. 227 00:13:26,760 --> 00:13:29,960 Speaker 1: But if you dig deeper, there were better ways to 228 00:13:30,040 --> 00:13:32,400 Speaker 1: look at what his future e r A should be. 229 00:13:32,480 --> 00:13:34,880 Speaker 1: And you do that by looking at his skill sets, 230 00:13:35,280 --> 00:13:38,720 Speaker 1: not his results, because e r A is dependent on 231 00:13:39,400 --> 00:13:43,000 Speaker 1: the defense behind him. UM, it's dependent on the luck 232 00:13:43,120 --> 00:13:46,880 Speaker 1: of sequencing, which I call cluster luck UM in terms 233 00:13:46,880 --> 00:13:50,240 Speaker 1: of of what what tends to be much more sticky 234 00:13:50,400 --> 00:13:53,240 Speaker 1: is the skill set of what percentage of batters does 235 00:13:53,240 --> 00:13:55,760 Speaker 1: he strike out, what percentage of batters does he walk? 236 00:13:56,040 --> 00:14:00,199 Speaker 1: What percentage of of hit balls are groundballs? And at 237 00:14:00,440 --> 00:14:03,520 Speaker 1: can't And you will find some pictures that you will 238 00:14:03,559 --> 00:14:06,160 Speaker 1: look at those inputs and you'll say, oh, you know, 239 00:14:06,200 --> 00:14:09,480 Speaker 1: a regression analysis tells me he should have an e 240 00:14:09,720 --> 00:14:14,360 Speaker 1: r A of upper threes instead of upper twos. So 241 00:14:14,880 --> 00:14:18,120 Speaker 1: you know, he might be overvalued on a single game. 242 00:14:18,160 --> 00:14:22,880 Speaker 1: And the important thing with any UM, with any endeavor 243 00:14:22,920 --> 00:14:25,560 Speaker 1: of of capital, of course, is you want to find 244 00:14:25,600 --> 00:14:28,160 Speaker 1: a small egge and then you want to put a 245 00:14:28,160 --> 00:14:30,640 Speaker 1: small amount of money on it. You know, it's that 246 00:14:30,720 --> 00:14:33,840 Speaker 1: old idea that you never want to risk tomorrow's egge 247 00:14:33,880 --> 00:14:37,840 Speaker 1: by overallocating today UM. And that's you know, that's just 248 00:14:38,880 --> 00:14:41,200 Speaker 1: and that that's again a lot of the overlap. I 249 00:14:41,280 --> 00:14:45,600 Speaker 1: find that the world of gamblers UM and even poker players, 250 00:14:46,240 --> 00:14:50,240 Speaker 1: they they tend to overestimate their edge and I tried 251 00:14:50,280 --> 00:14:52,160 Speaker 1: to talk about that in the book too, that Hey, 252 00:14:52,200 --> 00:14:54,760 Speaker 1: one thing that's the financial industry is really good at. 253 00:14:54,760 --> 00:14:57,960 Speaker 1: One thing hedge fund pros are really good at is 254 00:14:58,040 --> 00:15:00,800 Speaker 1: understanding survival. Um. And then of course I drew the 255 00:15:00,800 --> 00:15:03,920 Speaker 1: comparison to Dick Fold, who did not understand that, who 256 00:15:03,960 --> 00:15:08,600 Speaker 1: had a wonderful franchise and risked it all, um, you know, 257 00:15:08,640 --> 00:15:13,440 Speaker 1: by over betting on real estate. So, Joe, the subtitle 258 00:15:13,480 --> 00:15:15,720 Speaker 1: of your book is how a Wall Street trader made 259 00:15:15,720 --> 00:15:19,800 Speaker 1: a fortune betting on baseball. Walk us through exactly how 260 00:15:19,880 --> 00:15:22,240 Speaker 1: you made your bets and how much you actually made. 261 00:15:23,160 --> 00:15:25,040 Speaker 1: I will I will address that, but I will tell 262 00:15:25,080 --> 00:15:28,240 Speaker 1: you we can get a little inside sort of publishing here. Um. 263 00:15:28,320 --> 00:15:31,400 Speaker 1: And and you may know this, um, but don't tell 264 00:15:31,440 --> 00:15:38,120 Speaker 1: me the exaggerated the title refus y right exactly. And 265 00:15:38,120 --> 00:15:41,320 Speaker 1: you know this even from you know publication that you 266 00:15:41,360 --> 00:15:44,120 Speaker 1: know bylines. The person who writes an article doesn't write 267 00:15:44,120 --> 00:15:46,880 Speaker 1: the headline right, and they get trapped by the headlines sometimes. Well, 268 00:15:46,960 --> 00:15:49,600 Speaker 1: I certainly learned from the publishing industry that the author 269 00:15:49,600 --> 00:15:53,120 Speaker 1: owns everything between the covers, but the cover itself, including 270 00:15:53,160 --> 00:15:56,200 Speaker 1: the title, belongs to the to the publisher. Now, fortunately 271 00:15:56,200 --> 00:15:59,440 Speaker 1: the hard back cover. That was my title. Um. The 272 00:15:59,480 --> 00:16:02,120 Speaker 1: title of the book was Irustrating basis a story about 273 00:16:02,120 --> 00:16:05,160 Speaker 1: Wall Street gambling in baseball, not necessarily in that order. 274 00:16:05,960 --> 00:16:10,080 Speaker 1: When these when the UH paperback rights were sold, I 275 00:16:10,080 --> 00:16:12,280 Speaker 1: think to random House, they you know, they renamed it 276 00:16:12,360 --> 00:16:15,640 Speaker 1: or they put the subtitle there. Um. I never wanted 277 00:16:15,640 --> 00:16:19,720 Speaker 1: the focus to be on UM, you know that sort 278 00:16:19,720 --> 00:16:21,920 Speaker 1: of thing like Calvin how somebody made a fortune. Because 279 00:16:21,920 --> 00:16:26,320 Speaker 1: for one thing, a fortune is is UH is relative? Right? Um. 280 00:16:26,360 --> 00:16:28,000 Speaker 1: And for another, one of the things I kind of 281 00:16:28,000 --> 00:16:32,760 Speaker 1: talked about in the book is that professional UM investors 282 00:16:32,800 --> 00:16:34,760 Speaker 1: they never talk about like you will never hear a 283 00:16:34,760 --> 00:16:36,920 Speaker 1: hedge fund say, hey, we were up two million dollars yesterday. 284 00:16:37,160 --> 00:16:39,720 Speaker 1: You will hear them say we were forty basis points right. 285 00:16:39,760 --> 00:16:42,080 Speaker 1: And that is really what I tried to get across 286 00:16:42,080 --> 00:16:44,320 Speaker 1: in the book is it doesn't matter how little or 287 00:16:44,360 --> 00:16:47,600 Speaker 1: how much money you have, the the idea of capital 288 00:16:47,640 --> 00:16:50,760 Speaker 1: allocation is the same for everyone. Well, Joe, let me 289 00:16:51,040 --> 00:16:53,960 Speaker 1: let me rephrase my question then, because this is actually 290 00:16:53,960 --> 00:16:56,240 Speaker 1: what I wanted to get into. So you're betting on 291 00:16:56,320 --> 00:16:59,520 Speaker 1: a sport, you're betting on a particular outcome. Um, it 292 00:16:59,600 --> 00:17:02,280 Speaker 1: seems to me like that outcome is probably going to be, 293 00:17:02,480 --> 00:17:04,600 Speaker 1: you know, either win or lose. So how do you 294 00:17:05,200 --> 00:17:08,960 Speaker 1: how do you risk adjust whatever return that you're actually 295 00:17:09,000 --> 00:17:13,280 Speaker 1: making from sports spending. Fantastic question because I really did 296 00:17:13,600 --> 00:17:16,720 Speaker 1: dive into this um. And to to to the story 297 00:17:16,720 --> 00:17:20,040 Speaker 1: about you know, you know a fortune I did for 298 00:17:20,080 --> 00:17:23,560 Speaker 1: the for the apologue of the book, I did raise 299 00:17:23,600 --> 00:17:25,480 Speaker 1: a fund. It was a million dollar fund, and I 300 00:17:25,520 --> 00:17:28,720 Speaker 1: went to Vegas for the summer of twelve and actually 301 00:17:28,760 --> 00:17:32,000 Speaker 1: ran a baseball betting fund, you know, and legally in Vegas. 302 00:17:32,560 --> 00:17:35,639 Speaker 1: And that you're the The idea of how much to 303 00:17:35,680 --> 00:17:39,000 Speaker 1: bet on each game is very important because each game's 304 00:17:39,080 --> 00:17:42,520 Speaker 1: binary event. It's it's you're either gonna you know, it's 305 00:17:42,560 --> 00:17:44,280 Speaker 1: not double because the odds of the game might be, 306 00:17:44,320 --> 00:17:46,320 Speaker 1: say two to one, but you're either going to lose 307 00:17:46,359 --> 00:17:48,960 Speaker 1: everything you bet or you're going to you know, win 308 00:17:49,359 --> 00:17:51,679 Speaker 1: you know, essentially what you bet, or a little more, 309 00:17:51,800 --> 00:17:54,479 Speaker 1: maybe a little less, depending on the odds. So the 310 00:17:54,520 --> 00:17:57,240 Speaker 1: idea was what I had. The idea was, Okay, let's 311 00:17:57,240 --> 00:18:01,080 Speaker 1: look back to the financial industry. We know baseball has 312 00:18:01,160 --> 00:18:06,080 Speaker 1: this great um, this idea of the replacement player, and 313 00:18:06,119 --> 00:18:09,359 Speaker 1: it's it's kind of an amorphous concept. But um, and 314 00:18:09,440 --> 00:18:12,280 Speaker 1: you hear that. You know, every player is essentially judged 315 00:18:12,359 --> 00:18:15,440 Speaker 1: in the in the moneyball world by how much how 316 00:18:15,440 --> 00:18:19,600 Speaker 1: many wins they create over the replacement player. And Tracy, wait, 317 00:18:19,800 --> 00:18:22,600 Speaker 1: remember Tracy when we were when we were chatting, and 318 00:18:22,640 --> 00:18:25,080 Speaker 1: Tracy's like, what's one thing I should know about baseball? 319 00:18:25,680 --> 00:18:28,399 Speaker 1: And our colleague said, the one thing you should know 320 00:18:28,560 --> 00:18:31,480 Speaker 1: is war, and uh, that was here you go wins 321 00:18:31,520 --> 00:18:33,520 Speaker 1: above replacement. All right, So go on, I just wanted 322 00:18:33,560 --> 00:18:36,800 Speaker 1: to sorry and point out that this was something that 323 00:18:36,920 --> 00:18:39,399 Speaker 1: came up in Tracy's prep for the episode. Well, if 324 00:18:39,440 --> 00:18:41,560 Speaker 1: you think about the and the the idea is the 325 00:18:41,600 --> 00:18:44,600 Speaker 1: replacement player is readily available to anyone. You could pick 326 00:18:44,720 --> 00:18:47,960 Speaker 1: up this player from either the minor leagues or on waivers, 327 00:18:47,960 --> 00:18:50,600 Speaker 1: and any team has access to them for essentially a 328 00:18:50,640 --> 00:18:54,840 Speaker 1: minimal contract. There's actually that concept actually applies perfectly in 329 00:18:54,880 --> 00:18:58,240 Speaker 1: the financial world, and that is, you know, the sp 330 00:18:59,119 --> 00:19:02,520 Speaker 1: that's the replaced player for every investor. That's the passive 331 00:19:02,560 --> 00:19:07,280 Speaker 1: alternative that is readily available for any investor. So if 332 00:19:07,280 --> 00:19:12,199 Speaker 1: you're gonna pay up um the you know, that's if 333 00:19:12,200 --> 00:19:14,119 Speaker 1: you're gonna pay up for active management, right, they have 334 00:19:14,200 --> 00:19:16,600 Speaker 1: to beat this passive benchmark. Well, the nice thing about 335 00:19:16,600 --> 00:19:19,200 Speaker 1: the passive benchmark as well as it works as a 336 00:19:19,760 --> 00:19:22,960 Speaker 1: investment tool in the sense that I know the standard 337 00:19:23,000 --> 00:19:26,159 Speaker 1: deviation of returns of the SNP five dum and I 338 00:19:26,240 --> 00:19:29,639 Speaker 1: know the expected return, so that if I'm really running 339 00:19:29,680 --> 00:19:33,879 Speaker 1: a baseball fund that is truly in an alternative asset, well, 340 00:19:33,960 --> 00:19:37,520 Speaker 1: I should have daily returns that either have this you know, 341 00:19:37,680 --> 00:19:40,160 Speaker 1: hopefully have a higher return than the SNP five hundred, 342 00:19:40,240 --> 00:19:43,080 Speaker 1: but the same amount of volatility. So to get back 343 00:19:43,080 --> 00:19:46,119 Speaker 1: to your question, Tracy, that was really how I played 344 00:19:46,160 --> 00:19:48,720 Speaker 1: with how much should I be betting on single games 345 00:19:49,200 --> 00:19:54,760 Speaker 1: was to really find that amount that gave me returns 346 00:19:54,880 --> 00:19:58,040 Speaker 1: without giving me excess volatility. And what it did turn 347 00:19:58,040 --> 00:20:00,280 Speaker 1: out was, specifically, if you looked at a eight of 348 00:20:00,320 --> 00:20:02,280 Speaker 1: games in a day, there's about fifteen games every day 349 00:20:02,280 --> 00:20:05,679 Speaker 1: in Major League Baseball. If I identified five or six 350 00:20:05,760 --> 00:20:08,840 Speaker 1: to bet on, it was rare that when I would 351 00:20:08,880 --> 00:20:11,560 Speaker 1: even put one percent of the capital on one game, 352 00:20:11,960 --> 00:20:14,560 Speaker 1: usually the bets were somewhere between a third of a 353 00:20:14,600 --> 00:20:16,879 Speaker 1: percent to maybe three quarters of a percent based on 354 00:20:16,960 --> 00:20:19,600 Speaker 1: how much I thought there was a perceived edge. So 355 00:20:19,720 --> 00:20:22,040 Speaker 1: really it was almost like I always kind of likened 356 00:20:22,080 --> 00:20:24,760 Speaker 1: it to owning a roulette wheel. Right, If you own 357 00:20:24,800 --> 00:20:27,080 Speaker 1: a roulette wheel, if you're the house, you have a 358 00:20:27,080 --> 00:20:29,159 Speaker 1: small egg, and you just want to spin that wheel 359 00:20:29,200 --> 00:20:31,320 Speaker 1: as many times as possible in a day. And you 360 00:20:31,359 --> 00:20:33,800 Speaker 1: don't want people place a million dollar bets, right, you 361 00:20:33,840 --> 00:20:36,679 Speaker 1: want them place in a whole bunch of smaller bets, 362 00:20:37,400 --> 00:20:40,000 Speaker 1: because that's how you know, that's where you have your egge, 363 00:20:40,000 --> 00:20:42,280 Speaker 1: and that's where you extract your your games in the end, 364 00:20:42,680 --> 00:20:46,160 Speaker 1: And that was the I. I applied that same capital 365 00:20:46,160 --> 00:20:49,200 Speaker 1: application theory to a slate of baseball games each day. 366 00:20:49,320 --> 00:20:52,280 Speaker 1: All right, Well, let's just talk results for a second, though. 367 00:20:52,480 --> 00:20:55,880 Speaker 1: You you raised a million dollars for your fund. First 368 00:20:55,880 --> 00:20:59,639 Speaker 1: of all, A, uh, who how did you raise the 369 00:20:59,680 --> 00:21:03,280 Speaker 1: million dollars? And B how'd you do that? Well? The 370 00:21:03,280 --> 00:21:06,359 Speaker 1: the the the A is fascinating. So I handed in 371 00:21:06,440 --> 00:21:08,440 Speaker 1: my book. I handed in the manuscript for the book 372 00:21:08,960 --> 00:21:13,399 Speaker 1: UM after the Season, which is what the bulk of 373 00:21:13,440 --> 00:21:15,359 Speaker 1: the book is really about. It's sort of my my 374 00:21:15,600 --> 00:21:19,120 Speaker 1: like I say, my memoir of being injured during UH. 375 00:21:19,160 --> 00:21:22,520 Speaker 1: And I handed it in and the the publisher, Penguin 376 00:21:23,000 --> 00:21:25,480 Speaker 1: UM came back to me. Once I handed the manuscript 377 00:21:25,760 --> 00:21:27,720 Speaker 1: in March, and they they said, we love it, which 378 00:21:27,800 --> 00:21:30,439 Speaker 1: was certainly satisfying to me because I was, you know, 379 00:21:30,480 --> 00:21:34,080 Speaker 1: an unknown author with no works behind me. Uh. And 380 00:21:34,119 --> 00:21:37,959 Speaker 1: they said, we're going to publish it in March, but 381 00:21:37,960 --> 00:21:40,440 Speaker 1: we're gonna need something for and there. And they said, 382 00:21:40,560 --> 00:21:42,760 Speaker 1: if we gave you the marketing budget for the book, 383 00:21:43,320 --> 00:21:46,200 Speaker 1: would you go to Vegas and that on baseball games force. 384 00:21:46,280 --> 00:21:48,359 Speaker 1: We kind of think that would be a cool pitch. 385 00:21:48,920 --> 00:21:52,119 Speaker 1: And I of course, of course said absolutely that that 386 00:21:52,240 --> 00:21:55,199 Speaker 1: really does sound like fun. And I knew if I 387 00:21:55,240 --> 00:21:57,359 Speaker 1: did that that I really I had better talk to 388 00:21:57,400 --> 00:22:00,480 Speaker 1: some of my degenerate family and friends too and see 389 00:22:00,480 --> 00:22:03,240 Speaker 1: if they wanted to be involved as well. And that's 390 00:22:03,240 --> 00:22:05,320 Speaker 1: how I raised a million dollars. It was essentially family 391 00:22:05,320 --> 00:22:09,359 Speaker 1: and friends, UM and the publisher. And that was the 392 00:22:09,359 --> 00:22:12,760 Speaker 1: the apologue of the book. UM. In In that year, 393 00:22:12,840 --> 00:22:17,080 Speaker 1: I was up for the year in terms of of 394 00:22:17,080 --> 00:22:21,359 Speaker 1: of the fund um, which is about what I think 395 00:22:21,480 --> 00:22:26,399 Speaker 1: my egg would be. UM. The eleven season, which was 396 00:22:26,560 --> 00:22:30,119 Speaker 1: just me and I was up. And that's not it 397 00:22:30,200 --> 00:22:33,199 Speaker 1: was not repeatable. Just a lot went right. UM. You know, 398 00:22:33,240 --> 00:22:35,320 Speaker 1: that's the old skill versu luck. The actual egg was 399 00:22:35,440 --> 00:22:38,040 Speaker 1: much much slower, and I knew that, but it was 400 00:22:38,080 --> 00:22:41,640 Speaker 1: a fun ride. UM, and I view Twelves as being 401 00:22:41,720 --> 00:22:44,639 Speaker 1: much more indicative of what you could expect from a 402 00:22:44,760 --> 00:22:47,680 Speaker 1: data driven model. You know that really tries to conquer 403 00:22:47,960 --> 00:22:52,840 Speaker 1: baseball betting. So is sports betting? Is that a legitimate 404 00:22:52,920 --> 00:22:58,359 Speaker 1: replacement for investing or trading more traditional financial assets? And 405 00:22:58,400 --> 00:23:01,480 Speaker 1: if it is, then then what is actually the difference 406 00:23:01,520 --> 00:23:05,959 Speaker 1: between trading and betting? Great to uh to quote George 407 00:23:05,960 --> 00:23:13,359 Speaker 1: Washington and Hamilton's the musical, not yet because it's uh. 408 00:23:13,600 --> 00:23:16,080 Speaker 1: The epilogue of the book, I really tried to write 409 00:23:16,119 --> 00:23:18,639 Speaker 1: almost as a business school case study in this in 410 00:23:18,760 --> 00:23:22,679 Speaker 1: sort of looking at it is is there actually a 411 00:23:22,880 --> 00:23:26,280 Speaker 1: market as an alternative asset? Because one thing we do 412 00:23:26,400 --> 00:23:29,280 Speaker 1: know is if you're betting on sports, or if you're 413 00:23:29,320 --> 00:23:32,199 Speaker 1: investing with someone who's running a fund, we do know 414 00:23:32,280 --> 00:23:35,040 Speaker 1: that it's not correlated to you know, stocks and bots. 415 00:23:35,080 --> 00:23:39,359 Speaker 1: It is truly an uncorrelated asset, So it meets that requirement. 416 00:23:40,160 --> 00:23:44,399 Speaker 1: What it doesn't meet, however, is there's not enough liquidity. Um. 417 00:23:44,400 --> 00:23:47,480 Speaker 1: I estimated that I could have run two maybe three 418 00:23:47,520 --> 00:23:51,120 Speaker 1: million dollar fund and that was it, because past that 419 00:23:51,200 --> 00:23:53,440 Speaker 1: I couldn't have scaled up the bets. There just wasn't 420 00:23:53,560 --> 00:23:56,399 Speaker 1: enough liquidity in the market to be making bets. But 421 00:23:56,560 --> 00:24:02,960 Speaker 1: most importantly, and while I tried to give well, I 422 00:24:03,000 --> 00:24:05,400 Speaker 1: tried to point out the ways that the financial industry 423 00:24:05,440 --> 00:24:08,440 Speaker 1: can learn from baseball. In the book, I also tried 424 00:24:08,480 --> 00:24:11,920 Speaker 1: to point out that Vegas and specifically the the industry 425 00:24:11,960 --> 00:24:15,840 Speaker 1: of running sports markets could really learn a lot from 426 00:24:15,840 --> 00:24:20,320 Speaker 1: Wall Street. And unfortunately they're not there yet. It is 427 00:24:20,359 --> 00:24:24,399 Speaker 1: still an antagonistic relationship between the sports book and the 428 00:24:24,480 --> 00:24:28,200 Speaker 1: better which is the way when I entered the Nasdaq market, 429 00:24:28,480 --> 00:24:31,159 Speaker 1: That's the way NASTAC trading was. It was fragmented. We 430 00:24:31,160 --> 00:24:33,800 Speaker 1: were suspicious of every customer who walked through the door, 431 00:24:33,880 --> 00:24:38,280 Speaker 1: and every trade was us first them. Once NASDACK evolved 432 00:24:38,320 --> 00:24:40,679 Speaker 1: in the more of an agency market, it became the 433 00:24:40,760 --> 00:24:45,200 Speaker 1: business of asset collection and bringing together buyers and sellers. 434 00:24:45,200 --> 00:24:48,120 Speaker 1: And what it's what poker does poker you know, as 435 00:24:48,240 --> 00:24:51,639 Speaker 1: as you may know in the in in poker, the 436 00:24:51,720 --> 00:24:55,080 Speaker 1: house doesn't bet against the players. The how simply collects 437 00:24:55,080 --> 00:24:58,879 Speaker 1: rents by getting the players into the same room. And 438 00:24:59,200 --> 00:25:03,760 Speaker 1: if sport betting ever evolved into that, it could be 439 00:25:03,880 --> 00:25:06,359 Speaker 1: a huge market and it would turn into an asset 440 00:25:06,359 --> 00:25:09,840 Speaker 1: gathering market as opposed to you know, sort of what 441 00:25:09,920 --> 00:25:12,760 Speaker 1: it is now, which is us first then, um, I 442 00:25:12,920 --> 00:25:16,840 Speaker 1: see that I cannot get some of the people in 443 00:25:16,880 --> 00:25:19,240 Speaker 1: the industry to to see that, and I have tried 444 00:25:20,359 --> 00:25:24,040 Speaker 1: Joe Peter, he's the author of Trading Basses and he's 445 00:25:24,119 --> 00:25:28,040 Speaker 1: currently at Kingsford Capital. That was a great discussion. Loved 446 00:25:28,080 --> 00:25:32,280 Speaker 1: that last bit about market structure that lesson there. Really 447 00:25:32,320 --> 00:25:35,520 Speaker 1: appreciate you coming on. Odd Loves Joe and Tracy. Thank 448 00:25:35,560 --> 00:25:47,160 Speaker 1: you so much for having me so Tracy, another episode 449 00:25:47,200 --> 00:25:50,199 Speaker 1: about gambling and sports which you claim to not know 450 00:25:50,240 --> 00:25:52,120 Speaker 1: anything about, but you didn't know stuff and you ask 451 00:25:52,160 --> 00:25:55,720 Speaker 1: great questions. Well, I don't know that much about it, 452 00:25:55,760 --> 00:25:58,720 Speaker 1: but what fascinates me is it's really that question of, 453 00:25:58,880 --> 00:26:01,439 Speaker 1: you know, like what makes a market and what's the 454 00:26:01,480 --> 00:26:06,000 Speaker 1: difference between betting and trading and investing and where's the overlap? 455 00:26:06,040 --> 00:26:08,280 Speaker 1: And I thought Joe did a really really good job 456 00:26:08,280 --> 00:26:11,720 Speaker 1: of identifying that. Yeah, I I did too. I mean, 457 00:26:11,880 --> 00:26:14,000 Speaker 1: I really liked his answer about sort of how to 458 00:26:14,560 --> 00:26:18,800 Speaker 1: a where he found the statistical edge from sports betting 459 00:26:18,880 --> 00:26:21,159 Speaker 1: and you know, basically there's still a lot of emotion 460 00:26:21,480 --> 00:26:25,399 Speaker 1: home team biases. Uh, streak biases, things like that that 461 00:26:25,440 --> 00:26:28,320 Speaker 1: you can spot in the odds of the game. And 462 00:26:28,359 --> 00:26:31,760 Speaker 1: then that last uh answer sort of about the difference 463 00:26:31,800 --> 00:26:34,560 Speaker 1: between the sports book at a casino, which he described 464 00:26:34,560 --> 00:26:38,000 Speaker 1: as antagonistic versus a poker room where they just want to, 465 00:26:38,160 --> 00:26:40,639 Speaker 1: you know, sort of get liquidity and bring people together. 466 00:26:41,000 --> 00:26:44,280 Speaker 1: Sort of very interesting lesson back to think about how uh, 467 00:26:44,359 --> 00:26:47,960 Speaker 1: sort of more traditional financial markets are structured. Yeah, for sure. 468 00:26:48,520 --> 00:26:50,440 Speaker 1: So Joe, when are we going to go on our 469 00:26:51,040 --> 00:26:56,280 Speaker 1: crayfish eating poker playing baseball watching tour of the US. 470 00:26:56,880 --> 00:27:00,240 Speaker 1: We gotta do that very soon. But until then, this 471 00:27:00,320 --> 00:27:04,080 Speaker 1: has been another episode of the Odd Lots Podcast. I'm Joe, 472 00:27:04,119 --> 00:27:06,480 Speaker 1: wi Isn't thal. You could follow me on Twitter at 473 00:27:06,520 --> 00:27:09,800 Speaker 1: the Stalwart, and I'm Tracy Alloway. I'm on Twitter at 474 00:27:09,840 --> 00:27:13,440 Speaker 1: Tracy Alloway. And you can find Joe on Twitter at 475 00:27:13,640 --> 00:27:17,680 Speaker 1: at Magic rat SF and our producer Sarah Patterson on 476 00:27:17,720 --> 00:27:21,200 Speaker 1: Twitter at Sarah pat With two Teas. Thanks for listening.