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In Virginia one eight seven 24 00:01:12,000 --> 00:01:15,240 Speaker 1: eight nine seven seven seven seven, or visit CCPG dot 25 00:01:15,360 --> 00:01:19,600 Speaker 1: org slash chat in Connecticut one eight seven zero seven 26 00:01:19,959 --> 00:01:22,679 Speaker 1: seven for confidential help in Michigan one eight seven seven 27 00:01:22,720 --> 00:01:26,080 Speaker 1: seven seven zero s t o P in Louisiana called 28 00:01:26,080 --> 00:01:28,600 Speaker 1: the Tennessee Redline one eight hundred eight eight nine nine 29 00:01:28,640 --> 00:01:31,640 Speaker 1: seven eight nine, or visit one eight hundred gambler dot 30 00:01:31,680 --> 00:01:42,680 Speaker 1: net in West Virginia. Welcome to the Favorites the podcast 31 00:01:42,680 --> 00:01:45,160 Speaker 1: from the Volume and Podcast Network. I am Chad Mellman, 32 00:01:45,240 --> 00:01:47,960 Speaker 1: Chief Content Officer of the Action Network. We have a 33 00:01:48,040 --> 00:01:51,240 Speaker 1: very special guest today. You could argue that my guest 34 00:01:51,280 --> 00:01:58,080 Speaker 1: today is the greatest magazine writer of his generation. He's 35 00:01:58,080 --> 00:02:01,680 Speaker 1: written for The Atlantic, He's written for Esquire, for which 36 00:02:01,840 --> 00:02:07,640 Speaker 1: he wrote multiple stories that won national magazine Awards. The 37 00:02:07,760 --> 00:02:10,720 Speaker 1: very first columnist I hired when I took over as 38 00:02:11,120 --> 00:02:13,240 Speaker 1: editor in chief DSP in the magazine. He wrote some 39 00:02:13,280 --> 00:02:14,960 Speaker 1: of the best stories we did at the magazine, and 40 00:02:15,000 --> 00:02:18,639 Speaker 1: sort of the two thousand tens. He has written a 41 00:02:18,720 --> 00:02:23,360 Speaker 1: new book that tries to undo everything that is important 42 00:02:23,400 --> 00:02:27,520 Speaker 1: to me. It's called The Eye Test, A case for 43 00:02:27,680 --> 00:02:31,920 Speaker 1: human creativity in the age of analytics. I can't think 44 00:02:32,080 --> 00:02:37,600 Speaker 1: of a writer more equipped to try to tear down 45 00:02:37,880 --> 00:02:41,280 Speaker 1: the way the world is going the Mr Chris Jones, 46 00:02:41,760 --> 00:02:45,200 Speaker 1: how are you, buddy, dude? That was the nicest introduction 47 00:02:46,000 --> 00:02:49,040 Speaker 1: of all time. You've earned it. We could talk about 48 00:02:49,840 --> 00:02:53,600 Speaker 1: the amazing stories you wrote for the magazine. I will say, 49 00:02:53,639 --> 00:02:55,880 Speaker 1: and I've said this to you before, there's a there's 50 00:02:55,919 --> 00:02:59,680 Speaker 1: a seminal magazine piece that Right Thompson wrote about Michael 51 00:02:59,720 --> 00:03:03,160 Speaker 1: Jordan and turning fifty. And I think it was two 52 00:03:03,160 --> 00:03:07,360 Speaker 1: thousand twelve is when it came out. Maybe and um, 53 00:03:07,400 --> 00:03:09,359 Speaker 1: maybe it was two thousand eleven, I can't remember exactly. 54 00:03:10,240 --> 00:03:14,800 Speaker 1: That same year you wrote a story about a Japanese 55 00:03:15,360 --> 00:03:18,519 Speaker 1: high school baseball pitcher that I just thought was better 56 00:03:18,680 --> 00:03:22,040 Speaker 1: than writes Michael Jordan's story. It was like, you know, 57 00:03:22,120 --> 00:03:25,600 Speaker 1: when right like is in a subject, he can like 58 00:03:26,480 --> 00:03:30,680 Speaker 1: write you epic, epic stories. And this was a beautiful, 59 00:03:30,720 --> 00:03:33,600 Speaker 1: amazing story that it was so inside, one of the 60 00:03:33,600 --> 00:03:37,160 Speaker 1: people who was harder to get inside than anyone. But 61 00:03:37,200 --> 00:03:39,480 Speaker 1: there was something about the Japanese baseball story that to me, 62 00:03:39,520 --> 00:03:41,360 Speaker 1: and you should explain to people what it was. It 63 00:03:41,400 --> 00:03:44,320 Speaker 1: actually speaks to a lot of what your book is about, 64 00:03:44,760 --> 00:03:50,480 Speaker 1: which was so clean and interesting and dramatic in very 65 00:03:50,520 --> 00:03:53,280 Speaker 1: subtle ways. I just loved it. Tell people what that 66 00:03:53,280 --> 00:03:54,680 Speaker 1: was about, and then we can get into the book. 67 00:03:55,040 --> 00:03:57,600 Speaker 1: Having enjoyed this conversation. So far, it's been good for 68 00:03:57,640 --> 00:04:01,040 Speaker 1: my ego. Um. The story was about out a young 69 00:04:01,120 --> 00:04:05,200 Speaker 1: Japanese high school picture expected to do great things. And 70 00:04:05,440 --> 00:04:09,800 Speaker 1: in Japan there's a high school of tournament coation that happens. 71 00:04:09,840 --> 00:04:11,760 Speaker 1: There's two spring and fall, but the Spring is the 72 00:04:11,800 --> 00:04:15,720 Speaker 1: big one every district in Japan. Since team was astrate 73 00:04:15,840 --> 00:04:17,919 Speaker 1: to me as sort of the Super Bowl plus Marty 74 00:04:17,960 --> 00:04:21,320 Speaker 1: Gras plus July four. It's like the biggest that the 75 00:04:21,360 --> 00:04:24,520 Speaker 1: country stops to watch this tournament and these kids play 76 00:04:24,560 --> 00:04:27,160 Speaker 1: each other, and that that picture, you know, there's his 77 00:04:27,240 --> 00:04:29,680 Speaker 1: team kept winning and he threw I think it was 78 00:04:29,720 --> 00:04:34,039 Speaker 1: seven hundred and seventy two pitches in five days. Um 79 00:04:34,760 --> 00:04:37,480 Speaker 1: and his arm fell off in the final basically and 80 00:04:37,600 --> 00:04:41,839 Speaker 1: his career didn't really become what it was expected to become. 81 00:04:42,400 --> 00:04:44,200 Speaker 1: And it was sort of an exploration of how the 82 00:04:44,279 --> 00:04:48,320 Speaker 1: Japanese approach baseball, which is a very different way than Americans. 83 00:04:48,360 --> 00:04:51,760 Speaker 1: It's a lot less individualistic, and that what he did 84 00:04:51,800 --> 00:04:53,919 Speaker 1: was sort of sacrifice. It was he was helping his 85 00:04:53,960 --> 00:04:56,280 Speaker 1: brothers try to win this tournament, and he had no 86 00:04:56,360 --> 00:04:59,840 Speaker 1: regrets even though he lost, you know, potentially hundreds of 87 00:04:59,839 --> 00:05:02,159 Speaker 1: mill in some of dollars, and that's just for me. 88 00:05:02,200 --> 00:05:04,719 Speaker 1: It was. It was just such a different perspective on baseball. 89 00:05:04,760 --> 00:05:07,200 Speaker 1: It was It was a really great experience. So thank 90 00:05:07,200 --> 00:05:09,160 Speaker 1: you for your kind words about the story. But as 91 00:05:09,200 --> 00:05:13,680 Speaker 1: an experience, as a selfish sort of travel and cultural experience, 92 00:05:13,680 --> 00:05:16,520 Speaker 1: it was fantastic. I think about it all the time. Yeah, 93 00:05:16,520 --> 00:05:20,159 Speaker 1: we really did you right on that one. You're really dead? Yeah, yeah, 94 00:05:20,200 --> 00:05:22,920 Speaker 1: spectacularly right. T J. Quinn. I don't know if you remember. 95 00:05:22,960 --> 00:05:25,000 Speaker 1: T J. Quinn joined me to do the TV side 96 00:05:25,040 --> 00:05:27,159 Speaker 1: of things, and I was a little offside about it 97 00:05:27,200 --> 00:05:29,200 Speaker 1: at first, but then I realized the TV side of 98 00:05:29,240 --> 00:05:32,320 Speaker 1: things lived a lot better um, and so it was 99 00:05:32,440 --> 00:05:34,800 Speaker 1: even that was great. I got some move hotels and yeah, 100 00:05:34,800 --> 00:05:36,920 Speaker 1: it was fantastic. All right, So you wrote this book, 101 00:05:36,920 --> 00:05:39,080 Speaker 1: The I Test, The Case for Human Creativity in the 102 00:05:39,120 --> 00:05:42,400 Speaker 1: Age of Analytics. It's out now. I've read it. It's 103 00:05:42,520 --> 00:05:45,520 Speaker 1: really good. Thank you. You would think it's counter to 104 00:05:45,560 --> 00:05:47,720 Speaker 1: have someone like you on a podcast that is a 105 00:05:47,720 --> 00:05:51,000 Speaker 1: betting podcast in which analytics are the core of what 106 00:05:51,040 --> 00:05:54,560 Speaker 1: we do and at Action Network, analytics drive our entire engine, right. 107 00:05:54,600 --> 00:05:59,440 Speaker 1: We are constantly on the lookout for the number, the metrics, 108 00:05:59,480 --> 00:06:03,720 Speaker 1: the edge edge, lots of times driven by models and algorithms, 109 00:06:03,760 --> 00:06:07,520 Speaker 1: which you lay waste too in your book, And yet 110 00:06:08,400 --> 00:06:13,359 Speaker 1: I found it fascinating because it does speak to a 111 00:06:13,440 --> 00:06:17,719 Speaker 1: lot of things that matter when you are trying to 112 00:06:17,800 --> 00:06:22,760 Speaker 1: make decisions and why you can't just rely on analytics, 113 00:06:23,400 --> 00:06:25,960 Speaker 1: including the Super Bowl that we just saw between the 114 00:06:26,680 --> 00:06:29,600 Speaker 1: Bengals and the Rams. My co host Simon Hunter was 115 00:06:29,640 --> 00:06:32,320 Speaker 1: a professional better. He and I in the podcast earlier 116 00:06:32,320 --> 00:06:36,719 Speaker 1: this week talked about the need to have feel the 117 00:06:36,839 --> 00:06:39,240 Speaker 1: eye test elements of what the Bengals did all season 118 00:06:39,520 --> 00:06:42,600 Speaker 1: and incorporating that into our numbers. So my question for 119 00:06:42,640 --> 00:06:45,760 Speaker 1: you is why do you hate analytics so much and 120 00:06:45,800 --> 00:06:49,240 Speaker 1: the people who use them. I don't. I don't. This 121 00:06:49,320 --> 00:06:52,719 Speaker 1: is the thing the book is going to get cast 122 00:06:52,880 --> 00:06:55,960 Speaker 1: is like the anti money ball, and it's it's not 123 00:06:56,000 --> 00:06:58,600 Speaker 1: that at all. What I'm trying to say is that 124 00:06:58,760 --> 00:07:02,760 Speaker 1: data is useful. Models are useful. Algorithms in certain situations 125 00:07:02,760 --> 00:07:07,360 Speaker 1: are useful, but they're not the entire answer to many questions, 126 00:07:07,400 --> 00:07:11,480 Speaker 1: particularly in complicated situations. So they're pretty good at that. 127 00:07:11,840 --> 00:07:14,680 Speaker 1: You know, I get the use of statistics in baseball 128 00:07:14,720 --> 00:07:18,480 Speaker 1: when you're deciding whether to match up a particular picture 129 00:07:18,480 --> 00:07:22,680 Speaker 1: with a particular hitter, totally understood. When things get more complicated, 130 00:07:22,760 --> 00:07:26,000 Speaker 1: like when you're trying to dissect a hockey game, for instance, 131 00:07:26,240 --> 00:07:29,160 Speaker 1: five guys playing together against five other guys, lots of 132 00:07:29,200 --> 00:07:33,480 Speaker 1: moving parts, they can also be helpful, but maybe maybe 133 00:07:33,600 --> 00:07:36,840 Speaker 1: don't give you the entire answer, and something like sports 134 00:07:36,840 --> 00:07:39,600 Speaker 1: betting what I would argue. And by the way, I 135 00:07:39,640 --> 00:07:42,840 Speaker 1: regret very deeply not having an entire chapter about gambling, 136 00:07:42,840 --> 00:07:47,680 Speaker 1: which I should have done. Um, But the the idea 137 00:07:47,840 --> 00:07:52,160 Speaker 1: that you can rely entirely on models, I don't see 138 00:07:52,320 --> 00:07:56,120 Speaker 1: what your edges. Then unless your model just happens to 139 00:07:56,120 --> 00:07:58,600 Speaker 1: be better than any other model on the planet, or 140 00:07:58,680 --> 00:08:02,800 Speaker 1: your access to numbers is better, or your understanding of 141 00:08:02,920 --> 00:08:05,960 Speaker 1: numbers is better. But if you're if you're not number 142 00:08:06,000 --> 00:08:08,800 Speaker 1: one at that, and I feel like you must need 143 00:08:08,880 --> 00:08:15,200 Speaker 1: some ten or that little flivver that that comes from 144 00:08:15,360 --> 00:08:19,480 Speaker 1: field or knowledge or experience or or not. GOT. I 145 00:08:19,480 --> 00:08:24,200 Speaker 1: wouldn't advocate FORGOT. For me, it's like experience wisdom, Like 146 00:08:24,240 --> 00:08:26,960 Speaker 1: what's the value of that? And so you have your 147 00:08:27,160 --> 00:08:29,440 Speaker 1: model and you have I'm assuming you plug in all 148 00:08:29,440 --> 00:08:31,960 Speaker 1: sorts of numbers, but I'm assuming other people have those 149 00:08:32,040 --> 00:08:35,599 Speaker 1: numbers as well. What's the difference? What's that? What's the 150 00:08:35,640 --> 00:08:38,000 Speaker 1: competitive advantage of doing it exactly the same way as 151 00:08:38,000 --> 00:08:42,520 Speaker 1: anybody else? Well, you're right, I mean your argument really 152 00:08:43,000 --> 00:08:46,720 Speaker 1: is that the fallibility of the models that everybody uses. 153 00:08:47,320 --> 00:08:50,160 Speaker 1: And like I said, you know, for the Super Bowl, 154 00:08:51,920 --> 00:08:56,319 Speaker 1: I relied there were three experts at Action Network who 155 00:08:57,120 --> 00:08:59,480 Speaker 1: we were podcasting with, who were doing a lot of shows, 156 00:08:59,600 --> 00:09:01,280 Speaker 1: and they're a lot of experts at Action Network who 157 00:09:01,280 --> 00:09:06,880 Speaker 1: are amazing, but three people who build ostensibly three different 158 00:09:06,920 --> 00:09:12,079 Speaker 1: models coming from three entirely different approaches. The fallibility in 159 00:09:12,120 --> 00:09:14,760 Speaker 1: the model is that there is a human element that 160 00:09:14,800 --> 00:09:18,360 Speaker 1: builds the model. With any of these things, they're sort 161 00:09:18,360 --> 00:09:22,280 Speaker 1: of treated as these objectives like hugely rational, like they 162 00:09:22,320 --> 00:09:24,959 Speaker 1: were found in nature and we build the models, so 163 00:09:25,080 --> 00:09:29,320 Speaker 1: the algorithms and the models contain all the bias that 164 00:09:29,520 --> 00:09:32,080 Speaker 1: the people who make them contain. So if I'm building 165 00:09:32,080 --> 00:09:34,480 Speaker 1: a model and I think something is particularly important, it 166 00:09:34,480 --> 00:09:36,640 Speaker 1: needs to be waited a certain way. I'm adding a 167 00:09:36,760 --> 00:09:41,200 Speaker 1: human input into what is allegedly an objective model, and 168 00:09:41,240 --> 00:09:44,840 Speaker 1: it's it's that's not wrong. I mean, it's just I 169 00:09:44,880 --> 00:09:47,960 Speaker 1: think it's important for people to be aware that numbers 170 00:09:48,120 --> 00:09:51,679 Speaker 1: can be used as subjective instruments just as much as 171 00:09:51,720 --> 00:09:54,240 Speaker 1: stories can, or just as much as gut can't. You know, 172 00:09:54,240 --> 00:09:57,400 Speaker 1: It's it's it's this idea that math is black or 173 00:09:57,400 --> 00:10:00,080 Speaker 1: white that sort of bothers me. It's it's it's not 174 00:10:00,240 --> 00:10:03,040 Speaker 1: necessarily like statistics are used to lie all the time. 175 00:10:03,480 --> 00:10:05,920 Speaker 1: I can massage numbers to make a certain case if 176 00:10:05,960 --> 00:10:08,520 Speaker 1: I want to, or the opposite case if I want to. 177 00:10:08,960 --> 00:10:11,200 Speaker 1: And what you're describing is three different people have all 178 00:10:11,240 --> 00:10:14,520 Speaker 1: the same numbers, but they approached them with different um 179 00:10:14,640 --> 00:10:17,160 Speaker 1: weights and measures, and that again, I think that's okay. 180 00:10:17,200 --> 00:10:19,120 Speaker 1: But then if you're picking between those three models, like 181 00:10:19,120 --> 00:10:21,599 Speaker 1: how do you decide what's the right model? That's that 182 00:10:21,840 --> 00:10:24,480 Speaker 1: then you're sort of employing these other things. I think 183 00:10:24,640 --> 00:10:28,040 Speaker 1: that I'm talking about, which is the best way I 184 00:10:28,040 --> 00:10:30,079 Speaker 1: can describe. Stole this out of the New Yorker. Um 185 00:10:30,440 --> 00:10:33,360 Speaker 1: the New Yorker, a guy named Burkhardt Builder wrote a 186 00:10:33,400 --> 00:10:38,040 Speaker 1: story about a carpenter named Mark Ellison who was using 187 00:10:38,040 --> 00:10:41,120 Speaker 1: a table saw to make a curved cut. Now, if 188 00:10:41,120 --> 00:10:43,680 Speaker 1: you know anything about carpentry, table saws are designed to 189 00:10:43,720 --> 00:10:46,360 Speaker 1: make straight cuts. That is the purpose of a table saw. 190 00:10:46,880 --> 00:10:49,800 Speaker 1: Mark Allison, this master carpenter was using a table saw 191 00:10:49,880 --> 00:10:53,400 Speaker 1: to make this intricate curve cut. While talking to Burkhardt 192 00:10:53,440 --> 00:10:56,000 Speaker 1: Builder and an apprentice, he wasn't really paying attention to 193 00:10:56,120 --> 00:10:58,880 Speaker 1: what he was doing, and Burkhard Builders like, how do 194 00:10:58,920 --> 00:11:01,559 Speaker 1: you not have to focus on what you're doing? And 195 00:11:01,840 --> 00:11:06,439 Speaker 1: the term that they came up with together was embodied analysis, 196 00:11:06,559 --> 00:11:11,280 Speaker 1: which is like almost like muscle memory, but intellectual. And 197 00:11:11,400 --> 00:11:14,200 Speaker 1: that that is the case that make is that you 198 00:11:14,240 --> 00:11:18,280 Speaker 1: want to be the person who has the best embodied analysis. 199 00:11:18,360 --> 00:11:22,560 Speaker 1: The carpenter used the example of Roberto Clemente how he 200 00:11:22,600 --> 00:11:25,360 Speaker 1: would turn his back on a on a fly ball 201 00:11:25,720 --> 00:11:27,880 Speaker 1: run to the spot where he knew the ball was coming, 202 00:11:28,320 --> 00:11:32,960 Speaker 1: turn around and catching, and that sort of deep understanding 203 00:11:33,000 --> 00:11:36,840 Speaker 1: of a situation has a value. It doesn't matter, uh 204 00:11:36,880 --> 00:11:41,720 Speaker 1: that a model um might run slightly countered your your thinking. 205 00:11:41,760 --> 00:11:45,840 Speaker 1: They're like, the model has is a worthy metric and 206 00:11:45,920 --> 00:11:48,960 Speaker 1: your experience is a worthy metric. And what I'm saying 207 00:11:49,040 --> 00:11:52,880 Speaker 1: is the best decision making comes with a delicate, nuanced 208 00:11:53,040 --> 00:11:56,079 Speaker 1: combination of those two things. It's not a black or 209 00:11:56,120 --> 00:11:57,800 Speaker 1: white way of thinking. And I used to be you 210 00:11:57,840 --> 00:11:59,240 Speaker 1: know this about me. I used to be a very 211 00:11:59,280 --> 00:12:01,679 Speaker 1: black and white thing like this is right, and this 212 00:12:01,760 --> 00:12:04,880 Speaker 1: is wrong, this is universal and this is not the 213 00:12:04,920 --> 00:12:07,200 Speaker 1: world doesn't work like that, and and for me, there's 214 00:12:07,200 --> 00:12:10,559 Speaker 1: an advantage to being really smart and good about football 215 00:12:10,600 --> 00:12:15,600 Speaker 1: is a great example, Like those models, did they incorporate 216 00:12:15,640 --> 00:12:19,640 Speaker 1: anything about stress? Like like the super Bowl is a 217 00:12:19,760 --> 00:12:24,160 Speaker 1: different game. So any game analysis that you've done, like 218 00:12:24,320 --> 00:12:27,680 Speaker 1: did you look at which guys are going to perform 219 00:12:27,720 --> 00:12:30,080 Speaker 1: under pressure or not? That to me is an element 220 00:12:30,120 --> 00:12:32,400 Speaker 1: that you need to think about. But did the models 221 00:12:32,400 --> 00:12:35,800 Speaker 1: think about that? I don't know. Well, a couple of things. 222 00:12:35,800 --> 00:12:39,280 Speaker 1: On that one you would say you were a black 223 00:12:39,320 --> 00:12:45,720 Speaker 1: and white thinker. I would just say you were judging too. 224 00:12:46,760 --> 00:12:50,080 Speaker 1: I think I think what you're talking about is the 225 00:12:50,200 --> 00:12:56,320 Speaker 1: best model ends up becoming a combination of learned experience 226 00:12:56,559 --> 00:12:59,800 Speaker 1: and data. Right, It's like, and you wrote about this 227 00:12:59,840 --> 00:13:02,720 Speaker 1: in the Look, it's more than trust your gut, um, 228 00:13:02,760 --> 00:13:06,600 Speaker 1: it's really maybe trust your eyes. Right obviously, you know 229 00:13:06,800 --> 00:13:09,200 Speaker 1: if if if you are if, if you know what 230 00:13:09,240 --> 00:13:13,079 Speaker 1: you're talking about, lots of opinions are not valid. They 231 00:13:13,080 --> 00:13:16,560 Speaker 1: are dumb, and my gut would be useless in a 232 00:13:16,600 --> 00:13:20,440 Speaker 1: lot of situations because I don't have any knowledge, you know, 233 00:13:20,640 --> 00:13:24,360 Speaker 1: to to back up my my feeling. What I'm saying 234 00:13:24,480 --> 00:13:27,280 Speaker 1: is if you have that experience, if you have watched 235 00:13:27,280 --> 00:13:29,600 Speaker 1: a thousand football games, and if you've been on a 236 00:13:29,640 --> 00:13:34,080 Speaker 1: thousand football games, that has value. That knowledge has value. 237 00:13:34,120 --> 00:13:36,320 Speaker 1: And I think we're we sort of live in a 238 00:13:36,440 --> 00:13:40,360 Speaker 1: time when that human element is sort of dismissed as 239 00:13:40,920 --> 00:13:44,760 Speaker 1: bad almost like it's it's dismissed as a rational or subjective. 240 00:13:44,800 --> 00:13:46,599 Speaker 1: There's all these words that we use to describe that 241 00:13:46,679 --> 00:13:49,719 Speaker 1: sort of that little eggs that that humans bring. The 242 00:13:49,840 --> 00:13:53,640 Speaker 1: things I'm saying, I'm saying that has some worth. It's 243 00:13:53,720 --> 00:13:56,200 Speaker 1: it's it's and especially in the situation like you're talking about, 244 00:13:56,200 --> 00:13:58,679 Speaker 1: where everyone's got the same numbers, like what's going to 245 00:13:58,760 --> 00:14:01,840 Speaker 1: separate why there's no business book? I could write where 246 00:14:01,840 --> 00:14:03,160 Speaker 1: I go. You know what, the best thing you can 247 00:14:03,200 --> 00:14:06,320 Speaker 1: do is follow the crowd. Just do what everyone else 248 00:14:06,400 --> 00:14:08,679 Speaker 1: is doing. That's the right thing. Like you would think 249 00:14:08,720 --> 00:14:10,520 Speaker 1: that was insane, but that's the argument that we have 250 00:14:10,520 --> 00:14:13,480 Speaker 1: about analytics all the time, that it's the one right 251 00:14:13,520 --> 00:14:16,200 Speaker 1: tool and will always find the right answer. It's it's nonsense, 252 00:14:19,720 --> 00:14:23,680 Speaker 1: missing football. Well, you can still turn every Thursday into 253 00:14:23,720 --> 00:14:26,680 Speaker 1: pay day with NBA on t n T on fan 254 00:14:26,760 --> 00:14:29,600 Speaker 1: Duel sports Book. It doesn't matter if you would to 255 00:14:29,680 --> 00:14:33,360 Speaker 1: lose fanduels giving all customers ten dollars back every Thursday 256 00:14:33,400 --> 00:14:36,280 Speaker 1: in site credit. Just bet ten dollars or more on 257 00:14:36,320 --> 00:14:39,800 Speaker 1: the same game parlay on any NBA on t NT game. 258 00:14:40,200 --> 00:14:42,600 Speaker 1: Same game parlays like you combine the money, line points 259 00:14:42,640 --> 00:14:45,880 Speaker 1: by player props, and more all into one wager. I 260 00:14:46,040 --> 00:14:49,320 Speaker 1: like same game parlay bets because there are so many 261 00:14:49,640 --> 00:14:52,680 Speaker 1: NBA markets to bet. I can root from my favorite 262 00:14:52,680 --> 00:14:55,960 Speaker 1: players like Julius Randall, and it's the perfect way to 263 00:14:56,040 --> 00:14:59,640 Speaker 1: turn a small bet into a big time score and 264 00:15:00,000 --> 00:15:03,040 Speaker 1: gonna lose your guaranteed to get ten dollars added to 265 00:15:03,120 --> 00:15:05,800 Speaker 1: your account. 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Refund 275 00:15:41,560 --> 00:15:44,920 Speaker 1: issued as non withdrawal side credit that expires in seven days. 276 00:15:45,320 --> 00:15:49,800 Speaker 1: Max refund ten dollars. Restrictions apply see terms at sports 277 00:15:49,840 --> 00:15:53,400 Speaker 1: book dot fandel dot com. Same game parlay available to 278 00:15:53,640 --> 00:15:57,920 Speaker 1: multiple sports in all states on mobile and web. Gambling 279 00:15:57,960 --> 00:16:01,720 Speaker 1: problem called one next up or text next step to 280 00:16:01,840 --> 00:16:04,840 Speaker 1: five three three F two in Arizona one eight hundred 281 00:16:04,840 --> 00:16:09,080 Speaker 1: gambler or visit vanduel dot com, slash RG in Colorado, Indiana, 282 00:16:09,120 --> 00:16:12,240 Speaker 1: New Jersey. In Virginia one eight eight seven eight nine 283 00:16:12,280 --> 00:16:16,600 Speaker 1: seven seven seven seven or visit CCPG dot org slash 284 00:16:16,840 --> 00:16:20,400 Speaker 1: chat in Connecticut one eight hundred to seven zero seven 285 00:16:20,440 --> 00:16:23,040 Speaker 1: one one seven for confidential health in Michigan called the 286 00:16:23,040 --> 00:16:25,800 Speaker 1: Tennessee Red Line one eight hundred eight eight nine nine 287 00:16:25,840 --> 00:16:29,200 Speaker 1: seven eight nine, or visit one eight hundred gambler dot 288 00:16:29,240 --> 00:16:36,240 Speaker 1: net in West Virginia. You know, it's interesting I mentioned 289 00:16:36,280 --> 00:16:39,200 Speaker 1: the Bengals Ram super Bowl. That's a big football game 290 00:16:39,240 --> 00:16:42,400 Speaker 1: that happens here in the United States. I know the 291 00:16:42,400 --> 00:16:45,240 Speaker 1: Gray Cup is Mike, Yeah, so it's like the Gray Cup. 292 00:16:45,800 --> 00:16:47,840 Speaker 1: I guess it's a little bit. It's a little bit 293 00:16:48,120 --> 00:16:52,440 Speaker 1: like a combination of sort of the Gray Cup and uh, 294 00:16:52,720 --> 00:16:59,720 Speaker 1: the World Juniors Finals, maybe the Tournament of Hearts curling exactly. 295 00:17:01,680 --> 00:17:03,920 Speaker 1: The thing that there were so many people who were 296 00:17:03,960 --> 00:17:06,640 Speaker 1: betting the Rams. The Rams got the propondents of money. 297 00:17:06,800 --> 00:17:09,040 Speaker 1: There were a lot of professional betters who liked the Rams. 298 00:17:09,040 --> 00:17:11,200 Speaker 1: The Rams were opened his three and three and a 299 00:17:11,200 --> 00:17:13,080 Speaker 1: half point favorites, It went up to four, It got 300 00:17:13,160 --> 00:17:14,840 Speaker 1: up to four and a half and five in some places, 301 00:17:16,080 --> 00:17:19,159 Speaker 1: and everyone was saying, the reason like the Rams is 302 00:17:19,200 --> 00:17:25,600 Speaker 1: because the Bengals wins, we're flukey and there were too 303 00:17:25,600 --> 00:17:32,040 Speaker 1: many numbers that we're sort of violating their principles and 304 00:17:32,080 --> 00:17:35,480 Speaker 1: what their models said, and in every matchup, the Rams 305 00:17:36,280 --> 00:17:39,640 Speaker 1: win head to head. And there were a lot of us, 306 00:17:39,920 --> 00:17:44,439 Speaker 1: myself included Simon Hunter, who's professional better, Chris RayBan, Shaan Kroner, 307 00:17:44,480 --> 00:17:49,280 Speaker 1: a lot of Action Network experts who watched these games 308 00:17:49,280 --> 00:17:53,320 Speaker 1: and thought, sure, but if it's a fluke that happens 309 00:17:53,680 --> 00:17:56,600 Speaker 1: twenty out of times over the course of the season, 310 00:17:57,240 --> 00:18:00,600 Speaker 1: it's not a fluke, it's how they play. And if 311 00:18:00,640 --> 00:18:03,639 Speaker 1: the Bengals only lost three games by more than three points, 312 00:18:04,880 --> 00:18:07,280 Speaker 1: there's something happening within the context of those games that 313 00:18:07,359 --> 00:18:11,800 Speaker 1: is immeasurable in the overall stats they have to account 314 00:18:11,800 --> 00:18:13,520 Speaker 1: for some way. And I think that's a lot of 315 00:18:13,520 --> 00:18:17,760 Speaker 1: what you're arguing. That's we're watching the game matters like stats. 316 00:18:17,880 --> 00:18:22,520 Speaker 1: Stats can give you if you there's a the other 317 00:18:22,520 --> 00:18:28,400 Speaker 1: football soccer Liverpool were built largely by two people who 318 00:18:28,440 --> 00:18:32,119 Speaker 1: approached the game very differently. One they have a physicist 319 00:18:32,200 --> 00:18:36,040 Speaker 1: named Dr Ian Graham, who literally does not watch the game. 320 00:18:36,880 --> 00:18:41,000 Speaker 1: He strictly uses stats. He maintains this database of a 321 00:18:41,080 --> 00:18:45,720 Speaker 1: hundred thousand players. He thinks that watching film or watching 322 00:18:45,720 --> 00:18:49,200 Speaker 1: the game itself biases him. He doesn't want that. He's 323 00:18:49,200 --> 00:18:52,680 Speaker 1: a very It's as sure and analytical approach as you 324 00:18:52,720 --> 00:18:56,560 Speaker 1: can get. But you marry that or you marry that 325 00:18:56,880 --> 00:19:01,440 Speaker 1: with Jurgen Klopp, who's the manager who as this. He 326 00:19:01,840 --> 00:19:05,120 Speaker 1: played the game, he has an instinctual understanding of soccer. 327 00:19:05,520 --> 00:19:09,320 Speaker 1: He has a fluid sort of slashing style. He's brought 328 00:19:09,400 --> 00:19:11,399 Speaker 1: sort of the speed metal approach to the game, this 329 00:19:11,560 --> 00:19:15,119 Speaker 1: really human element to it. And both of those things 330 00:19:15,160 --> 00:19:18,960 Speaker 1: combined allowed them to win their first title in thirty years. 331 00:19:19,119 --> 00:19:21,919 Speaker 1: One or the other I don't think would have done it. 332 00:19:22,040 --> 00:19:24,480 Speaker 1: You're gonna needed that sort of sober second thought that 333 00:19:24,520 --> 00:19:27,760 Speaker 1: the analytics gave him. The analytical mind needed that little 334 00:19:27,800 --> 00:19:30,680 Speaker 1: what you're talking about, Well, if the numbers if the 335 00:19:30,760 --> 00:19:34,760 Speaker 1: numbers on the Bengals seemed like flukes and yet there 336 00:19:34,840 --> 00:19:37,439 Speaker 1: was you know how what you said twenty out of like, 337 00:19:37,720 --> 00:19:39,560 Speaker 1: then it's not a fluke. At some point it becomes 338 00:19:39,560 --> 00:19:42,159 Speaker 1: not an accident. Is how they play, And that you 339 00:19:42,240 --> 00:19:45,520 Speaker 1: only get I think from sort of watching the games. 340 00:19:45,520 --> 00:19:48,639 Speaker 1: Like statistics are are are can be indicative, but they 341 00:19:48,680 --> 00:19:51,680 Speaker 1: don't tell the whole story. Possession is another great stat 342 00:19:51,800 --> 00:19:54,720 Speaker 1: soccer that is used all the time. But there's such 343 00:19:54,720 --> 00:19:57,280 Speaker 1: a thing is useless possession, Like when you're just passing 344 00:19:57,280 --> 00:20:01,280 Speaker 1: it around in the midfield is not particularly construct Fruitful 345 00:20:01,320 --> 00:20:04,760 Speaker 1: possession is a very valuable thing, but that requires some 346 00:20:05,000 --> 00:20:08,120 Speaker 1: watching of the game to go, oh, that they're actually dangerous. 347 00:20:08,280 --> 00:20:11,440 Speaker 1: It's not useless possession, it's dangerous possession. And that's that's 348 00:20:11,440 --> 00:20:13,680 Speaker 1: what I'm talking about. Is that sort of well you're 349 00:20:13,680 --> 00:20:16,640 Speaker 1: talking about is exactly right, and as it turns out, 350 00:20:18,000 --> 00:20:21,959 Speaker 1: what was right. So the Bengals, if you got the Bengals, 351 00:20:21,960 --> 00:20:26,159 Speaker 1: it was the right call. I bet the Bengals. Um lies, 352 00:20:26,240 --> 00:20:28,680 Speaker 1: damn lies and statistics is what I keep thinking about 353 00:20:28,680 --> 00:20:31,359 Speaker 1: whenever you're talking about sort of how statistics can be 354 00:20:31,440 --> 00:20:35,880 Speaker 1: used to manipulate things. Also, you mentioned Liverpool. Uh, you'll 355 00:20:35,920 --> 00:20:37,720 Speaker 1: be glad to know that I was in l A 356 00:20:37,800 --> 00:20:40,000 Speaker 1: last week. On my flight out and on my flight back. 357 00:20:40,760 --> 00:20:43,320 Speaker 1: H the two books I was reading were The Eye 358 00:20:43,320 --> 00:20:47,800 Speaker 1: Test by Chris Jones and Lyrics by a famous Liverpool 359 00:20:47,880 --> 00:20:51,200 Speaker 1: Lean named Paul McCartney. And I spent more time reading 360 00:20:51,200 --> 00:20:56,040 Speaker 1: The Eye Test by Chris Jones than Lyrics by Paul McCartney. Um, 361 00:20:56,080 --> 00:20:59,960 Speaker 1: even though even though I do take issue with something 362 00:21:00,040 --> 00:21:07,400 Speaker 1: in the book, So, uh, this is the whole point. 363 00:21:07,480 --> 00:21:10,199 Speaker 1: It's the whole point. You talked about Michael Lewis uh 364 00:21:10,200 --> 00:21:12,760 Speaker 1: and Moneyball and sort of the impact that that Moneyball 365 00:21:12,800 --> 00:21:16,320 Speaker 1: has had on this sort of concept, right, And for 366 00:21:16,400 --> 00:21:21,280 Speaker 1: me like this, what Michael Lewis did is the epicenter 367 00:21:21,280 --> 00:21:24,760 Speaker 1: of our business action network doesn't exist if Michael Lewis 368 00:21:24,800 --> 00:21:31,240 Speaker 1: isn't writing about moneyball, if he's not capturing how people 369 00:21:31,240 --> 00:21:35,719 Speaker 1: are thinking opportunistically about being fans of sports, If the 370 00:21:35,720 --> 00:21:38,800 Speaker 1: Internet doesn't exist, if there's not a democratization of data, 371 00:21:39,160 --> 00:21:41,280 Speaker 1: if if there's not a perfect storm of all these things, 372 00:21:42,040 --> 00:21:44,760 Speaker 1: then all of a sudden, gambling doesn't become as widespread, 373 00:21:44,800 --> 00:21:47,720 Speaker 1: it doesn't become as accessible, it doesn't become something that 374 00:21:47,960 --> 00:21:51,159 Speaker 1: ultimately is legalized, you know, in many, many states, and 375 00:21:51,280 --> 00:21:55,879 Speaker 1: continues to be legalized in more states. Um, you're right 376 00:21:55,920 --> 00:21:58,520 Speaker 1: about the movie. I think the scene that you hate 377 00:21:58,520 --> 00:22:00,880 Speaker 1: the most in the movie is my favorite scene, which 378 00:22:00,920 --> 00:22:03,919 Speaker 1: is when God, Yeah, when Brad Pitt is sitting around 379 00:22:03,920 --> 00:22:08,040 Speaker 1: with his scouts and they're all saying, we've got to 380 00:22:08,119 --> 00:22:11,040 Speaker 1: you know, they're talking about he's got a good looking girlfriend, 381 00:22:11,040 --> 00:22:12,960 Speaker 1: I love his swing, he's a five to a player, 382 00:22:12,960 --> 00:22:15,720 Speaker 1: and Brad Pitt is saying, no, we don't have to 383 00:22:15,720 --> 00:22:20,520 Speaker 1: replace Jason Giandi, we have to replace this number of hits, 384 00:22:20,560 --> 00:22:22,840 Speaker 1: this number of runs, this number of times a guy 385 00:22:22,880 --> 00:22:25,560 Speaker 1: gets on base, and let's do that anyway we can. 386 00:22:26,320 --> 00:22:29,359 Speaker 1: That's how I think about building a team. And you 387 00:22:29,480 --> 00:22:32,800 Speaker 1: hated that part of the part of the movie. I 388 00:22:32,880 --> 00:22:35,840 Speaker 1: hated how the Scouts are portrayed. They were pertraders, like 389 00:22:35,960 --> 00:22:40,040 Speaker 1: bumbling idiots, uh death. They all at hearing aids, they 390 00:22:40,040 --> 00:22:44,080 Speaker 1: were all gray haired, they were combative, who's fabio like 391 00:22:44,400 --> 00:22:47,399 Speaker 1: just they were portrayed as complete morons, which is what 392 00:22:48,080 --> 00:22:52,119 Speaker 1: the analytics movement has done to like institutional knowledge. Like 393 00:22:52,160 --> 00:22:57,879 Speaker 1: if you if you dare say analytics, but you immediately 394 00:22:57,920 --> 00:23:02,320 Speaker 1: become moron. You're a BlooDye, you're a heathen, you'r you 395 00:23:02,359 --> 00:23:05,960 Speaker 1: believe in fairies. Like it's it's it's an extremely negative 396 00:23:06,119 --> 00:23:09,400 Speaker 1: like I've been attacked for what I think by analytics 397 00:23:09,400 --> 00:23:13,640 Speaker 1: people and it's aggressive. It's like, and I'm the scouts. 398 00:23:14,680 --> 00:23:19,160 Speaker 1: Those scouts found Roberto Clemented, The scouts found Derek Jeter, 399 00:23:19,200 --> 00:23:22,760 Speaker 1: those scouts found all the great baby baseball players that 400 00:23:23,040 --> 00:23:25,639 Speaker 1: we all grew up loving. That's not an accident. They 401 00:23:25,640 --> 00:23:32,359 Speaker 1: weren't lucky. They knew something about baseball was their knowledge, uh, infallible. No, 402 00:23:32,800 --> 00:23:35,960 Speaker 1: analytics taught us some really important things like clucke shitting, 403 00:23:36,119 --> 00:23:38,760 Speaker 1: Like I I agree that cluck shitting doesn't exist. I 404 00:23:38,840 --> 00:23:41,440 Speaker 1: agree that the sacrifice bunt is kind of dumb. You 405 00:23:41,440 --> 00:23:44,800 Speaker 1: shouldn't do it. Those are things that analytics taught us. 406 00:23:44,840 --> 00:23:48,280 Speaker 1: But all that knowledge, all the stuff that those guys 407 00:23:48,960 --> 00:23:52,440 Speaker 1: new in their bones, has the value. So to make 408 00:23:52,480 --> 00:23:55,640 Speaker 1: them seem like absolute morons I thought was like an injustice. 409 00:23:55,680 --> 00:24:00,400 Speaker 1: So some very smart people and and and we could 410 00:24:00,480 --> 00:24:02,840 Speaker 1: use more smart people. I just don't understand why you 411 00:24:02,840 --> 00:24:06,320 Speaker 1: would ever limit your perspective to the point where it's like, well, 412 00:24:06,359 --> 00:24:08,280 Speaker 1: I know you've seen the game for forty years. I 413 00:24:08,320 --> 00:24:11,480 Speaker 1: don't care what you think. That's that's not that's that's 414 00:24:11,600 --> 00:24:14,560 Speaker 1: that's ridiculous to me. That's like and we have this 415 00:24:14,720 --> 00:24:17,840 Speaker 1: this this, Uh, there's the strain of anti expertise that's 416 00:24:17,920 --> 00:24:21,760 Speaker 1: kind of crapt into society. That's really damaged it. Guess 417 00:24:21,760 --> 00:24:23,400 Speaker 1: to me, like, I'm I'm getting worked up. My voice 418 00:24:23,440 --> 00:24:25,960 Speaker 1: is about to crack. Um you know you see it 419 00:24:26,040 --> 00:24:29,000 Speaker 1: with the COVID debates. Oh, I don't trust the experts 420 00:24:29,040 --> 00:24:31,520 Speaker 1: because I have access to a computer. Well, they had 421 00:24:31,560 --> 00:24:34,280 Speaker 1: access to forty years of knowledge, like, let's rely on 422 00:24:34,280 --> 00:24:36,480 Speaker 1: that maybe a little bit. It's just that's why I 423 00:24:36,520 --> 00:24:38,560 Speaker 1: didn't like that scene. It just treated them like morons, 424 00:24:38,720 --> 00:24:41,119 Speaker 1: but not morals. They were just we're right about everything, 425 00:24:41,160 --> 00:24:43,480 Speaker 1: but neither is analytics. It's like, I don't care why 426 00:24:43,480 --> 00:24:46,240 Speaker 1: we're arguing. One has to be right and the other 427 00:24:46,240 --> 00:24:48,240 Speaker 1: has to be wrong. They can both be right, and 428 00:24:48,280 --> 00:24:50,280 Speaker 1: they're especially right when they work together to find the 429 00:24:50,280 --> 00:24:53,720 Speaker 1: best answer. So a couple of years last was it 430 00:24:53,880 --> 00:24:56,800 Speaker 1: last summer, I don't remember it was right now, it 431 00:24:56,880 --> 00:24:59,560 Speaker 1: was the summer or twenty when the pandemic was sort 432 00:24:59,560 --> 00:25:02,600 Speaker 1: of in full swing. I wrote a bunch of stories 433 00:25:02,640 --> 00:25:06,160 Speaker 1: like this series of stories for Action Network that really 434 00:25:06,240 --> 00:25:12,440 Speaker 1: traced the modern movement of sports betting. Started with UM 435 00:25:12,560 --> 00:25:15,119 Speaker 1: this guy Charles McNeil who invented the point spread, and 436 00:25:15,160 --> 00:25:17,919 Speaker 1: he was a mathematician and a trader in Chicago in 437 00:25:17,960 --> 00:25:21,639 Speaker 1: the nineteen thirties, and then went to the development of 438 00:25:21,680 --> 00:25:25,920 Speaker 1: sort of um more analytically driven models. And a guy 439 00:25:25,960 --> 00:25:30,240 Speaker 1: named Roxy Roxboro who, by the way, UH was a 440 00:25:30,280 --> 00:25:33,840 Speaker 1: son of Vancouver and was running a sports book and 441 00:25:33,880 --> 00:25:39,639 Speaker 1: sort of some of the Wild West oil trading offices 442 00:25:39,880 --> 00:25:42,680 Speaker 1: in Vancouver in the early seventies kind of got run 443 00:25:42,680 --> 00:25:45,640 Speaker 1: out of town um, and he moved to Vegas and 444 00:25:45,920 --> 00:25:49,040 Speaker 1: was betting and sort of developed this this concept that 445 00:25:49,160 --> 00:25:52,520 Speaker 1: he could be a bookmaker to everybody because he was 446 00:25:52,600 --> 00:25:56,159 Speaker 1: better at running the numbers, and so people started buying 447 00:25:56,240 --> 00:25:59,280 Speaker 1: his numbers on a consultancy basis. So he was the 448 00:25:59,280 --> 00:26:01,639 Speaker 1: bookmaker who was feeding the stardust, and he was feeding 449 00:26:01,640 --> 00:26:04,240 Speaker 1: books and renals and all that kind of stuff. And 450 00:26:04,280 --> 00:26:08,919 Speaker 1: then ended with a guy who was five years old 451 00:26:09,160 --> 00:26:12,880 Speaker 1: and a genius math genius who studied you know, computer 452 00:26:12,960 --> 00:26:16,800 Speaker 1: science at usc and got into bitcoin and you know, 453 00:26:17,320 --> 00:26:19,960 Speaker 1: made millions of dollars betting on sports, and then moved 454 00:26:19,960 --> 00:26:23,119 Speaker 1: to Costa Rican is now moving markets, right, And I 455 00:26:23,119 --> 00:26:26,800 Speaker 1: spoke to Michael Lewis about it, UM, and I asked 456 00:26:26,880 --> 00:26:32,480 Speaker 1: him like, and you comment about this, Is it better 457 00:26:33,000 --> 00:26:38,920 Speaker 1: that people can relinquished responsibility for for their decisions because 458 00:26:38,920 --> 00:26:41,640 Speaker 1: they've got a model and you have a line. I've 459 00:26:41,680 --> 00:26:46,560 Speaker 1: become a professional header. Welcome to my kingdom of doubt. 460 00:26:48,080 --> 00:26:50,160 Speaker 1: That's why you make the models so we don't have doubt, 461 00:26:50,240 --> 00:26:52,879 Speaker 1: and so we can say we have a handrail on 462 00:26:52,880 --> 00:26:56,320 Speaker 1: the roller coaster to hold onto I guess your argument, 463 00:26:56,320 --> 00:26:58,640 Speaker 1: Michael Lewis was like, no, it's great we have these. 464 00:26:58,680 --> 00:27:02,560 Speaker 1: I'm okay with people really relinquishing responsibility. The models do 465 00:27:02,600 --> 00:27:05,840 Speaker 1: the work. You would disagree, Well, I would say the 466 00:27:05,880 --> 00:27:09,440 Speaker 1: models give us an illusion of certainty that we find comforting, 467 00:27:09,520 --> 00:27:12,600 Speaker 1: but it actually doesn't really exist. And we saw that 468 00:27:12,640 --> 00:27:17,600 Speaker 1: with COVID all the time. Because the models predicted certain outcomes, 469 00:27:18,400 --> 00:27:23,560 Speaker 1: you interfere with the process in various ways, positively or negatively, 470 00:27:23,600 --> 00:27:28,560 Speaker 1: those outcomes change, like the models. You know, I I 471 00:27:28,600 --> 00:27:30,520 Speaker 1: go back to and this was in money Ball too, 472 00:27:30,720 --> 00:27:33,119 Speaker 1: like and you go to a ninety eightercent chance because well, 473 00:27:33,119 --> 00:27:37,960 Speaker 1: that's gonna happen. But two out of one out of 474 00:27:37,960 --> 00:27:43,359 Speaker 1: fifty that happens not infrequently. That's one out of how 475 00:27:43,400 --> 00:27:46,040 Speaker 1: many times in poker that the one card you don't 476 00:27:46,040 --> 00:27:48,720 Speaker 1: want comes on the river? It happens. And so we 477 00:27:48,800 --> 00:27:52,680 Speaker 1: have this like the models give us some I don't 478 00:27:52,680 --> 00:27:54,359 Speaker 1: know what you want to call it, but it's like this, 479 00:27:54,359 --> 00:27:57,119 Speaker 1: this this idea that life is less chaotic than it 480 00:27:57,160 --> 00:28:00,439 Speaker 1: really is. And I get the appeal of that, Like 481 00:28:00,480 --> 00:28:02,760 Speaker 1: we want to feel like we're in control, Like we 482 00:28:02,800 --> 00:28:05,320 Speaker 1: want to feel like we're masters of our own faith. 483 00:28:06,200 --> 00:28:10,040 Speaker 1: I don't believe in not planning like I plan. I 484 00:28:10,080 --> 00:28:13,080 Speaker 1: also don't believe in ignoring data. I'm not anti science, 485 00:28:13,600 --> 00:28:17,239 Speaker 1: but the idea that if this outcome seems likely, then 486 00:28:17,280 --> 00:28:21,679 Speaker 1: it's going to happen is really dangerous because then you 487 00:28:21,680 --> 00:28:25,040 Speaker 1: don't prepare for the other thing. This is Trump winning 488 00:28:25,040 --> 00:28:29,439 Speaker 1: the election, you know, like major consequential things happen all 489 00:28:29,480 --> 00:28:31,960 Speaker 1: the time, where we think, well, that's not likely, and 490 00:28:32,040 --> 00:28:36,600 Speaker 1: it's unlikely things happen, and so that that's certainty is 491 00:28:36,680 --> 00:28:40,120 Speaker 1: like it's a false sense of security. That again I 492 00:28:40,160 --> 00:28:42,800 Speaker 1: get we want the comfort because life seems really chaotic 493 00:28:42,800 --> 00:28:46,720 Speaker 1: and difficult sometimes, but it's not real. I think it 494 00:28:46,760 --> 00:28:51,920 Speaker 1: gives you. It lulls you into this sense that that um, 495 00:28:52,200 --> 00:28:53,720 Speaker 1: that things are gonna go the way you think they're 496 00:28:53,720 --> 00:28:56,040 Speaker 1: gonna go. And my life has taught me that sometimes 497 00:28:56,040 --> 00:28:59,040 Speaker 1: you get plunged in the mouth and then what really happened? 498 00:28:59,080 --> 00:29:01,520 Speaker 1: Then then you've you decide who you are, Like those 499 00:29:01,520 --> 00:29:04,720 Speaker 1: are the moments when And the idea of abdicating responsibility 500 00:29:04,720 --> 00:29:07,240 Speaker 1: in the numbers, I mean, why do we want anyone 501 00:29:07,240 --> 00:29:10,840 Speaker 1: to advocate responsibility seems hugely unethical. Well, I don't know 502 00:29:10,880 --> 00:29:12,840 Speaker 1: the machine told me to do it. How how mental 503 00:29:12,840 --> 00:29:14,720 Speaker 1: would that make you? If someone said if you went 504 00:29:14,720 --> 00:29:16,840 Speaker 1: to the bank and you said, well, there's this problem 505 00:29:16,920 --> 00:29:18,960 Speaker 1: and they were going, well, the machine decided that was right. 506 00:29:19,600 --> 00:29:23,040 Speaker 1: Would make you insane? You like, tell the machine that 507 00:29:23,160 --> 00:29:26,520 Speaker 1: is wrong? You know it's so this is this is 508 00:29:26,520 --> 00:29:28,960 Speaker 1: a larger debate about the path we're on as a 509 00:29:29,000 --> 00:29:31,920 Speaker 1: society and if it's good or bad to be so 510 00:29:32,040 --> 00:29:34,880 Speaker 1: removed from each other. And I think it's ultimately bad. 511 00:29:36,520 --> 00:29:37,920 Speaker 1: I don't know, brother, there are days I'd love to 512 00:29:37,960 --> 00:29:42,760 Speaker 1: addicate responsibility completely. People love it. That's that's why. Because 513 00:29:42,800 --> 00:29:44,400 Speaker 1: you can say, well, the model told me to do it, 514 00:29:44,440 --> 00:29:46,200 Speaker 1: and you obeyed the math, and the math told me. 515 00:29:46,920 --> 00:29:48,560 Speaker 1: I get the idea that you wanted to outside of 516 00:29:48,600 --> 00:29:52,080 Speaker 1: your yourself. But it's like it's it's I don't know, 517 00:29:52,120 --> 00:29:54,040 Speaker 1: it's not right. It's that you can tell yourself whatever, 518 00:29:54,080 --> 00:29:56,200 Speaker 1: lie do you want to tell yourself. But I mean, 519 00:29:56,200 --> 00:29:58,520 Speaker 1: look at the game last night, Like that game, if 520 00:29:58,560 --> 00:30:01,720 Speaker 1: we really want to break it down on you, analyze 521 00:30:01,760 --> 00:30:03,840 Speaker 1: the hell out of it in advance. Other people did 522 00:30:03,840 --> 00:30:06,520 Speaker 1: as well. That game really came down to two officiating 523 00:30:06,520 --> 00:30:13,480 Speaker 1: calls like that, that element it exists, so you could 524 00:30:13,520 --> 00:30:17,120 Speaker 1: plan all you want. Can you plan for officials suddenly 525 00:30:17,200 --> 00:30:19,400 Speaker 1: deciding to not make a call that was pretty agree 526 00:30:19,440 --> 00:30:21,720 Speaker 1: us and then to make a call that seems soft, 527 00:30:21,840 --> 00:30:25,240 Speaker 1: Like those are the that's the that's that game, and 528 00:30:25,320 --> 00:30:27,520 Speaker 1: it's like, so everything you put into it came down 529 00:30:27,520 --> 00:30:29,720 Speaker 1: to that. That's hard to think about it that way 530 00:30:29,720 --> 00:30:31,200 Speaker 1: because you want to think, oh, no, I knew what 531 00:30:31,240 --> 00:30:33,400 Speaker 1: I was doing, and I bet the bangals because I 532 00:30:33,440 --> 00:30:36,440 Speaker 1: did some smart analysis and you did. But the ultimately 533 00:30:36,520 --> 00:30:39,800 Speaker 1: the score line was dictated a lot by two calls 534 00:30:39,800 --> 00:30:44,719 Speaker 1: that were pretty random. Uh, you're right. That's also something 535 00:30:44,760 --> 00:30:47,040 Speaker 1: that we talked about it in our podcast earlier in 536 00:30:47,040 --> 00:30:49,000 Speaker 1: the week, is that at the end of the day, 537 00:30:49,040 --> 00:30:52,160 Speaker 1: the officials had a much bigger impact on this game 538 00:30:52,600 --> 00:30:56,000 Speaker 1: than they should have. But listen, I I actually love 539 00:30:56,040 --> 00:30:58,000 Speaker 1: everything you're sort of advocating for in the book. I 540 00:30:58,040 --> 00:31:00,880 Speaker 1: think for anyone who listens to this podcast, which tends 541 00:31:00,880 --> 00:31:02,800 Speaker 1: to be people who are looking for answers, looking for 542 00:31:02,840 --> 00:31:07,400 Speaker 1: ways to mitigate risk, looking for models and predictions that 543 00:31:08,080 --> 00:31:11,640 Speaker 1: help them find a three percent edge instead of being 544 00:31:12,400 --> 00:31:18,480 Speaker 1: but being in one of the best sort of moments 545 00:31:18,480 --> 00:31:20,520 Speaker 1: in the book for me, and this is what I 546 00:31:20,520 --> 00:31:22,160 Speaker 1: want you to answer before we get out of here 547 00:31:22,360 --> 00:31:27,840 Speaker 1: is you talked about how the best time for people 548 00:31:27,840 --> 00:31:31,280 Speaker 1: to shine is during life storms, right, the idea that 549 00:31:31,360 --> 00:31:36,880 Speaker 1: extreme moments are when people are going to outshine the 550 00:31:36,960 --> 00:31:41,480 Speaker 1: machines because of their imagination. Give me an example that 551 00:31:41,960 --> 00:31:46,600 Speaker 1: you can think of from the sports world when that's 552 00:31:46,600 --> 00:31:51,360 Speaker 1: been true. Oh gosh, sports. So in the book, I 553 00:31:51,400 --> 00:31:55,920 Speaker 1: talked about you know, weather, literally climate because the models 554 00:31:55,960 --> 00:31:59,880 Speaker 1: that we use the forecast weather are less and less 555 00:32:00,960 --> 00:32:04,600 Speaker 1: um good at predicting extreme weather because it's the models 556 00:32:05,120 --> 00:32:09,120 Speaker 1: I've never seen it before. Um so, like the data 557 00:32:09,200 --> 00:32:12,920 Speaker 1: mining or you know, it's basically relies on the future 558 00:32:13,080 --> 00:32:16,520 Speaker 1: behaving something like the past, and then climate it doesn't. 559 00:32:17,480 --> 00:32:21,080 Speaker 1: The same thing happened, you know, there was wildfires in 560 00:32:21,120 --> 00:32:25,720 Speaker 1: California and iPhones weren't capturing how red the skies were, 561 00:32:25,760 --> 00:32:27,720 Speaker 1: and people thought it was like this tech conspiracy to 562 00:32:27,720 --> 00:32:29,920 Speaker 1: make it seem less bad than it actually was. And 563 00:32:29,920 --> 00:32:33,840 Speaker 1: it's just because the algorithm that operates your camera just 564 00:32:33,880 --> 00:32:35,960 Speaker 1: didn't understand what it was seeing and it was like, well, 565 00:32:35,960 --> 00:32:38,120 Speaker 1: those skies can't be that red. I'm going to correct it, 566 00:32:38,520 --> 00:32:40,080 Speaker 1: you know, because it wasn't looking at a volcano it 567 00:32:40,160 --> 00:32:42,840 Speaker 1: was trained to know that volcanoes are red. Those guys 568 00:32:42,880 --> 00:32:44,680 Speaker 1: generally aren't that red, and so it was fixing a 569 00:32:44,760 --> 00:32:48,200 Speaker 1: problem that that was in fact reality. I'm trying to 570 00:32:48,200 --> 00:32:50,120 Speaker 1: come up with a good sports example where it's like 571 00:32:50,760 --> 00:32:55,680 Speaker 1: where the past did not behave Well, look, you had 572 00:32:55,680 --> 00:32:57,840 Speaker 1: one in the book. It might not be about imagination 573 00:32:57,880 --> 00:33:03,160 Speaker 1: and pressure, but the whole zero experience I thought was 574 00:33:03,200 --> 00:33:07,240 Speaker 1: a great reflection of lack of imagination of what could 575 00:33:07,240 --> 00:33:12,120 Speaker 1: happen not you know, I was trying to come up 576 00:33:12,120 --> 00:33:15,840 Speaker 1: with a possible Barry Zito was a negative one. Yeah, 577 00:33:15,880 --> 00:33:18,880 Speaker 1: because statistically Barry Zito seemed like a lock when when 578 00:33:18,880 --> 00:33:22,000 Speaker 1: the Giants signed him, Yeah he was. I know, wins 579 00:33:22,000 --> 00:33:24,320 Speaker 1: and losses are sort of fraught, but he was and 580 00:33:24,360 --> 00:33:28,640 Speaker 1: eleven when the a score more than three runs. Uh, 581 00:33:28,640 --> 00:33:33,600 Speaker 1: he'd never had an injury. He projected to be better 582 00:33:34,000 --> 00:33:37,520 Speaker 1: than Hall of Fame pictures and almost every category if 583 00:33:37,560 --> 00:33:41,840 Speaker 1: you follow the stats. Uh. And what this was This 584 00:33:41,960 --> 00:33:43,880 Speaker 1: was one of the times when I really started thinking 585 00:33:43,960 --> 00:33:46,600 Speaker 1: about what I believe in because I would have these 586 00:33:46,680 --> 00:33:48,959 Speaker 1: arguments with people about Barry Zito, who I knew as 587 00:33:48,960 --> 00:33:51,720 Speaker 1: a human being that spent a lot of time with him, 588 00:33:52,040 --> 00:33:55,120 Speaker 1: and I think he was damaged by the contract itself 589 00:33:55,160 --> 00:33:58,640 Speaker 1: when he signed with the Giants record contract, and I 590 00:33:58,680 --> 00:34:04,240 Speaker 1: think Barry it was a pretty sensitive soul, just just 591 00:34:04,360 --> 00:34:06,600 Speaker 1: took on too much weight and it screwed up how 592 00:34:06,640 --> 00:34:10,440 Speaker 1: he picked. Because the way he picked, he talked about 593 00:34:10,440 --> 00:34:12,960 Speaker 1: being an instrument that the universe played. You just let 594 00:34:12,960 --> 00:34:15,120 Speaker 1: it flow through you, you know that flow state, and 595 00:34:15,120 --> 00:34:18,040 Speaker 1: you you don't think about what you're doing. He talked 596 00:34:18,040 --> 00:34:21,600 Speaker 1: about being consciously something, you know, working in your subconc 597 00:34:21,719 --> 00:34:24,920 Speaker 1: consciously working in your subconscious like a like flipping a 598 00:34:24,960 --> 00:34:28,319 Speaker 1: switch and just turning everything off. And the contract made 599 00:34:28,360 --> 00:34:30,799 Speaker 1: it impossible for him to do that because now there's 600 00:34:30,800 --> 00:34:34,680 Speaker 1: all this outside influence and pressure and analytics. Guys were like, well, no, 601 00:34:34,800 --> 00:34:38,719 Speaker 1: there's more foul territory in Oakland than San Francisco, so 602 00:34:38,760 --> 00:34:41,359 Speaker 1: we didn't get those foul ball outs. I'm like, that's 603 00:34:41,400 --> 00:34:43,720 Speaker 1: not That can't be the whole answer to why berries 604 00:34:43,719 --> 00:34:45,480 Speaker 1: you don't fell off a cliff as soon as he 605 00:34:45,520 --> 00:34:48,040 Speaker 1: moved over to the Giants, Like, foul territory at home 606 00:34:48,160 --> 00:34:51,640 Speaker 1: is one small factor, and it's just the idea that 607 00:34:51,719 --> 00:34:54,480 Speaker 1: he that the contract might damage him never entered the 608 00:34:54,480 --> 00:34:57,560 Speaker 1: conversation and never entered the conversation from the giant's perspective, 609 00:34:58,000 --> 00:35:01,120 Speaker 1: It never entered berries thinking until that happened, his agents thinking, 610 00:35:01,120 --> 00:35:04,200 Speaker 1: of course, he wants the biggest contract. Like when Barry 611 00:35:04,280 --> 00:35:06,360 Speaker 1: found out about the contract, he was out with a 612 00:35:06,400 --> 00:35:10,279 Speaker 1: friend having sushi and sashimi and he choked, you know, 613 00:35:10,600 --> 00:35:13,719 Speaker 1: Scott Boris text them the numbers and and and he 614 00:35:13,840 --> 00:35:18,680 Speaker 1: choked on his fish. And I'm like, that matters, That matters, 615 00:35:18,719 --> 00:35:21,760 Speaker 1: And so I think when you're signing that's a great example. Actually, 616 00:35:21,760 --> 00:35:25,040 Speaker 1: when you're signing a player, like will this contract change 617 00:35:25,080 --> 00:35:28,000 Speaker 1: how he plays? Will it add pressure to him? Or 618 00:35:28,040 --> 00:35:30,759 Speaker 1: will it make him rise to greatness? And that the 619 00:35:30,840 --> 00:35:33,520 Speaker 1: numbers won't help you there. That's when you come down 620 00:35:33,520 --> 00:35:35,080 Speaker 1: and talking to someone and go on, what kind of 621 00:35:35,120 --> 00:35:37,440 Speaker 1: person is this? Is he gonna rise to the occasion 622 00:35:37,480 --> 00:35:40,200 Speaker 1: or is he gonna choke? And that's, you know, a 623 00:35:40,320 --> 00:35:44,440 Speaker 1: super interesting debate that I think you can't really answer 624 00:35:45,960 --> 00:35:48,000 Speaker 1: with a spreadsheet. That comes down to being in a 625 00:35:48,120 --> 00:35:51,160 Speaker 1: room with somebody or watching how they play, or watching 626 00:35:51,160 --> 00:35:54,239 Speaker 1: them in moments of stress. And it's it's that for me, 627 00:35:54,320 --> 00:35:56,800 Speaker 1: is that like that three percent you're talking about, that 628 00:35:56,800 --> 00:35:59,960 Speaker 1: that and of course retroactively you can go. Of course Barrios, 629 00:36:00,040 --> 00:36:02,319 Speaker 1: it was gonna fall off a cliff. Of course that 630 00:36:02,360 --> 00:36:04,880 Speaker 1: contract was gonna screw with his head. I mean, but 631 00:36:05,000 --> 00:36:07,359 Speaker 1: we didn't see it at the time, and that's that's Yeah, 632 00:36:07,400 --> 00:36:08,920 Speaker 1: that is a good example of negat I'm trying to 633 00:36:08,920 --> 00:36:12,840 Speaker 1: come up with a positive one where where you know, 634 00:36:13,040 --> 00:36:15,400 Speaker 1: like Bryson the Shambo and golf is really interesting to 635 00:36:15,480 --> 00:36:18,560 Speaker 1: me because he's playing in a different way and they 636 00:36:18,640 --> 00:36:21,800 Speaker 1: keep trying to tweak the game to mass with him 637 00:36:21,840 --> 00:36:24,279 Speaker 1: and um, and it's it's like they don't want what 638 00:36:24,360 --> 00:36:28,399 Speaker 1: he's doing, and that that sort of give and take, 639 00:36:28,520 --> 00:36:31,960 Speaker 1: like Canny survived golf screwing with him is sort of 640 00:36:31,960 --> 00:36:34,319 Speaker 1: a really interesting thing for me to watch because he's 641 00:36:34,360 --> 00:36:38,080 Speaker 1: pure analytics. Basically, Yeah, you're right about this book, like 642 00:36:38,160 --> 00:36:41,640 Speaker 1: he is going to completely reimagine and we've talked about 643 00:36:41,640 --> 00:36:43,879 Speaker 1: this in action. Jason Sobel has written about it really well, 644 00:36:44,440 --> 00:36:48,880 Speaker 1: like completely reimagine how he's going to swing the club 645 00:36:49,440 --> 00:36:52,960 Speaker 1: and what it is important to him, which is basically 646 00:36:53,680 --> 00:36:56,320 Speaker 1: hit the ball as far as you fucking can. Yeah, 647 00:36:56,400 --> 00:36:59,720 Speaker 1: he's changed what Matt, how what matters. It's not your store, 648 00:37:00,120 --> 00:37:02,399 Speaker 1: it's your boss. Speed is your swing. Speed is your 649 00:37:02,600 --> 00:37:06,480 Speaker 1: and and golf doesn't like it. And so you know, 650 00:37:06,560 --> 00:37:09,240 Speaker 1: there's an interesting thing happening with Bryson right now. We're golf. 651 00:37:10,160 --> 00:37:14,239 Speaker 1: The golfers themselves voted to ban green reading books, like 652 00:37:14,280 --> 00:37:17,200 Speaker 1: the books that had these beautiful laser red maps of 653 00:37:17,239 --> 00:37:20,440 Speaker 1: every green they played and they were perfectly legal. Bryson 654 00:37:20,520 --> 00:37:24,480 Speaker 1: used them all the time to figure out lines, because 655 00:37:24,640 --> 00:37:27,920 Speaker 1: Bryson was good at Bryson has a literal chart that 656 00:37:28,000 --> 00:37:30,960 Speaker 1: he consults. This is the this is the distance, this 657 00:37:31,040 --> 00:37:33,560 Speaker 1: is the slope, this is the blind, this is how 658 00:37:33,560 --> 00:37:35,960 Speaker 1: hard I hit it. And he knows, you know, he 659 00:37:36,000 --> 00:37:38,920 Speaker 1: can control how hard he hits it. But the line 660 00:37:38,960 --> 00:37:41,760 Speaker 1: reading was a big advantage to him. It helped them, 661 00:37:41,800 --> 00:37:44,200 Speaker 1: and they took it away, and they took it away. 662 00:37:44,480 --> 00:37:47,080 Speaker 1: It's like a pretty purposeful rebuke of how he plays 663 00:37:47,120 --> 00:37:49,880 Speaker 1: the game. Like, yeah, they took it away from everybody, 664 00:37:49,920 --> 00:37:52,200 Speaker 1: but it's it's really gonna mess with Bryson, like I 665 00:37:52,239 --> 00:37:54,120 Speaker 1: think you could. This year is going to be really 666 00:37:54,120 --> 00:37:57,160 Speaker 1: interesting to watch it because you know, he built this 667 00:37:57,239 --> 00:38:01,200 Speaker 1: system that now no longer can be used. And that's life. 668 00:38:01,680 --> 00:38:04,120 Speaker 1: That's life. This is Rice is also in a way 669 00:38:04,160 --> 00:38:07,560 Speaker 1: the argument against pure analytics because I can always mess 670 00:38:07,600 --> 00:38:10,640 Speaker 1: with the system a little bit and then what now? 671 00:38:11,360 --> 00:38:13,759 Speaker 1: And that's I guess the book. The book is basically 672 00:38:14,400 --> 00:38:17,600 Speaker 1: how do you succeed when you get asked what now? 673 00:38:17,960 --> 00:38:20,960 Speaker 1: And for me, the answer isn't always numbers. Sometimes you 674 00:38:21,080 --> 00:38:25,720 Speaker 1: just gotta be an awesome person. That's a lovely sentiment 675 00:38:25,760 --> 00:38:29,840 Speaker 1: to end it on. Chris, I hope the book is hopeful. 676 00:38:30,160 --> 00:38:35,520 Speaker 1: I hope it is. Hopefully it's totally people. People should 677 00:38:35,560 --> 00:38:38,319 Speaker 1: read it. It's a great book because there is like 678 00:38:39,239 --> 00:38:44,279 Speaker 1: not even in sports, right you mentioned in polling in 679 00:38:45,000 --> 00:38:50,520 Speaker 1: COVID in the way the economy is being discussed in sports. 680 00:38:50,520 --> 00:38:52,920 Speaker 1: That really is the reason we want to be here 681 00:38:52,920 --> 00:38:55,400 Speaker 1: and the thing that we want to escape from the 682 00:38:55,520 --> 00:39:00,000 Speaker 1: COVID and the economy and the politics and the environment, um, 683 00:39:00,120 --> 00:39:03,760 Speaker 1: all of which are sort of discussed in the book. Uh. 684 00:39:04,160 --> 00:39:08,000 Speaker 1: Like this helps us find clarity when we're looking for 685 00:39:08,120 --> 00:39:13,080 Speaker 1: new ways to be fans. And for that reason alone, 686 00:39:13,520 --> 00:39:18,719 Speaker 1: I would say everyone go by the eye test. A 687 00:39:18,840 --> 00:39:23,799 Speaker 1: Case for Human Creativity. Emphasis on the Creativity in an 688 00:39:23,840 --> 00:39:29,400 Speaker 1: Age of Analytics by multiple National Magazine Award winner Chris Jones. 689 00:39:30,040 --> 00:39:32,120 Speaker 1: Thank you, chat that's lovely to hear your boys, and 690 00:39:32,160 --> 00:39:34,360 Speaker 1: thank you for your earlier patriot Nation in my career. 691 00:39:34,440 --> 00:39:36,919 Speaker 1: No matter to love whatever. Man, you don't even mention 692 00:39:36,960 --> 00:39:40,799 Speaker 1: it in the book. It's like kind of weird it 693 00:39:40,840 --> 00:39:43,359 Speaker 1: did it didn't matter. It helped a lot of my thinking, 694 00:39:43,560 --> 00:39:47,600 Speaker 1: helped a lot of my thinking. Okay, thanks, all right. 695 00:39:47,600 --> 00:39:50,560 Speaker 1: I want to thank Chris Jones, brilliant writer, wrote an 696 00:39:50,600 --> 00:39:54,960 Speaker 1: amazing book called The Eye Test. Also want to remind 697 00:39:55,000 --> 00:39:57,120 Speaker 1: everybody I talked a little bit about this in the 698 00:39:57,120 --> 00:40:00,359 Speaker 1: podcast on Monday, the Super Bowl review. Sign and I 699 00:40:00,440 --> 00:40:03,600 Speaker 1: have gotten a lot of questions about how we'll handle 700 00:40:03,640 --> 00:40:06,560 Speaker 1: the show now that NFL season is over. Don't worry. 701 00:40:06,600 --> 00:40:10,719 Speaker 1: We will still be here every Tuesday and Thursday. We'll 702 00:40:10,760 --> 00:40:14,320 Speaker 1: cover everything that matters in the NFL off season, the combine, 703 00:40:14,520 --> 00:40:18,440 Speaker 1: the draft, free agency, all of it. It is a huge, 704 00:40:18,520 --> 00:40:20,239 Speaker 1: huge part of what we're going to continue to do. 705 00:40:20,680 --> 00:40:23,400 Speaker 1: We'll also cover NFL betting one of one educational stuff. 706 00:40:23,400 --> 00:40:26,320 Speaker 1: We'll talk about how Simon uses gametape, how he builds 707 00:40:26,320 --> 00:40:29,920 Speaker 1: his models. We finally have time for all that stuff. Honestly, 708 00:40:30,400 --> 00:40:33,080 Speaker 1: the podcast got so big so fast. We were always 709 00:40:33,080 --> 00:40:35,440 Speaker 1: in the midst of covering games and talking about games, 710 00:40:35,440 --> 00:40:36,960 Speaker 1: and people have a ton of questions, so we want 711 00:40:36,960 --> 00:40:39,279 Speaker 1: to get to those, but the gambling season is never over. 712 00:40:39,600 --> 00:40:41,280 Speaker 1: That's why we're going to talk about all the stuff 713 00:40:41,320 --> 00:40:43,560 Speaker 1: we love betting on between now in the summer, including 714 00:40:43,640 --> 00:40:48,600 Speaker 1: NBA March Madness, Oscars Masters, all that good stuff. Stay tuned, 715 00:40:49,080 --> 00:40:53,080 Speaker 1: it's going to be a ton of fun between now 716 00:40:53,320 --> 00:41:00,480 Speaker 1: an NFL training camp. I love you, no