1 00:00:00,400 --> 00:00:03,760 Speaker 1: Hello, I'm Evan Nobi Williams, usually accompanied by Michael Barr 2 00:00:03,760 --> 00:00:05,880 Speaker 1: and Scott Sashnik, but both of them are off today, 3 00:00:06,080 --> 00:00:08,840 Speaker 1: so we've done one better. We've got Ira Boudway Bloomberg 4 00:00:08,920 --> 00:00:11,760 Speaker 1: Business Week reporter. Ira, thanks for pitch hitting with us, 5 00:00:11,760 --> 00:00:14,280 Speaker 1: no problem, Thank you for doing this. So let's talk about, 6 00:00:14,320 --> 00:00:16,480 Speaker 1: you know, the biggest stories in the sports world right now, 7 00:00:16,520 --> 00:00:18,920 Speaker 1: and the one I want to lead off with Michael 8 00:00:18,960 --> 00:00:23,200 Speaker 1: Avanati and Nike for folks who don't know. And Michael 9 00:00:23,239 --> 00:00:26,119 Speaker 1: Avanati is now in part of an extortion lawsuit. You know, 10 00:00:26,160 --> 00:00:29,440 Speaker 1: he's been charged with extorting the company and as a result, 11 00:00:29,880 --> 00:00:32,199 Speaker 1: you know, we're getting a lot of discovery in this lawsuit, 12 00:00:32,520 --> 00:00:36,280 Speaker 1: and a lot of the things point to Nike potentially 13 00:00:36,400 --> 00:00:40,440 Speaker 1: paying high school basketball players. Ian Williamson one of the 14 00:00:40,520 --> 00:00:44,000 Speaker 1: names that's been thrown around. Romeo Langford another one. Ira, 15 00:00:44,080 --> 00:00:46,320 Speaker 1: Do you feel deja vu here? I feel like, you know, 16 00:00:46,360 --> 00:00:48,560 Speaker 1: it's it's the third straight year that we've been talking 17 00:00:48,600 --> 00:00:52,720 Speaker 1: about legal discovery and a big shoe company paying young 18 00:00:52,720 --> 00:00:55,720 Speaker 1: basketball players. Yeah, I think it's going to keep happening. 19 00:00:55,760 --> 00:00:59,800 Speaker 1: And I actually think Avati looked at the Adidas you know, 20 00:01:00,040 --> 00:01:02,520 Speaker 1: implicated in that federal probe and said, hey, why not 21 00:01:02,560 --> 00:01:04,800 Speaker 1: pull Nike into this or at least that's one possible 22 00:01:04,840 --> 00:01:08,080 Speaker 1: interpretation of what's going on here. So and I think 23 00:01:08,120 --> 00:01:10,720 Speaker 1: as long as we have the system that we do 24 00:01:10,840 --> 00:01:16,000 Speaker 1: have where these kids are not allowed to reap any benefits, 25 00:01:16,200 --> 00:01:19,360 Speaker 1: uh really directly, then then this is there's a great 26 00:01:19,360 --> 00:01:21,480 Speaker 1: market or black market, and and there's going to be 27 00:01:21,560 --> 00:01:24,720 Speaker 1: constant uh every year we're going to hear it. Yeah, 28 00:01:24,720 --> 00:01:27,160 Speaker 1: it's interesting that the n A kind of always seems 29 00:01:27,200 --> 00:01:30,080 Speaker 1: to be in the cross hairs when we have these discussions. 30 00:01:30,440 --> 00:01:33,759 Speaker 1: The information that dropped last week in in federal court. 31 00:01:33,959 --> 00:01:36,760 Speaker 1: You know, these are kids that are high schoolers, right 32 00:01:36,760 --> 00:01:39,119 Speaker 1: that we're talking about Zion Williamson and Romeo Langford when 33 00:01:39,120 --> 00:01:42,640 Speaker 1: they're playing a AU basketball and you know, the third 34 00:01:43,200 --> 00:01:46,360 Speaker 1: the third person reportedly a seventh grader, right, a thirteen 35 00:01:46,440 --> 00:01:49,800 Speaker 1: year old basketball player. So it does make me wonder, 36 00:01:49,880 --> 00:01:51,640 Speaker 1: if you know, you know, the the n c A 37 00:01:51,720 --> 00:01:53,840 Speaker 1: and and and and the fact that college basketball players 38 00:01:53,840 --> 00:01:57,760 Speaker 1: aren't compensated beyond their scholarships, if this also needs to 39 00:01:57,760 --> 00:02:00,840 Speaker 1: trickle down even below that, right, Yeah, I mean, if 40 00:02:00,840 --> 00:02:03,160 Speaker 1: you look at if this is all working properly, right, 41 00:02:03,200 --> 00:02:05,920 Speaker 1: these kids on these elite youth teams are going to 42 00:02:05,960 --> 00:02:09,080 Speaker 1: get a few shoes and some travel you know, paid for, 43 00:02:09,240 --> 00:02:12,280 Speaker 1: while their coaches get tens of thousands of dollars on 44 00:02:12,360 --> 00:02:15,519 Speaker 1: the up and up from UH, Nike, any other athletic 45 00:02:15,520 --> 00:02:18,800 Speaker 1: apparel companies. Everybody knows along the way that kids in 46 00:02:19,000 --> 00:02:21,200 Speaker 1: junior high, high school, they wear what they see their 47 00:02:21,240 --> 00:02:23,480 Speaker 1: friends wearing with the with the best player on the 48 00:02:23,560 --> 00:02:25,520 Speaker 1: on the au team, on the high school team is wearing, 49 00:02:25,520 --> 00:02:28,239 Speaker 1: So that's valuable these companies, kids get nothing. Then they're 50 00:02:28,240 --> 00:02:30,200 Speaker 1: supposed to wait at least a year go to college 51 00:02:30,480 --> 00:02:33,000 Speaker 1: while their coaches get paid big money by apparel companies. 52 00:02:33,000 --> 00:02:35,120 Speaker 1: And why the schools get big money from the apparel 53 00:02:35,120 --> 00:02:37,400 Speaker 1: companies and while the n C the A gets you know, 54 00:02:37,440 --> 00:02:40,400 Speaker 1: big money TV money, and they get nothing, and then 55 00:02:40,520 --> 00:02:42,760 Speaker 1: when they're done with a year, then they can finally 56 00:02:42,760 --> 00:02:44,960 Speaker 1: go cash in. And I think if you look at that, 57 00:02:44,960 --> 00:02:47,560 Speaker 1: you think, well, this is how is this supposed to work? 58 00:02:47,639 --> 00:02:49,600 Speaker 1: You know, how are we not supposed to have illegal 59 00:02:50,040 --> 00:02:52,440 Speaker 1: channeling of money in this situation? But what I wonder 60 00:02:52,440 --> 00:02:54,880 Speaker 1: watching all of this is what is the best puff 61 00:02:54,960 --> 00:02:57,639 Speaker 1: welcome for Nike at this point? Like, how do how 62 00:02:57,639 --> 00:03:01,400 Speaker 1: do they make this and you know they think I 63 00:03:01,480 --> 00:03:03,480 Speaker 1: have not is just gonna go quietly? Doesn't seem to 64 00:03:03,480 --> 00:03:05,799 Speaker 1: be his way? Like what do you think for them 65 00:03:05,880 --> 00:03:09,400 Speaker 1: is the ideal outcome? Yeah, So, I mean this is 66 00:03:09,720 --> 00:03:12,440 Speaker 1: again the strange part about this is that it's all 67 00:03:12,440 --> 00:03:16,000 Speaker 1: coming out of an extortion lawsuit. You know, Novnadi has 68 00:03:16,040 --> 00:03:19,919 Speaker 1: been essentially accused by federal prosecutors of trying to extort 69 00:03:20,600 --> 00:03:22,640 Speaker 1: Nike and and and he has said that he was 70 00:03:22,680 --> 00:03:26,240 Speaker 1: actually right operating as an attorney for a client of 71 00:03:26,280 --> 00:03:29,440 Speaker 1: his who was an a au coach. Um. The other 72 00:03:29,600 --> 00:03:33,919 Speaker 1: federal investigation, the one that sent or charged a number 73 00:03:33,919 --> 00:03:37,160 Speaker 1: of Adidas employees, and they were given jail sentences which 74 00:03:37,160 --> 00:03:39,840 Speaker 1: they are now appealing. But but that federal probe is 75 00:03:39,880 --> 00:03:43,160 Speaker 1: still ongoing theoretically, so I think there's still a question 76 00:03:43,200 --> 00:03:45,360 Speaker 1: as to whether or not information that maybe comes in 77 00:03:45,360 --> 00:03:48,480 Speaker 1: this lawsuit could eventually trickle its way over into that 78 00:03:48,680 --> 00:03:53,000 Speaker 1: federal probe. Um. But you know, right now, the people 79 00:03:53,760 --> 00:03:56,320 Speaker 1: in these disclosures, these Nike employees, they have not been 80 00:03:56,600 --> 00:03:59,600 Speaker 1: fired yet. They're still employed and there are probably some 81 00:03:59,600 --> 00:04:02,960 Speaker 1: some lead egal reasons for that as well. Um, But 82 00:04:03,080 --> 00:04:05,440 Speaker 1: I also want, I mean, is there. I'm not sure 83 00:04:05,440 --> 00:04:07,400 Speaker 1: if the public really cares about this any I mean, 84 00:04:07,440 --> 00:04:10,960 Speaker 1: I think there's among people who follow college basketball, there 85 00:04:11,000 --> 00:04:14,280 Speaker 1: is now kind of an assumption that there is money 86 00:04:14,360 --> 00:04:17,960 Speaker 1: changing hands under the table between shoe companies and college 87 00:04:17,960 --> 00:04:23,880 Speaker 1: coaches and a AU coaches and parents and uncles and representatives. Um. 88 00:04:24,080 --> 00:04:26,480 Speaker 1: It seems as though there's almost fatigue on this story, 89 00:04:26,480 --> 00:04:28,200 Speaker 1: which is why I think you're not seeing it covered 90 00:04:28,200 --> 00:04:31,880 Speaker 1: as much. Yeah. I think that is the truth about this, 91 00:04:31,960 --> 00:04:35,719 Speaker 1: that that ultimately fans either think that you know, what's 92 00:04:35,760 --> 00:04:37,840 Speaker 1: wrong with this? Why can't these kids get paid? Or 93 00:04:37,880 --> 00:04:40,520 Speaker 1: they think I don't care it's wrong, but it doesn't 94 00:04:40,560 --> 00:04:43,440 Speaker 1: bother me, you know. Uh. And So in the end, 95 00:04:43,520 --> 00:04:46,600 Speaker 1: I think for the inside blat for Nike, this can 96 00:04:46,680 --> 00:04:48,520 Speaker 1: just go on at a low level and it's a 97 00:04:48,600 --> 00:04:50,599 Speaker 1: kind of a cost of doing business, you know. And 98 00:04:50,600 --> 00:04:53,440 Speaker 1: and they'll deal with it because what people really care 99 00:04:53,440 --> 00:04:56,120 Speaker 1: about is who is the next Tim Williamson and and 100 00:04:56,240 --> 00:04:58,719 Speaker 1: watching him play, uh and and hopefully you know, in 101 00:04:58,760 --> 00:05:02,159 Speaker 1: their shoes. Uh. So I think they can definitely live 102 00:05:02,240 --> 00:05:03,960 Speaker 1: with it. But it is interesting that you could have 103 00:05:04,000 --> 00:05:07,839 Speaker 1: the FEDS going after Avanadi for for extorting Nike and 104 00:05:07,880 --> 00:05:09,680 Speaker 1: then using information that comes out of that to go 105 00:05:09,760 --> 00:05:12,760 Speaker 1: after Nike for what he's extorting them over. Yeah, and 106 00:05:13,200 --> 00:05:15,200 Speaker 1: there will be there will be a lot more coming 107 00:05:15,200 --> 00:05:18,159 Speaker 1: out of this, I'm sure in the next couple of months. 108 00:05:18,480 --> 00:05:21,839 Speaker 1: Let's move on in in Business Week this week issue 109 00:05:21,880 --> 00:05:25,520 Speaker 1: on stands, you have an article about the Athletic the 110 00:05:25,600 --> 00:05:28,360 Speaker 1: startups sports coverage website. Give us a sense, I mean, 111 00:05:28,520 --> 00:05:30,840 Speaker 1: everybody loves to talk about the Athletic. There's a lot 112 00:05:30,839 --> 00:05:34,440 Speaker 1: of speculation about their business model, whether it's sustainable, whether 113 00:05:34,520 --> 00:05:36,720 Speaker 1: it is bound to fail in a year or two. 114 00:05:37,120 --> 00:05:40,000 Speaker 1: From your reporting, how healthy is the athletics business model? 115 00:05:41,080 --> 00:05:42,880 Speaker 1: I mean, if you look at their growth, right, so 116 00:05:42,920 --> 00:05:46,320 Speaker 1: they're now up to six hundred thousand subscribers, which really 117 00:05:46,360 --> 00:05:49,520 Speaker 1: is a considerable number in the world of digital media subscriptions. 118 00:05:49,560 --> 00:05:52,120 Speaker 1: All everybody who's in the millions is for the most part, 119 00:05:52,160 --> 00:05:54,719 Speaker 1: you're talking about New York Times, Wall Street Journal, Washington Post, 120 00:05:54,800 --> 00:05:58,680 Speaker 1: really well known old publications. So that's an impressive achievement. 121 00:05:58,800 --> 00:06:01,240 Speaker 1: And I think it's extant out of the question of 122 00:06:01,279 --> 00:06:04,640 Speaker 1: like whether this is gonna just collapse any minute. I mean, 123 00:06:04,680 --> 00:06:06,320 Speaker 1: if they can be taken at their word that they 124 00:06:06,320 --> 00:06:11,080 Speaker 1: get their different markets to profitability within six or twelve months. Uh. 125 00:06:11,240 --> 00:06:15,640 Speaker 1: Then I think the question now is not can this survives? 126 00:06:15,680 --> 00:06:18,120 Speaker 1: The question is like what scale? Uh? And I think 127 00:06:18,560 --> 00:06:20,359 Speaker 1: you know, if they can grow to say three or 128 00:06:20,400 --> 00:06:22,240 Speaker 1: four or five times what they are now and have 129 00:06:22,360 --> 00:06:25,520 Speaker 1: even slim losses, Uh, then that's a business somebody might 130 00:06:25,520 --> 00:06:29,040 Speaker 1: want to buy or slim slim profits. Um. If they 131 00:06:29,040 --> 00:06:31,040 Speaker 1: can get to ten twenty times what they are now, 132 00:06:31,320 --> 00:06:33,479 Speaker 1: even again with losses, uh, you know, as long as 133 00:06:33,520 --> 00:06:36,200 Speaker 1: they're not huge, Uh, that's a company that maybe I 134 00:06:36,320 --> 00:06:40,279 Speaker 1: p o s. I mean, you look at companies Netflix, Amazon, Uber, 135 00:06:40,279 --> 00:06:41,600 Speaker 1: A lot of these companies they get all the way 136 00:06:41,600 --> 00:06:45,400 Speaker 1: to public markets without being profitable or with routinely showing losses. 137 00:06:45,480 --> 00:06:48,160 Speaker 1: So UM, I think that, you know, it's just a 138 00:06:48,240 --> 00:06:51,240 Speaker 1: question of the scale that it's going to reach. Um, 139 00:06:51,880 --> 00:06:54,040 Speaker 1: into my mind at this point, not a question of 140 00:06:54,240 --> 00:06:57,040 Speaker 1: whether this is like another national sports daily where it's 141 00:06:57,080 --> 00:06:59,520 Speaker 1: just going to explode someday. The thing I find so 142 00:06:59,600 --> 00:07:02,520 Speaker 1: compelling about the athletic is that it's it's really a 143 00:07:02,520 --> 00:07:08,840 Speaker 1: test for pay for content distribution model way beyond just sports, right. 144 00:07:08,920 --> 00:07:12,360 Speaker 1: I mean, I would think every major media company in 145 00:07:12,400 --> 00:07:14,440 Speaker 1: the in the country is looking at the athletic in 146 00:07:14,480 --> 00:07:17,280 Speaker 1: some capacity and using it as a litmus test for 147 00:07:17,320 --> 00:07:20,720 Speaker 1: how willing people are willing to pay for written content 148 00:07:20,800 --> 00:07:23,840 Speaker 1: if the content is good enough, right, And I think 149 00:07:23,840 --> 00:07:26,280 Speaker 1: that is one of the questions is whether how peculiar 150 00:07:26,320 --> 00:07:28,560 Speaker 1: are sports? I mean, you know, you talk to people 151 00:07:28,600 --> 00:07:31,760 Speaker 1: and not say what the tribalism around sports, the passion 152 00:07:31,800 --> 00:07:35,400 Speaker 1: around sports. People's willingness as die hard fans to pay 153 00:07:35,440 --> 00:07:38,320 Speaker 1: for information that they can't get elsewhere about their favorite 154 00:07:38,360 --> 00:07:41,920 Speaker 1: team is maybe different than your willingness to you know, 155 00:07:41,960 --> 00:07:44,960 Speaker 1: paid for a great read that you know about a 156 00:07:45,000 --> 00:07:48,240 Speaker 1: profile of somebody who you might see have seen in 157 00:07:48,280 --> 00:07:50,440 Speaker 1: the news. I think those are two different propositions. But 158 00:07:50,440 --> 00:07:54,080 Speaker 1: I think you're right that other companies are already thinking 159 00:07:54,080 --> 00:07:56,880 Speaker 1: about and are going to try as startups to move 160 00:07:56,960 --> 00:08:00,360 Speaker 1: to this model where you're not dependent on ads, don't 161 00:08:00,400 --> 00:08:03,560 Speaker 1: have to always try to hit the biggest possible traffic 162 00:08:03,640 --> 00:08:06,640 Speaker 1: or go viral, and you're not dependent on Facebook tweaking 163 00:08:06,640 --> 00:08:10,000 Speaker 1: its algorithm that you know, totally change your the state 164 00:08:10,040 --> 00:08:12,400 Speaker 1: of your business where you where you're dependent on a 165 00:08:12,480 --> 00:08:15,760 Speaker 1: direct relationship with your readers and you know that they 166 00:08:15,800 --> 00:08:18,200 Speaker 1: care a little bit already, right that they're interested in 167 00:08:18,240 --> 00:08:20,000 Speaker 1: what you have and are invested in what you have, 168 00:08:20,040 --> 00:08:23,200 Speaker 1: because they've shown that by by by paying for a subscription. 169 00:08:23,280 --> 00:08:26,200 Speaker 1: So I do think a lot of people are watching 170 00:08:26,320 --> 00:08:28,400 Speaker 1: the Athletic and trying to figure out whether there's a 171 00:08:28,560 --> 00:08:30,560 Speaker 1: broader lessons here. One of the things one of the 172 00:08:30,640 --> 00:08:33,959 Speaker 1: numbers that stuck out in reading your piece their attention rate. 173 00:08:34,000 --> 00:08:36,880 Speaker 1: I mean the as some people will know, a lot 174 00:08:36,880 --> 00:08:38,920 Speaker 1: of the people that signed up for the Athletic originally 175 00:08:38,960 --> 00:08:43,840 Speaker 1: we're doing so on kind of heavily discounted early early deals. 176 00:08:44,320 --> 00:08:46,800 Speaker 1: Um And And one of the main questions that I had, 177 00:08:46,840 --> 00:08:50,520 Speaker 1: I've always had was when when those discount rates expire, 178 00:08:50,840 --> 00:08:52,679 Speaker 1: how many people are willing to pay the full rate 179 00:08:52,760 --> 00:08:56,400 Speaker 1: moving forward? Um And what you reported is that you 180 00:08:56,920 --> 00:09:00,760 Speaker 1: people re up for the next year or the next months, 181 00:09:00,800 --> 00:09:04,000 Speaker 1: regardless of whether they were paying full freight before or 182 00:09:04,000 --> 00:09:06,680 Speaker 1: whether they were on a discount. Is that a number 183 00:09:06,720 --> 00:09:08,959 Speaker 1: that is heartening for for the Athletic. Is that a 184 00:09:09,040 --> 00:09:11,120 Speaker 1: number that is they think they can improve on? Kind 185 00:09:11,120 --> 00:09:13,079 Speaker 1: of how how did they approach that eight percent number? 186 00:09:13,760 --> 00:09:16,120 Speaker 1: I mean, that number from all I can gather is high. 187 00:09:16,280 --> 00:09:18,720 Speaker 1: And you know, by industry standards, there isn't there aren't 188 00:09:18,720 --> 00:09:20,959 Speaker 1: a ton of data points to compare them too. But 189 00:09:21,520 --> 00:09:24,319 Speaker 1: there's a place called the lens Fest Institute for Journalism 190 00:09:24,400 --> 00:09:26,640 Speaker 1: that looks at sort of what are the future models 191 00:09:26,640 --> 00:09:30,440 Speaker 1: for newspapers and journalism and and they estimated that usually 192 00:09:30,440 --> 00:09:33,839 Speaker 1: for newspapers, which are typically you know, maybe at different 193 00:09:33,840 --> 00:09:36,200 Speaker 1: price points and different size markets, but they only they 194 00:09:36,200 --> 00:09:38,200 Speaker 1: see turn of like a year over year, like half 195 00:09:38,200 --> 00:09:41,440 Speaker 1: of their people leave. Uh. And that's frequently, you know, 196 00:09:41,440 --> 00:09:43,240 Speaker 1: with a huge jump from a tease rate to a 197 00:09:43,360 --> 00:09:46,600 Speaker 1: full full rate. Uh. And so eight percent is high 198 00:09:46,640 --> 00:09:49,599 Speaker 1: that one of the investment bankers that backs one of 199 00:09:49,600 --> 00:09:52,559 Speaker 1: the venture capital companies that backs UM the Athletics, said 200 00:09:52,600 --> 00:09:56,199 Speaker 1: they've never seen retention at this height, at this scale. 201 00:09:56,600 --> 00:09:58,600 Speaker 1: The Athletics said that, you know, this is their largest 202 00:09:58,640 --> 00:10:01,320 Speaker 1: cohorts are coming through now and they're re upping. And 203 00:10:01,360 --> 00:10:04,040 Speaker 1: I think one of the things that the Athletic has 204 00:10:04,080 --> 00:10:06,200 Speaker 1: going for it is that the two guys who started 205 00:10:06,200 --> 00:10:08,720 Speaker 1: at Adam Handsman and Alex now that they came from Strava, 206 00:10:08,760 --> 00:10:11,640 Speaker 1: which is a subscription based company for like elite cyclists 207 00:10:11,640 --> 00:10:13,800 Speaker 1: and runners to track their runs. It's sort of a 208 00:10:13,800 --> 00:10:17,320 Speaker 1: social network and tracking thing. But they understand that model 209 00:10:17,520 --> 00:10:19,760 Speaker 1: and they understand. So one of the things they do 210 00:10:19,800 --> 00:10:21,560 Speaker 1: is they do have these teaser rates where you where 211 00:10:21,559 --> 00:10:23,160 Speaker 1: you can pay less for a while, but they also 212 00:10:23,200 --> 00:10:25,640 Speaker 1: have free trials that expire and then you come in 213 00:10:25,679 --> 00:10:28,600 Speaker 1: on the full rate. And so basically they've been able 214 00:10:28,640 --> 00:10:32,439 Speaker 1: to get sixty four dollars on average from a year 215 00:10:32,800 --> 00:10:36,600 Speaker 1: from from their subscribers, even though the cheapest way with 216 00:10:36,640 --> 00:10:39,880 Speaker 1: a full boat subscription is sixty dollars annual rate. But 217 00:10:39,920 --> 00:10:41,560 Speaker 1: they get more than that because there are enough people 218 00:10:41,559 --> 00:10:44,320 Speaker 1: who are doing monthly subscriptions which are ten dollars a month, 219 00:10:44,800 --> 00:10:47,760 Speaker 1: that they're actually coming in above sixty from every customer. 220 00:10:47,840 --> 00:10:50,120 Speaker 1: So I think they really know their business on that 221 00:10:50,160 --> 00:10:53,280 Speaker 1: side in terms of customer finding customers, retaining customers, and 222 00:10:53,400 --> 00:10:57,360 Speaker 1: handling churn. Speaking of retaining customers, let's get to our third. 223 00:10:57,840 --> 00:11:00,719 Speaker 1: Our third topic an interesting one actually at what it 224 00:11:00,800 --> 00:11:03,960 Speaker 1: has a kind of a sinister subplot. But a couple 225 00:11:03,960 --> 00:11:06,800 Speaker 1: of weeks ago, at an English soccer match in in Hull, 226 00:11:07,679 --> 00:11:12,040 Speaker 1: a soccer fan was essentially asked to leave a game 227 00:11:12,120 --> 00:11:14,760 Speaker 1: at halftime because he had been texting on his phone 228 00:11:14,800 --> 00:11:17,440 Speaker 1: and they were they were curious about what he had 229 00:11:17,480 --> 00:11:19,920 Speaker 1: been texting. He didn't actually end up leaving, but it 230 00:11:19,960 --> 00:11:22,960 Speaker 1: caused kind of a stir in England. Um. And the 231 00:11:23,000 --> 00:11:25,719 Speaker 1: reason he was you know, the reason why security was 232 00:11:25,760 --> 00:11:28,880 Speaker 1: suspicious of him is that there is and I know 233 00:11:28,920 --> 00:11:32,000 Speaker 1: you've written about this before, Um, there is a tendency 234 00:11:32,080 --> 00:11:35,320 Speaker 1: for you know, sports betting syndicates or sports bettors to 235 00:11:35,400 --> 00:11:38,280 Speaker 1: have people in the stands at sporting events who are 236 00:11:38,600 --> 00:11:41,120 Speaker 1: sending information as soon as it happens so that it 237 00:11:41,160 --> 00:11:44,560 Speaker 1: can maybe beat the betting feed wherever their friend, their 238 00:11:44,600 --> 00:11:46,560 Speaker 1: colleague is sitting, so that they can maybe make a 239 00:11:46,559 --> 00:11:50,400 Speaker 1: little money real quick before the markets adjust. Um, what 240 00:11:50,400 --> 00:11:52,839 Speaker 1: did you think when you read this story? I mean, 241 00:11:52,840 --> 00:11:55,120 Speaker 1: I think this is a whole fascinating world and maybe 242 00:11:55,120 --> 00:11:57,720 Speaker 1: not even just trying to front run the markets, right, 243 00:11:57,760 --> 00:11:59,680 Speaker 1: but it also just might be somebody trying to get 244 00:11:59,679 --> 00:12:01,840 Speaker 1: it a data feat for settling up bets and they 245 00:12:01,840 --> 00:12:04,080 Speaker 1: don't want to pay for the official data. You've reported 246 00:12:04,080 --> 00:12:07,280 Speaker 1: a lot on uh, you know these companies like sport Radar, 247 00:12:07,320 --> 00:12:10,160 Speaker 1: who just did their deal with the NFL for official data. 248 00:12:10,400 --> 00:12:12,360 Speaker 1: Sort of what all the what are the alternatives if 249 00:12:12,360 --> 00:12:14,120 Speaker 1: you don't want to use official data if you're a 250 00:12:14,120 --> 00:12:16,760 Speaker 1: betting operator, right, and one of them is spotters. But 251 00:12:16,840 --> 00:12:19,719 Speaker 1: I assume when they make these deals, when you know 252 00:12:19,760 --> 00:12:21,880 Speaker 1: sport Radar makes a deal with the NFL, that part 253 00:12:21,880 --> 00:12:23,600 Speaker 1: of that is that the league is going to take 254 00:12:23,600 --> 00:12:26,440 Speaker 1: it upon itself to try to police that spotting. I 255 00:12:26,480 --> 00:12:28,240 Speaker 1: don't know if that's written into these, but I would 256 00:12:28,240 --> 00:12:31,160 Speaker 1: guess it is. Uh. And then if you're a better, 257 00:12:31,920 --> 00:12:34,040 Speaker 1: you know, how is your bet being settled? Is it 258 00:12:34,080 --> 00:12:35,640 Speaker 1: by a spotter? Because I think we've all been in 259 00:12:35,840 --> 00:12:37,839 Speaker 1: at a game and suddenly realized we're not sure, we 260 00:12:37,880 --> 00:12:41,000 Speaker 1: couldn't see what was going on, or we got distracted, 261 00:12:41,280 --> 00:12:43,640 Speaker 1: or it's unclear what's happened. Like, are your bets being 262 00:12:43,640 --> 00:12:45,720 Speaker 1: settled up by somebody who's sitting there trying to punch 263 00:12:45,760 --> 00:12:47,480 Speaker 1: in the info? I think this is the world that 264 00:12:47,559 --> 00:12:51,400 Speaker 1: most people never even consider existing, even if you are 265 00:12:51,440 --> 00:12:55,480 Speaker 1: a regular better. It's it's it's a wild situation. And 266 00:12:55,520 --> 00:12:58,040 Speaker 1: that argument right there, I think is exactly the argument 267 00:12:58,120 --> 00:13:00,520 Speaker 1: that Major League Baseball and the NBA are aching here 268 00:13:00,520 --> 00:13:02,720 Speaker 1: in the US right now as they try to compel 269 00:13:03,520 --> 00:13:06,360 Speaker 1: sports book operators, whether it's Fan Duel or DraftKings or 270 00:13:06,400 --> 00:13:09,240 Speaker 1: William Hill, to pay for their official data. You know, 271 00:13:09,360 --> 00:13:11,920 Speaker 1: this is the alternative that they are kind of pitching right, 272 00:13:12,280 --> 00:13:14,880 Speaker 1: those those the people that aren't using official data are 273 00:13:14,920 --> 00:13:18,280 Speaker 1: either getting it off of the fastest TV stream or 274 00:13:18,400 --> 00:13:21,760 Speaker 1: radio stream they can find. Or there's Ira sitting in 275 00:13:21,800 --> 00:13:24,200 Speaker 1: the stands with his phone out, uh saying you know, 276 00:13:24,280 --> 00:13:26,480 Speaker 1: corner kicked for Manchester United, or you know, you know, 277 00:13:26,559 --> 00:13:30,920 Speaker 1: penalty on on Rashford, you know, etcetera. UM, I do wonder, 278 00:13:30,960 --> 00:13:34,120 Speaker 1: I mean, there is I've seen estimates that there are 279 00:13:34,160 --> 00:13:37,600 Speaker 1: at least twenty maybe twenty or more people at every 280 00:13:37,600 --> 00:13:39,920 Speaker 1: soccer game fans in the stands, are are people who 281 00:13:39,920 --> 00:13:42,640 Speaker 1: are relaying called court sighting, as you said, are spotters 282 00:13:43,200 --> 00:13:46,040 Speaker 1: who are doing this at every at every English football game. 283 00:13:46,200 --> 00:13:48,000 Speaker 1: That's a that's a crazy number to me. I think 284 00:13:48,000 --> 00:13:50,720 Speaker 1: that's that's that's really interesting. And I also wonder how 285 00:13:51,280 --> 00:13:53,400 Speaker 1: I mean, clearly Hull messed this one up right. It 286 00:13:53,440 --> 00:13:56,320 Speaker 1: seems like this fan was just texting his girlfriend or 287 00:13:56,320 --> 00:13:58,040 Speaker 1: maybe telling you know, a buddy what was going on 288 00:13:58,040 --> 00:14:01,160 Speaker 1: at the game. Um, but you can't you can't go 289 00:14:01,240 --> 00:14:03,760 Speaker 1: up to every fan who has who's got their nose 290 00:14:03,760 --> 00:14:05,840 Speaker 1: in their phone for the first half and asked them 291 00:14:05,880 --> 00:14:08,080 Speaker 1: what they're texting, and asked them to leave. How do 292 00:14:08,120 --> 00:14:11,400 Speaker 1: you even go about policing this? Right? And I mean 293 00:14:11,440 --> 00:14:13,600 Speaker 1: I was surprised that it would even be possible to 294 00:14:13,720 --> 00:14:16,440 Speaker 1: just be a text over some cell network, you know, 295 00:14:16,480 --> 00:14:20,960 Speaker 1: which are notoriously bad at in crowded stadiums, that you 296 00:14:20,960 --> 00:14:23,040 Speaker 1: could even try to do this, right? Would that actually 297 00:14:23,080 --> 00:14:26,440 Speaker 1: be faster than than TV or whatever the official data 298 00:14:26,480 --> 00:14:29,600 Speaker 1: feed is, you know, trying to type uh and send 299 00:14:29,600 --> 00:14:31,760 Speaker 1: it out. Like, I assumed that that spotters had to 300 00:14:31,800 --> 00:14:34,960 Speaker 1: have sort of specialized software where they're just tapping a 301 00:14:35,000 --> 00:14:37,320 Speaker 1: button for each thing, and that they've trained how to 302 00:14:37,400 --> 00:14:39,360 Speaker 1: do that. This was not a thing that you could 303 00:14:39,400 --> 00:14:43,000 Speaker 1: just do by texting quickly. Um. But I guess there, 304 00:14:43,000 --> 00:14:45,080 Speaker 1: you know, if the security guards thought so, then they 305 00:14:45,160 --> 00:14:48,680 Speaker 1: must have in the past run into this people trying 306 00:14:48,720 --> 00:14:52,200 Speaker 1: to do this kind of court sighting at their matches. Yeah, 307 00:14:52,240 --> 00:14:54,440 Speaker 1: I mean, I can't I can't wait until this is 308 00:14:54,520 --> 00:14:57,280 Speaker 1: something that happens at an NFL game or an NBA game. 309 00:14:57,320 --> 00:14:59,360 Speaker 1: And this, I think is I think there's a good 310 00:14:59,400 --> 00:15:01,680 Speaker 1: chance that it will where someone might get kicked out 311 00:15:01,720 --> 00:15:03,640 Speaker 1: if they are in fact doing this, and and and 312 00:15:03,720 --> 00:15:06,360 Speaker 1: to you know, this is to me a good example 313 00:15:06,400 --> 00:15:09,640 Speaker 1: of kind of the other ancillary side effects that you 314 00:15:09,680 --> 00:15:12,680 Speaker 1: may see as sports betting becomes a bigger deal in 315 00:15:12,680 --> 00:15:15,560 Speaker 1: the US and becomes kind of more normalized that you know, 316 00:15:15,960 --> 00:15:18,680 Speaker 1: these are now things that the people and venues need 317 00:15:18,720 --> 00:15:21,680 Speaker 1: to worry about, whereas you know last year at NBA 318 00:15:21,760 --> 00:15:23,840 Speaker 1: games is probably not something that was really top of 319 00:15:23,880 --> 00:15:27,280 Speaker 1: mind for for NBA teams, right, And I think it's driven, 320 00:15:27,320 --> 00:15:29,640 Speaker 1: like you said, by the by the companies, the data companies. 321 00:15:29,640 --> 00:15:32,360 Speaker 1: This is what differentiates them. They have this live speed 322 00:15:32,440 --> 00:15:36,040 Speaker 1: and accuracy and potentially also you know, more data they 323 00:15:36,040 --> 00:15:38,240 Speaker 1: can get that no spotter could you know. I think 324 00:15:38,280 --> 00:15:41,080 Speaker 1: if the players are wearing tracking equipment, then that allows 325 00:15:41,120 --> 00:15:44,080 Speaker 1: them to potentially move that data into betting markets. And 326 00:15:44,080 --> 00:15:46,440 Speaker 1: this is what they need to to be able to 327 00:15:46,520 --> 00:15:49,240 Speaker 1: charge a lot of money for this information. That's our 328 00:15:49,320 --> 00:15:52,080 Speaker 1: Bootway Business Week reporter Already. You gotta be careful. If 329 00:15:52,080 --> 00:15:53,640 Speaker 1: you do too well on this thing, we're gonna have 330 00:15:53,640 --> 00:15:57,080 Speaker 1: you back more. All right, I'll think that's the heart. Thanks, 331 00:15:57,080 --> 00:15:59,160 Speaker 1: thanks for joining us. This is the Bloomberg Business of 332 00:15:59,200 --> 00:16:02,240 Speaker 1: Sports podcast. I'm Evan Novie Williams along with Ira Boudway, 333 00:16:02,280 --> 00:16:05,000 Speaker 1: filling in for Michael Barr and Scott Sashnik. We're here 334 00:16:05,040 --> 00:16:08,440 Speaker 1: each and every Monday, Wednesday, and Thursday exploring the world 335 00:16:08,440 --> 00:16:10,360 Speaker 1: of money in sports. Join us again at the end 336 00:16:10,360 --> 00:16:12,600 Speaker 1: of the week when we speak with Michael Swimmer, former 337 00:16:12,760 --> 00:16:15,960 Speaker 1: MLB pitcher who's got a business investing in baseball players 338 00:16:16,200 --> 00:16:19,080 Speaker 1: and a business selling sports betting pigs. And download the 339 00:16:19,080 --> 00:16:20,520 Speaker 1: show wherever you get your podcast.