1 00:00:00,200 --> 00:00:02,480 Speaker 1: This is the Bloomberg Business of Sports show, but we 2 00:00:02,520 --> 00:00:04,680 Speaker 1: explore the big money issues in the world of sports. 3 00:00:04,720 --> 00:00:07,960 Speaker 1: I'm Michael Barn, I'm Scarlett Foul, and I'm Mike Lynch. 4 00:00:08,240 --> 00:00:11,920 Speaker 1: Coming up today, we are speaking with Carston Curl, founder 5 00:00:12,160 --> 00:00:16,759 Speaker 1: and CEO of sports Radar, the world's largest sports tech 6 00:00:16,840 --> 00:00:22,000 Speaker 1: company that provides real time sports data across several leagues, broadcasters, 7 00:00:22,079 --> 00:00:26,560 Speaker 1: and sports books. Carston joins us from Switzerland. I think 8 00:00:26,600 --> 00:00:28,880 Speaker 1: this is the farthest we've ever had anybody come on 9 00:00:28,960 --> 00:00:35,640 Speaker 1: from the show. Carston. Welcome to the show, Sir Wowa introduction. 10 00:00:36,159 --> 00:00:39,680 Speaker 1: Nice to media guys, and happy to be on the show. 11 00:00:39,880 --> 00:00:43,120 Speaker 1: Thank you. So I gotta how this whole thing started. 12 00:00:43,560 --> 00:00:46,040 Speaker 1: It was founded by you more than twenty years ago. 13 00:00:46,200 --> 00:00:49,519 Speaker 1: You had one fifty thousand dollars in you and and 14 00:00:49,720 --> 00:00:54,760 Speaker 1: a dream, and I guess it turned out okay, it 15 00:00:55,000 --> 00:00:59,800 Speaker 1: was one hundred fifty thouros to be precise. You're right. 16 00:01:00,040 --> 00:01:03,760 Speaker 1: The dream was there two partners in Norway. We started 17 00:01:03,880 --> 00:01:07,280 Speaker 1: with a company with three prison and as a very 18 00:01:07,319 --> 00:01:12,240 Speaker 1: small text startup there in Trondhan and I traveled to Norway, 19 00:01:12,720 --> 00:01:16,920 Speaker 1: and at this time I had a pretty successful business 20 00:01:16,920 --> 00:01:22,759 Speaker 1: before and I decided after exiting this that I should 21 00:01:22,760 --> 00:01:25,760 Speaker 1: take a couple of months off. So on the very 22 00:01:25,760 --> 00:01:28,640 Speaker 1: beginning when we started that I was even not present. 23 00:01:28,720 --> 00:01:31,440 Speaker 1: I was traveling in Australia with a bush campers through 24 00:01:31,680 --> 00:01:35,000 Speaker 1: the complete continent, and you can envision it in this way. 25 00:01:35,120 --> 00:01:37,920 Speaker 1: I was standing on iris Rock, not precisely in Iris Rock, 26 00:01:38,040 --> 00:01:40,640 Speaker 1: because you should not stand as a tourist on this 27 00:01:40,760 --> 00:01:43,440 Speaker 1: holy mountains, but I was standing in a good spot 28 00:01:43,520 --> 00:01:46,479 Speaker 1: where I had a nice signal. And then I gave 29 00:01:46,520 --> 00:01:50,480 Speaker 1: them some squibbles and drafts how I think a customized 30 00:01:50,560 --> 00:01:53,160 Speaker 1: user interfac should look like. And that was our starting 31 00:01:53,200 --> 00:01:56,320 Speaker 1: point of the business and it's been an incredible run 32 00:01:56,400 --> 00:01:59,440 Speaker 1: since then. Right now you generate one point two billion 33 00:01:59,520 --> 00:02:03,240 Speaker 1: live d points per year, which I'm sure was unthinkable 34 00:02:03,360 --> 00:02:06,680 Speaker 1: when you were standing there with a hundred fifty euros 35 00:02:06,680 --> 00:02:10,280 Speaker 1: and your other partners. What if you could summarize for us, 36 00:02:10,320 --> 00:02:13,120 Speaker 1: what specific problem were you looking to solve when you 37 00:02:13,200 --> 00:02:16,400 Speaker 1: came up with this idea. And at this time Scarlett 38 00:02:16,560 --> 00:02:20,720 Speaker 1: I exited the business which did sports batting over the 39 00:02:20,720 --> 00:02:24,200 Speaker 1: internet fully digital. The vision there was to be the 40 00:02:24,240 --> 00:02:29,880 Speaker 1: biggest sports batting company digital, and we reached this long 41 00:02:30,040 --> 00:02:32,440 Speaker 1: after I left the business. It was called be Win. 42 00:02:33,040 --> 00:02:36,440 Speaker 1: So I had a good understanding how sports batting digital 43 00:02:36,520 --> 00:02:39,400 Speaker 1: should work, how much information and data you need for this, 44 00:02:40,080 --> 00:02:42,440 Speaker 1: and how do you match the two things together that 45 00:02:42,520 --> 00:02:46,960 Speaker 1: you can get properly abilities in pricing and understand the sport. 46 00:02:47,120 --> 00:02:50,280 Speaker 1: So that was the starting point, and from there many 47 00:02:50,360 --> 00:02:53,440 Speaker 1: things have developed. Of course, be so very quickly that 48 00:02:53,520 --> 00:02:56,000 Speaker 1: there is a need to validate results. It's not so 49 00:02:56,160 --> 00:03:00,320 Speaker 1: easy to have results for sixty different sports. Not thinking 50 00:03:00,320 --> 00:03:02,880 Speaker 1: about life at this time. It was all pre match 51 00:03:03,200 --> 00:03:07,640 Speaker 1: and and so's It went then step by step going 52 00:03:07,720 --> 00:03:13,040 Speaker 1: deeper into this, understanding that player performance is important, understanding 53 00:03:13,040 --> 00:03:16,600 Speaker 1: that life data is important. On the very beginning, we 54 00:03:17,240 --> 00:03:20,840 Speaker 1: had no idea how detailed that gets. Hey, Carson, it's 55 00:03:21,000 --> 00:03:23,919 Speaker 1: Mike Lynch up in Boston. I couldn't help but notice 56 00:03:24,000 --> 00:03:28,240 Speaker 1: your revenue from last year up thirty. But what's staggering 57 00:03:28,320 --> 00:03:31,640 Speaker 1: is that in the United States up over. Did you 58 00:03:31,840 --> 00:03:34,040 Speaker 1: hit the wave of legalized sports betting at the right 59 00:03:34,080 --> 00:03:38,800 Speaker 1: time in the United States. Look, my personal story about 60 00:03:38,880 --> 00:03:42,960 Speaker 1: this is I got interested in the market in the 61 00:03:43,080 --> 00:03:47,680 Speaker 1: US in twenty fourteen, and when I'm interested that, I'm 62 00:03:47,680 --> 00:03:51,200 Speaker 1: always trying to understand it. As deep as possible. So 63 00:03:51,240 --> 00:03:55,800 Speaker 1: I decided to move myself to New York City. I 64 00:03:55,840 --> 00:03:59,560 Speaker 1: rented there with the family and apartment UM down on 65 00:03:59,640 --> 00:04:03,160 Speaker 1: the River of fifty seven West and and I began 66 00:04:03,320 --> 00:04:07,680 Speaker 1: to study the passion from US sports fans on the 67 00:04:07,760 --> 00:04:10,360 Speaker 1: sport and what is different to what I know in 68 00:04:10,400 --> 00:04:13,480 Speaker 1: Europe or in Asia. And it was a pretty amazing time. 69 00:04:13,560 --> 00:04:16,240 Speaker 1: So whenever I went to our office in Fifth Avenue, 70 00:04:16,320 --> 00:04:19,479 Speaker 1: close to Central Station, I passed most of the time 71 00:04:19,560 --> 00:04:22,760 Speaker 1: time square, and I saw all these billboards, which I thought, 72 00:04:22,800 --> 00:04:25,680 Speaker 1: it's only there for the share prices and for the 73 00:04:25,720 --> 00:04:28,520 Speaker 1: stock market, but it's not. If you go there, you 74 00:04:28,560 --> 00:04:31,240 Speaker 1: will see a lot of sport information. And that was 75 00:04:31,640 --> 00:04:34,479 Speaker 1: when the market catched me. Where I'm saying, wow, this 76 00:04:34,640 --> 00:04:37,679 Speaker 1: is for somebody like me who loves sport, who loves data, 77 00:04:37,839 --> 00:04:41,240 Speaker 1: who loves that expression, who loves to be deeply engaged 78 00:04:41,279 --> 00:04:44,280 Speaker 1: into it. Um, that is the market where I want 79 00:04:44,320 --> 00:04:46,520 Speaker 1: to grow and where I want to get bigger. And 80 00:04:46,560 --> 00:04:50,360 Speaker 1: of course in twenty fourteen, nobody has talked about sports setting, 81 00:04:50,560 --> 00:04:53,400 Speaker 1: but we had a knowledge that a lot of wages 82 00:04:53,440 --> 00:04:57,120 Speaker 1: are done abroad, more than twenty million American citizens at 83 00:04:57,160 --> 00:05:01,080 Speaker 1: this time, I recall, and it was very clear it's 84 00:05:01,120 --> 00:05:05,359 Speaker 1: a question of time, Um that this market needs to 85 00:05:05,480 --> 00:05:08,800 Speaker 1: be regulated, because that's the only way how you can 86 00:05:08,880 --> 00:05:12,640 Speaker 1: protect players, how you can protect underage gaming, how you 87 00:05:12,720 --> 00:05:17,760 Speaker 1: can play responsible. So we lay the foundation and my 88 00:05:17,839 --> 00:05:21,760 Speaker 1: passion for the US Sports and UH and then form 89 00:05:21,839 --> 00:05:24,880 Speaker 1: for that function, and we we have. We had the 90 00:05:24,960 --> 00:05:29,880 Speaker 1: regulation kicking off much quicker than many people has had anticipated. 91 00:05:29,640 --> 00:05:34,000 Speaker 1: But for us, the journey started in you had a 92 00:05:34,080 --> 00:05:36,800 Speaker 1: chance to ring the opening bell when you were here 93 00:05:37,120 --> 00:05:41,000 Speaker 1: in the New York area alongside investors Todd Bowley and 94 00:05:41,120 --> 00:05:43,960 Speaker 1: Michael Jordan. We're not talking about the Michael Jordan that 95 00:05:44,040 --> 00:05:46,880 Speaker 1: I knew back in my childhood playing dodgeball. This is 96 00:05:46,960 --> 00:05:50,520 Speaker 1: the Michael Jordan's. You got a big name here. What 97 00:05:50,680 --> 00:05:53,760 Speaker 1: does that mean when you have an investor like that 98 00:05:54,600 --> 00:05:58,960 Speaker 1: for your business? I have you a small story about 99 00:05:59,080 --> 00:06:02,160 Speaker 1: is ringing the bell Michael Um. When I was standing 100 00:06:02,200 --> 00:06:06,279 Speaker 1: there was well, Michael John is the legend the best 101 00:06:06,480 --> 00:06:11,159 Speaker 1: ever basketball player on this planet. And of course all 102 00:06:11,279 --> 00:06:15,000 Speaker 1: eyes are our Michael when he's entering a room, and 103 00:06:15,080 --> 00:06:17,120 Speaker 1: we have been standing there on the bell and they 104 00:06:17,200 --> 00:06:21,359 Speaker 1: counted down, and I know Michael as the most competitive 105 00:06:21,520 --> 00:06:25,320 Speaker 1: person on first I ever met. He wants to win everything. 106 00:06:25,400 --> 00:06:28,000 Speaker 1: And I looked at him and said, Michael, I'm quicker 107 00:06:28,040 --> 00:06:30,280 Speaker 1: on the bell than you, at the spite of me, 108 00:06:30,360 --> 00:06:33,640 Speaker 1: because don't you think so well? I was quicker if 109 00:06:33,640 --> 00:06:36,760 Speaker 1: you look on the feature I I smashed it. So 110 00:06:37,480 --> 00:06:41,200 Speaker 1: that was that was a very funny moment. But of 111 00:06:41,240 --> 00:06:44,719 Speaker 1: course it's um. It's the dream of attack entrepreneur that 112 00:06:45,440 --> 00:06:48,240 Speaker 1: you have such an opportunity. I had a lot of 113 00:06:48,320 --> 00:06:52,119 Speaker 1: luck to make this happen, and I had sensational team 114 00:06:52,160 --> 00:06:55,680 Speaker 1: and partners around this, and Marthel is one of them, UM, 115 00:06:55,760 --> 00:06:58,440 Speaker 1: And I can't think think too much that that they 116 00:06:58,440 --> 00:07:01,640 Speaker 1: helped me, that we could do this unique moment in 117 00:07:01,720 --> 00:07:04,520 Speaker 1: time for a company. If you said double or nothing, 118 00:07:04,600 --> 00:07:08,400 Speaker 1: don't take the bet, just take what you get. You 119 00:07:08,440 --> 00:07:15,600 Speaker 1: know that in my working contract. That's not Also, I 120 00:07:15,640 --> 00:07:18,800 Speaker 1: wonder if someone like Michael Jordan was able to share 121 00:07:19,360 --> 00:07:23,720 Speaker 1: what kind of insight he UM derives from the data 122 00:07:23,760 --> 00:07:28,120 Speaker 1: points as radar generates. How does he use that data 123 00:07:28,440 --> 00:07:31,120 Speaker 1: and how does he incorporate it into his thinking and 124 00:07:31,280 --> 00:07:35,480 Speaker 1: into his analysis of the situation, And how my users 125 00:07:35,760 --> 00:07:37,280 Speaker 1: at the end of the day be able to take 126 00:07:37,320 --> 00:07:41,640 Speaker 1: that data and turn it into something actionable. M hmm. Look, 127 00:07:41,720 --> 00:07:45,040 Speaker 1: Michael is is pretty unique. He played that match on 128 00:07:45,400 --> 00:07:49,240 Speaker 1: in a different dimension, and I think that's one of 129 00:07:49,280 --> 00:07:52,480 Speaker 1: the problems. If you are if you, if you are 130 00:07:52,600 --> 00:07:57,000 Speaker 1: so outstanding, you're seeing many things with your own eyes 131 00:07:57,440 --> 00:08:01,400 Speaker 1: and that's not based on data points. That's based on 132 00:08:01,400 --> 00:08:04,400 Speaker 1: a knowledge which you have because you played that sport 133 00:08:05,040 --> 00:08:08,680 Speaker 1: in a separate league. So that's how I would classify Michael. 134 00:08:08,920 --> 00:08:11,840 Speaker 1: And it's not so much about the data. He's seeing 135 00:08:11,920 --> 00:08:15,720 Speaker 1: things in the match which even data can't put out 136 00:08:15,760 --> 00:08:18,200 Speaker 1: at the moment. Maybe in the future we can do this, 137 00:08:18,720 --> 00:08:20,520 Speaker 1: but there are a lot of things to be learned 138 00:08:20,560 --> 00:08:24,080 Speaker 1: from him how he's viewing a match, and that's something 139 00:08:24,280 --> 00:08:26,600 Speaker 1: that's one of the reasons why we said we want 140 00:08:26,600 --> 00:08:29,560 Speaker 1: to have Michael Closer collected to a company, and as 141 00:08:29,600 --> 00:08:32,640 Speaker 1: you know, we want to create an advisory board around this. 142 00:08:33,000 --> 00:08:35,280 Speaker 1: We also want to look to other legends in sport 143 00:08:35,400 --> 00:08:39,200 Speaker 1: which have this outstanding profile and knowledge about their sport. 144 00:08:39,240 --> 00:08:41,880 Speaker 1: I think it's very beneficial for a company like sport Radar. 145 00:08:42,400 --> 00:08:47,320 Speaker 1: Beside of this um competitiveness is something which which I love. 146 00:08:47,440 --> 00:08:50,840 Speaker 1: I love sport and I love technology, and I'm promoting 147 00:08:50,880 --> 00:08:54,199 Speaker 1: this in the company and saying we want to be outstanding, 148 00:08:54,280 --> 00:08:56,960 Speaker 1: we want to be super competitive, but we want to 149 00:08:57,000 --> 00:08:59,760 Speaker 1: look to sport on this and saying it also be 150 00:09:00,200 --> 00:09:02,839 Speaker 1: the stair rules. Um, it should be a level of 151 00:09:02,880 --> 00:09:05,520 Speaker 1: playing field. But of course we want to win that game. 152 00:09:05,720 --> 00:09:08,960 Speaker 1: That's the second thing which I think you can learn 153 00:09:09,040 --> 00:09:12,240 Speaker 1: from such heroes. If I'm looking now to the data points, 154 00:09:12,400 --> 00:09:15,480 Speaker 1: there is an observation if I'm looking specifically to the 155 00:09:15,559 --> 00:09:19,439 Speaker 1: US UM, it's getting more and more player related data 156 00:09:19,640 --> 00:09:23,440 Speaker 1: and the volume is getting every year exponential higher. We 157 00:09:23,559 --> 00:09:26,960 Speaker 1: think that computer vision is the key technology for this. 158 00:09:27,520 --> 00:09:30,199 Speaker 1: So we need fast video signals, we need it from 159 00:09:30,240 --> 00:09:33,760 Speaker 1: different camera angles, and then you can extract a lot 160 00:09:33,800 --> 00:09:37,000 Speaker 1: of additional data points UM, and then you need to 161 00:09:37,000 --> 00:09:39,040 Speaker 1: decide what do you want to do out of these 162 00:09:39,080 --> 00:09:42,120 Speaker 1: data points. We want to use them for predictive models. 163 00:09:42,160 --> 00:09:45,480 Speaker 1: In sports batting, we understand we want to understand how 164 00:09:45,520 --> 00:09:49,640 Speaker 1: a single player can with his performance, influenced the outcome 165 00:09:49,679 --> 00:09:52,640 Speaker 1: of a match. We want to simulate how does that 166 00:09:52,760 --> 00:09:55,600 Speaker 1: happen when there is a different lineup, when you're playing 167 00:09:55,640 --> 00:09:59,320 Speaker 1: against different teams, and what is what is his strength? 168 00:09:59,400 --> 00:10:01,720 Speaker 1: What is a way no for this? So because far 169 00:10:01,800 --> 00:10:04,520 Speaker 1: beyond the cover of which coverage which is now there, 170 00:10:05,000 --> 00:10:08,560 Speaker 1: and then it gets into the entertainment space for getting 171 00:10:08,600 --> 00:10:12,160 Speaker 1: that fan engagement and somehow showing with the data points 172 00:10:12,200 --> 00:10:15,600 Speaker 1: and the visualization why the match is now very special 173 00:10:15,640 --> 00:10:18,120 Speaker 1: and why this player is very special, why this player 174 00:10:18,200 --> 00:10:21,240 Speaker 1: for example, is now the superhero of this match, and 175 00:10:21,400 --> 00:10:25,360 Speaker 1: underline it with data and then giving the batting opportunity. 176 00:10:25,440 --> 00:10:27,840 Speaker 1: This is where this all is heading to. And of 177 00:10:27,880 --> 00:10:31,520 Speaker 1: course we're speaking a lot about visualizations matter worse, how 178 00:10:31,559 --> 00:10:34,360 Speaker 1: do you map this in? But you will feel in 179 00:10:34,400 --> 00:10:37,440 Speaker 1: the future sports betting is a part of the game 180 00:10:37,559 --> 00:10:41,360 Speaker 1: because of these things. You marry this kind of fan engagement, 181 00:10:41,679 --> 00:10:44,800 Speaker 1: the deep passion for data with batting opportunity, and it 182 00:10:44,880 --> 00:10:48,560 Speaker 1: will feel natural with the visualization. That's the vision which 183 00:10:48,600 --> 00:10:52,560 Speaker 1: we see Carson. What gets more activity pregame betting or 184 00:10:52,640 --> 00:10:57,800 Speaker 1: in game betting. That's cltal clearly in game betting. So 185 00:10:58,480 --> 00:11:02,200 Speaker 1: the world wide market is that it depended on the 186 00:11:02,240 --> 00:11:05,680 Speaker 1: country in the region. But let's say is a fair 187 00:11:05,720 --> 00:11:09,800 Speaker 1: assumption is the volume from handling it in play batting, 188 00:11:10,080 --> 00:11:15,160 Speaker 1: so worldwide of old wages are done in game and 189 00:11:15,360 --> 00:11:17,920 Speaker 1: in the US at the moment a bit depending on 190 00:11:17,960 --> 00:11:21,120 Speaker 1: the sports, for basketball is a better life setting sport 191 00:11:21,400 --> 00:11:25,000 Speaker 1: than for example, American football has to do with the 192 00:11:25,040 --> 00:11:28,400 Speaker 1: speed of the sport. But at the moment we see 193 00:11:28,520 --> 00:11:33,040 Speaker 1: seventy percent in average is pre match patting and only 194 00:11:34,040 --> 00:11:37,240 Speaker 1: is life patting. No doubt that this trend will turn 195 00:11:37,360 --> 00:11:41,400 Speaker 1: upside down. It's only the question how quickly does this happen? 196 00:11:41,679 --> 00:11:45,079 Speaker 1: And we think it happens a little bit quicker than 197 00:11:45,160 --> 00:11:48,559 Speaker 1: many analysts are thinking, because it's so much more fun 198 00:11:48,640 --> 00:11:51,760 Speaker 1: and so much more engagement when you watch a match, 199 00:11:51,840 --> 00:11:54,680 Speaker 1: when you have that moment of passion, when you feel 200 00:11:54,800 --> 00:11:58,000 Speaker 1: something can happen here and there, that's life and that 201 00:11:58,200 --> 00:12:01,960 Speaker 1: is the moment when you are impulsive, want to wager 202 00:12:02,400 --> 00:12:06,160 Speaker 1: some money on on your opinion about this. So without 203 00:12:06,200 --> 00:12:10,079 Speaker 1: any doubt, the trends is that pretty much patting goes 204 00:12:10,120 --> 00:12:14,440 Speaker 1: down and be shifting into life betting. So here I 205 00:12:14,480 --> 00:12:18,800 Speaker 1: am Joe Blowbar and I want this information because I 206 00:12:18,960 --> 00:12:22,000 Speaker 1: want to win when I make this bet. And then 207 00:12:22,080 --> 00:12:26,280 Speaker 1: I see the words enterprise level, which means I can't 208 00:12:26,440 --> 00:12:28,640 Speaker 1: really get out and get it yet. But is this 209 00:12:28,880 --> 00:12:32,400 Speaker 1: something down the road that you're looking at? So a 210 00:12:32,520 --> 00:12:35,600 Speaker 1: guy like me, I don't have to go through many 211 00:12:35,600 --> 00:12:40,600 Speaker 1: of the booking anymore. Of course. Look that's UM, that's 212 00:12:40,600 --> 00:12:43,680 Speaker 1: what we that's the mission UM which we want to 213 00:12:43,720 --> 00:12:47,000 Speaker 1: fulfill together with our partners in sport. UM. We have 214 00:12:47,160 --> 00:12:51,599 Speaker 1: two um sensational leagues of s partners, the NBA and 215 00:12:51,800 --> 00:12:55,960 Speaker 1: the NHL both have that deep passion for making the 216 00:12:56,000 --> 00:12:59,719 Speaker 1: sport more digital, transporting this information closer to the fan. 217 00:13:00,000 --> 00:13:03,800 Speaker 1: It's those of them in from the from product teams 218 00:13:03,960 --> 00:13:07,360 Speaker 1: and we are looking on fulfilling that mission. So we 219 00:13:07,559 --> 00:13:09,920 Speaker 1: want to tell this story, we want to enrich it 220 00:13:09,960 --> 00:13:12,240 Speaker 1: with data, and they want to give it to the 221 00:13:12,320 --> 00:13:16,680 Speaker 1: sports fan. And yes, sports has the interest that the 222 00:13:16,760 --> 00:13:20,640 Speaker 1: next generation is getting engaged. That there are fans for 223 00:13:20,720 --> 00:13:23,800 Speaker 1: the NHL and for the NBA. So it's for exactly 224 00:13:23,800 --> 00:13:26,040 Speaker 1: it is so you will get it in the future. 225 00:13:26,080 --> 00:13:30,840 Speaker 1: I can promise it to you. Speaking of the future, Carson, 226 00:13:30,880 --> 00:13:33,280 Speaker 1: which sport do you think has the longest legs in 227 00:13:33,400 --> 00:13:37,560 Speaker 1: terms of really captivating a younger generation. We've been talking 228 00:13:37,600 --> 00:13:40,520 Speaker 1: a lot this week about baseball and how it's struggling 229 00:13:40,559 --> 00:13:43,640 Speaker 1: to capture the younger audience because the games are long, 230 00:13:43,840 --> 00:13:48,920 Speaker 1: and it's expensive, and it's ruled by tradition and rules. 231 00:13:49,320 --> 00:13:51,640 Speaker 1: I wonder if there is any sport that you see 232 00:13:51,679 --> 00:13:54,439 Speaker 1: that's really ready to take off. The NFL obviously is 233 00:13:54,559 --> 00:13:57,480 Speaker 1: king in every way in the US market, But you know, 234 00:13:57,640 --> 00:14:01,800 Speaker 1: is there a smaller, lower profile that that hasn't pretty 235 00:14:01,840 --> 00:14:07,000 Speaker 1: incredible growth prospects? Oh, there are incredible sports, um and 236 00:14:07,000 --> 00:14:10,120 Speaker 1: and and some of them if you speak specifically about 237 00:14:10,240 --> 00:14:14,920 Speaker 1: young players, UM what I see um in Asia is 238 00:14:14,920 --> 00:14:20,280 Speaker 1: definitely cricket. Cricket is huge. It's huge in India, it's 239 00:14:20,400 --> 00:14:24,520 Speaker 1: huge in in many Asian countries, and of course it's 240 00:14:24,520 --> 00:14:26,960 Speaker 1: also big in Australia, but it's a small continent or 241 00:14:27,120 --> 00:14:30,600 Speaker 1: there is not much population there. But for betting as well, Carson, 242 00:14:31,080 --> 00:14:33,720 Speaker 1: is that the case when it comes to betting, it's 243 00:14:33,720 --> 00:14:38,200 Speaker 1: a sensational betting sport. This sport is played for five days. 244 00:14:38,280 --> 00:14:40,960 Speaker 1: One match is played for five days, so we have 245 00:14:41,080 --> 00:14:45,440 Speaker 1: five days in running betting opportunities. And that's the best 246 00:14:45,520 --> 00:14:48,160 Speaker 1: what you can get from this perspective. I think the 247 00:14:48,240 --> 00:14:52,600 Speaker 1: NBA is doing very well with with the passion for 248 00:14:52,680 --> 00:14:55,920 Speaker 1: the sport and addressing it to young sports fans and 249 00:14:55,920 --> 00:14:58,480 Speaker 1: two young players. That's what we see around the globe. 250 00:14:58,800 --> 00:15:02,400 Speaker 1: That's very important for US. Partner with a leak which 251 00:15:02,520 --> 00:15:06,040 Speaker 1: is really global and and we see a lot of 252 00:15:06,120 --> 00:15:09,560 Speaker 1: drive here, but there are sports on the horizon which 253 00:15:09,600 --> 00:15:13,600 Speaker 1: are super super interesting for US. E sport is such 254 00:15:13,640 --> 00:15:16,520 Speaker 1: a sample. Whatever it is getting in the Eastport environment 255 00:15:16,920 --> 00:15:19,760 Speaker 1: closer to the sport is something where you want to 256 00:15:19,760 --> 00:15:24,280 Speaker 1: be in sore. Sport is something super interesting. A twenty 257 00:15:24,320 --> 00:15:28,880 Speaker 1: twenty is super interesting. Also some tennis games. Um. But 258 00:15:28,880 --> 00:15:31,400 Speaker 1: but if you ask me about the young generation, those 259 00:15:31,440 --> 00:15:33,480 Speaker 1: are the things. And if you want to have something 260 00:15:33,560 --> 00:15:36,240 Speaker 1: which is at the moment not on the radar screen 261 00:15:36,280 --> 00:15:39,640 Speaker 1: of many people, I would say, paddle like we call 262 00:15:39,720 --> 00:15:42,040 Speaker 1: it in Europe, or pickleball like you call it in 263 00:15:42,080 --> 00:15:45,600 Speaker 1: the US, is something super interesting for the younger generation. 264 00:15:46,320 --> 00:15:51,600 Speaker 1: Did you say, yes, have you heard of this? Linji? Oh, 265 00:15:51,680 --> 00:15:57,840 Speaker 1: it's it's it's the race, It's it's a fun sport 266 00:15:57,880 --> 00:16:00,960 Speaker 1: that combines many elements of tennis, bad innton and ping pong. 267 00:16:01,120 --> 00:16:04,920 Speaker 1: Thank you to us a pickleball, duck. I'm looking out 268 00:16:04,920 --> 00:16:08,440 Speaker 1: my window right now when people are playing pickleball. You guys, 269 00:16:08,640 --> 00:16:14,120 Speaker 1: you guys, you're getting now some education. It's really picking up. 270 00:16:14,160 --> 00:16:18,520 Speaker 1: And you spoke about young generation, that's exactly what happens here. 271 00:16:18,720 --> 00:16:21,480 Speaker 1: And and I think that's a huge potentially in there. 272 00:16:21,560 --> 00:16:24,120 Speaker 1: We are looking to all those trends, um and of 273 00:16:24,160 --> 00:16:26,760 Speaker 1: course we try to be early adopters, were trying to 274 00:16:26,880 --> 00:16:29,960 Speaker 1: understand it. We try to understand the need of sport 275 00:16:30,040 --> 00:16:33,080 Speaker 1: for data points, and of course also how can we 276 00:16:33,200 --> 00:16:36,160 Speaker 1: promoted in the media space, how can we promoted in 277 00:16:36,200 --> 00:16:39,240 Speaker 1: the patting space. That's our job. And right now snapshot 278 00:16:39,600 --> 00:16:42,440 Speaker 1: that looks good. So there is a lot of interest 279 00:16:42,840 --> 00:16:45,320 Speaker 1: from the young crowd around it. Okay, let me just 280 00:16:45,360 --> 00:16:47,640 Speaker 1: interrupt for a second, because now I'm looking at pickleball 281 00:16:47,680 --> 00:16:50,240 Speaker 1: and apparently Gary Vanna Shuk has bought into an expanding 282 00:16:50,240 --> 00:16:53,600 Speaker 1: professional picketball league. There's an Atlanta Journal Constitution article that 283 00:16:53,640 --> 00:16:57,520 Speaker 1: says pickleball gives Metro Atlanta older adults activity and socialization. 284 00:16:57,720 --> 00:17:00,280 Speaker 1: So there you go. It's it's the new hot growing 285 00:17:00,280 --> 00:17:03,400 Speaker 1: sport scarlet. If next time over in New York we 286 00:17:03,400 --> 00:17:07,560 Speaker 1: should play a round of pick up it, you really 287 00:17:07,560 --> 00:17:14,359 Speaker 1: will like it sport. I'm a dad, what am I? 288 00:17:14,359 --> 00:17:18,440 Speaker 1: Gonn Carson, I have to ask you, this is sport 289 00:17:18,520 --> 00:17:23,120 Speaker 1: Radar still doing business in Russia. We are still doing business. Um. 290 00:17:23,359 --> 00:17:27,560 Speaker 1: We we feel for all the people there. It's it's 291 00:17:27,680 --> 00:17:30,639 Speaker 1: it's heartbreaking, and I'm in Europe and that's very close. 292 00:17:30,800 --> 00:17:33,800 Speaker 1: I was flying to our office in Warsaw to talent. 293 00:17:34,280 --> 00:17:38,160 Speaker 1: The office is bordering this area, trying to understand people, 294 00:17:38,320 --> 00:17:41,680 Speaker 1: learn from it and learning what we can do. And 295 00:17:41,760 --> 00:17:44,399 Speaker 1: what we're doing is first, we donated a million dollars 296 00:17:44,440 --> 00:17:48,000 Speaker 1: half a million from myself, and we created help for 297 00:17:48,080 --> 00:17:50,840 Speaker 1: the Red Cross. We created help which we give to 298 00:17:50,880 --> 00:17:53,879 Speaker 1: the unit stuff and we created an emergency relief fund 299 00:17:54,600 --> 00:17:58,719 Speaker 1: for our employees and their relatives. So we made it 300 00:17:58,760 --> 00:18:03,040 Speaker 1: now very easy. Many of our employees have relatives or 301 00:18:03,080 --> 00:18:06,280 Speaker 1: family in the Ukraine, so this fund is there for 302 00:18:06,760 --> 00:18:09,680 Speaker 1: emergency help on it. We of course, we are ready 303 00:18:09,720 --> 00:18:13,440 Speaker 1: to double down here and provide emergency help. From our 304 00:18:13,480 --> 00:18:17,800 Speaker 1: company perspectives. We stopped to onboard new business in Russia. 305 00:18:17,840 --> 00:18:21,159 Speaker 1: There's a complete stop for any investment related to this, 306 00:18:21,760 --> 00:18:25,400 Speaker 1: and that's something I think we did significant more than 307 00:18:25,400 --> 00:18:29,200 Speaker 1: many other companies in the space. It's a heartbreaking situation, 308 00:18:29,280 --> 00:18:32,119 Speaker 1: like I told you, but that's the measurement of responsible 309 00:18:32,240 --> 00:18:35,400 Speaker 1: company should do. Now, speaking of employees, you have more 310 00:18:35,440 --> 00:18:39,840 Speaker 1: than twenty three hundred full time employees across nineteen countries 311 00:18:39,880 --> 00:18:42,879 Speaker 1: all around the world. When you started out did you 312 00:18:42,920 --> 00:18:46,200 Speaker 1: imagine you're going to have that many employees in the beginning? 313 00:18:46,480 --> 00:18:48,760 Speaker 1: How many did you start out with? And outside of you, 314 00:18:49,000 --> 00:18:52,400 Speaker 1: how many did you have to start out with two partners? 315 00:18:52,560 --> 00:18:56,000 Speaker 1: I started with two partners from Norway. They both left 316 00:18:56,000 --> 00:18:59,280 Speaker 1: the business since a long time. But no, it was 317 00:18:59,280 --> 00:19:01,840 Speaker 1: an ever planned in the way that we are getting 318 00:19:01,840 --> 00:19:05,080 Speaker 1: over three thousands. And that's for me with the I 319 00:19:05,200 --> 00:19:08,679 Speaker 1: p O. It's a new beginning and and it's a 320 00:19:08,680 --> 00:19:11,720 Speaker 1: new chapter which we are opening up. And what I 321 00:19:11,760 --> 00:19:14,639 Speaker 1: see is a lot of passion from my team and 322 00:19:14,840 --> 00:19:18,000 Speaker 1: from everybody who joins new data team. So that's the 323 00:19:18,040 --> 00:19:21,200 Speaker 1: beginning of the journey for me, and it's a milestone 324 00:19:21,200 --> 00:19:23,960 Speaker 1: which we have there. But to answer your question, of course, 325 00:19:24,160 --> 00:19:27,919 Speaker 1: I never thought about getting three thousand employees of four thousand, 326 00:19:27,960 --> 00:19:31,000 Speaker 1: and probably never think we get six or seven thousand. 327 00:19:31,440 --> 00:19:36,159 Speaker 1: But looking to our growth and looking to the opportunities worldwide, um, 328 00:19:36,200 --> 00:19:39,159 Speaker 1: that might be not unrealistic that we reached this very 329 00:19:39,240 --> 00:19:42,800 Speaker 1: quickly and being our home for the ones which love technology, 330 00:19:42,880 --> 00:19:46,199 Speaker 1: spot which share the passion to be competitive. Do you 331 00:19:46,240 --> 00:19:51,440 Speaker 1: bet in your personal life on sports? No? Really educated, 332 00:19:52,119 --> 00:19:57,760 Speaker 1: I'm an educated matter which is you ever been on 333 00:19:57,880 --> 00:20:02,280 Speaker 1: professional sports, of course, as I did, and what what 334 00:20:02,760 --> 00:20:04,720 Speaker 1: was your greatest hit and what was your greatest miss? 335 00:20:05,320 --> 00:20:08,080 Speaker 1: I never bet a lot of money, so it's something 336 00:20:08,160 --> 00:20:11,679 Speaker 1: for me. I sometimes I want to test interfaces. I 337 00:20:11,720 --> 00:20:17,159 Speaker 1: want to understand what works better from usability perspective. Sometimes 338 00:20:17,200 --> 00:20:20,359 Speaker 1: I simply want to see how quickly clearings are done 339 00:20:20,560 --> 00:20:23,520 Speaker 1: and what is the service um on the clients that 340 00:20:23,600 --> 00:20:26,359 Speaker 1: I need to understand how our clients are working with 341 00:20:26,440 --> 00:20:29,520 Speaker 1: their clients to help them to optimize these things or 342 00:20:29,560 --> 00:20:33,199 Speaker 1: to optimize our products. Um. For me, that is that 343 00:20:33,359 --> 00:20:37,200 Speaker 1: is the main reason. It's market research in due diligence. Yeah, 344 00:20:38,000 --> 00:20:40,760 Speaker 1: that's look, that's how you can say it. It's also 345 00:20:40,800 --> 00:20:43,640 Speaker 1: a bit of passion um to to wager at ten 346 00:20:43,680 --> 00:20:46,640 Speaker 1: dollars or twenty dollars. You will not hear me to 347 00:20:46,280 --> 00:20:49,280 Speaker 1: to to tell you it was ever five thousand or something. 348 00:20:49,560 --> 00:20:53,280 Speaker 1: I never bet at such amounts. Um, that's simply that's 349 00:20:53,320 --> 00:20:55,720 Speaker 1: simply not in my nature. All right, bar same question 350 00:20:55,760 --> 00:21:00,000 Speaker 1: to you greatest hit? No, I Carston said the exact same, 351 00:21:00,000 --> 00:21:03,960 Speaker 1: And think that's my speed. I might go fifty in 352 00:21:04,040 --> 00:21:05,920 Speaker 1: a NASCAR race. It's like, you know, I'm going to 353 00:21:06,040 --> 00:21:08,560 Speaker 1: take those odds. I hit it one time when I 354 00:21:08,640 --> 00:21:11,159 Speaker 1: hit a long shot in the dayton of five hundred, 355 00:21:11,200 --> 00:21:13,120 Speaker 1: trying to think it was I think it was Austin 356 00:21:13,160 --> 00:21:16,680 Speaker 1: Dillon and he won the Daytona five hundred. And then 357 00:21:16,680 --> 00:21:19,120 Speaker 1: that's how I got the hit the worst one. Well, 358 00:21:19,160 --> 00:21:21,520 Speaker 1: we won't talk about it because my wife is listening, 359 00:21:22,760 --> 00:21:25,159 Speaker 1: but let's just say that I was out a hundred 360 00:21:25,160 --> 00:21:28,320 Speaker 1: dollars and that and see that's my speed. I get it, Carson. 361 00:21:28,400 --> 00:21:34,760 Speaker 1: I'm with you, Carson. The sport radar create the odds 362 00:21:34,920 --> 00:21:38,120 Speaker 1: or is that left to the bookmaker. No, we are 363 00:21:38,200 --> 00:21:42,480 Speaker 1: creating this and we call it. We call it life 364 00:21:42,480 --> 00:21:46,560 Speaker 1: adds or of service. Um. This has started as a 365 00:21:46,600 --> 00:21:50,760 Speaker 1: normal quantz exercise with some algorithms, looking to statistics, looking 366 00:21:50,800 --> 00:21:53,600 Speaker 1: to real time data, looking to what is on the 367 00:21:53,640 --> 00:21:56,840 Speaker 1: sustension list, what is new in there, what is the 368 00:21:56,880 --> 00:21:59,560 Speaker 1: tendency of teams? That was the starting point, and then 369 00:21:59,600 --> 00:22:03,800 Speaker 1: you come with some basic agorithms. That was the early days. 370 00:22:04,000 --> 00:22:07,080 Speaker 1: Then we went with the algorithms into the machine machine 371 00:22:07,119 --> 00:22:10,480 Speaker 1: learning procedures on this, and now we are the machine 372 00:22:10,600 --> 00:22:13,880 Speaker 1: learned this and the machine begins to optimize itself. That's 373 00:22:13,920 --> 00:22:17,119 Speaker 1: called artificial intelligence. So we are now on this level 374 00:22:17,600 --> 00:22:20,879 Speaker 1: that we are running all our odds production around this, 375 00:22:21,040 --> 00:22:24,520 Speaker 1: and you, guys, you you can imagine that life is 376 00:22:24,560 --> 00:22:26,840 Speaker 1: so much more data points which you need to process. 377 00:22:27,200 --> 00:22:30,199 Speaker 1: You get the real time data from the arenas. You 378 00:22:30,320 --> 00:22:34,159 Speaker 1: capture this nowadays with computers and computer visions, so we 379 00:22:34,240 --> 00:22:37,320 Speaker 1: get terror byto of data from every match which we 380 00:22:37,359 --> 00:22:38,800 Speaker 1: need to map. We need to make it all to 381 00:22:38,840 --> 00:22:41,400 Speaker 1: a fast and then put it into the models. That's 382 00:22:41,440 --> 00:22:46,920 Speaker 1: one of of the really exciting applications because we predict 383 00:22:47,000 --> 00:22:51,120 Speaker 1: potential match outcomes were predicted every second. We're not predicting 384 00:22:51,160 --> 00:22:53,720 Speaker 1: it pre match, what is the final result? That is 385 00:22:53,840 --> 00:22:58,199 Speaker 1: more easy exercise. Nowadays, the life prediction is getting more 386 00:22:58,240 --> 00:23:01,199 Speaker 1: and more important. And then the next dimension is that 387 00:23:01,280 --> 00:23:05,440 Speaker 1: we do risk management and clearing for bookmakers. So we're 388 00:23:05,480 --> 00:23:09,119 Speaker 1: getting a useriety, we're getting a stake, and we're getting 389 00:23:09,119 --> 00:23:11,280 Speaker 1: match I D S or one match I D depending 390 00:23:11,320 --> 00:23:14,159 Speaker 1: if it's only one match. And then we're beginning to 391 00:23:14,200 --> 00:23:17,760 Speaker 1: aggregate this matter to the real time data and running 392 00:23:17,800 --> 00:23:20,800 Speaker 1: simulations of risk exposure. Um, that's the reason that I 393 00:23:20,840 --> 00:23:24,440 Speaker 1: loved before with the batting and and being a mathematician, 394 00:23:24,520 --> 00:23:27,040 Speaker 1: you you don't go on that too much of course 395 00:23:27,040 --> 00:23:30,119 Speaker 1: there's mathematics behind it. It's not a game of chance. 396 00:23:30,359 --> 00:23:34,640 Speaker 1: It's managing risk, managing exposure. With this, you're changing your 397 00:23:34,680 --> 00:23:37,760 Speaker 1: prices and lines, and that's exactly what we provided. A 398 00:23:37,880 --> 00:23:41,560 Speaker 1: risk management service in the nature it is then Monte 399 00:23:41,560 --> 00:23:45,439 Speaker 1: Carlo simulations massively where you have to risk exposure of 400 00:23:45,480 --> 00:23:48,600 Speaker 1: aggregated liquidity and to give you a bit of flavor 401 00:23:48,640 --> 00:23:51,600 Speaker 1: of the volume here that is at the moment on 402 00:23:51,640 --> 00:23:54,240 Speaker 1: the run rate level, on the yearly level of round 403 00:23:54,240 --> 00:23:59,119 Speaker 1: about twenty billion dollars which we run with this risk management. 404 00:23:59,359 --> 00:24:04,360 Speaker 1: So that gets into softbore business and matching sport with 405 00:24:04,400 --> 00:24:09,360 Speaker 1: the liability. Carston Curl, founder and CEO of sport Radar, 406 00:24:10,200 --> 00:24:12,920 Speaker 1: called all the way from Switzerland. You get a special 407 00:24:12,920 --> 00:24:14,960 Speaker 1: t bone steak from all three of us because that 408 00:24:15,280 --> 00:24:17,960 Speaker 1: thank you that that was really neat. We really do 409 00:24:18,080 --> 00:24:20,479 Speaker 1: appreciate it, and I hope you enjoyed talking with us. 410 00:24:20,720 --> 00:24:23,280 Speaker 1: I enjoyed it a lot. Thanks for having me, guys, 411 00:24:23,320 --> 00:24:25,000 Speaker 1: and I will knock on your door with the t 412 00:24:25,119 --> 00:24:31,800 Speaker 1: bone steak, pickle pickle ball, pickleball. This is the Bloomberg 413 00:24:31,840 --> 00:24:35,000 Speaker 1: Business and Sports Podcast. I'm Michael bar along with Scarlett 414 00:24:35,000 --> 00:24:37,399 Speaker 1: Fu and Mike Lynch catches here each and every Monday, 415 00:24:37,400 --> 00:24:39,960 Speaker 1: Wednesday and Thursday exploring the world of money in sports. 416 00:24:39,960 --> 00:24:42,679 Speaker 1: And catch me on Twitter and Big Bar Sports. And 417 00:24:42,720 --> 00:24:45,480 Speaker 1: I'm on Twitter at Scarlett Fou. I'm Mike Lynch. Follow 418 00:24:45,520 --> 00:24:49,679 Speaker 1: me at Lynch w CBB. You're listening the Bloomberg Business 419 00:24:49,680 --> 00:24:52,040 Speaker 1: of Sports on Bloomberg Radio around the world.