1 00:00:00,080 --> 00:00:02,800 Speaker 1: But welcome into an all new episode of her playbook, 2 00:00:02,840 --> 00:00:05,880 Speaker 1: presented by Kendra Scott. Gained they just got Personal fund 3 00:00:05,880 --> 00:00:08,720 Speaker 1: You're winning a look at kendras Scott where team spirit 4 00:00:08,800 --> 00:00:12,039 Speaker 1: meets personal styles. Shop jewelry, accessories and more at your 5 00:00:12,080 --> 00:00:16,040 Speaker 1: New York area Kendra Scott store or online at Kendrascott 6 00:00:16,079 --> 00:00:18,040 Speaker 1: dot com. My name is Madeline Burke and I am 7 00:00:18,079 --> 00:00:20,759 Speaker 1: thrilled to be joined this week by Julie Susa, the 8 00:00:20,760 --> 00:00:23,639 Speaker 1: Global head of Sports at Amazon Web Services, where she 9 00:00:23,680 --> 00:00:27,480 Speaker 1: leads a company strategy and innovation efforts in the sports industry. Julie, 10 00:00:27,760 --> 00:00:29,680 Speaker 1: thank you so much for joining us today. 11 00:00:30,200 --> 00:00:31,320 Speaker 2: Thanks for having me Madelin. 12 00:00:31,760 --> 00:00:34,600 Speaker 1: It's been Uh. It's cool to read about your career 13 00:00:34,640 --> 00:00:36,960 Speaker 1: and your career path. I mean you've worked across media, 14 00:00:37,200 --> 00:00:42,760 Speaker 1: data and now cloud technology. If you were to look 15 00:00:42,800 --> 00:00:45,720 Speaker 1: back and think about what originally drew you to sports 16 00:00:45,840 --> 00:00:47,800 Speaker 1: and what kept you there, how did you know that 17 00:00:47,840 --> 00:00:50,400 Speaker 1: this was a career path that was enticing to you. 18 00:00:52,680 --> 00:00:53,240 Speaker 2: It wasn't. 19 00:00:53,440 --> 00:00:55,600 Speaker 3: I don't think I started off with, oh, I want 20 00:00:55,640 --> 00:00:58,920 Speaker 3: to work in sports, right. I went to the University 21 00:00:58,960 --> 00:01:02,440 Speaker 3: of Michigan undergrad and I always say, if you're not 22 00:01:02,480 --> 00:01:06,160 Speaker 3: a sports fan coming in, you are coming out, and 23 00:01:06,240 --> 00:01:08,720 Speaker 3: if you aren't coming out, then you probably did it wrong. 24 00:01:08,760 --> 00:01:12,080 Speaker 2: No, I'm just kidding, but it's. 25 00:01:11,760 --> 00:01:14,440 Speaker 3: You know, see, I of course had it infinity for sports, 26 00:01:14,440 --> 00:01:16,760 Speaker 3: and I was a sports fan, and I was thinking like, oh, 27 00:01:16,760 --> 00:01:19,160 Speaker 3: it would be fun to take what I like to 28 00:01:19,240 --> 00:01:23,319 Speaker 3: do functionally, right, just strategy, business development, and where's sort 29 00:01:23,319 --> 00:01:26,319 Speaker 3: of the puck going to mix my sports metaphors here? 30 00:01:28,280 --> 00:01:30,200 Speaker 2: And I loved that work right. 31 00:01:30,280 --> 00:01:32,319 Speaker 3: And then at the time, I was working in financial 32 00:01:32,319 --> 00:01:36,000 Speaker 3: services and I liked it, but I didn't love it, 33 00:01:36,080 --> 00:01:38,000 Speaker 3: you know, And could I do functionally what I love 34 00:01:38,040 --> 00:01:40,639 Speaker 3: in an industry that I'm passionate about? And I started 35 00:01:40,640 --> 00:01:43,040 Speaker 3: doing some informational interviews. I'll never forget. 36 00:01:43,120 --> 00:01:46,199 Speaker 2: It was such a great sort of slap down because 37 00:01:46,200 --> 00:01:49,680 Speaker 2: I was talking to somebody doing an informational interview and 38 00:01:49,240 --> 00:01:51,640 Speaker 2: he said, well, why do you want to work in sports? 39 00:01:51,920 --> 00:01:55,080 Speaker 3: And I started the way everybody starts that answer, which 40 00:01:55,160 --> 00:01:57,600 Speaker 3: is well, I'm a huge sports fan, right, And he 41 00:01:57,640 --> 00:01:59,440 Speaker 3: cut me off right there and he said, yeah, you 42 00:01:59,480 --> 00:02:01,480 Speaker 3: and everybody else who walks in the door. That doesn't 43 00:02:01,480 --> 00:02:02,240 Speaker 3: make you special. 44 00:02:02,600 --> 00:02:06,160 Speaker 2: Why right? It really made me think about why right? 45 00:02:06,240 --> 00:02:08,360 Speaker 3: And then it and for that, I think is a 46 00:02:08,360 --> 00:02:11,600 Speaker 3: big motivator for me is like, what is so compelling 47 00:02:11,639 --> 00:02:14,440 Speaker 3: about this industry? Look the affinity that it drives, right 48 00:02:14,440 --> 00:02:16,880 Speaker 3: and the community it drives, How it brings people together, 49 00:02:17,440 --> 00:02:21,320 Speaker 3: How it could be a vehicle for social change, how 50 00:02:21,360 --> 00:02:24,880 Speaker 3: it could be a vehicle for technological change. That's what's 51 00:02:24,880 --> 00:02:26,840 Speaker 3: really compelling to me about sports and why I wanted 52 00:02:26,880 --> 00:02:28,680 Speaker 3: to work in this industry and have been fortunate to 53 00:02:28,680 --> 00:02:31,280 Speaker 3: be in it for a couple decades now. 54 00:02:31,400 --> 00:02:34,840 Speaker 1: So absolutely absolutely, And what you do right now now 55 00:02:34,880 --> 00:02:36,960 Speaker 1: at AWS you kind of sit at the intersection of 56 00:02:36,960 --> 00:02:41,360 Speaker 1: sports and technology and business. Was that a convergence that 57 00:02:41,400 --> 00:02:44,760 Speaker 1: you deliberately pursued along your career path or did it 58 00:02:44,800 --> 00:02:45,760 Speaker 1: emerge organically? 59 00:02:47,240 --> 00:02:50,560 Speaker 3: That's a great question, and it actually is a budget 60 00:02:50,560 --> 00:02:52,800 Speaker 3: of things coming together right out of underground. 61 00:02:52,840 --> 00:02:54,600 Speaker 2: I worked in technology. 62 00:02:55,520 --> 00:02:59,480 Speaker 3: And then I went to business school and then worked 63 00:02:59,520 --> 00:03:03,280 Speaker 3: more you know, strategy and business molment roles after business school. 64 00:03:03,720 --> 00:03:06,200 Speaker 3: So it sort of brought together, you know, the affinity 65 00:03:06,280 --> 00:03:09,760 Speaker 3: and you know, for sport, it brought together the tech 66 00:03:09,760 --> 00:03:12,680 Speaker 3: background I had early days in my career and the 67 00:03:12,720 --> 00:03:15,840 Speaker 3: business background that I have, you know, in and. 68 00:03:15,919 --> 00:03:19,760 Speaker 2: Post business schools. So it was just a really nice convergence. 69 00:03:19,880 --> 00:03:24,280 Speaker 3: And you know, I'm not I understand how technology can 70 00:03:24,320 --> 00:03:26,960 Speaker 3: get deployed to drive business outcomes. I am not a 71 00:03:26,960 --> 00:03:29,840 Speaker 3: person who's just like, oh, technology for technology's sake, that's 72 00:03:29,840 --> 00:03:31,839 Speaker 3: super fun, right, it's got to buy. 73 00:03:32,200 --> 00:03:34,880 Speaker 2: Why does it matter what outcome Cannet deliver? How can 74 00:03:34,880 --> 00:03:37,080 Speaker 2: it make fans feel closer to the game or the team. 75 00:03:37,640 --> 00:03:38,920 Speaker 2: That's where it's interesting for me. 76 00:03:38,960 --> 00:03:42,240 Speaker 3: It's bringing sort of the business purpose to the technology 77 00:03:42,640 --> 00:03:44,880 Speaker 3: in a field that I care quite deeply about. 78 00:03:45,360 --> 00:03:47,840 Speaker 1: Right well, And I mean your your career path to 79 00:03:47,920 --> 00:03:49,880 Speaker 1: getting here has been so unique, and you've worked in 80 00:03:49,920 --> 00:03:52,600 Speaker 1: so many various capacities of that. You know, you've worked 81 00:03:52,640 --> 00:03:55,960 Speaker 1: in media for places like ESPN and CBS Sports, who 82 00:03:56,000 --> 00:03:58,320 Speaker 1: worked in data for places like Second Spectrum and now 83 00:03:58,360 --> 00:04:02,280 Speaker 1: at a WS and having these different perspectives on the industry, 84 00:04:02,320 --> 00:04:04,480 Speaker 1: does that shape how you approach your day to day 85 00:04:04,560 --> 00:04:05,160 Speaker 1: in this role. 86 00:04:06,080 --> 00:04:07,440 Speaker 2: Yeah, I mean one hundred percent. 87 00:04:07,640 --> 00:04:10,680 Speaker 3: It's you know, I started my first job in sports 88 00:04:10,720 --> 00:04:14,200 Speaker 3: was at College Sports Television, which is a startup, you know, 89 00:04:14,480 --> 00:04:16,760 Speaker 3: a startup at the time, a cable network that was 90 00:04:16,800 --> 00:04:20,040 Speaker 3: then bought by CBS, and so that was really good 91 00:04:20,080 --> 00:04:22,479 Speaker 3: in terms of being able to look broadly across the 92 00:04:22,480 --> 00:04:25,640 Speaker 3: sports media industry. Right as a startup, I was involved 93 00:04:25,640 --> 00:04:28,760 Speaker 3: in distribution and programming and production, like all of those 94 00:04:28,800 --> 00:04:30,960 Speaker 3: sorts of conversations were happening. 95 00:04:31,000 --> 00:04:31,320 Speaker 2: And so. 96 00:04:32,760 --> 00:04:35,160 Speaker 3: When I went to go lead business development at ESPN, 97 00:04:35,200 --> 00:04:37,280 Speaker 3: it was taking a lot of that experience with me, 98 00:04:37,440 --> 00:04:41,200 Speaker 3: but also seeing it at a much you know, bigger place, right, 99 00:04:42,520 --> 00:04:46,480 Speaker 3: And with that was introduced to other facets of the industry. 100 00:04:46,560 --> 00:04:48,719 Speaker 3: And so Second Spectrum was a company that I was 101 00:04:48,720 --> 00:04:53,000 Speaker 3: introduced to by one of our stats and information group 102 00:04:53,279 --> 00:04:56,799 Speaker 3: leads at ESPN, gentleman by the name of Ben Alamar, 103 00:04:56,839 --> 00:05:00,159 Speaker 3: who's just brilliant, and you know, I think it's just 104 00:05:00,200 --> 00:05:03,840 Speaker 3: being curious, you know. He piqued my curiosity told me 105 00:05:03,880 --> 00:05:06,760 Speaker 3: about this company's Second Spectrum, and the more I leaned 106 00:05:06,760 --> 00:05:08,960 Speaker 3: in and learned it, just for me, that was a 107 00:05:09,040 --> 00:05:11,880 Speaker 3: light bulb moment really, Like all of this data that's 108 00:05:11,920 --> 00:05:16,640 Speaker 3: being collected out of sport, holy volumes. I mean, in 109 00:05:16,680 --> 00:05:19,560 Speaker 3: the NFL, you know, for next Gen stats, that's five 110 00:05:19,600 --> 00:05:22,120 Speaker 3: hundred million points of data over the course of a season, right, 111 00:05:22,200 --> 00:05:25,200 Speaker 3: So just really tremendous amount of data coming out of 112 00:05:25,200 --> 00:05:25,600 Speaker 3: sport now. 113 00:05:25,640 --> 00:05:27,520 Speaker 2: And I remember sitting there thinking like, oh my gosh, 114 00:05:27,520 --> 00:05:28,920 Speaker 2: this is going to change everything. 115 00:05:29,360 --> 00:05:31,520 Speaker 3: I give no idea where like how but the fact 116 00:05:31,560 --> 00:05:34,839 Speaker 3: that we have all this data, like I can't you know, 117 00:05:35,760 --> 00:05:38,279 Speaker 3: you only limited by the questions you ask of it, right, 118 00:05:38,360 --> 00:05:40,920 Speaker 3: And so I think as humans we tend not to 119 00:05:41,000 --> 00:05:44,120 Speaker 3: run out of questions, which makes it exciting. So it's 120 00:05:44,160 --> 00:05:46,240 Speaker 3: just that sort of natural curiosity like what's next, what 121 00:05:46,279 --> 00:05:48,440 Speaker 3: are the people doing in this industry? How is that working? 122 00:05:48,560 --> 00:05:52,520 Speaker 3: What is that advancing? So I would say it hasn't 123 00:05:52,520 --> 00:05:54,760 Speaker 3: been like a straight line like oh, I'm shooting my shot, 124 00:05:54,800 --> 00:05:55,680 Speaker 3: this is where I want to go. 125 00:05:55,800 --> 00:05:58,320 Speaker 2: It's been a little bit more of like. 126 00:05:58,320 --> 00:06:02,360 Speaker 3: A a lazy river ride third and white, a career 127 00:06:03,680 --> 00:06:06,039 Speaker 3: where things are interesting and you sort of, you know, 128 00:06:06,160 --> 00:06:08,359 Speaker 3: dip in and try to understand a little bit better. 129 00:06:08,480 --> 00:06:13,520 Speaker 3: So it's been organic and really really wonderful. 130 00:06:13,920 --> 00:06:16,480 Speaker 1: Yeah, it's funny how sometimes these these things make more 131 00:06:16,520 --> 00:06:18,760 Speaker 1: sense in hindsight when you look back and it's like, okay, 132 00:06:18,800 --> 00:06:21,120 Speaker 1: that's of course how we got here. But you mentioned 133 00:06:21,160 --> 00:06:23,760 Speaker 1: just wow, how much has changed and how much data 134 00:06:23,760 --> 00:06:26,720 Speaker 1: has impacted things? I mean, from your vantage point when 135 00:06:26,760 --> 00:06:31,160 Speaker 1: you look at the sports ecosystem from athletes, coaches, broadcasters, fans. 136 00:06:31,760 --> 00:06:35,279 Speaker 1: Which area do you think has most been transformed by 137 00:06:35,680 --> 00:06:38,159 Speaker 1: the data and the cloud technology so far? And which 138 00:06:38,200 --> 00:06:41,239 Speaker 1: area do you think is next? 139 00:06:41,560 --> 00:06:42,040 Speaker 2: All of them? 140 00:06:42,200 --> 00:06:45,239 Speaker 3: First of all of them been affected by this, And 141 00:06:44,400 --> 00:06:49,039 Speaker 3: I'm going to because we're talking about the Giants and 142 00:06:49,080 --> 00:06:53,120 Speaker 3: we're talking about football, like, I'm going to just root 143 00:06:53,200 --> 00:06:57,359 Speaker 3: some of these use cases and stories in football. You 144 00:06:57,400 --> 00:06:58,960 Speaker 3: look at the data that's coming out of sport and 145 00:06:58,960 --> 00:07:02,760 Speaker 3: it's coming into ways to root this, it's either sensors 146 00:07:02,760 --> 00:07:04,840 Speaker 3: that are in that the players are chipped, right, their 147 00:07:05,120 --> 00:07:08,840 Speaker 3: shoulder pads are tipped, the ball is chipped, and then 148 00:07:08,839 --> 00:07:11,720 Speaker 3: there's their cameras, so optical tracking. So all of this 149 00:07:11,880 --> 00:07:15,200 Speaker 3: data is being collected and it's feeding different use cases 150 00:07:15,400 --> 00:07:17,360 Speaker 3: like next Gen Stats as we mentioned, which are sort 151 00:07:17,400 --> 00:07:20,400 Speaker 3: of these engaging fan analytics to let people understand at 152 00:07:20,400 --> 00:07:22,920 Speaker 3: a deeper level what they're watching and how, you know, 153 00:07:23,920 --> 00:07:28,840 Speaker 3: exceptional it may be. So that's one use case there. 154 00:07:29,720 --> 00:07:32,520 Speaker 3: The optical tracking data combined with that tracking data that 155 00:07:32,600 --> 00:07:35,920 Speaker 3: inform next Gen stats, that's been really important in the 156 00:07:35,920 --> 00:07:37,680 Speaker 3: player healthy safety side of things. 157 00:07:38,080 --> 00:07:38,720 Speaker 2: And so the. 158 00:07:38,800 --> 00:07:42,040 Speaker 3: League created a what they call the digital athlete, which 159 00:07:42,160 --> 00:07:44,400 Speaker 3: is a player Health and Safety portal that they rolled 160 00:07:44,400 --> 00:07:46,240 Speaker 3: out to all thirty two clubs a couple of years. 161 00:07:46,040 --> 00:07:46,720 Speaker 2: Ago, and. 162 00:07:48,320 --> 00:07:51,560 Speaker 3: That's helping teams identify players they are risk of injury 163 00:07:52,000 --> 00:07:54,360 Speaker 3: and get to them before injuries sustained. 164 00:07:54,520 --> 00:07:55,280 Speaker 2: Right, whether it's a. 165 00:07:55,200 --> 00:07:59,760 Speaker 3: Load management issue or training regiment issue, or whatever the 166 00:07:59,760 --> 00:08:03,520 Speaker 3: case may be. We're I mean that first season, Madeline, 167 00:08:03,520 --> 00:08:06,360 Speaker 3: there were seven hundred fewer missed games by players, right 168 00:08:06,400 --> 00:08:10,280 Speaker 3: because teams were able to get to players and keep 169 00:08:10,280 --> 00:08:13,840 Speaker 3: them safe, which is great. You've seen rule changes, right, 170 00:08:14,280 --> 00:08:18,160 Speaker 3: the kickoff rule changed, hip drop tackle was banned. We've 171 00:08:18,160 --> 00:08:21,320 Speaker 3: seen equipment changes. Everybody's were the same helmet. Now there 172 00:08:21,320 --> 00:08:26,080 Speaker 3: are position specific helmets because how you know, wide receiver 173 00:08:26,240 --> 00:08:29,480 Speaker 3: absorbed the hit is very different than how an oline 174 00:08:29,840 --> 00:08:33,240 Speaker 3: you know player may receive a hit. So these sorts 175 00:08:33,240 --> 00:08:35,880 Speaker 3: of changes and this sort of data is really affecting 176 00:08:36,240 --> 00:08:38,640 Speaker 3: every part of the ecosystem. Yeah. 177 00:08:38,720 --> 00:08:42,960 Speaker 2: Well, and even officiating, right, officiating analysis, How can we 178 00:08:43,040 --> 00:08:46,120 Speaker 2: use this to have much more you know, identify twelve 179 00:08:46,160 --> 00:08:47,760 Speaker 2: men on the field when it happens instantly. 180 00:08:48,080 --> 00:08:51,760 Speaker 1: Yeah, right, And you mentioned officiating. You talked about how 181 00:08:51,800 --> 00:08:53,800 Speaker 1: the ball is shipped and players are shipped, and we 182 00:08:53,880 --> 00:08:56,240 Speaker 1: see you know next gen stats. You know, football fans 183 00:08:56,280 --> 00:08:58,480 Speaker 1: are well familiar with watching the little dots of the 184 00:08:58,520 --> 00:09:02,880 Speaker 1: play kind of unfold in that regard. What's uh in 185 00:09:02,920 --> 00:09:05,040 Speaker 1: your opinion, how important is it though, to kind of 186 00:09:05,040 --> 00:09:08,480 Speaker 1: balance that data availability with the human element. Right, We've 187 00:09:08,480 --> 00:09:10,640 Speaker 1: got the changing on the sides, we've got the officials 188 00:09:10,640 --> 00:09:12,400 Speaker 1: in the game. If the ball is chipped and the 189 00:09:12,400 --> 00:09:15,240 Speaker 1: players are chipped, you'd think it'd be so easy to say, oh, well, 190 00:09:15,240 --> 00:09:17,640 Speaker 1: the first down according to the GPS of this player 191 00:09:18,080 --> 00:09:21,199 Speaker 1: has been accomplished. But you know the league still holds 192 00:09:21,240 --> 00:09:24,680 Speaker 1: on to the way things have been done over the 193 00:09:24,720 --> 00:09:26,680 Speaker 1: years too. How do you balance that with all that 194 00:09:26,679 --> 00:09:27,720 Speaker 1: that data available? 195 00:09:28,840 --> 00:09:32,960 Speaker 3: They are using it, actually there they are, so but 196 00:09:33,040 --> 00:09:34,720 Speaker 3: you know what, I think you're hitting on a really 197 00:09:34,800 --> 00:09:35,640 Speaker 3: really important thing. 198 00:09:35,720 --> 00:09:39,000 Speaker 2: The change takes time, right, and so people want to 199 00:09:39,000 --> 00:09:39,840 Speaker 2: be able to trust it. 200 00:09:39,880 --> 00:09:44,720 Speaker 3: So they are using the ball chip being chipped to 201 00:09:44,760 --> 00:09:47,559 Speaker 3: be able to mark. Now you have not exclusion issues, 202 00:09:47,720 --> 00:09:50,280 Speaker 3: none as but right, but you still have but you 203 00:09:50,280 --> 00:09:52,839 Speaker 3: still want the ceremonial like I mean, you don't actually 204 00:09:52,840 --> 00:09:54,440 Speaker 3: have the chain anymore, right, but you do have the 205 00:09:54,480 --> 00:09:57,920 Speaker 3: markers so players know where they're going and what they're 206 00:09:58,640 --> 00:09:59,079 Speaker 3: going for. 207 00:09:59,080 --> 00:10:01,000 Speaker 2: But that that's an evolution. 208 00:10:01,720 --> 00:10:04,960 Speaker 3: And so the point you're making, which is, how do 209 00:10:05,040 --> 00:10:07,000 Speaker 3: you you know there are things we know how to do. 210 00:10:06,920 --> 00:10:08,800 Speaker 2: Why don't we do them right away? 211 00:10:09,840 --> 00:10:11,760 Speaker 3: Well, sometimes it's you have to do it a while, 212 00:10:11,840 --> 00:10:15,280 Speaker 3: like in in companion with the old way of doing things, 213 00:10:15,320 --> 00:10:17,080 Speaker 3: so people have the confidence that the new way is 214 00:10:17,160 --> 00:10:19,760 Speaker 3: just as effective and maybe even more so than the old. 215 00:10:19,559 --> 00:10:23,440 Speaker 2: Way of doing things. And then it's just a comfort level, 216 00:10:23,600 --> 00:10:24,199 Speaker 2: really it is. 217 00:10:24,600 --> 00:10:28,160 Speaker 3: It's, uh, you know people teams student used to have 218 00:10:28,240 --> 00:10:31,360 Speaker 3: analytics staff like that wasn't a thing, right, and now 219 00:10:31,800 --> 00:10:34,880 Speaker 3: every major team has an analytics staff. So it's just 220 00:10:34,920 --> 00:10:38,240 Speaker 3: a matter of people being able to understand the value 221 00:10:38,720 --> 00:10:42,360 Speaker 3: that's coming out of this. And that's and that's the point, right, 222 00:10:42,360 --> 00:10:44,400 Speaker 3: Like if we're just doing something like, oh isn't this cool? 223 00:10:44,480 --> 00:10:48,200 Speaker 2: Well, what value does a drive? What this does outcome? 224 00:10:48,280 --> 00:10:49,160 Speaker 2: Is it? 225 00:10:49,200 --> 00:10:51,960 Speaker 3: Is it you know, improving the game, speeding up the game, 226 00:10:52,040 --> 00:10:55,719 Speaker 3: making players see for making fans more you know, engaged. 227 00:10:55,920 --> 00:10:58,760 Speaker 3: Then then it's you know, it's easier to sell once 228 00:10:58,800 --> 00:11:00,439 Speaker 3: there's the value is it to it? 229 00:11:00,679 --> 00:11:01,359 Speaker 1: Yeah? 230 00:11:01,600 --> 00:11:01,840 Speaker 3: Yeah? 231 00:11:01,880 --> 00:11:05,280 Speaker 1: And it's you know, you've got so much data at 232 00:11:05,280 --> 00:11:08,160 Speaker 1: your fingertips and you know, aws positions itself is as 233 00:11:08,200 --> 00:11:10,240 Speaker 1: a partner rather than a vendor. So what does that 234 00:11:10,280 --> 00:11:12,960 Speaker 1: look like in practice when it comes to, you know, 235 00:11:13,000 --> 00:11:16,120 Speaker 1: working with leagues that are protective of the tradition and 236 00:11:16,160 --> 00:11:18,560 Speaker 1: the competitive advantage in that regard. 237 00:11:19,040 --> 00:11:20,640 Speaker 3: It's a great question, and thank you for calling that 238 00:11:20,640 --> 00:11:22,800 Speaker 3: out because I do. I very much want to be 239 00:11:22,960 --> 00:11:26,199 Speaker 3: innovation partners with our partners, not just a tech vendor, 240 00:11:26,280 --> 00:11:28,600 Speaker 3: and that means really leaning in and trying to understand 241 00:11:28,640 --> 00:11:31,880 Speaker 3: their business and their priorities. And so if it is 242 00:11:32,200 --> 00:11:35,640 Speaker 3: look the NFL, there there was a concussion problem, there 243 00:11:35,720 --> 00:11:39,000 Speaker 3: was a you know, this is a high contact intent sport. 244 00:11:39,720 --> 00:11:42,480 Speaker 3: How do you you know, try and there's going to 245 00:11:42,480 --> 00:11:44,960 Speaker 3: be injury as a as a as a you know 246 00:11:45,040 --> 00:11:47,800 Speaker 3: output of that. But how do we make around the 247 00:11:47,920 --> 00:11:51,240 Speaker 3: edges and maybe markedly so make some real improvements here. 248 00:11:51,800 --> 00:11:54,760 Speaker 3: So it's just really lining up around those priorities and 249 00:11:54,840 --> 00:11:57,320 Speaker 3: making sure that we're thinking creatively and how do we 250 00:11:57,360 --> 00:12:00,000 Speaker 3: bring tech to bear to help solve some of those problems. 251 00:12:00,280 --> 00:12:03,240 Speaker 3: So a lot of what we're talking about with skeletal 252 00:12:03,280 --> 00:12:07,120 Speaker 3: pose and the tracking data is letting us understand more 253 00:12:07,240 --> 00:12:11,440 Speaker 3: about you know, potential injury causes for injuries and ways 254 00:12:11,480 --> 00:12:16,760 Speaker 3: to maybe uh reduce them in specific ways. 255 00:12:16,800 --> 00:12:17,439 Speaker 2: Officiating. 256 00:12:17,480 --> 00:12:19,680 Speaker 3: Nobody likes to end on a bad call, right that, 257 00:12:19,800 --> 00:12:21,439 Speaker 3: So how do we make sure that there are some 258 00:12:21,480 --> 00:12:25,520 Speaker 3: plays that we we understand that there is going to 259 00:12:25,520 --> 00:12:27,600 Speaker 3: be human jouled in a lot of these calls, but 260 00:12:27,600 --> 00:12:31,080 Speaker 3: there are some that twelve men on the field, like computers, 261 00:12:31,080 --> 00:12:32,920 Speaker 3: should be able to count and tell you right like you, 262 00:12:33,160 --> 00:12:36,840 Speaker 3: you don't need the refs wasting time necessarily on that 263 00:12:36,920 --> 00:12:40,520 Speaker 3: call when when they're you know, maybe more subjective calls 264 00:12:40,520 --> 00:12:40,920 Speaker 3: to be made. 265 00:12:41,240 --> 00:12:45,640 Speaker 1: Yeah, if we consider just how quickly technology evolves and 266 00:12:45,679 --> 00:12:47,680 Speaker 1: it almost snowballs, right is it gets going, It gets 267 00:12:47,720 --> 00:12:50,920 Speaker 1: going faster and faster. It's wee fast forward five years 268 00:12:50,920 --> 00:12:54,080 Speaker 1: from now. Is there a part of today's sports experience 269 00:12:54,080 --> 00:12:57,079 Speaker 1: that you think will feel obviously outdated? And how do 270 00:12:57,120 --> 00:13:00,160 Speaker 1: you think people will be and is there something that 271 00:13:00,200 --> 00:13:02,800 Speaker 1: you think people will be surprised hasn't changed at that point. 272 00:13:04,800 --> 00:13:07,120 Speaker 3: Yeah, I think that's such a fun question. I think 273 00:13:07,160 --> 00:13:09,880 Speaker 3: there are some things behind the scenes that will fundamentally 274 00:13:10,000 --> 00:13:13,520 Speaker 3: change that you won't see necessarily. So, for example, right now, 275 00:13:13,800 --> 00:13:15,720 Speaker 3: the way we produce sporting events is we roll up 276 00:13:15,760 --> 00:13:17,880 Speaker 3: these big O b trucks, right, and there's all this 277 00:13:17,960 --> 00:13:20,199 Speaker 3: cabling and equipment and people that get slept all over 278 00:13:20,240 --> 00:13:22,679 Speaker 3: the place. I think you're going to see a lot 279 00:13:22,679 --> 00:13:24,679 Speaker 3: of that move to the cloud and so people can 280 00:13:24,800 --> 00:13:27,400 Speaker 3: do production from the comfort of their own home or 281 00:13:27,440 --> 00:13:30,640 Speaker 3: from their you know, single home based studio or whatnot. 282 00:13:32,440 --> 00:13:36,600 Speaker 3: And so how we produce I think will change to 283 00:13:36,679 --> 00:13:42,600 Speaker 3: be more scalable, more flexible, way less carbon emitting. So 284 00:13:42,640 --> 00:13:45,040 Speaker 3: I think some of that on the back end you 285 00:13:45,040 --> 00:13:49,680 Speaker 3: will see. I also think the trend towards personalization is real, right, 286 00:13:49,760 --> 00:13:52,360 Speaker 3: so allowing fans to consume the content the way they 287 00:13:52,400 --> 00:13:53,240 Speaker 3: want to consume it. 288 00:13:53,280 --> 00:13:54,360 Speaker 2: So I always talk about my. 289 00:13:56,000 --> 00:13:58,880 Speaker 3: Future state of how we watch, you know, a Get 290 00:13:58,960 --> 00:14:01,880 Speaker 3: Live game. Right now, we still have this notion of 291 00:14:01,880 --> 00:14:03,480 Speaker 3: second screen, all the things that we do on our 292 00:14:03,480 --> 00:14:07,719 Speaker 3: phones while we're watching a game, whether it's a you know, 293 00:14:07,760 --> 00:14:11,960 Speaker 3: on a computer on a big screen or whatnot. With 294 00:14:12,160 --> 00:14:16,360 Speaker 3: the adoption of streaming consumption of content, you can do 295 00:14:16,480 --> 00:14:19,680 Speaker 3: all of those secon screen experiences within the streaming platform, right. 296 00:14:19,720 --> 00:14:21,640 Speaker 3: So when you think about it, you want to choose 297 00:14:21,640 --> 00:14:25,040 Speaker 3: your camera angle, choose your audio feed, choose your level 298 00:14:25,040 --> 00:14:27,560 Speaker 3: of augmentation. Play with real money that followed your fantasy 299 00:14:27,600 --> 00:14:30,200 Speaker 3: team in real time, you know, chat with friends, take 300 00:14:30,200 --> 00:14:31,840 Speaker 3: a pool, buy tickets to the next game, by your 301 00:14:31,880 --> 00:14:35,320 Speaker 3: favorite player's jersey, order Uber eats twenty minutes before halftime. 302 00:14:35,400 --> 00:14:37,200 Speaker 3: Like all of the things that you would do, you 303 00:14:37,240 --> 00:14:39,520 Speaker 3: can build into that experience, and then you can get 304 00:14:39,600 --> 00:14:43,800 Speaker 3: even more granular, which is, hey, when my favorite player 305 00:14:44,200 --> 00:14:48,240 Speaker 3: scores a touchdown, I want a virtual confetti canon to 306 00:14:48,400 --> 00:14:51,720 Speaker 3: like go off on the screen. You know, So you 307 00:14:51,720 --> 00:14:56,440 Speaker 3: can start getting these really personalized, engrossing experiences and people 308 00:14:56,520 --> 00:14:59,720 Speaker 3: can experience the sport, you know, the way it's going 309 00:14:59,760 --> 00:15:02,240 Speaker 3: to be most impactful to them. And then we're going 310 00:15:02,240 --> 00:15:04,640 Speaker 3: from really what has traditionally been a little bit of 311 00:15:04,680 --> 00:15:07,600 Speaker 3: a lean back experience and a more passive viewing experience 312 00:15:07,800 --> 00:15:10,760 Speaker 3: to a really highly engaged, interactive experience. And I think 313 00:15:10,800 --> 00:15:13,000 Speaker 3: that just makes for more avid fans. 314 00:15:13,680 --> 00:15:17,400 Speaker 1: Yeah, I mean, it's fascinating. And the fan experience has 315 00:15:17,400 --> 00:15:21,040 Speaker 1: evolved so much, as you mentioned in the analytics of teams, 316 00:15:21,080 --> 00:15:24,240 Speaker 1: the scouting, all of it, with so much data and 317 00:15:24,440 --> 00:15:27,800 Speaker 1: possibility at our fingertips. And now, you know, as leagues 318 00:15:27,840 --> 00:15:30,640 Speaker 1: and teams become more dependent on cloud partners with these 319 00:15:30,680 --> 00:15:33,440 Speaker 1: analytics departments, so we kind of referenced earlier on how 320 00:15:33,480 --> 00:15:35,720 Speaker 1: do you guys think about data ownership? 321 00:15:35,800 --> 00:15:35,960 Speaker 3: Right? 322 00:15:36,000 --> 00:15:38,040 Speaker 1: I mean data has got a lot of value when 323 00:15:38,040 --> 00:15:41,960 Speaker 1: it comes to certain competitive edges and you know, long 324 00:15:42,080 --> 00:15:44,320 Speaker 1: term dependency. What kind of safeguards are how do you 325 00:15:44,320 --> 00:15:47,600 Speaker 1: guys keep leagues in control of their own futures and 326 00:15:47,840 --> 00:15:49,080 Speaker 1: in control of their own information? 327 00:15:49,800 --> 00:15:52,200 Speaker 3: One hundred percent? And that is like job one, right, 328 00:15:52,280 --> 00:15:55,440 Speaker 3: the security of our customers data is where it stops. 329 00:15:55,440 --> 00:15:58,240 Speaker 3: It starts for us, and so all of the data 330 00:15:58,400 --> 00:16:00,960 Speaker 3: that the league is collecting is in their own instance 331 00:16:01,000 --> 00:16:03,840 Speaker 3: that is guarded and managed and secured by them, right. 332 00:16:04,000 --> 00:16:05,920 Speaker 2: And so then what they do with that data to 333 00:16:05,960 --> 00:16:07,560 Speaker 2: make it available to. 334 00:16:07,680 --> 00:16:12,000 Speaker 3: Clubs, to broadcast partners, to betting partners, whatever, that's all 335 00:16:12,040 --> 00:16:14,680 Speaker 3: within the discretion of the league. And we give them 336 00:16:14,800 --> 00:16:19,240 Speaker 3: infrastructure to host and manage and secure that data, right, 337 00:16:20,080 --> 00:16:21,960 Speaker 3: But then how it gets deployed out there in the 338 00:16:21,960 --> 00:16:26,280 Speaker 3: world that is still entirely under the jurisdiction and control 339 00:16:26,320 --> 00:16:29,480 Speaker 3: and purview of you know, of the customer, which in 340 00:16:29,520 --> 00:16:30,600 Speaker 3: this case is the NFL. 341 00:16:31,200 --> 00:16:34,640 Speaker 1: Yeah, yeah, I love that. I mean you've built such 342 00:16:34,800 --> 00:16:37,200 Speaker 1: a presence and a stronghold in a career that is 343 00:16:37,560 --> 00:16:40,960 Speaker 1: you know sport and you know male dominated field. If 344 00:16:40,960 --> 00:16:43,480 Speaker 1: you look at you your career path and how you've 345 00:16:43,480 --> 00:16:45,560 Speaker 1: gotten to this place right now, what advice would you 346 00:16:45,600 --> 00:16:49,040 Speaker 1: give to your younger self in building the influence that 347 00:16:49,080 --> 00:16:51,440 Speaker 1: you've had in your career? 348 00:16:51,480 --> 00:16:57,360 Speaker 3: Here? The one thing I didn't have a lot of 349 00:16:57,440 --> 00:16:59,680 Speaker 3: in my career, and it saddens me a little bit, 350 00:16:59,720 --> 00:17:03,240 Speaker 3: is meant right? I will say I had some really 351 00:17:03,280 --> 00:17:08,000 Speaker 3: phenomenal female colleagues, right, and and male colleagues, but just 352 00:17:08,119 --> 00:17:11,480 Speaker 3: you know who banned arms and and even. 353 00:17:11,320 --> 00:17:14,679 Speaker 2: Later in my career I have met. 354 00:17:15,400 --> 00:17:18,800 Speaker 3: There's a particular chat thread of like nine of us 355 00:17:19,359 --> 00:17:21,880 Speaker 3: that is probably the most active chet thread. I have 356 00:17:22,160 --> 00:17:25,560 Speaker 3: other women in this industry who can talk very seriously 357 00:17:25,560 --> 00:17:27,560 Speaker 3: about things, and then you know. 358 00:17:28,440 --> 00:17:30,800 Speaker 2: Would devolved into hockey romance novels yesterday. 359 00:17:30,800 --> 00:17:34,080 Speaker 3: But finding your people, I guess this is my point, right, 360 00:17:34,160 --> 00:17:37,800 Speaker 3: find those people who will support you, but also in 361 00:17:37,800 --> 00:17:40,760 Speaker 3: a mentor somebody will hold you to account, right and 362 00:17:41,320 --> 00:17:44,240 Speaker 3: really talk things. Hold you know, this is what I 363 00:17:44,280 --> 00:17:45,960 Speaker 3: want for my career. Okay, I'm going to read it 364 00:17:45,960 --> 00:17:47,879 Speaker 3: back to you when you start telling me, you know 365 00:17:47,920 --> 00:17:50,199 Speaker 3: that you're to being distracted by something, right, So how 366 00:17:50,200 --> 00:17:52,919 Speaker 3: do you, how do you use mentors not only to 367 00:17:52,920 --> 00:17:55,919 Speaker 3: support you, but to push you right and to and 368 00:17:56,000 --> 00:18:00,439 Speaker 3: to really put you in a position that you are 369 00:18:00,520 --> 00:18:03,720 Speaker 3: challenging yourself. So I think that's part of it, to 370 00:18:03,720 --> 00:18:06,439 Speaker 3: seek out people who who will not just pet you 371 00:18:06,520 --> 00:18:08,879 Speaker 3: on the head but sort of you know, push you 372 00:18:08,920 --> 00:18:11,679 Speaker 3: along a little bit at times too. And then the 373 00:18:11,720 --> 00:18:15,760 Speaker 3: other thing I would say is really develop a functional expertise. Right, 374 00:18:16,480 --> 00:18:18,040 Speaker 3: there's the what you do and the way you do it. 375 00:18:18,480 --> 00:18:21,439 Speaker 3: I really like strategy. I really like building partnerships. I 376 00:18:21,480 --> 00:18:23,600 Speaker 3: really like, you know, trying to envision what the future 377 00:18:23,640 --> 00:18:28,359 Speaker 3: it looks like with those partners and having some analytical 378 00:18:28,440 --> 00:18:31,480 Speaker 3: rigor to how you do that. Where I do it 379 00:18:31,520 --> 00:18:34,000 Speaker 3: is in sports, that is awesome. I have both things 380 00:18:34,000 --> 00:18:36,480 Speaker 3: that I love, right, But I will say, at the 381 00:18:36,560 --> 00:18:39,679 Speaker 3: end of the day, what you do you need to 382 00:18:39,720 --> 00:18:42,560 Speaker 3: love that, right, So if it's marketing, if it's finance, 383 00:18:42,600 --> 00:18:46,560 Speaker 3: if it's operations, like really hone some expertise in that area, 384 00:18:46,640 --> 00:18:49,440 Speaker 3: because then it becomes a little bit more transferable. And 385 00:18:49,480 --> 00:18:51,680 Speaker 3: if you're in a consumer package goods company right now 386 00:18:51,680 --> 00:18:53,960 Speaker 3: and really want to get to sports, but you being 387 00:18:54,000 --> 00:18:56,920 Speaker 3: a great social marketer, that's transferable. 388 00:18:57,080 --> 00:19:00,920 Speaker 2: It absolutely is right. So find that like what of expertise, 389 00:19:01,320 --> 00:19:03,200 Speaker 2: and then look for ways to make it extend into 390 00:19:03,240 --> 00:19:04,800 Speaker 2: the industries that you're passionate about. 391 00:19:05,160 --> 00:19:08,000 Speaker 1: Yeah, and now going back to that point about mentorship too, 392 00:19:08,040 --> 00:19:09,960 Speaker 1: Now that you are in the chapter of your career 393 00:19:10,000 --> 00:19:12,600 Speaker 1: where you are a mentor to a lot of people, 394 00:19:12,640 --> 00:19:16,159 Speaker 1: I'd imagine what advice would you give a younger person 395 00:19:16,280 --> 00:19:19,919 Speaker 1: in pursuing that relationship and pursuing a mentorship with somebody, 396 00:19:19,960 --> 00:19:24,440 Speaker 1: because you know, it's a challenging choice to navigate. It's 397 00:19:24,600 --> 00:19:26,359 Speaker 1: how do how do you kind of build that relationship? 398 00:19:27,760 --> 00:19:29,960 Speaker 2: So, first of all, there's different ways of approaching it. Right. 399 00:19:30,000 --> 00:19:32,480 Speaker 3: I happen to be a mentor within the wise for 400 00:19:33,119 --> 00:19:37,639 Speaker 3: you know, women in sports and events to several fantastic 401 00:19:37,800 --> 00:19:41,439 Speaker 3: young women. And so you can have that sort of 402 00:19:41,720 --> 00:19:43,800 Speaker 3: formal program you can get into, or you could just 403 00:19:43,880 --> 00:19:46,919 Speaker 3: you know, if you stumble across people, and sometimes it's not. 404 00:19:47,040 --> 00:19:48,320 Speaker 2: Just will you be my mentor? 405 00:19:48,480 --> 00:19:51,000 Speaker 3: It is hey, could I set up some sort of 406 00:19:51,040 --> 00:19:53,000 Speaker 3: regular cadence with you? I would just love to get 407 00:19:53,000 --> 00:19:55,760 Speaker 3: your advice on thics and show up prepared. It's it's 408 00:19:56,440 --> 00:19:58,760 Speaker 3: it's your relationship. What do you want out of it? 409 00:19:58,840 --> 00:20:01,440 Speaker 3: And that's how I usually start little as conversations, which 410 00:20:01,480 --> 00:20:02,760 Speaker 3: is what do you want from this? 411 00:20:02,920 --> 00:20:04,679 Speaker 2: I am happy to be a sounding board. One of 412 00:20:04,680 --> 00:20:05,920 Speaker 2: my mentees is, you. 413 00:20:05,880 --> 00:20:10,160 Speaker 3: Know, looking at two different jobs, you know, promotion opportunities, 414 00:20:10,200 --> 00:20:11,600 Speaker 3: and how to weigh those and how to consider it 415 00:20:11,680 --> 00:20:14,720 Speaker 3: was a week went through the pros and con Some 416 00:20:14,760 --> 00:20:17,399 Speaker 3: people are like, I want to get into this industry. Okay, 417 00:20:17,440 --> 00:20:18,920 Speaker 3: well what kinds of jobs do you want? Go do 418 00:20:19,000 --> 00:20:21,760 Speaker 3: a little bit of homework, right, so, and I think, 419 00:20:22,240 --> 00:20:24,200 Speaker 3: you know, showing up prepared with what you want to 420 00:20:24,240 --> 00:20:27,480 Speaker 3: get out of that, and then I think it naturally 421 00:20:27,600 --> 00:20:31,359 Speaker 3: just that sets the foundation of okay, that you're. 422 00:20:31,240 --> 00:20:32,120 Speaker 2: Treating this seriously. 423 00:20:32,200 --> 00:20:34,399 Speaker 3: You know, you have intentionality over what you want in 424 00:20:34,440 --> 00:20:38,040 Speaker 3: this relationship, and now it's easy, right. Sometimes it's just 425 00:20:38,080 --> 00:20:40,399 Speaker 3: catching up or like you know, hey, I'm doing this 426 00:20:40,440 --> 00:20:42,399 Speaker 3: one project at work. You have any thoughts on this? 427 00:20:42,480 --> 00:20:45,640 Speaker 3: If you heard anything in this space, and I'll tell 428 00:20:45,640 --> 00:20:48,440 Speaker 3: you I learn as much from them as is probably 429 00:20:48,480 --> 00:20:51,240 Speaker 3: more than that they learned from me at this point. 430 00:20:51,320 --> 00:20:55,040 Speaker 2: But it's I believe deeply in it. 431 00:20:55,080 --> 00:20:58,199 Speaker 3: And because I didn't have strong mentorship in my career, 432 00:20:59,320 --> 00:21:00,760 Speaker 3: let's up be the two Jewish to say in the 433 00:21:00,760 --> 00:21:03,800 Speaker 3: world sort of thing, right, Like I it's very important 434 00:21:03,800 --> 00:21:04,480 Speaker 3: to me that I. 435 00:21:04,560 --> 00:21:08,040 Speaker 2: Try to, you know, help others. 436 00:21:08,080 --> 00:21:09,800 Speaker 3: And and look, I have a daughter who's a senior 437 00:21:09,840 --> 00:21:12,240 Speaker 3: in high school right now applying to sports management programs, 438 00:21:12,320 --> 00:21:14,840 Speaker 3: Like I want to put out. 439 00:21:14,720 --> 00:21:17,320 Speaker 2: There what I hope people will do for her one day. 440 00:21:17,400 --> 00:21:20,800 Speaker 1: So right, yeah, absolutely, And it's you know, it's such 441 00:21:20,840 --> 00:21:23,640 Speaker 1: a fun industry, a competitive industry, and then one that's 442 00:21:23,680 --> 00:21:26,879 Speaker 1: continuously evolving as well. And as we talked about earlier, 443 00:21:26,920 --> 00:21:29,560 Speaker 1: you know, the curiosity and the data and the evolution 444 00:21:29,680 --> 00:21:33,360 Speaker 1: of what technology can do for sport. You know, what 445 00:21:33,440 --> 00:21:36,960 Speaker 1: responsibility do you feel that leaders like yourself have to protect? 446 00:21:36,960 --> 00:21:40,520 Speaker 1: So what makes sports human? Not just make them smarter? 447 00:21:41,920 --> 00:21:45,000 Speaker 3: I love that question so much because and I get 448 00:21:45,040 --> 00:21:48,000 Speaker 3: it in a if you asked it so artfully, because 449 00:21:48,040 --> 00:21:50,520 Speaker 3: I've been asked it before in ways like you and 450 00:21:50,560 --> 00:21:54,000 Speaker 3: all the data you're screwing everything up, right, like you're 451 00:21:53,440 --> 00:21:57,399 Speaker 3: taking the emotion out of the game. Not if that 452 00:21:57,640 --> 00:22:00,320 Speaker 3: is true, if that is the outcome, then we are 453 00:22:00,480 --> 00:22:03,520 Speaker 3: doing it wrong, Madeline. I will give you a very 454 00:22:03,520 --> 00:22:08,320 Speaker 3: specific example of when data actually like because you know, 455 00:22:08,680 --> 00:22:11,159 Speaker 3: and you don't want to overwhelm people right. So, giving 456 00:22:11,200 --> 00:22:14,880 Speaker 3: that personalization capability and that optionality, you can watch the 457 00:22:14,920 --> 00:22:17,800 Speaker 3: prime vision broadcast of a THURSDA night football game, which 458 00:22:17,840 --> 00:22:21,040 Speaker 3: is the data cast, or you can just the traditional broadcast. Right. 459 00:22:21,080 --> 00:22:23,040 Speaker 3: So we're not going to force down anybody, but those 460 00:22:23,040 --> 00:22:24,960 Speaker 3: who want to lean in certainly want to give them 461 00:22:25,000 --> 00:22:27,720 Speaker 3: that option. But I will tell you I was watching 462 00:22:28,760 --> 00:22:31,000 Speaker 3: it was hockey. I was watching Western Conference finals a 463 00:22:31,040 --> 00:22:34,360 Speaker 3: few years ago. Didn't care who won, No, I didn't care. 464 00:22:34,600 --> 00:22:36,080 Speaker 3: I was just watching name and I happened to be 465 00:22:36,080 --> 00:22:38,760 Speaker 3: watching the data cast. So for someone who doesn't care 466 00:22:38,760 --> 00:22:40,680 Speaker 3: about what she's watching to watch the data cast is weird. 467 00:22:40,720 --> 00:22:41,280 Speaker 2: But here we are. 468 00:22:41,680 --> 00:22:44,920 Speaker 3: And they just took a very simple metric of time 469 00:22:44,960 --> 00:22:48,159 Speaker 3: on ice for each for all five skaters, right, And 470 00:22:48,200 --> 00:22:50,080 Speaker 3: so as you know fresh pair of legs or a 471 00:22:50,080 --> 00:22:52,520 Speaker 3: skater gets on the ice, that number that time clock 472 00:22:52,600 --> 00:22:55,000 Speaker 3: is green as it approach is forty five seconds, which 473 00:22:55,040 --> 00:22:57,840 Speaker 3: is the optimal shift change time. 474 00:22:58,200 --> 00:23:00,400 Speaker 2: That number starts to get yellow. Once you seen forty 475 00:23:00,440 --> 00:23:04,640 Speaker 2: five seconds, that number goes red. Madeline, I am screaming 476 00:23:04,680 --> 00:23:08,199 Speaker 2: at my television like shift change as I'm watching an 477 00:23:08,280 --> 00:23:10,879 Speaker 2: entire line that's red and another one that's green, and 478 00:23:10,920 --> 00:23:11,800 Speaker 2: I'm like, what this happened? 479 00:23:11,840 --> 00:23:14,680 Speaker 3: And I'm like, I'm literally like a little bit stressed, 480 00:23:14,760 --> 00:23:16,880 Speaker 3: a lot stressed so much though that I'm yelling shift 481 00:23:16,960 --> 00:23:20,879 Speaker 3: change at my television because you showed me data in 482 00:23:20,920 --> 00:23:24,560 Speaker 3: a way that like actually got me more invested in 483 00:23:24,640 --> 00:23:28,160 Speaker 3: You got me leaning in, right, So I think, how 484 00:23:28,560 --> 00:23:31,840 Speaker 3: you know all of this machine learning, all of these analytics, 485 00:23:31,960 --> 00:23:34,560 Speaker 3: all of this data processing, that's all the math and 486 00:23:34,600 --> 00:23:38,560 Speaker 3: the science, right. The art is the storytelling. How do 487 00:23:38,600 --> 00:23:41,680 Speaker 3: we use this data to tell you why it matters? Hey, athlete, 488 00:23:41,800 --> 00:23:43,440 Speaker 3: I need you to wear this sort of helmet because 489 00:23:43,520 --> 00:23:46,359 Speaker 3: it is seventy four percent safer for you right in 490 00:23:46,400 --> 00:23:47,120 Speaker 3: your position. 491 00:23:47,359 --> 00:23:49,560 Speaker 2: I'm using that data to wait, you know that that's 492 00:23:49,560 --> 00:23:51,320 Speaker 2: communicating to you value. 493 00:23:51,520 --> 00:23:53,840 Speaker 3: Hey, I'm going to give you defensive alerts when you're 494 00:23:53,840 --> 00:23:55,879 Speaker 3: watching an NFL game to let you know who might 495 00:23:55,960 --> 00:23:56,960 Speaker 3: be rushing the quarterback. 496 00:23:56,960 --> 00:23:58,800 Speaker 2: And you're going to feel smarter once that happened because 497 00:23:58,840 --> 00:23:59,960 Speaker 2: you were given that visual tip. 498 00:24:00,200 --> 00:24:00,400 Speaker 3: Right. 499 00:24:00,520 --> 00:24:03,560 Speaker 2: So it's like, how we use it in a really 500 00:24:03,680 --> 00:24:06,960 Speaker 2: artful way. That is the challenge. And when you just 501 00:24:07,040 --> 00:24:11,119 Speaker 2: throw it a bunch of people, No that doesn't work, right, No, 502 00:24:11,320 --> 00:24:11,880 Speaker 2: it's so true. 503 00:24:11,880 --> 00:24:13,840 Speaker 1: I mean it's it's really just the goal of closing 504 00:24:13,880 --> 00:24:16,000 Speaker 1: the gap between the fans and the teams and the 505 00:24:16,000 --> 00:24:17,800 Speaker 1: players that they love. And for so many years we've 506 00:24:17,840 --> 00:24:21,240 Speaker 1: been doing that with narrative and storytelling, but now using 507 00:24:21,359 --> 00:24:24,440 Speaker 1: data and information to make the fans smarter and more 508 00:24:24,440 --> 00:24:27,320 Speaker 1: invested is a great thing that aight of us provides. 509 00:24:27,320 --> 00:24:29,000 Speaker 1: And that's that's fantastic. 510 00:24:29,040 --> 00:24:29,840 Speaker 2: I can't wait. 511 00:24:29,840 --> 00:24:32,640 Speaker 1: I'm gonna have to tune into the next ANWUS cast 512 00:24:32,680 --> 00:24:34,119 Speaker 1: of a hockey game too, and maybe we get a 513 00:24:34,160 --> 00:24:35,800 Speaker 1: little bit smarter of a hockey fan there. 514 00:24:37,359 --> 00:24:38,119 Speaker 2: I mean, but that's you. 515 00:24:38,200 --> 00:24:40,360 Speaker 3: You were hitting the point, which is, if we can 516 00:24:40,359 --> 00:24:43,000 Speaker 3: make you feel a little bit smarter about what you're watching, 517 00:24:43,600 --> 00:24:46,000 Speaker 3: probably watch more of it. Right, how do you use 518 00:24:46,040 --> 00:24:48,480 Speaker 3: this data as an edification tool? And how do we 519 00:24:48,520 --> 00:24:50,560 Speaker 3: meet fans where they are? If you go really heavy, 520 00:24:50,600 --> 00:24:52,880 Speaker 3: really fast, people are going to feel alienated by that, 521 00:24:53,200 --> 00:24:55,879 Speaker 3: and again that's us not doing it right, but bringing 522 00:24:55,880 --> 00:24:58,720 Speaker 3: them along and you know, this is what's important, or 523 00:24:58,800 --> 00:25:01,880 Speaker 3: this is what's so accept He had a three percent 524 00:25:02,000 --> 00:25:05,960 Speaker 3: chance of making that shot for you know, hitting that 525 00:25:06,080 --> 00:25:07,040 Speaker 3: target or whatever. 526 00:25:07,840 --> 00:25:11,280 Speaker 2: How exceptional was right. So it's just it gives you 527 00:25:11,320 --> 00:25:13,800 Speaker 2: some context for what you're watching, because I think sometimes 528 00:25:14,840 --> 00:25:18,400 Speaker 2: you know, we watch sports and we're not because everybody's 529 00:25:18,440 --> 00:25:21,040 Speaker 2: so amazing, you're you're sort of sort of lose sight 530 00:25:21,119 --> 00:25:23,880 Speaker 2: of Wow, he's running at twenty miles per hour right now, 531 00:25:24,000 --> 00:25:25,240 Speaker 2: like that banana right. 532 00:25:25,800 --> 00:25:28,399 Speaker 1: Yeah, I don't. Yeah, driving at that speed is a 533 00:25:28,400 --> 00:25:30,760 Speaker 1: differentiating than running at it. I can't imagine it. Yeah, 534 00:25:30,760 --> 00:25:33,080 Speaker 1: it definitely comes you understand the gravity of what these 535 00:25:33,080 --> 00:25:35,040 Speaker 1: players are doing at such a high level that they make. 536 00:25:34,960 --> 00:25:35,600 Speaker 2: It look easy. 537 00:25:36,160 --> 00:25:39,760 Speaker 1: Yeah, exactly, I love it. Julius's a global head of 538 00:25:39,800 --> 00:25:41,520 Speaker 1: sports for eight of you guys. Thank you so much 539 00:25:41,520 --> 00:25:43,360 Speaker 1: for taking the time with me today on her playbook. 540 00:25:43,400 --> 00:25:44,119 Speaker 1: It's been a pleasure. 541 00:25:44,560 --> 00:25:46,320 Speaker 2: Oh, Melan's been so much fun. Thank you.