1 00:00:01,280 --> 00:00:03,520 Speaker 1: So you're talking about big data, and we have lots 2 00:00:03,560 --> 00:00:06,840 Speaker 1: of new big data, but we also have old big data. 3 00:00:07,280 --> 00:00:09,800 Speaker 1: And it's very interesting to us because we have the 4 00:00:09,880 --> 00:00:14,680 Speaker 1: historical archives of baseball video footage going back into the fifties, 5 00:00:14,720 --> 00:00:17,800 Speaker 1: but it's been sort of locked away on older media 6 00:00:17,920 --> 00:00:21,160 Speaker 1: and so digitizing this data making it available for fans, 7 00:00:21,440 --> 00:00:24,159 Speaker 1: true fans of baseball that just love this stuff. The 8 00:00:24,239 --> 00:00:27,120 Speaker 1: intention is that all that data is democratized and you 9 00:00:27,120 --> 00:00:28,720 Speaker 1: can do whatever you want with it. So it's not 10 00:00:28,800 --> 00:00:32,200 Speaker 1: just our curated content, but we're effectively turning that over 11 00:00:32,280 --> 00:00:37,520 Speaker 1: to fans to have fun with. Welcome to the Restless Ones. 12 00:00:37,880 --> 00:00:42,120 Speaker 1: I'm Jonathan Strickland. I've spent more than a decade really 13 00:00:42,400 --> 00:00:46,680 Speaker 1: learning about technology one makes it tick, and then describing 14 00:00:46,760 --> 00:00:50,159 Speaker 1: and explaining that to my audience. But it's the conversations 15 00:00:50,280 --> 00:00:54,080 Speaker 1: with the world's most unconventional thinkers, the leaders at the 16 00:00:54,080 --> 00:00:58,160 Speaker 1: intersection of technology and business that fascinate me the most. 17 00:00:58,800 --> 00:01:02,000 Speaker 1: In partnership with Team Mobile for Business, I explore the 18 00:01:02,080 --> 00:01:04,880 Speaker 1: unique set of challenges that see I o S and 19 00:01:05,040 --> 00:01:08,399 Speaker 1: c t o S face from advancements in cloud and 20 00:01:08,560 --> 00:01:12,880 Speaker 1: edge computing, software as a service, Internet of Things, and 21 00:01:13,319 --> 00:01:17,080 Speaker 1: of course five G we are often left wondering how 22 00:01:17,120 --> 00:01:26,120 Speaker 1: the leading minds and business continue to thrive. Let's find out. Today. 23 00:01:26,160 --> 00:01:29,759 Speaker 1: We have two guests from Major League Baseball or MLB 24 00:01:30,440 --> 00:01:33,160 Speaker 1: on the show. We're honored to have the Sons Williams, 25 00:01:33,240 --> 00:01:36,400 Speaker 1: Chief Product Officer and e VP of Product and Engineering, 26 00:01:36,640 --> 00:01:40,720 Speaker 1: as well as Truman Boys, s VP of Infrastructure. When 27 00:01:40,760 --> 00:01:43,880 Speaker 1: it comes to high tech, the game of baseball might 28 00:01:43,959 --> 00:01:47,880 Speaker 1: not leap to mind right away. The fundamentals of the 29 00:01:47,920 --> 00:01:52,400 Speaker 1: game have had relatively few changes over the years, but 30 00:01:52,520 --> 00:01:57,320 Speaker 1: as it turns out, sophisticated technology underlies every aspect of 31 00:01:57,360 --> 00:02:02,440 Speaker 1: the modern MLB organization, from establishing high speed connections between 32 00:02:02,480 --> 00:02:06,120 Speaker 1: the thirty baseball parks and the home offices to creating 33 00:02:06,440 --> 00:02:12,480 Speaker 1: unforgettable fan experiences. Technology is a core element in modern baseball. 34 00:02:13,320 --> 00:02:16,280 Speaker 1: The sant and Truman walked me through how MLB is 35 00:02:16,320 --> 00:02:20,040 Speaker 1: making use of technology and the challenges the organization faces 36 00:02:20,080 --> 00:02:22,640 Speaker 1: to make certain that the experiences they want to share 37 00:02:22,680 --> 00:02:26,720 Speaker 1: with the fans have the technological support to make them possible. 38 00:02:27,200 --> 00:02:30,280 Speaker 1: It turns out there's a lot to talk about, and 39 00:02:30,360 --> 00:02:34,240 Speaker 1: so our conversation will span two episodes, because well, I've 40 00:02:34,240 --> 00:02:37,480 Speaker 1: got to be honest there's just too much great information 41 00:02:37,520 --> 00:02:40,919 Speaker 1: to edit it all down. I started off asking each 42 00:02:40,960 --> 00:02:44,960 Speaker 1: of them about their own backgrounds. Thank you for both 43 00:02:45,080 --> 00:02:47,640 Speaker 1: being on the show. The sant I'd like to start 44 00:02:47,680 --> 00:02:50,959 Speaker 1: off by learning how you first became interested in technology. 45 00:02:51,919 --> 00:02:58,080 Speaker 1: So my background, I started as a chemical engineer, so it's, uh, 46 00:02:58,080 --> 00:03:00,680 Speaker 1: it's very different from what I'm doing right now. But 47 00:03:00,800 --> 00:03:03,360 Speaker 1: as I finished my undergrad and one of the things 48 00:03:03,440 --> 00:03:05,880 Speaker 1: I did during my grad was to be an intern 49 00:03:06,280 --> 00:03:09,840 Speaker 1: at a chemical factory. And that's when internet was taken out, 50 00:03:09,919 --> 00:03:14,160 Speaker 1: is the late ninety mid nineties, ninety nineties. I'm dating 51 00:03:14,160 --> 00:03:17,399 Speaker 1: myself here, and I was starting to double in technology 52 00:03:17,440 --> 00:03:21,040 Speaker 1: and computers, and I was comparing myself my internship but 53 00:03:21,680 --> 00:03:24,840 Speaker 1: as a chemical factory, which you know, these are factories 54 00:03:24,880 --> 00:03:27,680 Speaker 1: that are built fifty years ago or thirty years ago, 55 00:03:28,200 --> 00:03:31,000 Speaker 1: and you're looking at oh, we got to change the boiler, 56 00:03:31,400 --> 00:03:33,799 Speaker 1: which is now you have this, and I'm looking at 57 00:03:33,960 --> 00:03:37,520 Speaker 1: software and computers and it was so nascent at the time, 58 00:03:37,560 --> 00:03:40,480 Speaker 1: and I was like, this is exciting, things changing every 59 00:03:40,520 --> 00:03:44,280 Speaker 1: six months every year. And so that's when I said, okay, 60 00:03:44,400 --> 00:03:47,400 Speaker 1: you know what, I'm going to get myself more uh 61 00:03:47,440 --> 00:03:50,360 Speaker 1: into computers, and I did my masses and computer science. 62 00:03:50,400 --> 00:03:53,400 Speaker 1: I went straight from chemical injuring undergrad two masses and 63 00:03:53,440 --> 00:03:56,640 Speaker 1: computer science. And here I am right, so I could 64 00:03:56,640 --> 00:03:59,920 Speaker 1: have been working in a chemical factory. Uh right now, 65 00:04:00,040 --> 00:04:03,119 Speaker 1: but what I am I have to admit I did 66 00:04:03,120 --> 00:04:05,480 Speaker 1: not expect to hear that kind of a journey from 67 00:04:05,560 --> 00:04:10,720 Speaker 1: chemical engineering to working in computer science with MLB. That 68 00:04:10,880 --> 00:04:15,360 Speaker 1: is a phenomenal start to this conversation. Uh. And I'll 69 00:04:15,440 --> 00:04:17,559 Speaker 1: probably want to dive into that more in a second. 70 00:04:17,640 --> 00:04:19,400 Speaker 1: But Truman, I would also like to know from you, 71 00:04:19,440 --> 00:04:23,840 Speaker 1: how did you first get interested in tech? So I 72 00:04:23,839 --> 00:04:27,960 Speaker 1: got interested in online services prior to the Internet. So 73 00:04:28,240 --> 00:04:31,760 Speaker 1: you know, early services in the eighties actually compu Serve 74 00:04:31,839 --> 00:04:35,840 Speaker 1: and Prodigy and all of these really you know, predating 75 00:04:35,880 --> 00:04:39,400 Speaker 1: the Internet services. And I was completely enthralled by it. 76 00:04:39,720 --> 00:04:42,040 Speaker 1: I ended up, you know, as a teenager, actually making 77 00:04:42,240 --> 00:04:46,200 Speaker 1: early bulletin board systems, connecting them up to other bulletin 78 00:04:46,200 --> 00:04:49,159 Speaker 1: board systems, and I just knew at that point how 79 00:04:49,200 --> 00:04:52,960 Speaker 1: important all of this would be. UM connecting with information, 80 00:04:53,120 --> 00:04:56,880 Speaker 1: connecting with other people. Early email, that was just mind 81 00:04:56,880 --> 00:04:59,360 Speaker 1: blowing that you could actually you could communicate with people 82 00:04:59,360 --> 00:05:02,960 Speaker 1: when you'd get sponses back and at nineteen, I started 83 00:05:02,960 --> 00:05:05,240 Speaker 1: working at a small I s P with some friends. 84 00:05:05,880 --> 00:05:08,920 Speaker 1: We basically created a dial up I s P for 85 00:05:09,120 --> 00:05:11,320 Speaker 1: New Jersey and New York, and you know, we just 86 00:05:11,320 --> 00:05:15,640 Speaker 1: started building things and I just kept going. Um, loved 87 00:05:15,680 --> 00:05:18,960 Speaker 1: building networks, loved connecting people, and you know, from there 88 00:05:19,000 --> 00:05:22,760 Speaker 1: it was just a journey into technology and just loving 89 00:05:22,760 --> 00:05:25,359 Speaker 1: every minute of it. This is so interesting. So we 90 00:05:25,480 --> 00:05:29,800 Speaker 1: have Massan who you went into an industry where it 91 00:05:29,839 --> 00:05:33,200 Speaker 1: was heavily dependent upon legacy systems that had not really 92 00:05:33,320 --> 00:05:39,480 Speaker 1: changed for decades, and was just entering into that revolutionary 93 00:05:39,720 --> 00:05:45,159 Speaker 1: transformational period where you start seeing more software implementations and automation, 94 00:05:45,600 --> 00:05:48,680 Speaker 1: and that sparked your interest. And then Truman with you, 95 00:05:48,760 --> 00:05:52,520 Speaker 1: we have someone who got into online service providers, the 96 00:05:52,560 --> 00:05:56,800 Speaker 1: predecessors to I s p s, and really cutting your 97 00:05:56,800 --> 00:06:00,400 Speaker 1: teeth in that world. It's interesting how that ex variants, 98 00:06:00,440 --> 00:06:04,000 Speaker 1: that encountering of technology really was what set you on 99 00:06:04,040 --> 00:06:07,200 Speaker 1: this pathway. I'm very curious of the Truman, what actually 100 00:06:07,200 --> 00:06:10,160 Speaker 1: brought you over to MLB? Where were you before that 101 00:06:10,200 --> 00:06:13,719 Speaker 1: and how did you move over here? So I was 102 00:06:14,120 --> 00:06:19,480 Speaker 1: at a financial services technology organization and was working on 103 00:06:20,320 --> 00:06:23,600 Speaker 1: a whole different set of problems ultra low latency trading, 104 00:06:23,800 --> 00:06:28,440 Speaker 1: you know, market data, all the fintech components embargoed data, 105 00:06:28,880 --> 00:06:32,840 Speaker 1: and it's a massive shift to transition from that world 106 00:06:33,000 --> 00:06:34,680 Speaker 1: to this other world. I think there's kind of two 107 00:06:34,680 --> 00:06:38,280 Speaker 1: common things that in terms of career and passion, just 108 00:06:38,480 --> 00:06:40,560 Speaker 1: making sure that I'm doing things that feel like they 109 00:06:40,560 --> 00:06:43,720 Speaker 1: have value one of them. You know, from the financial 110 00:06:43,760 --> 00:06:46,200 Speaker 1: services side, you know, market transparency I think is really 111 00:06:46,240 --> 00:06:50,800 Speaker 1: important just holistically across the world, and that's what brought 112 00:06:50,800 --> 00:06:55,240 Speaker 1: me into that world. And then making people happy is baseball. 113 00:06:55,760 --> 00:06:59,240 Speaker 1: So I had an opportunity to uh come in and 114 00:07:00,000 --> 00:07:03,400 Speaker 1: are working in the infrastructure space, which was greatly needed 115 00:07:03,400 --> 00:07:05,680 Speaker 1: at the time. As we were, you know, MLB itself 116 00:07:05,760 --> 00:07:10,400 Speaker 1: was transitioning um parts of its staff and technologies had 117 00:07:10,440 --> 00:07:13,160 Speaker 1: moved to another organization, and so it was a bit 118 00:07:13,160 --> 00:07:16,960 Speaker 1: of a rebuilding exercise and for me it was a challenge, 119 00:07:17,080 --> 00:07:19,760 Speaker 1: and I was looking for a wonderful set of challenges 120 00:07:19,840 --> 00:07:23,160 Speaker 1: with organization and technology and a good mix of both. 121 00:07:23,760 --> 00:07:25,920 Speaker 1: So in a way, you were coming into MLB while 122 00:07:25,920 --> 00:07:28,880 Speaker 1: it was going through its own transformational stage. It's so 123 00:07:28,920 --> 00:07:31,920 Speaker 1: fascinating to me when we see these organizations that have 124 00:07:32,480 --> 00:07:37,080 Speaker 1: incredible histories behind them and the amount of work and 125 00:07:37,120 --> 00:07:42,040 Speaker 1: effort it takes in order to reinvent these various industries 126 00:07:42,080 --> 00:07:45,080 Speaker 1: so that they can keep pace with the times the 127 00:07:45,120 --> 00:07:47,679 Speaker 1: steenth What about you, I'm very curious how the chemical 128 00:07:47,760 --> 00:07:52,320 Speaker 1: engineer ended up over at MLB. The chemical engineer was 129 00:07:52,360 --> 00:07:56,840 Speaker 1: at Microsoft and just before coming to MLB, I was 130 00:07:56,880 --> 00:07:59,600 Speaker 1: at Amazon for five years prior to that, right, so 131 00:08:00,400 --> 00:08:03,400 Speaker 1: um at Amazon, I was running the product and technology 132 00:08:03,520 --> 00:08:05,880 Speaker 1: for one of the fastest growing business which was the 133 00:08:06,080 --> 00:08:09,960 Speaker 1: online advertising technology there and it was going great. That's 134 00:08:10,000 --> 00:08:13,200 Speaker 1: when MLB reached out to me. And at that time, 135 00:08:13,400 --> 00:08:16,679 Speaker 1: and you know, I use this framework called them regret 136 00:08:16,760 --> 00:08:21,120 Speaker 1: minimization framework, right, So what that is is effectively, what 137 00:08:21,160 --> 00:08:25,440 Speaker 1: would you regret not doing ten or twenty years from now, 138 00:08:25,600 --> 00:08:29,360 Speaker 1: but I regret leaving Amazon, and or would you regret 139 00:08:29,440 --> 00:08:31,960 Speaker 1: not working for one of the top sports leagues that 140 00:08:32,200 --> 00:08:36,760 Speaker 1: really brick that brings people together, creates communities. And that's 141 00:08:36,880 --> 00:08:39,320 Speaker 1: was very simple. At that point, was talking to the 142 00:08:39,360 --> 00:08:42,960 Speaker 1: commissioner and Chris Mannach at at the league. They said, hey, look, 143 00:08:42,960 --> 00:08:47,000 Speaker 1: we're at the inflection point right sports media and things 144 00:08:47,040 --> 00:08:49,559 Speaker 1: that we need to do. You know, it's it's a 145 00:08:49,600 --> 00:08:53,240 Speaker 1: green field you know, you get to come into rematch 146 00:08:53,360 --> 00:08:56,160 Speaker 1: and what we could do to get as a sports league. 147 00:08:56,200 --> 00:08:57,840 Speaker 1: We tend to get caught up when you're in a 148 00:08:57,880 --> 00:09:02,280 Speaker 1: particular role or particular organ station, like you know what's next? 149 00:09:02,400 --> 00:09:05,160 Speaker 1: What are you pushing so important to this organization? But 150 00:09:06,360 --> 00:09:09,160 Speaker 1: using the regret minimization framework, we really take a step back. 151 00:09:09,280 --> 00:09:13,120 Speaker 1: It becomes very clear I would definitely regret not joining 152 00:09:13,120 --> 00:09:16,640 Speaker 1: the league. That's what brought me to MLP and my 153 00:09:17,360 --> 00:09:20,559 Speaker 1: role here. That's also a really interesting approach to sort 154 00:09:20,600 --> 00:09:25,200 Speaker 1: of risk assessment, right, the idea of well, I'm measuring risk, 155 00:09:25,280 --> 00:09:28,240 Speaker 1: but I'm also measuring is this going to be the 156 00:09:28,280 --> 00:09:30,079 Speaker 1: thing that a month from now I'm going to wake 157 00:09:30,200 --> 00:09:32,280 Speaker 1: up and say, why didn't I try that? That could 158 00:09:32,280 --> 00:09:35,240 Speaker 1: have been a challenge that really energized me and I 159 00:09:35,240 --> 00:09:39,720 Speaker 1: could have made a real impact there. Well. Normally, at 160 00:09:39,720 --> 00:09:43,440 Speaker 1: this point I would ask my guest to describe their 161 00:09:43,559 --> 00:09:47,160 Speaker 1: job as if they were trying to tell someone in 162 00:09:47,200 --> 00:09:50,480 Speaker 1: a casual setting what it is they do. However, since 163 00:09:50,520 --> 00:09:53,720 Speaker 1: I have two of you, I have the unique opportunity 164 00:09:54,000 --> 00:09:55,960 Speaker 1: to make you try and do that to each other. 165 00:09:56,440 --> 00:09:59,600 Speaker 1: So the scent, could you try and tell me what 166 00:10:00,000 --> 00:10:05,240 Speaker 1: Truman's job is Yeah. Absolutely, So Truman is a counterpart 167 00:10:05,280 --> 00:10:10,160 Speaker 1: to me. Truman brands are infrastructure. Think about every thing 168 00:10:10,360 --> 00:10:15,280 Speaker 1: that technology infrastructure that from the ballpark to the MLB offices, 169 00:10:15,559 --> 00:10:19,600 Speaker 1: all the thirty ballparks across the country, the WiFi to 170 00:10:20,040 --> 00:10:24,720 Speaker 1: the systems, the security systems to the cybersecurity, all the 171 00:10:24,800 --> 00:10:30,120 Speaker 1: base infrastructure that we need to have to execute things 172 00:10:30,120 --> 00:10:33,160 Speaker 1: on top of it. My digital products does not exist 173 00:10:33,240 --> 00:10:36,560 Speaker 1: if Truman does not do his job. And that's not 174 00:10:36,720 --> 00:10:39,560 Speaker 1: just at all the thirty ballparks, but also all our 175 00:10:39,600 --> 00:10:43,040 Speaker 1: offices and all of the infrastructure. So he's really a 176 00:10:43,120 --> 00:10:46,520 Speaker 1: critical and important partner to me. Well, Truman, the Santh 177 00:10:46,679 --> 00:10:49,240 Speaker 1: really talk to you up, so it's now your job 178 00:10:49,640 --> 00:10:51,680 Speaker 1: to tell me what the SANS job is all about. 179 00:10:52,240 --> 00:10:55,920 Speaker 1: And and and likewise, phenomenal collaboration between the SANTH and 180 00:10:55,920 --> 00:10:59,680 Speaker 1: I and the SANTH coming in and running product and 181 00:11:00,040 --> 00:11:03,600 Speaker 1: engineering has completely transformed the way that we build products. 182 00:11:03,600 --> 00:11:06,240 Speaker 1: So I would say that the SANS job is basically 183 00:11:06,280 --> 00:11:08,400 Speaker 1: to plot out what it is that we want to 184 00:11:08,400 --> 00:11:12,600 Speaker 1: build from a product perspective, taking a look at existing 185 00:11:12,600 --> 00:11:14,880 Speaker 1: products that we already have that you have to life cycle, 186 00:11:15,240 --> 00:11:18,000 Speaker 1: continue to mature, that's where the fans are, that's where 187 00:11:18,040 --> 00:11:21,560 Speaker 1: all of our user bases. Finding ways to build and 188 00:11:21,679 --> 00:11:24,720 Speaker 1: invest in those properties, and then you know, to run 189 00:11:24,720 --> 00:11:28,480 Speaker 1: a team that continues to add features and connect back 190 00:11:28,520 --> 00:11:31,080 Speaker 1: with the market. What do they want? What are they 191 00:11:31,120 --> 00:11:33,960 Speaker 1: looking for? And that's what his team is embraced and 192 00:11:34,000 --> 00:11:37,439 Speaker 1: we've see in phenomenal you know impact there around engagement 193 00:11:37,559 --> 00:11:41,320 Speaker 1: around uh, you know, gaming opportunities, Um, you know what 194 00:11:41,360 --> 00:11:44,800 Speaker 1: we're doing in terms of second screen experiences, just getting 195 00:11:44,840 --> 00:11:47,600 Speaker 1: even advertising right, you know, how do we insert the 196 00:11:47,679 --> 00:11:51,400 Speaker 1: right personalized add into media streams? And all of that 197 00:11:51,559 --> 00:11:54,040 Speaker 1: falls into the sans work. So I view it as 198 00:11:54,840 --> 00:11:58,240 Speaker 1: it's a really hard problem to solve, which is always 199 00:11:58,240 --> 00:12:01,679 Speaker 1: knowing what people are looking for and and building it. Well, 200 00:12:01,720 --> 00:12:03,920 Speaker 1: then this is really fortunate for me to be able 201 00:12:03,960 --> 00:12:06,840 Speaker 1: to talk with two people whose work complements one another, 202 00:12:07,040 --> 00:12:09,280 Speaker 1: that the one enables the other, and there's sort of 203 00:12:09,280 --> 00:12:13,120 Speaker 1: a feedback loop going on where you are able to 204 00:12:13,160 --> 00:12:16,240 Speaker 1: determine what is it that the organization needs and then 205 00:12:16,280 --> 00:12:20,280 Speaker 1: how do you meet those needs both from infrastructure and 206 00:12:20,360 --> 00:12:24,400 Speaker 1: from an experience. Well, what is something about your job 207 00:12:24,480 --> 00:12:28,400 Speaker 1: that you suspect the average person is not aware of? 208 00:12:28,640 --> 00:12:32,720 Speaker 1: Someone who perhaps is even an ardent fan and goes 209 00:12:32,800 --> 00:12:37,400 Speaker 1: to baseball games and things, but doesn't really get that 210 00:12:37,440 --> 00:12:39,719 Speaker 1: this is part of what you do. And Truman, I 211 00:12:39,800 --> 00:12:42,520 Speaker 1: guess we'll start with you. I'd say most people don't 212 00:12:42,559 --> 00:12:45,440 Speaker 1: know that some of the hardest work is happening during 213 00:12:45,440 --> 00:12:48,520 Speaker 1: the off season. So you know, when baseball is not 214 00:12:48,679 --> 00:12:51,360 Speaker 1: being played, that's when we're going out to ballparks. We're 215 00:12:51,360 --> 00:12:55,720 Speaker 1: installing new cameras, we're refreshing all our infrastructure, we're building 216 00:12:55,720 --> 00:12:58,760 Speaker 1: applications and getting ready to release them. So there's so 217 00:12:58,840 --> 00:13:02,199 Speaker 1: much that's happening in the off season. And so i'd say, 218 00:13:02,200 --> 00:13:06,319 Speaker 1: you know, November through March, we're busy, very busy. Every 219 00:13:06,360 --> 00:13:08,600 Speaker 1: time that we think of a project, it's always times 220 00:13:08,640 --> 00:13:10,680 Speaker 1: thirty and sometimes you know, if it has to go 221 00:13:10,720 --> 00:13:14,640 Speaker 1: out to minor league ballparks or or other areas that 222 00:13:14,640 --> 00:13:18,400 Speaker 1: we're investing in in other leagues. Um, you know, there's 223 00:13:18,480 --> 00:13:20,720 Speaker 1: there's quite a bit that goes into that travel schedule, 224 00:13:20,880 --> 00:13:24,000 Speaker 1: and so, um, you know, it's just every task is 225 00:13:24,040 --> 00:13:27,040 Speaker 1: a big task, right the soth What about you? What 226 00:13:27,160 --> 00:13:29,920 Speaker 1: is something that about your job that people just aren't 227 00:13:30,000 --> 00:13:33,800 Speaker 1: aware of? One is how much fun it is, even 228 00:13:33,880 --> 00:13:37,439 Speaker 1: the toughest day at at Major League Baseball is fun 229 00:13:37,520 --> 00:13:39,600 Speaker 1: And at any point do you feel frustrated. You just 230 00:13:39,640 --> 00:13:41,880 Speaker 1: go to a game and you feel you'll know that 231 00:13:42,520 --> 00:13:45,400 Speaker 1: your reason why you exist. So it's uh, it gives 232 00:13:45,400 --> 00:13:49,120 Speaker 1: a man sense of satisfaction, so that on an everyday basis. 233 00:13:49,400 --> 00:13:53,280 Speaker 1: The other thing about the job an average UH fan 234 00:13:53,440 --> 00:13:57,280 Speaker 1: or a person may not know is UH how much 235 00:13:57,440 --> 00:14:01,040 Speaker 1: tracking that happens on the field. In a single play 236 00:14:01,320 --> 00:14:04,800 Speaker 1: each player at any instant, we're at tracking eighteen points 237 00:14:04,880 --> 00:14:07,280 Speaker 1: of the player. We're tracking the spin on the ball. 238 00:14:07,360 --> 00:14:10,080 Speaker 1: We're tracking u at a at a frame rate that 239 00:14:10,440 --> 00:14:14,200 Speaker 1: high fidelity that never existed before, and we provided back 240 00:14:14,240 --> 00:14:16,439 Speaker 1: to these players, back to these clubs so so they 241 00:14:16,440 --> 00:14:21,880 Speaker 1: can leverage this information for better coaching or injury prevention. 242 00:14:22,000 --> 00:14:26,160 Speaker 1: So what seems like a regular baseball game, we generate 243 00:14:26,560 --> 00:14:29,560 Speaker 1: petabytes of data that we pass back to the club 244 00:14:29,600 --> 00:14:32,160 Speaker 1: and we're making sure each of the data is accurate. 245 00:14:32,280 --> 00:14:34,960 Speaker 1: Is what a lot of my teams do. Every hardcore 246 00:14:34,960 --> 00:14:38,760 Speaker 1: fan of baseball I know is a statistics junkie. And 247 00:14:38,840 --> 00:14:40,760 Speaker 1: as you say, that's going to lead us to some 248 00:14:41,000 --> 00:14:43,600 Speaker 1: pretty cool conversations a little bit later in this episode 249 00:14:43,880 --> 00:14:47,400 Speaker 1: to talk about ways to to leverage that both behind 250 00:14:47,400 --> 00:14:51,680 Speaker 1: the scenes and for the fan experience as well. Well, 251 00:14:51,720 --> 00:14:54,640 Speaker 1: then I'll follow that up, Truman, What is something about 252 00:14:54,640 --> 00:14:57,360 Speaker 1: your job you find just really exciting, like the thing 253 00:14:57,400 --> 00:14:59,680 Speaker 1: that gets you out of bed in the morning. I 254 00:15:00,080 --> 00:15:03,560 Speaker 1: think it's it's, you know, similar to the points that 255 00:15:03,640 --> 00:15:06,360 Speaker 1: this ath is raising, which is really around the connection 256 00:15:06,400 --> 00:15:10,320 Speaker 1: to the game. So it's taking technology, bridging it and 257 00:15:10,360 --> 00:15:14,080 Speaker 1: just seeing how it directly impacts what we're doing. And 258 00:15:14,160 --> 00:15:16,440 Speaker 1: many times, you know, technology is just you know, kind 259 00:15:16,440 --> 00:15:18,440 Speaker 1: of the mundane. You know, here's how we all like 260 00:15:18,520 --> 00:15:20,760 Speaker 1: log in, we check our email. This thing actually has 261 00:15:20,800 --> 00:15:23,080 Speaker 1: a direct impact on the experience and so it makes 262 00:15:23,120 --> 00:15:25,320 Speaker 1: the fans happy. And you know things in point being 263 00:15:25,760 --> 00:15:29,120 Speaker 1: the accuracy of calls, making sure that a replay review 264 00:15:29,160 --> 00:15:32,240 Speaker 1: can happen within a minute. How do you do that, Well, 265 00:15:32,280 --> 00:15:34,000 Speaker 1: you have to have lots of cameras and lots of 266 00:15:34,040 --> 00:15:36,000 Speaker 1: bandwidth and send that back to New York and have 267 00:15:36,120 --> 00:15:38,760 Speaker 1: operators that know how to pull up those angles very quickly, 268 00:15:39,240 --> 00:15:42,720 Speaker 1: increasing the pace of the game itself. Historically, you know, 269 00:15:42,800 --> 00:15:44,880 Speaker 1: baseball is a long game, and you know, how do 270 00:15:44,920 --> 00:15:47,840 Speaker 1: you keep it relevant and how do you keep it 271 00:15:47,920 --> 00:15:50,560 Speaker 1: you know, something that people want to enjoy and watch 272 00:15:50,600 --> 00:15:52,480 Speaker 1: a you know, a two and a half three hour game. 273 00:15:52,960 --> 00:15:56,280 Speaker 1: So you know, finding ways to build products that allow 274 00:15:56,360 --> 00:15:59,240 Speaker 1: us to do that, you know, consolidated games, catch up games, 275 00:15:59,400 --> 00:16:01,760 Speaker 1: just all of the attech. But it makes people happy. 276 00:16:01,800 --> 00:16:04,120 Speaker 1: I would say the number one thing is tech makes 277 00:16:04,160 --> 00:16:07,840 Speaker 1: people happy because it's you know, improving the experience. And 278 00:16:07,840 --> 00:16:09,840 Speaker 1: and Basant, I think you kind of touched on this 279 00:16:09,920 --> 00:16:12,680 Speaker 1: in your last answer, but is there anything else about 280 00:16:12,680 --> 00:16:14,800 Speaker 1: your job that really excites you that you think of 281 00:16:14,920 --> 00:16:18,800 Speaker 1: as this. I'm so glad when I did that regret 282 00:16:18,840 --> 00:16:23,880 Speaker 1: minimization approach, that that I took this choice. Yeah, absolutely right. 283 00:16:23,960 --> 00:16:26,320 Speaker 1: One thing I can tell you is no two seasons 284 00:16:26,320 --> 00:16:29,200 Speaker 1: are the same, right, No even two months are the same. 285 00:16:29,320 --> 00:16:33,280 Speaker 1: In baseball, it's exciting the constant pace of change, not 286 00:16:33,400 --> 00:16:37,480 Speaker 1: just what's happening on the field, but also what we're 287 00:16:37,480 --> 00:16:40,120 Speaker 1: seeing now with a fan base, like the way they 288 00:16:40,160 --> 00:16:43,680 Speaker 1: consume baseball, the way they engage with baseball is changing 289 00:16:43,720 --> 00:16:46,240 Speaker 1: fast than ever before. And you don't think about it 290 00:16:46,280 --> 00:16:48,080 Speaker 1: is it's, oh, it's a hundred twenty year old spot, 291 00:16:48,160 --> 00:16:52,280 Speaker 1: but rather thinking about the changes that are happening in 292 00:16:52,320 --> 00:16:56,160 Speaker 1: real time. You know, last year was not a great change, 293 00:16:56,200 --> 00:16:58,280 Speaker 1: but still there was a lot of change. So and 294 00:16:58,320 --> 00:17:00,080 Speaker 1: I think it's just going to continue to act the 295 00:17:00,160 --> 00:17:02,520 Speaker 1: right thing. And that to me is exciting about this 296 00:17:02,600 --> 00:17:05,239 Speaker 1: job and it gets me up every day. Uh. And 297 00:17:05,280 --> 00:17:07,680 Speaker 1: I have to say most people who work in tech 298 00:17:07,720 --> 00:17:11,640 Speaker 1: fields in businesses, they don't get to hear their consumers 299 00:17:11,760 --> 00:17:15,400 Speaker 1: burst out into a standing ovation when something truly amazing 300 00:17:15,440 --> 00:17:19,439 Speaker 1: shows on the screen, or when they're able to augment 301 00:17:20,200 --> 00:17:23,200 Speaker 1: the already incredible experience of being at a baseball game 302 00:17:23,600 --> 00:17:30,160 Speaker 1: through technology. So that has to be pretty astounding. Absolutely. Yeah, 303 00:17:30,200 --> 00:17:32,360 Speaker 1: it's been fun. I've been here to years and it's 304 00:17:32,880 --> 00:17:36,000 Speaker 1: every day it's been fun. I gotta say that's great. 305 00:17:44,760 --> 00:17:48,040 Speaker 1: If there's one thing most businesses can agree on these days, 306 00:17:48,320 --> 00:17:51,440 Speaker 1: it's that change has never come about so quickly. New 307 00:17:51,480 --> 00:17:54,520 Speaker 1: ways of working have become the norm. As a result, 308 00:17:54,800 --> 00:17:57,120 Speaker 1: the status quo no longer cuts it when it comes 309 00:17:57,160 --> 00:18:00,639 Speaker 1: to helping businesses adapt and innovate. That's why T Mobile 310 00:18:00,640 --> 00:18:05,080 Speaker 1: for Business uses unconventional thinking to help businesses work smarter 311 00:18:05,280 --> 00:18:09,359 Speaker 1: and grow faster. 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Slash Unconventional Open Signal awarded T mobile 323 00:18:48,800 --> 00:18:51,200 Speaker 1: fastest five G network based on average speeds USA five 324 00:18:51,240 --> 00:18:54,760 Speaker 1: G User Experience Report January. Capable device required coverage not 325 00:18:54,840 --> 00:18:57,560 Speaker 1: available in some areas. Some users may require certain planner features. 326 00:18:57,560 --> 00:19:07,840 Speaker 1: Set mobile dot Com next. I wanted to learn more 327 00:19:07,960 --> 00:19:12,159 Speaker 1: about how MLB is using technology today, and I was 328 00:19:12,200 --> 00:19:15,160 Speaker 1: shocked to learn that MLB took the concept of big 329 00:19:15,280 --> 00:19:19,480 Speaker 1: data and cranked it up to eleven. Can you sort 330 00:19:19,520 --> 00:19:23,600 Speaker 1: of explain in what ways MLB now is really leaning 331 00:19:23,840 --> 00:19:28,600 Speaker 1: on technology, both in the ballparks and also like within 332 00:19:28,680 --> 00:19:30,359 Speaker 1: the office. Is kind of give us an idea of 333 00:19:31,080 --> 00:19:36,680 Speaker 1: your job is dealing with infrastructure, What does that actually encapsulate. Sure. Well, 334 00:19:36,960 --> 00:19:39,000 Speaker 1: first of all, it's it's a lot of data. So 335 00:19:39,200 --> 00:19:44,560 Speaker 1: there's there's constantly streaming data from statistics and video that's 336 00:19:44,600 --> 00:19:47,280 Speaker 1: coming out of all thirty ballparks and on some days 337 00:19:47,520 --> 00:19:49,520 Speaker 1: you know, it's coming out of fifteen of those ballparks 338 00:19:49,520 --> 00:19:52,439 Speaker 1: at the same time. So the way which we lean 339 00:19:52,520 --> 00:19:56,040 Speaker 1: on technology, it's really at every layer. It's from production, 340 00:19:56,359 --> 00:20:01,040 Speaker 1: it's from being able to have highly accurate replay and 341 00:20:01,520 --> 00:20:04,600 Speaker 1: really highly accurate understanding of what's happening on the field. 342 00:20:05,200 --> 00:20:07,440 Speaker 1: And so with all of that technology which we've invested 343 00:20:07,480 --> 00:20:10,040 Speaker 1: in at all of the ballparks to create a common platform, 344 00:20:10,520 --> 00:20:13,880 Speaker 1: we now have a uniform way to address what actually happened, 345 00:20:14,160 --> 00:20:17,120 Speaker 1: what is playing out on that field, you know, describing 346 00:20:17,760 --> 00:20:20,760 Speaker 1: how detailed it is. It also provides a way for 347 00:20:20,920 --> 00:20:23,399 Speaker 1: us to take that data and provide it back for coaching, 348 00:20:23,920 --> 00:20:26,520 Speaker 1: so you see over time that not only is the 349 00:20:26,640 --> 00:20:29,800 Speaker 1: game being affected you know, that's currently being played, but 350 00:20:29,960 --> 00:20:32,600 Speaker 1: that there's this feedback loop to give back to UMP, 351 00:20:32,680 --> 00:20:35,680 Speaker 1: to the replay team and also two coaches so that 352 00:20:35,720 --> 00:20:38,760 Speaker 1: they're able to over time you have just better calls 353 00:20:38,880 --> 00:20:41,960 Speaker 1: and better players. I think overall, the way that technology 354 00:20:42,040 --> 00:20:44,200 Speaker 1: is affecting it is that we're having a better experience 355 00:20:44,280 --> 00:20:47,120 Speaker 1: for the fans, but we're also providing a better experience 356 00:20:47,720 --> 00:20:50,360 Speaker 1: for the game itself. And we're improving just the way 357 00:20:50,440 --> 00:20:53,040 Speaker 1: that everything comes together, and we start to have better 358 00:20:53,160 --> 00:20:55,840 Speaker 1: on field dynamics. And I think the next piece that 359 00:20:56,000 --> 00:20:58,560 Speaker 1: you know, to kind of tease together is what is 360 00:20:58,600 --> 00:21:01,000 Speaker 1: it like to experience the game of baseball in a ballpark. 361 00:21:01,400 --> 00:21:03,680 Speaker 1: We don't really have that opportunity last year, and and 362 00:21:03,800 --> 00:21:06,159 Speaker 1: as people are returning, you know, this year and the 363 00:21:06,240 --> 00:21:09,680 Speaker 1: numbers have been phenomenal, it's how do we make this 364 00:21:09,960 --> 00:21:13,080 Speaker 1: more frictionless? You know, the long lines that's going to 365 00:21:13,160 --> 00:21:15,200 Speaker 1: go away, and how do we get to a place 366 00:21:15,280 --> 00:21:18,360 Speaker 1: where it's easy to get in, it's easy to sit down, 367 00:21:18,520 --> 00:21:21,000 Speaker 1: get your food, watch a game, and leave. And so 368 00:21:21,160 --> 00:21:23,359 Speaker 1: we're interested in all the technologies that would allow that 369 00:21:23,440 --> 00:21:27,600 Speaker 1: to happen. And I love that this is also a 370 00:21:27,720 --> 00:21:31,359 Speaker 1: way of leveling the playing field across different ballparks and 371 00:21:31,560 --> 00:21:34,320 Speaker 1: very different regions that they all have access to this 372 00:21:35,000 --> 00:21:38,359 Speaker 1: same sort of technological infrastructure so that you are able 373 00:21:38,480 --> 00:21:41,920 Speaker 1: to have that experience no matter where a game is 374 00:21:42,000 --> 00:21:45,520 Speaker 1: being played and bassans, so we touched on a little 375 00:21:45,520 --> 00:21:48,600 Speaker 1: bit about this technology gathering up all this data. This 376 00:21:48,880 --> 00:21:51,560 Speaker 1: is I think the very definition of big data, the 377 00:21:51,720 --> 00:21:56,760 Speaker 1: idea of of collecting enormous amounts of information. So I 378 00:21:56,840 --> 00:22:00,000 Speaker 1: assume that that part of your responsibility also is figure 379 00:22:00,160 --> 00:22:03,879 Speaker 1: out ways of leveraging all that information and actually finding 380 00:22:03,960 --> 00:22:08,920 Speaker 1: meaning and usefulness out of that. Is that an accurate representation? Yeah? Absolutely, 381 00:22:09,200 --> 00:22:11,680 Speaker 1: So there's two aspects to this, right. So there we 382 00:22:11,800 --> 00:22:15,240 Speaker 1: talked about the data collection on the fear right, we 383 00:22:15,400 --> 00:22:18,200 Speaker 1: installed the new tracking system Truman's team a clear and 384 00:22:18,560 --> 00:22:21,359 Speaker 1: help do all of that across all the thirty Major 385 00:22:21,640 --> 00:22:25,440 Speaker 1: League ballparks. So basically now we track every movement on 386 00:22:25,520 --> 00:22:27,680 Speaker 1: the field with such high fidelity. So we got a 387 00:22:27,720 --> 00:22:29,159 Speaker 1: lot of the data. So what do we do with this? 388 00:22:29,800 --> 00:22:32,240 Speaker 1: There's two ways to think about it, right. One is 389 00:22:32,520 --> 00:22:35,320 Speaker 1: how could we use to better the game itself? Right? 390 00:22:35,359 --> 00:22:38,200 Speaker 1: So so we uh, that's we give this data to 391 00:22:38,280 --> 00:22:40,600 Speaker 1: the clubs. We give this data to the players to 392 00:22:40,880 --> 00:22:42,880 Speaker 1: help them refine the game. You know, you can see 393 00:22:42,920 --> 00:22:45,200 Speaker 1: that a lot of them as leveraging that pretty significantly, 394 00:22:45,320 --> 00:22:47,080 Speaker 1: and you know pictures are getting better and better with 395 00:22:47,160 --> 00:22:50,680 Speaker 1: that information. And then there's the second aspect to it. 396 00:22:50,960 --> 00:22:53,520 Speaker 1: What can we do from a fan perspective? Can we 397 00:22:53,760 --> 00:22:58,120 Speaker 1: take this data and showcase and provide fans and experience 398 00:22:58,440 --> 00:23:00,680 Speaker 1: that they're never used to get before. Right. So a 399 00:23:00,840 --> 00:23:03,280 Speaker 1: one use case that we have been dabbling with is 400 00:23:03,880 --> 00:23:06,840 Speaker 1: because we have so much data, we can literally recreate 401 00:23:07,000 --> 00:23:11,520 Speaker 1: the game from any angle you want. We launched the 402 00:23:11,640 --> 00:23:16,360 Speaker 1: first generation of that called field vision, which basically being 403 00:23:16,440 --> 00:23:18,720 Speaker 1: able to see the game from an angle there was 404 00:23:18,920 --> 00:23:21,919 Speaker 1: there was where there's no camera. So can we provide 405 00:23:21,920 --> 00:23:25,359 Speaker 1: an experience to fans that is kind of fun? Like 406 00:23:25,520 --> 00:23:27,639 Speaker 1: let's say from the ice of Mookie bat when he's 407 00:23:27,640 --> 00:23:30,760 Speaker 1: sliding in into first space, right, So can you see 408 00:23:30,800 --> 00:23:34,960 Speaker 1: that what was he seen when at that point in time? 409 00:23:35,040 --> 00:23:36,920 Speaker 1: So we're trying to step take a step back and 410 00:23:37,000 --> 00:23:39,879 Speaker 1: see how could we take this data and use this 411 00:23:39,920 --> 00:23:42,600 Speaker 1: in a way that it's kind of fun for fans 412 00:23:42,680 --> 00:23:45,080 Speaker 1: to immerse themselves into the game. We're in early stages 413 00:23:45,119 --> 00:23:48,880 Speaker 1: of that, but for folks, you should just go look 414 00:23:48,960 --> 00:23:52,080 Speaker 1: up field vision. I think back to when I was 415 00:23:52,119 --> 00:23:55,240 Speaker 1: watching baseball growing up, and if something happened where a 416 00:23:55,359 --> 00:23:58,200 Speaker 1: camera operator wasn't, you would hear about it, but you 417 00:23:58,200 --> 00:24:00,720 Speaker 1: wouldn't really necessarily see it. And now we've reached a 418 00:24:00,760 --> 00:24:04,440 Speaker 1: point where we've got this proliferation of cameras and this 419 00:24:04,600 --> 00:24:10,200 Speaker 1: amazing way of recreating a moment, and also just that 420 00:24:10,359 --> 00:24:16,760 Speaker 1: idea of leveraging data that quickly and that effectively. That really, 421 00:24:16,880 --> 00:24:20,920 Speaker 1: to me is one of those amazing instances of the 422 00:24:21,280 --> 00:24:24,880 Speaker 1: actual implementation of a big data solution. So now having 423 00:24:24,960 --> 00:24:28,760 Speaker 1: this real time approach to making use to enormous amounts 424 00:24:28,800 --> 00:24:32,200 Speaker 1: of data that's streaming in constantly from all these different sources, 425 00:24:33,000 --> 00:24:35,840 Speaker 1: that to me is really kind of a snapshot of 426 00:24:35,920 --> 00:24:39,720 Speaker 1: how amazing technology has advanced over the past decade. But 427 00:24:39,960 --> 00:24:43,120 Speaker 1: I am also curious if if you could maybe walk 428 00:24:43,240 --> 00:24:45,320 Speaker 1: us through to kind of get an idea of what 429 00:24:45,560 --> 00:24:49,080 Speaker 1: this looks like, uh, in your regular jobs at Truman. 430 00:24:49,280 --> 00:24:52,000 Speaker 1: I'll start with you, if you could talk about a 431 00:24:52,119 --> 00:24:55,960 Speaker 1: project like a big tech implementation you've worked on with 432 00:24:56,240 --> 00:25:01,040 Speaker 1: MLB and kind of go through the point of ideation 433 00:25:01,359 --> 00:25:06,640 Speaker 1: where the decision is made through the actual implementation process 434 00:25:06,720 --> 00:25:09,080 Speaker 1: to kind of give us an idea of scale and 435 00:25:09,440 --> 00:25:12,439 Speaker 1: timing on these sorts of things. Sure, yeah, I think 436 00:25:12,480 --> 00:25:14,760 Speaker 1: there's a really cool one. Actually. So you're talking about 437 00:25:14,800 --> 00:25:17,520 Speaker 1: big data, and we have lots of new big data, 438 00:25:17,760 --> 00:25:20,919 Speaker 1: but we also have old big data, and it's very 439 00:25:21,000 --> 00:25:23,840 Speaker 1: interesting to us because we have the historical archives of 440 00:25:23,880 --> 00:25:27,879 Speaker 1: baseball video footage going back into the fifties, and so 441 00:25:28,880 --> 00:25:30,320 Speaker 1: we took a look at this and we've you know, 442 00:25:30,680 --> 00:25:33,440 Speaker 1: we maintained it as an asset and there's wonderful things 443 00:25:33,480 --> 00:25:36,080 Speaker 1: in there, and it's you know, games, it's full games, 444 00:25:36,160 --> 00:25:39,760 Speaker 1: it's press pressors that you know, came out in the eighties, 445 00:25:39,800 --> 00:25:42,440 Speaker 1: and there's interesting content there, but it's been sort of 446 00:25:42,560 --> 00:25:45,879 Speaker 1: locked away on older media. And so we've had this 447 00:25:46,040 --> 00:25:50,439 Speaker 1: on a bunch of magnetic tape and sixteen millimeter film 448 00:25:50,560 --> 00:25:54,280 Speaker 1: and all these other technologies that are effectively aging, and 449 00:25:54,920 --> 00:25:58,840 Speaker 1: worse than that is there decaying, and so digitizing this 450 00:25:59,000 --> 00:26:01,680 Speaker 1: data making an availa or for fans, the true fans 451 00:26:01,720 --> 00:26:03,800 Speaker 1: of baseball that just love this stuff and they want 452 00:26:03,840 --> 00:26:07,639 Speaker 1: to see consolidated games or a clip from growing up 453 00:26:07,680 --> 00:26:09,320 Speaker 1: and they want to see they remember the game, but 454 00:26:09,400 --> 00:26:11,159 Speaker 1: they can't find it on you know, one of the 455 00:26:11,240 --> 00:26:15,320 Speaker 1: online platforms. The Song's team built something really cool which 456 00:26:15,400 --> 00:26:18,119 Speaker 1: is called film Room, and it lets fans pull up. 457 00:26:18,240 --> 00:26:20,560 Speaker 1: There's over three million clips in there right now, and 458 00:26:20,640 --> 00:26:23,840 Speaker 1: we're adding more. The intention is that all that data 459 00:26:23,920 --> 00:26:25,920 Speaker 1: is democratized and you can do whatever you want with it. 460 00:26:26,000 --> 00:26:27,600 Speaker 1: You can pull it up, you can create your own 461 00:26:27,640 --> 00:26:30,440 Speaker 1: clip and if there's things that you like, uh you 462 00:26:30,560 --> 00:26:32,320 Speaker 1: like to see home runs from this team, or you 463 00:26:32,400 --> 00:26:34,560 Speaker 1: like to see double plays, whatever that is, you sort 464 00:26:34,600 --> 00:26:38,600 Speaker 1: of build these yourself. So it's not just are curated content, 465 00:26:38,720 --> 00:26:41,440 Speaker 1: but we're effectively turning that over to fans to have 466 00:26:41,560 --> 00:26:44,560 Speaker 1: fun with. So the way this ties back in with 467 00:26:44,640 --> 00:26:47,920 Speaker 1: the data is we have decaying media, which we realized, 468 00:26:48,080 --> 00:26:50,119 Speaker 1: you know, as we started to pull through this content 469 00:26:50,200 --> 00:26:51,920 Speaker 1: and we realized that we needed to get all of 470 00:26:52,000 --> 00:26:55,399 Speaker 1: this such stuff digitized, make it available, and start to 471 00:26:55,480 --> 00:26:59,280 Speaker 1: move those assets into secure on prem and also in cloud. 472 00:27:00,080 --> 00:27:02,879 Speaker 1: Um monumental effort. And the size of this data is 473 00:27:03,119 --> 00:27:06,240 Speaker 1: north of forty peta bytes of data, so you know, 474 00:27:06,400 --> 00:27:09,680 Speaker 1: in terms of what that is, it's a large footprint 475 00:27:09,760 --> 00:27:12,000 Speaker 1: in a data center, and you can imagine moving that 476 00:27:12,440 --> 00:27:14,600 Speaker 1: over time into cloud. It you know, it takes takes 477 00:27:14,600 --> 00:27:16,960 Speaker 1: a lot of time. So um, you know, we scoped 478 00:27:17,000 --> 00:27:19,359 Speaker 1: it out as a project, we've invested in it, and 479 00:27:19,640 --> 00:27:21,760 Speaker 1: we've put a team together to even digitize some of 480 00:27:21,760 --> 00:27:25,240 Speaker 1: the old content. So they're rescanning in sixteen millimeter film 481 00:27:25,880 --> 00:27:27,960 Speaker 1: pulling this stuff in and it's just it's a really 482 00:27:28,040 --> 00:27:30,399 Speaker 1: cool The tech behind it is really interesting. But the 483 00:27:30,520 --> 00:27:33,560 Speaker 1: net result is that some footage that hasn't been seen 484 00:27:33,800 --> 00:27:36,240 Speaker 1: is going to be seen. Um, and that's just you know, 485 00:27:36,359 --> 00:27:39,359 Speaker 1: it really connects us back to the game. I love that. 486 00:27:39,560 --> 00:27:41,679 Speaker 1: I also love the idea that I can I can 487 00:27:41,760 --> 00:27:45,040 Speaker 1: watch the Braves win the world series over and over 488 00:27:45,119 --> 00:27:49,240 Speaker 1: in various ways, because I feel sometimes that's the only 489 00:27:49,280 --> 00:27:53,040 Speaker 1: way I'm going to be able to enjoy it. I'm 490 00:27:53,080 --> 00:27:55,879 Speaker 1: a I'm a hometown Atlanta boy, So let's say just 491 00:27:56,000 --> 00:27:58,200 Speaker 1: keep looping it. That's right, and I'll just be yelling 492 00:27:58,280 --> 00:28:01,119 Speaker 1: Braves wind, Braves win over and he again, that was 493 00:28:01,160 --> 00:28:03,680 Speaker 1: a great example the someth Do you have any other 494 00:28:04,160 --> 00:28:06,840 Speaker 1: sort of projects that that you've worked on personally that 495 00:28:06,960 --> 00:28:09,560 Speaker 1: you feel would be a great sort of example of 496 00:28:10,160 --> 00:28:14,360 Speaker 1: from beginning to implementation. Yeah, absolutely, So I will rip 497 00:28:14,400 --> 00:28:16,639 Speaker 1: off a little bit of what Truman talked about, Like, 498 00:28:16,840 --> 00:28:19,600 Speaker 1: you know, while he moves all of that to the 499 00:28:19,760 --> 00:28:23,439 Speaker 1: digitizing all the media, how do we provide an experience 500 00:28:23,520 --> 00:28:27,240 Speaker 1: to fans that is actually utilized. We have so much data, 501 00:28:27,400 --> 00:28:30,760 Speaker 1: So basically what it is is we use technology to 502 00:28:30,920 --> 00:28:35,440 Speaker 1: go through this digital media and cut every pitch. We 503 00:28:35,640 --> 00:28:40,120 Speaker 1: tagged every pitch programmatically with all the additional information as 504 00:28:40,160 --> 00:28:43,920 Speaker 1: a tool for fans. For example, you could say show 505 00:28:44,000 --> 00:28:47,560 Speaker 1: me all the place by Fernando Tattys in the seventh 506 00:28:47,720 --> 00:28:51,920 Speaker 1: inning on a fastball that resulted in a home run. Right. 507 00:28:52,000 --> 00:28:54,680 Speaker 1: So it's as detailed as that. So it has like 508 00:28:54,880 --> 00:28:58,240 Speaker 1: a twenty thirty different levels that you can pull and 509 00:28:58,400 --> 00:29:01,320 Speaker 1: precisely so if you want to build a narrative around 510 00:29:01,360 --> 00:29:04,240 Speaker 1: a player, you want to have dragging rights with your friends. 511 00:29:04,440 --> 00:29:06,840 Speaker 1: It's a great tool. J allows you to create reels 512 00:29:06,880 --> 00:29:08,960 Speaker 1: around it and share it on Twitter and all the 513 00:29:09,040 --> 00:29:11,720 Speaker 1: different social media. It's all just started as the Hackathon 514 00:29:11,840 --> 00:29:14,360 Speaker 1: project and now it's you know, it's a It's one 515 00:29:14,400 --> 00:29:17,800 Speaker 1: of the ways the beat writers and the writers use 516 00:29:18,040 --> 00:29:22,760 Speaker 1: clips together with is directly through this tool. I love 517 00:29:22,840 --> 00:29:26,600 Speaker 1: that too. I can think of endless conversations I've been 518 00:29:26,720 --> 00:29:31,320 Speaker 1: part of among other baseball fans where everyone's debating the 519 00:29:31,480 --> 00:29:34,600 Speaker 1: various aspects of one player versus another, and having a 520 00:29:34,680 --> 00:29:36,800 Speaker 1: tool where you can start like, no, no, I I 521 00:29:36,960 --> 00:29:39,080 Speaker 1: have evidence to back up my point. I'm just gonna 522 00:29:39,120 --> 00:29:41,640 Speaker 1: pull let me show you how hope dies in the 523 00:29:41,720 --> 00:29:44,240 Speaker 1: eyes of a batter when smalt took the mound. And 524 00:29:45,360 --> 00:29:48,720 Speaker 1: this also is just a great conversation to show how 525 00:29:49,720 --> 00:29:54,760 Speaker 1: data can have a truly incredible impact on people when 526 00:29:55,080 --> 00:29:59,440 Speaker 1: implemented and leveraged properly. I think that's that is like 527 00:29:59,720 --> 00:30:03,560 Speaker 1: the big business of the next century, right, It's the 528 00:30:03,640 --> 00:30:07,800 Speaker 1: technology enables it, but the data is what powers it. Well, then, 529 00:30:07,880 --> 00:30:11,080 Speaker 1: I am. I'm also very curious because obviously the songs 530 00:30:11,120 --> 00:30:16,600 Speaker 1: you alluded to this earlier obviously was a truly tumultuous 531 00:30:16,720 --> 00:30:22,240 Speaker 1: year had an enormous impact on live events in all industries. Uh, 532 00:30:22,920 --> 00:30:28,560 Speaker 1: how did you leverage technology during the pandemic in order 533 00:30:28,640 --> 00:30:33,600 Speaker 1: to keep things moving as smoothly as you possibly could. Yeah, 534 00:30:33,680 --> 00:30:36,800 Speaker 1: it was a pretty um roughier to say the least. 535 00:30:37,200 --> 00:30:40,160 Speaker 1: It's not just for MLB, but for everyone going through 536 00:30:40,200 --> 00:30:43,240 Speaker 1: it in so at the same time, we knew that 537 00:30:43,760 --> 00:30:46,480 Speaker 1: baseball we should, we have to have baseball, right this 538 00:30:46,680 --> 00:30:51,040 Speaker 1: is this is one avenue where people can come together 539 00:30:51,280 --> 00:30:53,880 Speaker 1: and despite all the craziness is going around in the 540 00:30:53,920 --> 00:30:57,560 Speaker 1: world to create create a sense of normalcy. And baseball 541 00:30:57,600 --> 00:30:59,800 Speaker 1: has always played the strong in history and we wanted 542 00:30:59,840 --> 00:31:03,640 Speaker 1: to continue to do that. UM, digital or technology became 543 00:31:03,640 --> 00:31:05,840 Speaker 1: a lot more critical at this point. So what became 544 00:31:05,960 --> 00:31:09,160 Speaker 1: relevant is how do you bring baseball to homes? Right? 545 00:31:09,240 --> 00:31:13,479 Speaker 1: So when we didn't have the season start in April, 546 00:31:13,760 --> 00:31:16,760 Speaker 1: what we did is we allowed people to watch all 547 00:31:16,880 --> 00:31:21,040 Speaker 1: the old opening day games and UH and we started 548 00:31:21,080 --> 00:31:23,400 Speaker 1: giving a lot more video on demand content for our 549 00:31:23,440 --> 00:31:26,560 Speaker 1: fans during the time. So that's one big push we did. 550 00:31:26,680 --> 00:31:29,040 Speaker 1: And the other one was if they're not going to 551 00:31:29,120 --> 00:31:31,920 Speaker 1: be at the ballpark, what can we do to help 552 00:31:32,000 --> 00:31:35,440 Speaker 1: them feel like they're they're right? So a couple of 553 00:31:35,480 --> 00:31:37,480 Speaker 1: things we did. We had we launched something called the 554 00:31:37,560 --> 00:31:40,680 Speaker 1: Cheer at the Ballpark, which is basically digital sharing UM, 555 00:31:40,960 --> 00:31:43,560 Speaker 1: and we had the Fake Noise you know, like it 556 00:31:43,720 --> 00:31:46,920 Speaker 1: or not, It was actually it was seemed like a 557 00:31:46,960 --> 00:31:50,400 Speaker 1: little normal. We had a lot of engagement with that 558 00:31:50,480 --> 00:31:53,320 Speaker 1: product because people are so craving for something at that point, 559 00:31:53,680 --> 00:31:56,680 Speaker 1: and the same thing even when if you couldn't be 560 00:31:56,680 --> 00:31:59,120 Speaker 1: at the ballpark, we allowed them to send a picture 561 00:31:59,160 --> 00:32:00,800 Speaker 1: and we will actually put up cut outs there. It 562 00:32:00,920 --> 00:32:03,320 Speaker 1: was actually created an emotional connection to people like it 563 00:32:03,480 --> 00:32:06,000 Speaker 1: matters to show up and be part of the community. 564 00:32:06,160 --> 00:32:09,920 Speaker 1: So I think, you know it accelerated our people watching 565 00:32:10,400 --> 00:32:13,880 Speaker 1: streaming products that we had significant demand for for that 566 00:32:14,200 --> 00:32:17,160 Speaker 1: and what we're seeing now is as we're coming out 567 00:32:17,200 --> 00:32:20,840 Speaker 1: of the pandemic, we're still seeing that engagement, that behavior 568 00:32:20,920 --> 00:32:25,120 Speaker 1: we've created continue. We have one of the best streaming 569 00:32:25,280 --> 00:32:29,120 Speaker 1: viewer viewership numbers and engagement numbers ever in the history. Well, 570 00:32:29,200 --> 00:32:31,160 Speaker 1: and as you point out the SOTH, I mean, one 571 00:32:31,200 --> 00:32:35,080 Speaker 1: of the big draws of any live event is that 572 00:32:35,480 --> 00:32:39,640 Speaker 1: that communal experience and that you are a part of 573 00:32:39,920 --> 00:32:42,920 Speaker 1: something and it elevates everything. Having a game where it 574 00:32:42,960 --> 00:32:47,480 Speaker 1: would just be dead silent would just be unnatural and unsettling. 575 00:32:47,880 --> 00:32:51,200 Speaker 1: And I think that that using technology to help address 576 00:32:51,280 --> 00:32:55,440 Speaker 1: that was a genius move, something that without that you 577 00:32:55,480 --> 00:32:57,840 Speaker 1: would have really felt like this just doesn't feel right. 578 00:32:58,000 --> 00:33:01,440 Speaker 1: And Truman, I I imagine that for your team, the 579 00:33:01,720 --> 00:33:04,680 Speaker 1: COVID probably caused an enormous pivot as well. Can you 580 00:33:05,080 --> 00:33:07,920 Speaker 1: talk about what was going on in for you and 581 00:33:08,000 --> 00:33:14,120 Speaker 1: your team? Yes. In January, the expectation was for all 582 00:33:14,200 --> 00:33:16,880 Speaker 1: of us too to come into the office, and we 583 00:33:16,960 --> 00:33:20,080 Speaker 1: had just built a brand new office for MLB, all 584 00:33:20,160 --> 00:33:22,160 Speaker 1: new technology. It was the first time that we brought 585 00:33:22,160 --> 00:33:24,000 Speaker 1: our tech teams and the rest of the Office of 586 00:33:24,000 --> 00:33:27,120 Speaker 1: the Commissioner together. A lot of new technology was just 587 00:33:27,280 --> 00:33:30,240 Speaker 1: being stood up, and we were expecting that we had 588 00:33:30,320 --> 00:33:32,600 Speaker 1: this runway of you know, three months to get ready 589 00:33:32,600 --> 00:33:36,280 Speaker 1: for opening day, and so this was infrastructure work that 590 00:33:36,320 --> 00:33:38,280 Speaker 1: lived in our building. It was getting out to all 591 00:33:38,280 --> 00:33:41,080 Speaker 1: the ballparks to install all new equipment. There was lots 592 00:33:41,120 --> 00:33:45,560 Speaker 1: of new projects that were landing. In March eleven, we 593 00:33:45,640 --> 00:33:48,640 Speaker 1: all went home and we were just trying to figure 594 00:33:48,680 --> 00:33:51,240 Speaker 1: out what does this mean for the game, and a 595 00:33:51,320 --> 00:33:55,000 Speaker 1: bunch of things happened. Firstly, we just I think people 596 00:33:55,240 --> 00:33:58,240 Speaker 1: are resilient and they just started to figure out how 597 00:33:58,320 --> 00:34:00,040 Speaker 1: do we take the technology that we have to a 598 00:34:01,040 --> 00:34:05,560 Speaker 1: and adapt. So even prior to baseball starting, there were 599 00:34:05,640 --> 00:34:09,319 Speaker 1: lots of press release related streaming and things that still 600 00:34:09,400 --> 00:34:11,239 Speaker 1: support the game of baseball, and we needed to have 601 00:34:11,320 --> 00:34:15,040 Speaker 1: this thing running. In March, we effectively ran our broadcast 602 00:34:15,120 --> 00:34:19,400 Speaker 1: operations over zoom so multi view, all these you know 603 00:34:20,280 --> 00:34:23,480 Speaker 1: panels for quality control and streaming. All of that was 604 00:34:23,560 --> 00:34:27,160 Speaker 1: being operated at people's houses at the venues. We had 605 00:34:27,360 --> 00:34:31,040 Speaker 1: such dedicated employees that wanted to get this thing stood up, 606 00:34:31,080 --> 00:34:33,160 Speaker 1: and yet there were a lot of restrictions on travel, 607 00:34:33,960 --> 00:34:38,160 Speaker 1: so you know, the ingenious solution was renting r vs, 608 00:34:38,760 --> 00:34:40,800 Speaker 1: and some folks ended up you know, putting themselves in 609 00:34:40,840 --> 00:34:43,640 Speaker 1: a bubble and driving ballpark to ballpark, which I wish 610 00:34:43,680 --> 00:34:45,680 Speaker 1: we actually had this stuff, you know, captured on video 611 00:34:45,680 --> 00:34:47,680 Speaker 1: because I think it would be like a wonderful experience, 612 00:34:47,760 --> 00:34:49,719 Speaker 1: like to just see what that what that was like 613 00:34:49,840 --> 00:34:53,040 Speaker 1: for them. But basically, the tech was midflight and we 614 00:34:53,160 --> 00:34:55,600 Speaker 1: had to adapt and so you know, using these tools 615 00:34:55,640 --> 00:34:58,239 Speaker 1: I think allowed us to really get through and get 616 00:34:58,280 --> 00:35:00,960 Speaker 1: the game going. Uh. I agree, Truman. I think that 617 00:35:01,040 --> 00:35:04,839 Speaker 1: the RV story would have made a phenomenal documentary look 618 00:35:04,880 --> 00:35:07,359 Speaker 1: at the amount of dedication and the amount of work 619 00:35:07,440 --> 00:35:12,160 Speaker 1: that went into ensuring that these initiatives could continue as 620 00:35:12,280 --> 00:35:24,880 Speaker 1: best they could under beyond trying circumstances. My conversation with 621 00:35:25,000 --> 00:35:28,440 Speaker 1: Truman and Bassant will continue in the next episode of 622 00:35:28,560 --> 00:35:31,239 Speaker 1: The Restless Ones. We covered a lot of ground and 623 00:35:31,320 --> 00:35:35,480 Speaker 1: I gained a deeper appreciation for how MLB integrates technology 624 00:35:35,560 --> 00:35:39,040 Speaker 1: into every aspect of baseball, from the experience of attending 625 00:35:39,120 --> 00:35:42,320 Speaker 1: a game in person to the fundamentals of the game itself. 626 00:35:42,960 --> 00:35:45,879 Speaker 1: Be sure to tune into our next episode, where we'll 627 00:35:45,960 --> 00:35:48,719 Speaker 1: learn about some of the emerging technologies that will lead 628 00:35:48,800 --> 00:35:52,280 Speaker 1: to a variety of experiences at both parks that appealed 629 00:35:52,320 --> 00:35:56,080 Speaker 1: to a broad spectrum of baseball fans. Thanks for listening. 630 00:35:56,680 --> 00:36:07,360 Speaker 1: I'm Jonathan Strickland. 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