I am very pleased to introduce you first to our CEO, Mark Locke. Thanks. Hi, everybody, I just want to echo the thanks for making the effort to come over today. It means, well, it means a huge amount to come over and listen to us show you the thing we've been talking about for ages. Mike D'Auria here, who is the commercial director of Sal, Second Spectrum, stood up on stage the other day at the Sloan Analytics Conference and said, "You've seen a lot of vision pieces, you know, up here today. I'm gonna show you some reality pieces," which he then did. Really, that's a lot of what we're hoping to do today. We're gonna show you what it is, what how we do it, and really give you a feel for why we're so excited about the opportunity we've got and so excited to have you here all today. Thank you very much for coming. Just to introduce sort of a very high level people who are sitting in front of you. I think most of you probably have met or engaged with most of us, but it's probably worth just running along the top and having everyone introduce themselves. And then Mike Slade, who I'll introduce in a sec. He'll introduce himself, and he'll tell you a bit of a story about his background as well. Steve. My name is Steve Bornstein. I was CEO of ESPN for about 20 years, then I was at the NFL for another 12 years, and I'm now with Genius. I am the least important person up here on the dais. I can promise you that. Yeah. Hey, guys. I think, you probably recognize most people in the room. I'm Nick Taylor. I'm the CFO. Been around Genius for three and a half years now. It's round up. Hi, everyone. I'm Josh Linforth, the CRO for Genius, and I've been with Genius for about eight or nine years. Hi, I'm Mike Slade, and I'll talk to you more in a minute. Hey, everyone, I'm Mike D'Auria. I'm the chief commercial officer for Second Spectrum. Hi. Oh. I'm Rajiv Maheswaran. I was the founder of Second Spectrum, and I'm the president of Second Spectrum inside Genius Sports. Before I hand you over to our own math scientist, Rajiv here for the full demo. People are here for that. I'm gonna let Mike Slade, who, he's been with us for about a year. He's our Chief Product Officer. To give you a bit of background on where he's come from and, you know, where we're going with Second Spectrum certainly. Thanks. Over to you, Mike. Thanks, Mark. Yeah. I was really excited to come help these guys because they're at the intersection of the two things I've done and love most in my career, which are sports and technology. I have a kind of a crazy background. I worked at Microsoft in the early days and introduced Excel and then introduced Office and was in charge of all the Mac products for a long time. Then I worked for Steve Jobs for two years as the head of marketing at NeXT Computer. Then I came to Seattle, and I worked for this guy, Paul Allen, who was the co-founder of Microsoft, and we did a company called Starwave that I ran that was a very early internet pioneer. We created a bunch of consumer websites in 1995 to give you a flavor. It's four years before Google was formed and Yahoo was kind of a big deal, and there were no such thing as search engines. There were portals. We launched in the 12 months of the company's operation, ESPN.com, NBA.com, NFL.com, NASCAR.com, and ABCNews.com. We built all of them from scratch. Nobody had ever done anything like them before, and everything in them had never been done before. Publishing systems, load balancing system, ad management system, premium subscription system, e-commerce system, you name it. We sold this to Disney, which was about as much fun as getting a root canal. Anyway, dentists don't lie. Anyway, that's another story. After I was in rehab from that, I spent six years as an executive advisor to Steve Jobs at Apple. I worked half-time for Steve and was part of the executive team at Apple from 1998 to about 2004 when he got sick. After that, I'd become more of a venture capital and consultant kind of a guy and have a lot of fun and do lots of interesting stuff. I just wanted to tell you a couple of very quick war stories because what we're doing at Second Spectrum and Genius involving this idea called Dragon is what I call an end run. An end run is when you are in a position where you have the ability to exploit technology to not do a little bit better job than the competition, but to change the rules of the game and do something that's way better and way different. So you could use the word leapfrog. I tend to prefer end run 'cause it means you're going around all the, you're redefining the rules of the game a little bit, and that's kinda what we're doing. We did that with Excel. When I was at Microsoft, I was lucky enough to launch Excel. At the time, spreadsheets- For which he apologizes to most of you. Quite the opposite. because Excel might have been the as much fun as I've ever had except for maybe ESPN.com because at Excel, spreadsheets were character-based, and there was this company called Lotus with this product called Lotus 1-2-3 that every single person in New York, all they did all day was sit at their computer and type in 1- 2- 3 at the C prompt, and then at 5:00, they quit and went home. That's all they did. It was a character-based experience, and Microsoft decided to change the rules of the spreadsheet by doing a graphical spreadsheet and doing it on the Mac, which was kinda crazy. I ran around telling people that Excel on a Mac, which was considered a toy, was better than Lotus 1-2-3 on an IBM PC, and it pissed everybody off. It pissed people off inside of Microsoft who liked royalties from IBM, and I didn't care, and Apple loved it. Of course, it was number one on the Mac, and it was number one forever. When we did ESPN.com, pretty much everything about it had never been done before. Nobody had ever built a big hunkin' sports, the world's biggest sports section, as I like to call it. Nobody had ever parsed wire feeds interactively before. Nobody had ever done things like hit charts and spray charts and interactive shot trackers in basketball and nobody. We invented fantasy football. Sorry. One million other things. It was the same thing where by doing it on the web, which no one was using then except for scientists and educators, we were considered crazy. I sat in a meeting where Paul Allen yelled at me about my business plan, and I was like, "Well, I literally can't build you a business plan, but I know it's gonna work," which it did. Then at Apple, the same thing happened, where Steve basically was stubborn about redefining the rules of the game. His efforts in building a digital hub for all your personal media, photos, videos, and music had never really been done before. It'd been talked about for a long time, and he did it on the Mac. He didn't worry about having it run on Windows or whatever. Everything that you see today at Apple came from the decision to port the NeXT operating system to the Mac and build those apps. That all became the Mac, then it became the iPod, then it became the iPhone, it became the iPad, it became this thing. Again, so I have all this weird, cool personal experience from doing end runs that work. When I started meeting with these guys, I got really excited about the fact that that building Dragon and building software that understands everything that's going on in sporting events, had never really been done before and is a game changer. That's why I'm excited. All right. Rajiv has prepared a presentation. He's gonna take you through it, and I'll pass it over to him. Wow. Okay. Well, I have one rule in life, and that rule is never follow Mike Slade 'cause I'm gonna break it. I'm pretty grateful because I think when Steve Bornstein and Mike Slade got together and made Starwave. I mean, I was the kid on the other side who was watching Starwave. I remember the launch of Starwave. I remember going on browsers and going to sort of espn.starwave.com, and it was espn.go.com and then espn.com. I remember all those transitions. I mean, to some degree, this is all just. It talks about how stuff cascades, how technology keeps stacking because, like, I'm here doing what I'm doing because they were able to feed the fire that I had for sports in a way that was completely new. I feel like to some degree, what they did was build sports for a new generation of people, for people on the internet. I think we wanna do something similar to sort of build the next generation of sports content. I'm gonna Just a little bit of background on myself. I was a professor of computer science studying AI at the University of Southern California with my, I guess my colleague. He was my partner. We ran a research lab together, Yu-Han Chang. We were sort of happily doing AI. The area we studied was called the science of moving dots. It was basically the study of machine learning on movement. It's just funny, we had a large research group. I happened to be a huge sports fan. I thought they were different interests. Yu-Han tells the story of how once the basketball game started, he would have to talk. We would sit next to each other and talk to all the students, but once the basketball game started, he would talk to the students, and I would watch the games on my laptop pretending to listen. I am a, I am a professor, so normally that just means I can keep talking indefinitely. Normally I tell everybody, please interrupt me and ask questions. Charles and Brandon said, "No, we're gonna, we're gonna save it for the end." It's gonna be a little weird for me. Actually, not that weird. How did we actually end up getting into sports? About 10 years ago, basically a lot of machine-generated data was coming out. You know, they call it tracking data. Piles and piles of machine-generated data was coming out, and no one knew what to do with it. They just collected it and put it in a corner and never looked at it. For us, that was like, "Oh, this is gold," because that's what our research group was about. We got a hold of some of this data. We wrote a research paper. If you're familiar, this is the MIT Sloan Sports Analytics Conference. They had a research paper track. We wrote a paper with a couple of students for fun, we ended up winning the, what's known as the Alpha Award. I think the key thing there was we showed there was something in the data that no one knew. Coaches didn't know, once we showed it to them, NBA coaches didn't know, they couldn't even explain it. It was like the seminal moment to say that there was all this data could be transformed into something that the sports world had never seen. On the back of that, we formed Second Spectrum. We won another Alpha Award, then we said, "We're not publishing any more papers. We're just gonna make products." Stopping all the papers. Then we decided that there was really going to be a big value in transforming this data in the sports world. We just started. Said, "It's going to be a technological revolution. Let's put together the best tech team we could possibly get." This is some of our leadership. The people, the left four are all former computer science professors, PhDs from sort of top schools, with publications in top AI, CV, ML journals. This is, our leadership is basically nothing but a bunch of PhDs, and Mike. But even to be a Chief Commercial Officer at Second Spectrum, you have to have a bachelor's in computer science from MIT. Mike can do some things that we cannot do. Here's Mike. The rest of the management team cannot do that. You know, he does bring something to the table. What's Second Spectrum? Like, what is Second Spectrum? The name actually means quite a bit. When we were starting the company, what we wanted to be is we wanted to be the next way of seeing things. The next way of seeing things. How do we be the next way of seeing things? We want seeing, vision, all, you know, spectrum, and we basically came up with the name Second Spectrum to be the next way of seeing things, in particular sports. What is the next way of seeing sports? When you're talking about what you think is next, again, like Mike said, you normally show a vision piece, and that's what we did when we first started the company. Over the last 10 years, that vision has become a reality. Now, and I love this, it took me a while to adjust to this, I don't show stuff that's gonna happen. Show stuff that we've already done. Everything you see here, for many people, it's what they're gonna do. This is all stuff that's already been out there. Check it out. I think there's such an opportunity to change the way people experience sports by using technology. Second Spectrum, they do the analytics software. What they've really done is built a machine that can understand the game of basketball. You have access to Clippers CourtVision powered by AWS. Control and customize your live viewing experience. Unique experiences from NBA broadcast ever. Second Spectrum and ESPN are bringing you Full Court Press. It's the first time that this technology is used on a national broadcast. Rondo. Oh, Dad, give me that explosion. It also helped that LeBron had a great first half. Second Spectrum numbers show what a great perimeter he is. We have Sam, Courtney Cronin. Second Spectrum, you're probably thinking to yourself, "Well, what is Second Spectrum? You saw- Getting heat map, that's such a great stat. Ooh! God, okay. You guys mentioned Rondo. He's been terrific. Seeing these percentages up there by Second Spectrum, but he's defying it. Numbers say that that's a good shot that you're forcing them into, but Rondo has stepped up. Time to download the BT Sport app if you haven't done already, because we've made it even better experience. It's called Manager Mode. It's Newcastle on the attack here with Miguel Almirón. Oh, that legs it in. Rumbles in. Good gracious. It was even higher than that. Curry. Oh-ho! He is inevitable. There is the Herald Emmy Award-winning Wilmer Valderrama. Basically went up like he's gonna run a choice route. You got Cam right here. He's gonna bounce this outside. Look at the fog trail. He's gonna get to the one-yard line. Blitz coming. Mahomes, man wide open. Touchdown Chiefs. It's Dalmore. Yeah. What I like to say at this point is just like Steph Curry, we are inevitable. The reason for that is that this has already been done. It's important to understand what's going on, for example, in the entire content industry. If you look at all the forms of what we would call content, information, news, books, movies, music, pictures, video, and sports. I'm dating myself. When I was a kid, I went to libraries, and I read papers, and I went to Barnes & Noble and Blockbuster and Tower Records, and I had a Kodak and video camera, and I watched SportsCenter like a religion. I think we all know that because of technology, because of the Internet, compute, mobile devices, Internet devices, laptops, everything has been completely disrupted. On the left, you have a bunch of things in the cemetery. On the right, you have trillions of dollars of market value. The key thing is that sports has not. There is no obvious thing there on the sports. There's a reason for that, and sort of I can get into that. Our hope at Second Spectrum was to be the game changer for sports, to bring sports into the place where every other content class has gone. I think there's also a playbook. Why have all those companies on the right one and all the ones on the left gone away, right? Here's the key thing. You can look at all of them. In the world, we are all different people, but when you look at sort of 20th century business models, you think about the words product and distribution. You make a product, you make it as good as possible, and get as many people to engage with it, right? Write the best book you can, make the best movie you can, the best TV show you can, the best paper you can every day, the best store you can, right? You get to as many people. Once you have the Internet, you don't have to give the same thing to every single person. That's number one. Number two, they can talk back with the Internet. They can interact with you. The third thing is with compute, you can basically get enough data to understand the content and give different pieces of content to everyone. There's a radio station in the past, now there's Spotify, right? You could go to a store, now Amazon rebuilds the store for you every single time you visit the website. This goes on and on. Yahoo was a list of stuff, the same list of stuff for everyone. Google builds a dynamic list for every single person on the planet. The key is it's machine understanding of content. The reason that sports is last is the content is the hardest. Google's was the easiest because it was text, and then there was, like, music and photos. Video is even harder. Real-time video is the hardest. Sports is the hardest. That's why it was hard to do. The other thing was digital came to all these other things much, much sooner because this doesn't happen without digital, without the Internet, without devices. Frankly, I thought digital will come to sports a lot longer, but it's clearly coming now, right? You guys have seen Amazon's in the NFL, YouTube's in the NFL, Apple's in MLS, and all three of them have ambitions to grow beyond that. You see what's going on in leagues. Leagues are knowing. We all know what's going on with the RSNs. All the leagues know they have to take ownership of more of their product because there's, frankly, a lot more competition for eyeballs with this thing, even if they wasn't around. You have to take more care of your products. You've got NFL+, NBA League Pass, MLS Season Pass. Everyone's launching their direct-to-consumer businesses, which are digital. You've got the traditional companies say, "We gotta go digital." There are new companies forming which are saying, "I'm gonna be completely digital from scratch." Even people in ancillary businesses are saying digital streaming, digital interaction. Everything is digital now, right? If you look at it, basically linear, we know where it's going. Digital, we know where it's going. The part we might not know is manual work, and you can see this in lots of industries, and I'm sure as you follow the world is going down. AI and automation is going up. The key that people might not know is what where the AI is in sports, and I'm gonna try and help you understand that. This is where we are. Anybody could draw this graph. The question is you don't know where across time that you are. Well, I'm here to tell you we are right here. It's pretty clear from the linear digital side, we are also here from the manual to AI side. We are about to make the transition from there to there. The key, and this is where we have to get into this, like why is that transitioning happening? Because of machine understanding of sports. You know, we were professors, and we were said very soon in the company, like, "What are you doing?" It's like, "Well, we are teaching machines to understand sports." Right? Machine understanding of sports was always sort of our driving thing because we knew that would unlock a lot of this stuff. You can't personalize a billion experiences without a machine that understands the content, right? I'm trying to give you a little bit of an understanding of what it means to understand sports and what the technologies we're building are that are associated with that, and how that's gonna let us unlock all kinds of opportunities. The same way the Internet unlocked opportunities, the way mobile unlocked opportunities, everything unlocked opportunities. This sort of seismic change is going to unlock a lot of opportunities beyond all the great opportunities we already have right now. Let's look at the technology stack. If I looked at the technology stack, and I looked at a human being, and you said, "How well do you understand sports?" There's a person who can watch sports, there's a person who can explain sports, and there's a people who can create sports stories. What I mean by watch is I call watch meaning numbers. For example, I can watch a sport I don't know, but I can still say, "That's number 13, and they seem to be on the 40-yard line," or, "That's number 39, and they seem to be halfway or in the painted area or at this location," because I know what a grid looks like. Any human being who has spatial awareness and can read numbers can watch a sport they don't understand and say the basics of who's standing there and where they are. It's not that compelling. It's almost as useless as tracking data. The real stuff comes in words where you say, "Oh, that's somebody who's throwing a Hail Mary pass on the right-hand side on a go route, you know, into the end zone." You can say, "This is somebody making a between-the-lines pass to somebody who does a cross, puts a header into the goal," or, you know, a pick and roll and, you know, drives into the paint and does a floater that goes in as a layup. These are words that we use to describe the game to somebody else who only knows the numbers and is, you know, observing just the people moving around. The ultimate level of expertise of understanding is people who can create stories, and video is the ultimate. It used to be writing text stories. Video is really, you know, the biggest form of content out there, though text is also pretty big. On the technological side, the words we use for that is tracking, eventing, and content. Tracking is basically the idea that machines can watch the locations of players. It's just the equivalent of a human saying where everyone is. Eventing is what basically we do as a business. We're a sports data business. There are other sports data business. We collect events, right? Shots, passes, goals, points. That was a big thing that could machines actually understand the game at the level of a human. Once you do that, all kinds of content opportunities are locked up. We're at the point where machines have gone way past humans in the ability to track an event, and that's basically gonna unlock sort of superhuman possibilities in terms of content. Content and content capabilities and content classes that could not have been invented because you had to have a machine there to understand sports and able to unlock them, and I'm gonna show you what they are. Let's build from the bottom up, okay? The first thing is tracking. What is tracking? When we first started, what that meant was putting a little puddle under your center of mass. Like I would tell you where your body collapsed to a point was as you ran across a court, a field. It took several hours after the game to be able to get, you know, the quality of data to be able to do anything with it. Pretty useless, right? It's like I have a friend, I have a buddy that can tell you where all the players were standing throughout the whole game a few hours after the game. I'm like, "I'm not gonna do much with that," right? Where we have come is we have taken that quality, and not only have we gotten to the point where we can tell the full skeletal pose of a person, which basically is sports. Sports is people moving their bodies around. We went from hours to minutes to seconds to as most recently, we can do this in a few hundred milliseconds. What I'm gonna show you is stuff we can do in a few hundred milliseconds, and it's only getting faster. Let's check this out. That's basketball. I'm gonna show you some stuff that we did just It's also worth mentioning. Sure. They were real games. Yeah. Yeah. They were real games that were played in real time and converted into those avatars, within a few hundred milliseconds. You know, we recorded them, but they were actual real games, and that play was exactly what had happened. Yeah. 'Cause it's always worth framing. No, you're right. I keep forgetting. Yeah. I'm gonna show you some stuff from the NFL. We actually put the video in there so you know what actual play that we were doing. It's actually basically a full recreation of what happened, you know, basically a second before. Here's some stuff. Some very recent work, hot off the press. Yeah. Quite a lot of people often just think that that's just a sort of. Yeah. just like a video game or a video representation. What you have to actually understand is that was a real camera converting in real, well, 200 milliseconds into something that was real. It was a version of it. I keep forgetting 'cause we did it, so I keep forgetting that we did it. Yeah. It's. Yeah. It's. Yeah. No, no. frame it, frame it now always. Yeah. No. That's... Yeah. Appreciate it. No. Go Go on here. Here's some stuff with the NFL. good man. Now they're fighting for their lives. The defense has to make a stand. Second and 15. The throw away, and it's intercepted. He's got another one, and he's gonna feed the crowd. Davis is gonna take it to the end zone. I mean, it is a game changer on so many levels. The fact that you can get this quality of just even raw data at this fast is gonna unlock many dimensions, and we're gonna try and show you some of them over the next few minutes. It's just, again, just to give a bit of a voice overlay on that. I mean, we've seen it millions of times. Yeah. You know, That's a product that we've worked on very recently, so it's less refined than the basketball product. Yeah. Yeah. Ultimately, all of the ways that you can interact with that game, you're able to do live. The way that you are following the player, you do it live. The way you're changing your positioning, the camera angles, putting yourself in the field, zooming out, all of that can be done live. What we're gonna show you, we're gonna show you something really cool and let you play with something. It's live stuff on a real game. Does that make sense? One of the things that's gonna change is just generally the sort of experience. This is sort of like the NBA Finals from last year. Again, we were watching this live, you know, with a few hundred milliseconds. Just the idea that you could sort of just have this. The idea is, like, okay, sure, you could control the camera. You can sort of focus, rail, you can make a 2K cam. You can go above the rim, center court, top down, baseline. You could simply have a free camera and sort of move it any way you want. There are all kinds of other cool features that you can have sort of when you play this up. You can sort of, this is sort of a different view where I could say first person and say, "Sure, what's Marcus Smart, the point guard, seeing? What's Jayson Tatum looking at? What's Draymond Green looking at?" Maybe I wanna do a match up, so maybe I wanna see... Let's say I do a match up and I wanna see Draymond Green versus Jayson Tatum. This thing will sort of move around to make sure that I have sort of both of them in the view, and you can sort of make it be whatever you want. I'm gonna pass it over to Mike, and the really most compelling way to use it that we have found is with the. Yeah, I'm gonna try to hold this up here for you 'cause it's like everything. We'll come. we'll... I wanna get it on. Okay. We'll hand it out. We'll let you guys play with this afterwards 'cause it's the best way to see it. You know, I slowed it down a little bit, and as Rajiv and Mark said, I was literally watching this on my couch during the NBA Finals last year, well ahead of the broadcast 'cause it was only coming in at about 100, couple hundred milliseconds delayed, and it's way faster than linear TV. You can not only go through all those different camera angles and kinda anything you want to hear, but I actually can just kind of fully control the environment by using these kind of really intuitive, you know, controls that you're used to from working on any touch-related device, right? This is an iPad, it's no reason it couldn't be an iPhone or whatever device you have. The idea is it really just creates all these new ways for you to interact with the game and kind of these new opportunities for viewing, for highlights, and for kind of everything else. While on one hand this is just the foundational underlying data that's gonna power a whole bunch of downstream products that Rajiv's about to take you through, this in its own right we believe is this new opportunity for fans to get closer to the game than they ever could before and start to personalize their experience. One of the things that, you know, you guys will always wanna come back to, and it's the right question to be asking, it's what we ask ourselves, you know, when we're investing is how does this relate to our current business? How does this add incremental value? How does it translate to revenue? How, you know, how do these investments come through? If you think about, you know, just our core business, so forget about the, you know, the impact that we're having on our relationships with the leagues, the relationships with the media companies, the relationships with broadcasters. Think about our old, core business of bookmaking. You know, if you think about bookmaker sites, in the nicest possible way, they're no different to what they were 25 years ago. They're still in. You know, the way I describe it is Excel spreadsheets with some pictures on. All right, the Excel spreadsheets have got a bit prettier. The boxes have got a bit nicer. You've got a few other ways of interacting with them. The websites, the actual bet slips, the way you interact with the betting company isn't much different. What's gonna happen is there's gonna be a quantum leap in the way that those bookmakers interact with their clients. you know, you'll see this forming the core of the actual interactive platform that the bookmakers have. You'll have the game, the bet types can be put on here. You'll be able to interact and click. Again, you know, we announced a deal last year, just sort of to bring it back to, you know, sort of some of the core releases that you guys see and the revenue drop-throughs that you'll be interested in. We announced a deal, sorry, earlier this year with, you know, with bet365, you know, biggest bookmaker in the world really, who's looking at the way that they're interfacing and they're interacting with their customer base. These sorts of products, which are live, they're real, they're working, you know, simple integration with their player account management systems allows you to have that next generation of bookmaking product. You know, Rajiv will show you some incredible stuff, and we'll keep going through it. You know, I always wanna try and anchor it back to the core business, the revenue. How do we increase our, you know, our revenue from our core business? How do we increase our leverage with the sports leagues? How do we increase our revenues from the broadcast market? These are ways of doing it. Sorry to interrupt you. I think that's what. When you have a sport and you're able to translate it into this form of data, what it allows you to sort of transform the experience to a completely different kind of person. One thing is like, yes, there's a person who wants to make bets. The virtual world that you have can be seamlessly integrated with all your betting opportunities. If it's brands and stuff that Josh does, that world is completely controllable in a way that the physical world is not. Brands can find places in that world in all kinds of new ways. Even for just fan experiences, like one of the things that's fully untapped is, you know, people watch a lot of sports on this, and it's very interactive. If you wanna make bets, purchases, advertising and click to things, this is for not optimized. In fact, most people just take the couch viewing experience and put it on here. With, with that, with that 3D world, you can optimize what a viewing experience should be for the phone, because the key thing there is you really want a free camera. You can't just like take what's on film for a big TV and put it onto a digital device. You need to optimize how you're viewing. You need to move around the world to optimize the experience for this, and you need that dataset to be able to optimize the experience for this. That's gonna be huge. The reason you want to be on this is we're talking about betting, and we talk about advertising. You want to keep. Like, you wanna be here 'cause this is the shortest path to an interaction, whether that interaction is a bet, a purchase, a ticket purchase, whatever it is, right? a social like, whatever it is, it's all on this. That is completely untapped in many, many dimensions, and I think that's one of the things that this kind of technology is gonna bring around. That's like again, the raw thing. I think, you know, we do, I think, quite a good job of collecting data at a degree of accuracy and latency that few can match. The thing that I'm going to show you is a pretty unique advantage that right now in the world, like kinda only we have. When we came around, our thesis when we talked to leagues and teams and broadcasters, they totally echoed, was tracking data by itself is completely useless. We were the people that were trying to make tracking data valuable, right? It's like a pile of data is not valuable by itself. It's what you can do with it, right? And that's what we made our name for. Now when we first started, the data was two minutes delayed to... two to two minutes to two hours delayed. We weren't gonna go there and say, "We're going to give you shots and passes two minutes after it happened." The way we decided to build the machine understanding is like instead of doing that, we're gonna do the hardest things we can possibly find. We're going to go and find the things that the most elite professional coaches on the planet know and teach a machine to know them as well as the most elite professionals. Again, this is tracking data. Almost zero people in the entire sports ecosystem can work on this. This, if you cannot tell, is actually a soccer game. What we did is we turned those numbers into shots on goal, off ball runs, xG, build up phases, accelerations, between the lines passes, pressing, pass probability, defenders bypassed. We, coaches, managers, pundits on TV, bookmakers can use all those words. They cannot use the big pile of data. We've done a similar thing in basketball. This is from actually like quite a few years ago, I think nine years ago. If you look at this, we can get the whip defense on a cutter, spacing the paint, switches, wide pins, people coming off wide pins, spacing the pond, you know, drop defense on a screen. Like really, really deep stuff that I don't know coming off a flare. Hopefully MIT over here does. But even if he doesn't, he actually talks to all the NBA coaches, and he knows what he- He's a great D&O insurance, by the way. Sorry. A really good HR department. That's what we call them. Yeah. No. It's also all done automatically in real time. I think. There's a bit. There's no retagging that stuff, and again, it's happening at that, at that same cadence of kind of real-time sport. One of the most, I think fulfilling experiences of my life and Mike's, I think we've shared this where we go and meet all the coaches. We work with every single team in the NBA, and that first moment where you're talking to... and I can't share these stories, but like all the most famous coaches and coaches I've actually met, and the moment that they realize that the machine understands sports as well as they do, it's like a quite a fulfilling moment, and I'll remember those things forever. I think that was the game changer, that we can do this. Sorry. No. Again, just to bring it back to the deals that we announced, the revenue model, how it flows through to the business, we recently announced the deal with the NBA. A big part of that was teams business. You'll have seen the teams announce this is what we're talking about. The reason, you know, the teams love us and Rajiv, I'm sure will major on this in a second. The reason that the teams love us, the reason this is so valuable is because this is the sort of information we can provide them, and no one else can. This is not something that any other company can replicate. Yep. This is just us, just our semantic layer, our, the layer that sits above the data that understands it. It's that thing that makes us embedded in the sport and really gives us that leverage. It's just again, worth bringing it back to things that you've read in the press and some of the revenues that are gonna be flowing through the business in the future. Exactly. This was all done, if you see, with just a bunch of white and blue dots, right? Now that we have the kind of data I showed you before, I mean, we were able to answer just with the white and blue dots, we can answer questions like this. We have a software platform where you can ask super complicated questions, and I'm not even gonna bother reading them because I think you get the sense of how complex the questions we ask. These are the kind of questions we ask and answer in basketball. These are the kind of questions we can answer in soccer. The colors actually mean stuff like whether they're about the player, about the semantic of the word. Is it a statistic? You know, so but it's sort of that's the language of sport. We've basically a machine that understands the language of sport as much as, you know, top hundreds of people on the planet know. That's sort of a big leap, in machine understanding. Now-With this new level of data, that is just going to, I don't know, 10x or 100x, because the things that we can pull out of all of these things are going to be incredible, and this is going to affect sort of every aspect of the, of the sports system. It's going to affect, how media tells stories, how teams, build their day-to-day workflows, how bookmakers build new models and new markets. There's almost nothing that... There are sort of elements that brand sponsors can like attach themselves to, which we'll talk about a little bit later. There's absolutely no aspect of the sports ecosystem that's not gonna be affected by that. That was basketball. Here's a bunch of stuff in soccer. Again, here's the key thing. There's a big leap between having raw data and getting words that people can use, right? The raw data by itself, no one can use. The words, everyone can use. We have the unique transformation of going from numbers to words. I think that is one of the absolute sort of superpowers of Second Spectrum, and that is only getting amplified by being part of Genius that has access to hundreds of thousands of games and relationships and rights. That's where sort of the combination between Genius and Second Spectrum is super powerful. There we go. I'm gonna go to the next thing. One of the other things that has been unlocked with the new level of tracking is since it's gone from dots and hours to bodies in hundreds of milliseconds, now we can start going... Now that we're part of Genius, we have great incentive to basically do the basics faster than any human can do. You may have seen all this. I'll show you. I'll example of this. We have this stuff you can see on ESPN that we've been doing in part with them. Shot clock turned off. No foul. Watch Stephen Curry. To Boston. Now's a guard Curry. Long-distance Curry. Takes a 3. At the buzzer. At his feet, before the ball hit the rim, we knew that a shot was taken and how far away that shot was. The reason we can do that is within a few hundred milliseconds, we can figure out, using his body motion, that a shot and a particular type of shot was taken. We can tell exactly where his feet were and where it was a 2-pointer or a 3-pointer, the exact distance. As you can tell from some of our pre-previous augmentations, we know exactly if the shot went in or not. What all this basically shows is that we are able to- It's in a long three as he scores. Basically event a shot. Right now you're watching a game, you go to the play-by-play feed, and you get shot, shot. Shooter, two or three, how far it was, make or miss. That takes human beings in all of the various sports five, 10, sometimes 30 seconds, depending on how they do it. We're showing that all of that is gonna be driven to less than a second, and that's going to change, again, media, betting, advertising, when you can get the raw events that quickly. That's gonna be, something you're gonna see, you know, over the next several years sort of being rolled out into the, into the world. Sorry. No. I'm gonna interject again just on how that affects the business and obviously revenue opportunities. Yeah. The application in betting, I think is probably fairly obvious. Other things to think about is we spend, I think, $15-$20 million a year. Do we disclose that? We have now. On some of the data collection. Scrub that. Scrub that. Scrub that. in certain parts of our business, you know, paying human beings. We've got this network of 7,000 agents to go out there and go and collect that data. All of those, all of those costs can be addressed over the coming years by this sort of technology. The auto eventing, rather than having to send people into grounds, collecting that data automatically through these systems reduces the overhead of the humans, reduces our need to run a network of 7,000 people. Also, the other benefit of it is increases the amount of data we're collecting. Collecting much, you know, much more data, much richer types of data. That data becomes, you know, once it's out there, once it's used, you can't walk away from it. Once that data is being used by the bookmakers, by the leagues, by the coaches, by anyone else, you know, we're the only business that can collect that type of data, collect that type of fidelity of data, then distribute that data. That data collection capability that we have then gives us an unrivaled position. again, going back to the terms we use a lot, increases our moat and makes us very sticky within those sports. just worth adding that as well. I mean, this is gonna be. Therefore, there's more of it's gonna be more accurate, it's gonna be faster, and it's gonna be cheaper. In every dimension there's opportunity. What all of that does, the ability to have the raw material and the raw material to turn that into words is going to basically change all the places where, to some degree, you can call it content in sports in all its various forms, right? I think we showed you a bunch of stuff before. One of the more recent things that we did that we think was like, you know, again, a game changer that you're probably familiar with is, you know... We've been working very closely with the NFL and various partners in the NFL. One of the big things that we did was, you know, one of the things you've probably seen was Prime Vision on Amazon, which we think it was like, you know, beautifully executed by them and. I think it showed, like it was a combination sort of something that was super important to people who play Fantasy and people who watch Madden. If you haven't seen it, here's a little bit of that and some other things that we've done on the content space. Watching that game in that Prime Vision where it looked like a video game almost. Yeah. With the names over everybody's head and then routes, I think it was genius. Wide, wide. Come on. Go 5 wide here. 4-man rush again. Cardinals spinning around. Way through the fourth quarter. Second down and seven with a pump fake. Edwards-Helaire room to roll. First down. More magic on fourth down. Yes! Joshua Palmer. That is going to be very beneficial for a lot of people. Anytime That's how I watched the whole second half that way. I didn't know if I would like it or not. I loved it. I loved it, too. I think the key thing for that is like, you know, there are many things you want to know. A lot of what we do with what data does is make the invisible visible. Things you want to know. Like the first down line was a great example because you wanted to know where the first down line was. You can't live with it. When I'm playing, watching, especially football, like I'm in several fantasy leagues. I'm just like, "Where are my players?" Right? You know, I mean, look, I'm gonna date myself. I'm a Tecmo Bowl guy. Let's say Madden for everybody who's younger. Like we've all stared at those play diagrams. I want to see what those things are when I'm watching it because like I can't make sense of all those receivers running out. It's such a cool passing league now. Like that just adds to the game. I think a key thing to understand here, and this is important for our business, is that while there are a lot of augmentations, tracking data has been around for a long time. People who can paint stuff on TV have been around for a long time. They haven't done anything like this. They haven't done anything on like the video I first showed you. The reason is, the key is Eventing. Eventing data is what allows us to do this. What we really have, the power is not the ability to augment, but the power is the semantics, the event data that lets us know when to turn something off and on, when to turn a QB timer off and on, when to draw the routes, when not to draw the routes. There's a lot underneath in terms of the having the eventing data to power this and all the types of interactions that are gonna come, and that is gonna be a new part of sports data beyond what we do right now that is gonna be considered sports data, and that is really the thing to unlock. There's this layer. There's a layer between tracking and sort of content augmentation, which is eventing. It's called semantics data, what you want that powers all of this. We are in a very, very good position to be the people who do that. That's what we wanna set ourselves up for. Obviously, I think this and the phone is fundamentally gonna change how we interact with betting. Here's a... This is a vision, again, of how we're doing that. I don't like visions, but I'll tell you everything in this vision I'll show you as reality. I'll show you the vision piece. The only vision piece in here, but... This is some time to cross a strike. Curry. Oh! Good pass here. That's somebody to cut through the middle. Second down and seven with a pump fake. Edwards-Helaire room to run. If you looked at that vision piece and it's like, "That's nice. There's no way you can paint all that stuff under players and put their stats in real time." The answer, we already did it. For the last part of the video, we showed you putting the player stats on the Super Bowl and their names in real time. A bunch of the stats is just sort of generic, sort of overlays. There's also the other part. We had the little hand come up there and tap on the player. That seems a little crazy that you can actually just tap on the player while you're watching them. I'm just gonna show you. This is a prototype that we made actually five years ago, where you're basically using data, you're just tapping on different players, that is completely changing what it's showing you. You tap on a player, it shows you, it highlights their name, it highlights some stats about them. This is a key thing to understand is like this is the power of data and eventing that you might not think of, which is changing video into an interface, right? You want the video that you're enjoying to be an interactive platform. Right now, we'll look back at a time where it's like, "Can you believe that I had to do interactions with all these ugly pop-ups and HTML5 overlays? Why couldn't I just touch the thing I cared about? You can see how interesting it is, whether it's a bet or a purchase, to simply go in there and say, "Do you want to do this right now?" Tap on the player who's your favorite player to score 10 more points. I mean, it's so easy, and that is another whole API, SDK, whatever the right form factor is to go out in the world that will change all the different things we're talking about, whether it's branding or betting or commerce or just social interactions. That is going to be a thing that data and sort of next-generation data is going to power. As you can see, it's already built, right? A lot of it's just like there are a lot of pieces being built. It's how we get them out in the world that really matter. Sponsorship's an interesting thing. This is stuff that we've shown a lot of leagues and a lot of things of how you think of sponsorship. You know, sure, maybe I can put your name on the floor, a name on a jersey, an ad in between games. You know, a banner ad on the side. The coolest part of sports is the actual events of the sports itself. The key thing is the most valuable asset on the TV, which is the key events and the key players, are not monetizable right now. With data, you can turn all those elements that we showed you as augmentation into monetizable events on a sponsorship side. Here's what that's gonna look like. Guendouzi pull up jumper is good. Guardiola wth one challenge. Now they found Ronaldo! What a fantastic finish. So we can- Again, you know, you'll have heard us on earnings calls and seen new presentations talking about our media business and, you know, the growth in the number of brands that we're working with. We've talked a lot about expanding to build brilliant brands, you know, Carlsberg, Heineken, Coca-Cola, Hennessy, Doritos, Gatorade. We, you know, we've built those relationships, built the relationship with the agencies, and hopefully you're starting now to see how all of this stuff comes together and the revenue opportunities that come as a part of doing that. Just to be clear, That was vision in the sense like we. Those are not a step. We're not working with those people. That wasn't out there. I will say. Well, we are working with those people. We're just not. So- working with those people on that problem. On that problem. Like Yeah. There are people who have seen this idea and who are excited about this idea and it is a thing that I think you will see coming out. Yeah. sooner rather than later. you know, I think Charles and Brendan will do all the appropriate qualifiers later. There's lots of appropriate qualifiers. Yeah, yeah. Yeah, yeah. I'm the professor. I just talk. I You should put me off here. Just another thing just to be aware of, is that it's almost like all these things that are important in the sports world, whether it's fan experience or betting or sponsorship or officiating, they're all just variants of eventing, which is just variants of next generation data, right? Officiating, I'll sort of show you this, some stuff where we're working with the English Premier League on officiating. Take a look. Skills workshop. Well, he did until he got dropped away, Mark. The chairman. Oh, Ferguson missed. Before the Guardiola reign actually started. Stretch. Davis one. I think, you know, even I've been learning, Offsides in soccer is super complicated. It's not just like there's a line, there's. Even like sort of our head of AI was sort of explaining to us like how complex and how much human, you know, judgment is involved in it. Automating that is like. It's just a non-trivial thing. That's totally in our wheelhouse. That's what we do. Like, when we're finding flares and wide pins and all these, you know, the, these drop defenses in the NBA and similarly complex of the NFL, that's what we bring, is the complexity of the sort of human judgment we know we can event. That's what puts us in sort of that, in that position. That's sort of a run up of a lot of the technologies. Again, a sort of couple things that we've actually already built, deployed, and distributed in real time in the world. What I think that has allowed us to do is sort of be able to be on a particular plateau where we can look and we can see what's coming next. I think everything we did basically led to the next thing because when we solved that problem, we saw the next problem that needed to be solved and the next opportunity that was there. I think what we see and what we call Dragon is basically the collective sort of insight and wisdom that we've gathered over doing this for a decade and basically having a very strong thesis of what is to come. At, you know, at a very high level, I think, without going to a great deal of detail, we're coming to a transformational time where we're at the X point where technology is gonna really fundamentally transform sports and the digital nature of sports is totally going to unlock it. Dragon, there's sports all over the world and Dragon is sort of, you know, our answer to sort of being what the next generation solution to what sports is. I'm not gonna go into great amount of detail, but sort of at a high level, we feel like the nature of data capture is going to change. There's a reason to be able to be sort of both either much more voluminous or much more flexible about how data is captured. We want to build a technology that can effectively scale that, in a way that, you know, various sports and various leagues can get access to it. It's gonna unlock basically the next generation of everything that I already showed you that exists today. I'll show you a little bit of what is to come. For example, we went from one point on a body to sort of 20, 30, 40 points on a body, depending on what skeletal pose model is gonna happen. This is some sort of early work that we've done where we take a player, and instead of 20, 30, 40 points, we're gonna be looking at, I think this model has about 7,000 points on it, right? Once you have 7,000 points on it, there's a whole bunch of stuff that you can't do if you only have 20 or 30- And- or 40 or 50. Again, in recent press releases and things you'll have seen, we'll have announced Mesh. This is Mesh. This is Mesh. Just to say that this is the reality. This is sort of a game this summer. If you look at those sort of green and red. Lines ...taint, it's actually, if you look at it, we're just painting the players. It's not segmentation. It's actually Mesh. This is actually the three recreations of those 7,000 point models. That's gonna unlock a lot of stuff, but we're very excited about it. Mike's excited about it. There's a couple of interesting points to make. Well, interesting, relevant points to make. One is this doesn't require super expensive cameras. One of the things that, you know, we're not a hardware company. We never have been a hardware company, and never wanna be a hardware company. This is done, you know, really with super cheap cameras, and some very complicated software. That's the first point to make. You know, the move from a 25 point pose model, which was, you know, frankly, you know, 2019, 2020 work to what we're doing now, which is Mesh, which is, you know, 7,000 plus points and real fluidity and complete recreation of a model, that jump is huge and really, really material to the future story and the future usage, which I'll leave. I just wanna make the point on the cameras. No, it is. You know, that is what we showed you. Dragon is going to be the next way of seeing sports. That's who we are and in the sort of immortal words of Pat McAfee, I think, that's Genius. I think I'm good for then, open to questions.