Genius Sports Limited (GENI) Earnings Call Transcript & Summary

I am very pleased to introduce you first to our CEO, Mark Locke.

Thanks. Hi, everybody. And I just want to echo the thanks for making the effort to come over today. 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 Director of Sales at Second Spectrum, stood up on stage the other day at the Sloan Analytics Conference and said you've seen a lot of vision pieces up here today. I'm going to show you some reality pieces, which you then did and really, that's a lot of what we're hoping to do today. We're going to show you what it is, 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. So, thank you very, very much for coming. Just to introduce a sort of a very high level, people who are sitting in front of the -- 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, he'll introduce himself and he'll tell you a bit of the story about his background as well. So, Steve?

My name is Steve Bornstein, I was the CEO of ESPN for about 20 years, and then I was at the NFL for another 12 years, and I'm now with Genius, and I am the least important person up here on the day, so I can promise you that.

Yes, guys, I think you probably -- I recognize most people in the room. I'm Nick Taylor. I'm the CFO, been around Genius for 3.5 years now.

Hi, everyone. I'm Josh Linforth, the CRO for Genius, and I've been with Genius for about 8 or 9 years.

Hi, I'm Mike Slade, and I'll talk to you more in a minute.

Hi, everyone. I'm Mike D'Auria. I'm the Chief Commercial Officer for Second Spectrum.

Hi, I'm Rajiv Maheswaran. I was the founder of Second Spectrum, and I'm the President of Second Spectrum inside Genius Sports.

So we're going to -- before I hand you over to our own mad scientist Rajiv here for the full demo. I'm going to let Mike Slade, who -- he's been with for about a year. He's our Chief Product Officer and to give you a bit of background on where he's come from and actually some of the stuff that drives him and where we're going with second spectrum and certain things.

Thanks, Mark. Yes. So, I was really excited to come help these guys because they're at the intersection of the 2 things I've done and love most of my career, which are sports and technology. And so, I have a kind of a crazy background. I worked at Microsoft in the early days and introduced Excel and then introduce the Office and was in charge of all the Mac products for a long time. And then I started my -- I worked for Steve Jobs for 2 years as the Head of Marketing at NeXT Computer and then I came to Seattle and I worked for this guy, Paul Allen, who is the Co-Founder of Microsoft, and we did a company called Starwave that I ran that was a very early Internet pioneer, and we created a bunch of consumer websites in 1995. To give you a flavor, which is right -- it's 4 years before Google was formed and Yahoo! was kind of a big deal and there were no such single search engines; there were portals. And we launched in the first 12 months of the company's operation; espn.com, nba.com, nfl.com, nascar.com, and abcnews.com. And we built all of them from scratch, nobody did anything like them before and everything in them had never been there before, publishing systems, load balancing system, ad management system, premium subscription system, e-commerce system, you name it. And then we sold this at Disney, which was about as much fun as getting a root canal. Anyway, but involved, but unlike -- but dentists don't live. But anyway, that's not a story. And then so then after I was in rehab from that, and so I spent 6 years as an Executive Adviser to Steve Jobs at Apple. I worked half time for Steve and was part of the Executive Team at Apple from '98 to about '04 when he got sick. And after that, I become more of a venture capital and consultant kind of a guy. And have a lot of fun and do lots of interesting stuff. And so, 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. And 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. And so, you can use the word leapfrog. I tend to prefer end run because it means you're going around all the -- you're redefining the rules of the game a little bit, and that's kind of what we're doing. We did that with Excel when I was at Microsoft, I was lucky enough to launch Excel at the time.

For which apologizes to most of you...

Quite the opposite because Excel might have been as much fun as I've ever had, except for maybe espn.com because at Excel, stretches 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 [indiscernible] their computer and type in 1, 2, 3 at the seat prompt and then at 5 o'clock, they quit and went home. That's all they did and it was a character-based experience and Microsoft decided to change the rules of the spreadsheet by doing a graphical spreadsheet and doing that on the Mac, which was kind of crazy and I ran around telling people that Excel in a Mac, which was considered a toy was better than Lotus 1-2-3 and an IBM PC and it p***** everybody off. It is p***** people off in Microsoft who like royalties from IBM, and I didn't care and Apple loved it. And of course, it was #1 on the Mac, and it was #1 forever. Then when we did the espn.com, pretty much everything about it had never been done before. Nobody ever built a big hunking sports -- the world's biggest sports section that I'd like to call it. Nobody ever parsed wire feeds interactively before. Nobody has ever done things like hit charts and spray charts and interactive shot trackers and basketball. And nobody -- we invented fancy football, and a million other things. And it was the same thing where by doing it on the web, which no one was using that 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 going to work", which it did. And then at Apple, the same thing happened, where Steve basically was stubborn about redefining the rules of the game. And so, his efforts in building a digital hub for all your personal media photos, videos, and music and never really even done before. I've been talking about for a long time. And he did it on the map. He didn't worry about having a run-on Windows or whatever. And everything that you see today at Apple came from the decision to report the next operating system to the Mac and build those apps. And that all became the Mac and then it became the iPod that it became the iPhone became the iPad, it became this thing. Again, I have all this weird cool personal experience from doing end runs that work. And so, 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 has never really been done before and is a game changer. So that's why I'm excited.

So Rajiv has prepared a presentation. He's going to 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 over Mike Slade. I guess I have to 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'm going on browsers and going to sort of espn.starwave.com and then espngo.com and then espn.com, and I remember all those transitions. And I think to some degree, this is all just -- it talks about how stuff cascades, our 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. And I feel like, to some degree, what they did was build sports for a new generation of people for people on the Internet. And I think we want to do something similar just to build the next-generation of sports content. So, I'm going to -- just a little bit back on myself. I was a professor, professor of computer science studying AI at the University in Southern California with my -- I guess I met my colleague. He's my partner. We ran the Research Lab together, Yu-han Chang and we were sort of happily doing AI. And the area we studied was called the science of moving dots. And it's basically a study of machine learning on movement. It's just we had a large research group. I happened to be a huge sport fan. I thought they were different interest. Yu-han tells the story of how once the basketball game started, he would have to talk -- we would sit next to and talk to all the students. But once the basketball game started, he would talk to students, and I would watch the games on my laptop pretending to listen. So I'm a professor. So normally, that just means I can keep talking indefinitely. And so normally I tell everybody, please interrupt me and ask questions, but Charles and Brendon said, "No, we're going to save for the end. So, it's going to be a little beard for me. Actually, not that weird, but -- so how did we actually end up getting into sports. So about 10 years ago, basically a lot of machine-generated data was coming out -- call it tracking data, but piles and piles machine generated [ data ] is coming out. And no one knew what to do with it. So, they just collected it and put it in a corner and never looked at it. And for us, that was like, "Oh, this is gold because that's what our research group is about." And we got to hold some of this data. We wrote a research paper for the MIT Sloan Sports Analytics Conference at a research paper track. We wrote a paper with a couple of students for fun and so we ended up winning the alpha award. And I think the key thing there was we showed there was something in the data that no one knew. Coaches didn't know and once we showed it to them, and even the coaches didn't know, they couldn't even explain it. So, it was like the seminal moment to say that there was all this data can be transformed into something that the sports world had never seen. So, on the back of that, we form Second Spectrum. We've won one another alpha award, and then we said, we're not publishing any more papers. We're just going to make products. So, stopping all the papers. We decided that there was really going to be a big value in transforming this data in the sports world. And we just started and said, it's going to be a technological revolution. Let's put together the best tech team we could possibly get and this is some of our leadership. The people over the left floor are all former computer science professors, PhDs from sort of top schools, publications in top AI, CVML Journal. So this is -- our leadership is basically nothing but a bunch of PhDs and Mike. But even our -- even to be a Chief Commercial Officer at Second Spectrum, you have to have a bachelor's in Materials Science from MIT. But Mike can do some things that we cannot do. Here's Mike. The rest of the management team cannot do that. So, he does bring something to the table. So, what is Second Spectrum? The name actually means quite a bit that when we were starting the company, what we wanted to be is we want to be the next way of seeing things. How do we be the next way of seeing things and so we were seeing vision spectrum and we basically came up with the name Second Spectrum to be the next way of seeing things in particular sports. So what is the next way of seeing sports? We were 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. But over the last 10 years, that vision has become a reality. So now, and I love this, it took me a while to address this. I don't show stuff that's going to happen, show stuff that we've already done. So, everything you see here for many people, it's what they're going to do. This is all stuff that's already been out there. So, check it out. [Presentation]

Such an opportunity to change the way people experience sports by using technology. At Second Spectrum, they do the analytic stuff. What they've really done is built a machine that can understand the game of basketball. You have access to Clippers' court vision powered by AWS. You control and customize your live viewing experience -- unique experiences from NBA broadcast member, Second Spectrum at ESPN bringing you home court. It's the first time that this technology is used on a national broadcast over that present... Second Spectrum numbers show what a good -- Seeing percentages up there by Second Spectrum -- shot that you're forcing the mention, but Rondo stepped up. Being put that you haven't done already because we've made it even better viewing experience... If you catch them on the attack here with me on... So what I'd like to say at this point is just like Steph Curry, we are inevitable and the reason for that is that this has already been done. So, 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 have a Kodak and video camera and I watch sports into like a religion. And 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. And the key thing is that sports has not. There is no obvious thing there on the sports. And there's a reason for that. And sort of -- I can get into that. But our hope at Second Spectrum was to be the game changer for sports, to bring sports into the place where every other content class is gone. And 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? So, here's the key thing if you 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 you get as many people to engage with it, right? So make the best book you can, make the best movie you can, best teams, you can, the best paper you can every day, the best story you can, right? And 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 2, they can talk back with the Internet. They can interact with you. And the third thing is with compute, you can basically get enough data to understand the content and give different pieces of content to everyone. So 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. And the key is, it's machine understanding of content. The reason that sports is last is the content is the hardest. Google 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 and 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, Apples in MLS, and all 3 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 there wasn't around, you have to take more care of your products. You've got NFL+, NBA league pass, MLS season pass, everyone is launching their direct-to-consumer businesses, which are digital. You've got the traditional companies say, we got to go digital. There are new companies forming, which are saying, "I'm going to be completely digital from scratch". Even people in ancillary business were saying digital streaming, digital interaction, everything is digital amount, right? So if you look at it, basically, linear, we know where it's going. Digital, we know where it's going. The part we might know -- 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. Now the key that people might not know is where the AI is in sports, and I'm going to try and help you understand that. So this is where we are. And 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. And the key -- and this is why we get is like, why is that transition happening because of machine understanding of sports. We were professors and we were said, very short in the company, like what are you doing? It's like, well, we are teaching machines to understand sports. Machine understanding as 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? And so 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 going to let us unlock all kinds of opportunities, the same way the Internet unlocked opportunities, the way mobile unlock opportunities, everything opportunities. This sort of seismic change is going to unlock a lot of opportunities beyond all the great opportunities we already have right now. So let's look at the technology stack. So if I look at the technology stack, I looked at the human being, and you said how well do you understand sports. There's a person who can watch sports. There's a question who can explain sports and there's people who can create sports stories. So what I mean by watch is, I call watch being numbers. So for example, I can watch a sport, I don't know. But I can still say that's #13, and they seem to be on the 40-yard line or that's #39, and they seem to be halfway or in the patent area or at this location because I know what a grid looks like. So any human being who knows 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 is throwing a Hail Mary pass on the right-hand side, on a go route into the end zone, or you can say this is somebody making a between the lines pass to somebody who does a cross put the header into the goal, or a pick and roll and drives into the pain and there's a floater that goes into the lab. These are words that we used to describe the game to somebody else who only knows the numbers and is 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 tech stories, now video is really the biggest form of content out there. Well, text is also pretty big. On the technological side, the words we use for that is tracking, eventing, and content. So 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. We collect events; shots, passes, goals, points, but that was a big thing that could machines actually understand the game at the level of a human. And once you do that, all kinds of content opportunities are locked up. So we're at the point where machines have gone way past humans in their ability to track an event, and that's basically going to unlock sort of super human 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 going to show you what they are. So let's build from the bottom up. So the first thing is tracking. What is tracking? So when we first started, what that meant was putting a little puddle under your center of mass. I would tell you where your body collapsed to a point was as you ran across a court, a field, and it took several hours after the game to be able to get the quality of data to be able to do anything with it. It's pretty useless, right? It's like I have a friend of a buddy, they 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 going to 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 out of a person, which basically is sports, of course, people are 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. So what I'm going to show you is stuff we can do in a few hundred milliseconds, and it's only getting faster. So check this out. [Presentation]

So that's basketball. I'm going to show you the stuff that we did just...

Also worth mentioning they were real games.

Yes.

So they were real games that were played in real time and converted into those advertisers within a few hundred millisecond. So we recorded them, but they were actual real games and that play was exactly what had happened. It's always worth framing...

No, you're right. I always keep forgetting -- so the next video, I will show you stuff from the NFL. We actually put the video in there, so you know what actual play that we were doing. So it's actually basically a full recreation of what happened basically a second before. So here is just some stuff with some very recent work how...

Yes. So quite a lot of people off just think that, that's just sort of... Just like a video game or a video representation, what you have to actually understand is that was a real camera converting in real couple of hundred milliseconds into something that was real. So it was a version of it. But...

I keep forgetting because we did it, so I keep forget it...

Framing now...

Appreciate it. So here's the stuff from the NFL, good man. [Presentation]

I think this is just going to create -- I mean, it is a game changer on so many levels. The fact that you can get this quality just even raw data at this fast is going to unlock many, many dimensions. And we're going to try and show you some of them over the next few minutes. One of them...

So it's just again, just to give a bit of a voice overlay on that. I mean we've seen it millions of times. And that's a product that we're working very recently. So, it's less refined than the basketball product... But ultimately, all of the ways that you can interact with that game, you're able to do live. So the way that you're 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 and what we're going to show you in to show you something really cool and let you play with something, but it's live staff on a real game. Does that make sense?

So one of the things that's going to change is that generally the sort of experience. So this is sort of like the NBA finals from last year. Again, we were watching this live with a few hundred milliseconds. So just the idea that you can sort of just have this. But the idea is like, okay, sure, you could control the camera or gives you focus, rail, you can make a 2K cam, you can go above the rim, center court, top-down, baseline. You typically have a free camera and sort of move it anywhere you want. There are all kinds of other cool features that you can have sort of when you play this up, so you can sort of -- this is sort of a different view where I can say, first person and say; sure, what's Marcus Smart the point guard is seeing, what's Jayson Tatum looking at, what's Draymond Green looking at. Maybe I want to do a matchup. So maybe I want to say, let's say, I do a matchup when I want to see Draymond Green versus adjacent data. 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 going to pass it over to Mike and the really most compelling way to use it that we have found is... With the...

Yes, I'm going to try to hold this up here for you because it's like everything -- Well, I want to get it on screen. We'll hand it out. We'll let you guys play with this afterwards. It's the best way to see it. But 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 because it was only coming in at about a couple of hundred milliseconds delayed and it's way faster than when you're in TV. And you cannot only go through all those different camera angles and kind of anything you want here, but I actually can just kind of fully control the environment by using these kinds of really intuitive controls that you're used to from working on any touch related device, right? This is an iPad, but it's no reason it couldn't be an iPhone or whatever device you have. And so the idea is it really just creates all these new ways for you to interact with the game and these new opportunities for viewing for highlights and for kind of everything else. So while on one hand, this is just the foundational underlying data that's going to 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 ever could before and start to personalize their experience.

And one of the things that you guys will always want to come back to, and it's the right question to be asking. It's what we ask ourselves and we 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 do these investments come through? And if you think about just our core business, so forget about the impact that we're having on our relationships with the leagues, the relationships with the media companies, the relationship with the broadcasters. Think about our old core business of bookmaking, 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 -- the way I describe it is Excel spreadsheets with some pictures on. All right, the Excels got a bit pretty other boxes got a little bit nicer. You've got a few other ways of interacting with them but the website, the actual bet slips, the way you interact with a betting company isn't much different. What's going to happen is there's going to be a quantum leap in the way that those bookmakers interact with their clients. And you'll see this forming the core of the actual interactive platform that the book bankers have. You'll have the game, the back types we put on here, you'll be able to interact and click. And again, we announced the deal last year just sort of to bring it back to 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 earlier this year with Bet365, biggest bookmaker in the world really, who's looking at the way that they're interfacing and they're interacting with their customer base. So these sorts of products, which are live, they're real, they're working, simple integration with their player account management systems allows you to have that next generation and bookmaking product. So hopefully, Rajiv will show you some incredible stuff and we'll keep going through it. But I always want to try and anchor it back to the core business, the revenue, how do we increase our revenue from our core business, how do we increase our leverage with the sports leagues, how to increase our revenues with the broadcast market. These are ways of doing it. So -- sorry to interrupt you.

No, I think that's what -- when you have a sport and you're able to translate in this form of data, what it allows you to sort of transform the experience to a completely different kind of person. So 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 brand and stuff that Josh does, that world is completely controllable in a way that the physical world is not. And brands can find places in that world in all kinds of new ways. And even for just fan experiences, like one of the things that's fully untapped is people watch a lot of sports on this. And it's very interactive. So if you want to make bets purchases, advertising, and click the things, this is not optimized. In fact, multiple just take the couch viewing experience and put it on here. 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 on to 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 data set to be able to optimize the experience for this. And that's going to be huge. And the reason you want to be on this is we're talking about betting when you talk about advertising, you like you want to be here because this is the shortest path to an interaction where 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. And that is completely untapped in many, many dimensions. And I think that's one of the things that this kind of technology is going to bring around. So that's like, again, the raw thing. And I think we do, I think, quite a good job of collecting data at a degree of accuracy and latency that few can match. But the thing that I'm going to show you is a pretty unique advantage that right now in the world like kind of only we have because when we came around. Our thesis, when we talk to leagues and teams and broadcasters, they totally echoed was tracking data by itself is completely useless and we were the people that are trying to make tracking data valuable, right? A pile of data is not valuable by itself. It's what you can do with it, and that's what we made our name for. Now when we first started, the data was 2 minutes delayed to 2 hours delayed. We weren't going to go there and say, "We're going to give you shots and passes 2 minutes after it happened." So the way we decide to build a machine understanding is like instead of doing that, we're going to do the hardest things we can possibly find. We're going to go and find the things the most elite professional coaches on the planet now and teach a machine to know them as well as the most elite professionals. [Presentation]

So again, this is tracking data. Almost zero people in the entire sports ecosystem can work on this. This, if you cannot tell, it's actually a soccer game. And what we did is we turned those numbers into shots on goal, off-mall runs, XG buildup phases, acceleration between lines passes, pressing, pass probability, defenders bypassed. So 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. [Presentation]

So this is from actually like quite a few years ago, I think 9 years ago. If you look at this, we can get the WIP defense on a cutter pacing the pain, switches wide pins, people coming off line pins, spacing the pond, drop defense on the 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. That's what we call them.

But it's also all done automatically in real time. So I think... There's nobody tagging that stuff. And again, it's happening at that same cadence of kind of real-time support.

And one of the most -- I think fulfilling experience my life and Mike said, 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 the codes actually met and the moment that they realize that the machine understands sports as well as they do, it's like quite a fulfilling moment. And I'll remember those things forever. But I think that was the game changer. We can do this...

And 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 announced. This is what we're talking about. The reason the Teams love us and Rajiv, I'm sure will measure on this in a second. But 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. This is just us, just automatic layer [ RRM ], the layer that sits above the data that understands it. And it's that thing that makes us embedded in the sport and really gives us that leverage. So it's just, again, worth bringing it back to things that you've read in the press and some of the revenues are going to be flowing through the business in the future.

Exactly. And so especially -- and this was all done if you see just a little much of all that stuff, we did with a bunch of white and blue dots, right? And so now that we have the kind of data I showed you before, it is -- I mean we were able to answer just with the white and blue dots, we can answer questions like this. [Presentation]

So we have a software platform where you can ask super complicated questions, and I'm not even going to bother reading them because I think you get the sense of how complex the question we have. 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 but it's sort of -- that's the language of sport. We basically made a machine that understands the language of sport as much as top hundreds of people on the planet now. And that's sort of a big leap in machine understanding. [Presentation]

And 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 sports system. It's going to affect how the media tell stories, how teams build their day-to-day workflows, how bookmakers build new models and new markets; and there's almost nothing that there are sort of elements that brand sponsors can attach themselves to, which we'll talk about a little bit later. So there's absolutely no aspect of the sports ecosystem that's not going to be affected by that. That that was basketball. Here's a bunch of stuff in soccer. [Presentation]

And 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. And we are -- we have the unique transformation of going from numbers to words. I think that is one of the absolute 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. So that's where sort of the combination between Genius and Second Spectrum is super, super powerful. There we go. I'm going to go to the next thing. So one of the other things that has been unlocked with the new level of tracking since it's gone from dots in hours to bodies in hundreds of milliseconds. Now we can start going -- and now that we're part of Genius, we have great incentive to basically do the basics faster than any human can do. So you may have seen all this -- I'll show a short example of this. We have this the stuff you can see on ESPN and that we've been doing in partnership with them. [Presentation]

Wide step, Curry. So at his feet, before the ball hit the rim, we knew that a shot was taken and how far away that shot was. And 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 it was a 2-pointer or a 3-pointer, the exact distance. And as you can tell from some of our previous augmentations, we know exactly if the shot went in or not. What all this basically shows is that we are able to basically event a shot. So right now, you're watching a game, you go to the play-by-play feed, you get shot, shot, shot, shooter, 2 or 3, how far it was, make or miss. That takes human beings in all of the various sports, 5, 10, sometimes 30 seconds, depending on how we do it. We're showing that all of that is going to 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. So that's going to be something you're going to see over the next several years, sort of being rolled out into the world. And...

Sorry. I'm going to go interject again just on how that affects the business and obviously, revenue opportunities, the application in betting, I think, is probably fairly obvious. But other things to think about is we spend, I think, $15 million to $20 million, on some of the data collection... In certain parts of our business, paying human beings, we got this network of 7,000 agents to go out there and go and collect that data. 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, electing a much, much more data, much richer types of data. That data becomes -- 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. And we're the only business that can collect that type of data, collect that type of fidelity of data and then distribute that data. That data collection capability that we have then gives us an unrivaled position. And again, going back to the terms we use a lot increases our moat and makes us very, very sticky within those sports. So just worth adding that as well.

I mean this is going to be -- and therefore, there's more of it. It's going to be more accurate. It's going to be faster, and it's going to be cheaper. And 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 and sports in all its various forms, right? So I think we showed you a bunch of stuff before. One of the more recent things that we did, we think was like, again, a game changer that you're probably familiar with is we've been working very closely with the NFL and various partners in the NFL. One of the big things that we did was -- one of the things we've probably seen was Prime Vision on Amazon, which we think was like beautifully executed by them and -- but it was -- I think it showed like it was a combination sort of something that was super important 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. [Presentation]

Watching that game in the line vision looks like video does with the names of everything said I think it was genius... 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 downline was a great example of you wanted to know when the first downline was, and you can't live with it. When I'm playing watching -- especially football, like I have -- I'm in several fantasy leagues. And I just like, where are my players, right? And I've played -- I mean, look, I'm going to date myself. I'm a [ techmobile ] guy. But let's say, Madden for everybody'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. And 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 pay 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. So 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 do turn a QB timer off or on, when to draw the routes, when not to do the right. There's a lot underneath in terms of having the eventing data to power this and all the types of interactions that are going to come. And that is going to be a new part of sports data beyond what we do right now that is going to be considered sports data. And that is really the thing to unlock. And there's this layer. This is the whole thing. 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. And we are in a very, very good position to be the people who do that. And that's what we're going to set ourselves up for. Obviously, I think this and the phone is fundamentally going to change how we interact with betting. So this is a vision, again, of how we do. I don't like visions, but I'll tell you everything in this vision. I'll show you a reality. So I'll share the vision piece, the only vision piece in here... [Presentation]

So if you look at that vision piece is like that's nice. There's no way you can paint all that stuff under players and put their stats in real time. Well, the answer we already did it. And 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 staff is just sort of generic sort of overlays. But there's also other part we have a little hand come up there and tap on the player. That seems a little crazy to actually just tap on the player while you're watching them. Well, I'm just going to show you... [Presentation]

This is a prototype that we made actually 5 years ago where you're basically using data and you're just tapping on different players, and that is completely changing what it's showing you. So you tap on a player, it shows you. It highlights their name. It highlights some stats about them. And 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. You want the video that you're enjoying to be an interactive platform. Right now, we'll look back at a time and said, "Can you believe that I had to do interaction with all these ugly pop-ups and HTML5 overlays, why couldn't I just touch the thing I cared about?" And you can see how we -- 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?" and tap on the player who's your favorite player to score 10 more points. I mean it's so easy. And that is another cold API, SDK, whatever the right form factors 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. And as you can see, it's already built, right? And so a lot of it is just like there are a lot of pieces being built. It's how we get them out in the world that really matter. Sponsorship is an interesting thing. This is stuff that we've shown a lot of leads, a lot of things of how you think of sponsors, sure, maybe I can put your name on the floor, name on a jersey and add in between games. But the coolest maybe a banner ad on the side. But the coolest part of sports is the actual events of the sports itself. And so 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. But 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 going to look like. [Presentation]

And again, you'd have heard us on earnings calls and seen new presentations talking about our media business and the growth in the number of brands that we're working with. We've talked a lot about expanding to bringing brands, Carlsberg, Heineken, Coca-Cola, Hennessy, Doritos, Gatorade, we've built those relationships, built 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.

And just to be clear, that was a vision in the sense like -- those are not -- we're not working with those people that wasn't out there. But I will say like...

Well, we are working with those people, we just don't work with this people on that product.

-- on that problem. Like there are people and 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 sooner rather than later. I think Charles and Brendan will be all the appropriate qualifiers... 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 variance of next-generation data, right? So officiating, I'll show you this some stuff where we're working with English Premier League on officiating... Take a look. [Presentation]

So I think even I've been learning -- offside from soccer is super complicated. It's not just like there's a line even like our Head of AI was sort of explaining to us like how complex and how much human judgment is involved in it. And automating that is like an -- it's just a nontrivial thing. But that's totally in our wheelhouse. That's what we do. Like when we're finding flares and wide pins and all these drop defenses in the NBA and similarly complex of NFL, that's what we bring is that the complexity of the sort of human judgment we know we can event. And so that's what puts us into in that position. And so that's sort of a run-up of a lot of the technologies, again, I sort of a couple of things that we've actually already built, deployed, 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 solve that problem, we saw the next problem that needed to be solved and the next opportunity that was there. And 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. And at a very high level, I think without going into a great deal of detail. I think there is -- we're coming to a transformational time where we're at the X point where technology is going to really fundamentally transform sports and the digital nature of sports is totally going to unlock it. So Dragon, the sports all over the world and dragons sort of our answer to sort of being with the next-generation solution to what sports is. Now I'm not going to go into a 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, and we want to build a technology that can effectively scale that in a way that various sports and various leagues can get access to it. And it's going to unlock basically the next generation of everything that I already showed you that exist today. I'll show you a little bit of what is to come. [Presentation]

And so for example, we went from 1 point on a body to sort of 20, 30, 40 points on a body depending on what skeletal pull model is going to 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 going to be looking at -- I think this model has about 7,000 points on it. And 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 or 40 or 50.

And again, in recent press releases and things you'll have seen, well, if announced mesh, this is mesh.

This is mesh. And so just to say that it's not -- this is the reality. This is sort of a game this summer. And if you look at those sorts of green and red in, it's actually -- we're just painting the players, it's not segmentation. It's actually mesh. So this is actually the 3 recreations of those 7,000-point models. So that's going to unlock a lot of stuff, but we're very excited about it. Mike's excited about it, but...

And 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 we're not a hardware company. We never have been our company onto a hardware company. This is done really with super cheap cameras and some very complicated software. So that's the first point to make. And the move from a 25-point post model, which we frankly, 2019, 2020 work to what we're doing now, which is mesh, which is 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...

And that is what we showed you, Dragon is going to be the next way of seeing sports. And that's who we are in the sort of immortal words of Pat McAfee, I think that's genius. I think I'm good for then, and open to questions.