Unofficial Partner Podcast
Unofficial Partner Podcast
UP519 Live at Sportradar Connect: Soccer Edition, featuring Jamie Carragher
This episode was recorded at the recent Sportradar Connect: Soccer Edition event at the London Stadium in Queen Elizabeth Olympic Park.
The podcast divides is in two parts.
First is an onstage conversation on the evolution of football data analytics and AI featuring Jamie Carragher, Erik Durm and Nicolo D’Ercole.
Jamie Carragher played 737 times for Liverpool and 38 times for England, in career that included the famous 2005 UEFA Champions League victory. Today he is one of the most popular and innovative analysts working in sports television, part of Sky Sports award winning Premier League production and CBS’s groundbreaking Golazzo show in the US.
Erik Durm was a star of the Bundesliga, playing for Borussia Dortmund and Eintracht Frankfurt and was a member of Germany’s 2014 FIFA World Cup winning team.
Nicolo D’Ercole is Executive Vice President of AI & technology at Sportradar.
The second part of this episode is a roundtable conversation we convened at the end of the day featuring Nicolo along with Darren Small (SVP Managed Trading Services) and Nikolaus Beier (SVP Marketing Services) from Sportradar to discuss the key marketing and technology trends that dominate the sports, betting and entertainment industries.
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I think, uh, in the early two thousands pro zone came in, that was like, almost felt like the, the, the phase thing that could track the, uh, the data of players, you know, how far they'd run really. But then you get a situation where certain players were just making runs across the pitch just so the date, the numbers would be, would go high that threw that. So they were
Speaker 7:gaming the
Jamie Carragher:Yeah, I remember playing right back and Danny Murphy just made a run across the pitch, where you going? He said, no, it's good. You get me stats up, get me me sprint. So
Richard Gillis, Unofficial Partner:that was the unmistakable voice of Jamie Carer, the former Liverpool and England footballer, who today is one of the most watched and admired commentators and analysts working in sports television. Jamie's one of the faces of Sky Sports Award-winning football coverage in Britain, and he also appears On the CBS hit show Gala Zo in North America. I spoke with Jamie as part of the Sportradar Soccer Connect event at the London Stadium in Quin Asbo Olympic Park, home of West Ham United Football Club in Stratford. And this podcast divides into two parts. First, you'll hear Jamie and fellow guests, Eric Derham in Nicolo Dural. recorded on stage in front of an invited audience. Eric Derham was the star of the Bundesliga for Baria Dortmund and Irak Frankfurt, and was a member of Germany's 2014 FIFA World Cup winning team. And Nicolo Dekar is Executive Vice President of AI and Technology at Sportradar. And Nicolo joins me for the second part of this episode, which is a round table conversation we convened at the end of the day with other senior Sport radar executives discussing the key marketing and technology trends that dominate the sports betting and entertainment industries. So welcome. I'm Richard Gillis, and this is Unofficial Partner. We are gonna talk about data, and analytics that's gonna run through this bit of the conversation. Jamie Carer, I was trying to work out when your first game for Liverpool was and what data analytics meant what was the conversation then?
Jamie Carragher:Uh, there was no conversation really, uh, at all about data analysis. The only data was, I think, the mark you got the next day in the paper. That was the only data we got. Uh, I think I got a seven on my first game. What was the first game? 1997? Uh, would've been against my, my full Ws against Aston Villa. So that would've been, uh, January the 18th, 1997. So a long time ago, but there was no, after the game, there was no analysis data wise provided by the coaches or anyone in the media at that time. It was just a case of, you know, through your own experience, you knew if you'd had a good game or, you know, the coaching staff. Of course, you know, football like it is now, even though we're we're inundated with, with data, uh, it's still about opinions and, you know, people going to the pub and, and thinking who'd play well and who didn't. So yet there was a, there was no data to back up my performance. It was just, uh, you know, what the coaching staff and the supporters felt.
Speaker 7:Did you watch the Kenny, Doug Le documentary? Yes. There's a bit in there where. It's the Bob Paisley sort of, uh, Ronnie Moran, Joe Fagan era. And they were meticulous note takers, aren't they? Yeah.
Jamie Carragher:Yeah. I think that that came from Shanks and the boot room was together for so long and they used to always go back to the sort of the books and the training books. I think a lot of that was more to do with actually the training, the, the than the actual games really, and how they went about things. And I think they'd look back and maybe when the results weren't going well, what happened years before when the results weren't going well, what they didn't train and maybe replicate that as well. So, uh, so yeah, they were definitely ahead of the time at that stage. And then Kenny, you know, mentions that there was nothing like that anywhere. Then at that stage. He'd obviously famously been married and coached and looked after by Jste, the great jockey and, uh, but yeah, he said, you know, coming to liver appeals are completely different. Uh, catfish.
Richard Gillis, Unofficial Partner:Okay. Right. Eric, there's the same question for you. We are moving forward a generation, sorry to do this, but you know. Take me back to the, the, the initial period.'cause there is a juergen lop moment in your career, which is transformational. Let's go, let's get there.
Erik Durm:Yeah. It was, uh, 2013, um, when I first came, like professional player for the first team for Mond. Um, and before I was striker my whole life. Um, so, um, yeah, he came to me after training, I was training with the first team, um, and he was just like, um, you know, on this position maybe it's a little difficult to, to reach the, the highest level because you are maybe a little bit too thin, you know, like, not like 11. And you were, you
Speaker 7:were a forward, you were an attacker.
Erik Durm:Yeah, I was, I was striker. Um, so he wanted to, to show me and, and make analyzers with me for the left and right fullback position because he said, um, you can run a lot, you can sprint a lot. So he really wanted to, to get this done with me together and he always had my bag and I was so, so delighted and happy. That he saw this. Um, so I didn't, I didn't really thought about it. I just say, let's, let's start it. And then normally I would have to go to the second team, uh, to prove or to learn, um, that I can do it. But at the end of the day, it worked much faster than we both thought. And yeah, then I made my first, first EO game in 2013 against Augsburg. And yeah, two 14, um, I was part of this, uh, of this, um, yeah, this Germany team who won the World Cup in two 14. And, um, yeah, it was just a unbelievable journey. I never thought about, or never dreamed about something like this.
Speaker 7:So you went from the, the, the speed of which you went from, okay. I'm a, I'm a center forward now I, you're a, a fallback and then within a year you are in the World Cup winning German team in 2014. It's a pretty extraordinary
Erik Durm:turnaround. Yeah, it was, uh, also surreal. So for me it was. Of course, I, I didn't get the minutes to play in, in the, in the, in the World Cup, but for me it was just everything to be part of that, part of that team that feel the team spirit. So it was, for me, it was like a, like a dream come true, but as you said, it was very, very quick. Um, so I couldn't really think about it. I think that was good at that time.
Speaker 7:And the, the same question I asked Jamie in terms of what data analytics meant at that point,
Erik Durm:um, for me, we had already the, the vests with the GPS in it, so they really could, um, could track our, um, the speed, like the, the recovery time, um, how many meters you make, um, how was the tackles, you know, the in fights and stuff like this. Um, but for me it was always, I remember when it was 2013, I always wanted to have it directly on my plate, uh, and wanted to check how far, you know, how many kilometers I did and stuff. Um, but that time the AI was not there, so, um, I sometimes had to wait till the next day. When I was on the recovery bike with the guys and then we all sat there and looked like, ah, you did this, you did that. And so we compared it. So I think this is a, a big thing for the ai that you can more or less have a directly like life. Yeah. Like during the game and halftime or after the game. And you can directly like tell from the coaches to the, to the players on the pitch. You have to stand a little more on the right. You have to accelerate a little more. I think it's very, very great.
Richard Gillis, Unofficial Partner:And Jamie, before I go to Nicola, what was it like towards the end of your career? I mean, you, you that period. 2013, did you? Yeah. Was the, the end, so that is a transformational sort of, sort of night and day, isn't it?
Jamie Carragher:Yeah, I think, uh, in the early two thousands pro zone came in, that was like, almost felt like the, the, the phase thing that could track the, uh, the data of players, you know, how far they'd run really. But then you get a situation where certain players were just making runs across the pitch just so the date, you know, the, the numbers would be, would go high that threw that. So they were
Speaker 7:gaming the
Jamie Carragher:Yeah, I remember playing right back and Danny Murphy just made a run across the pitch, where you going? He said, no, it's good. You get me stats up, get me me sprint. So yeah, so it was, uh, it was at the early stages then I think it was probably, it was seen as sort of groundbreaking. I think it was Steve McLaren, certainly in English football, who was the first sort of coach behind sort of pro zone. And that sort of pushed him up the ladder in terms of getting a job at Manchester United and getting involved with England as well. Uh, but then it just, it sort of gradually kept just, just moving the dial more and more. And as, uh, as Alex mentioned there about sort of, you know, the tracking data that wouldn't be used for games, that'd be more for training. We wouldn't put those on for games, but, uh, but yeah, you could just see it was slowly growing.
Richard Gillis, Unofficial Partner:Nicola, I want you to build a bridge between the player experience and actually what's happening on the pitch now. So just bring us up to date in terms of what, what is being taken from a Bundas League, A match or a Champions League game.
Nicolo D'Ercole:Yes. So, um, it, it all starts from, you know, tracking, as they said, devices. You either with devices or with cameras, you're basically getting the video feed and you have dispersed computer system that can, from the video feed track players where they are. We got to a point where you can track things very accurately with cameras, uh, to the point that you get 2030 skeletal position for every person. 60 times a second. So, you know, acceleration, speed, how fast you're running, how fast you're reacting. Same for the ball, you know how the ball is spinning, you know, you get to know all of these things and you can basically, at some point, the, the big step that is happening already happen in some form is the, uh, going from just the numbers as, yeah. Uh, we were saying, oh, you know, maybe you just game the number running more to how does these things are impacting your game? So like, is you are running, but like when you shoot, like how is the ball going? Is, was that a good shot or not? How are you in position? How are you fitting the system? These are all things that are happening also for I guess health and recovery. Are you landing on the same feet? Are you with two feet? Are you accelerating too much? When you land on the, on the pitch after a jump, is it hurting you? Like there, there's many things, uh, even how tired you are. Maybe it's time to pull you out because you risk getting injured. These things, you get real time now. Like how your speed is decreasing during a game. These are all things that really just deliver better insights, uh, to the coaching staff and to scouts and everyone. Yeah,
Richard Gillis, Unofficial Partner:I think there's a question about has it changed the game in terms of the way in which managers look at the game and look at strategy and the way in which the, the, the sort of thing that we, the, the game on the pitch that we see has evolved?
Jamie Carragher:Oh, yeah. I think it's completely changed football. I, I think especially within recruitment in terms of sort of playing players, I think, I think the days of, of managers and coaches, you know, traveling the country or Europe watching players, uh, you know, in the stadiums, I, I think it's long gone. I think that that will still happen, but I think most of it now, it'll be. First of all, who comes up high on the numbers in terms of what they're looking for, what sorts of data or suits, the style of play of the coach. And then they'll identify sort of four or five players and then maybe go and watch the players. So I, I think it'll definitely start with data initially as I think, as I said when I started playing, it'll be about watching people in the flesh and getting a feeling there. Whereas as you know, the people will say, now, you know, the numbers don't lie. It, it's there for you in black and white.
Richard Gillis, Unofficial Partner:What's it like being a player, being analyzed to that level? He's talking about 30 odd skeletal sort of, uh, data points. How does it impact you? Do you, are you, do you enjoy that process?
Erik Durm:Um, for me, I think when I look back on it now, I, I would hope they start a little earlier with the ai because I was injured almost my half, half of my career. So they maybe could tell me before, like, injury risk management, like settle down a little bit, like Eric is in a red, a red zone or whatever. I was in a red zone all the time or less. Um, but, um. Yeah, I, I think for me, I'm, I'm a guy who always like, try to everywhere get like, um, support or, or take something in to learn. So I think it's a great tool to learn. Um, uh, it's great things to know what happens with the game or with the accelerations of the game or with the whole team structure. If you see, like, my heat map for example, is like all the time on the left side. What happens if I go a little more to the right side that you can change it in half time? The coaches tell you what it, what it means to the game, what it can bring to the game when you stand a little, little more open and stuff like this. And this is what the AI does does. Yeah. Like, it's like a little assistant coach who never sleeps. It sees everything and, um, yeah, it's, but it's up to the, to the coaches, how they deliver it to the players and how the players take it in. So that's a good thing that this is, yeah, it's like a, it's like assistance, you know, it's cannot work without human beings, you know. It's like, it's like, um, you have this data. As, as you said, Jamie, um, it's black and white. It is there, but you have to deliver it. And that's the nice thing about it.
Speaker 10:I, I think lots of things are changing, but one thing doesn't change, which is like in sport, you're always trying to find the most minimal edge to win against your opponent. Yeah. And whatever you use this data is just useful. You need to find you it in the best possible way to figure out how you get a little advantage to, to beat, uh, your opponent.
Speaker 7:Because one of the, one of the worries I have or you know, is generally is the, the big will get bigger. So the is is the data analytics question just gonna, is that the edge when everyone's got the same data? Where is the edge do you think?
Speaker 10:Um, I think actually I, I have the opposite view. We were talking earlier as well, like, I think this helps democratize some stuff. There's like many examples of many teams that have been able to play from a kind of medium level to really exploit the data to find. Uh, best player that fits their system to really leverage analytics in a way that you don't need to spend big monies on the, the star players to find and really optimize, uh, your performance and your, uh, tactics, uh, to win. This is, I, I think really like democratizing some, some aspects of the game. Of course, there's always gonna be stars, uh, at the end. It's a human game.
Speaker 7:Jamie, what do you think about that, the balance between where the edge is is now and where is it gonna be?
Jamie Carragher:I, I, when I look at the sort of the, the team at the, the top end of the table and namely my own team, Liverpool, who have supposedly been ahead of the cave, if you like, in terms of, you know, data ana analytics, in terms of buying players, you know, the Moneyball project. And even though I say this, uh, through gritted teeth, it's probably in some ways for the game be nice that a team who have spent 400 million. This summer end up running away with the league or beating everyone. And it, and it shows, you know, it's still a team game. It's about being sort of clever. It's not about spending the most money, it's about getting the right players, creating a sort of a unit, a connection between the, you know, what's on the pitch and off the pitch as well. And that's it. It's really interesting Liverpool this season because as I said, for so long they've been seen as the people who are ahead of the game, uh, data wise, but it just hasn't happened for them this season. We're trying to work it out. Uh, I'm not sure they can work it out at the moment as well. So it shows, you know, even in this sort of data world where you feel as if, you know, you're trying to make less mistakes because the data is there for Yeah, at times it can, uh, it can still go wrong.
Speaker 7:It's quite hard for a, for a Liverpool or Manu United or a buy-in to do a Brentford Brighton type model, isn't it? So buy low, sell high, move the player at the trading of players in that way.'cause there is. Not data, you know, it's the, it's the fan ego, it's the club ego gets in the way. Do you, do you agree with that, Eric?
Erik Durm:A hundred percent. I think, um, there's an example in Denmark. My wife's from Denmark, so I know a little bit about a tennis football as well. Um, about, I don't know, I hope I spell it correct. Um, and they won the league, I think four times in the last 10 years. And this is a club I never heard before. Like they really bring the, the young players, the young talent players you never heard of. And all of a sudden you see them in a C league performing, um, and winning the league in, in Denmark. And there's a big club like Copenhagen, um, competing with them. And this shows like that. This IA in especially what Jamie also said, that they figure out, like with the data, like who is on top? This can, this fits to the team. And okay, this is, we need a left bag. Who plays a little bit like Alfonso Davis, let's, let's put it that way. And they show you, okay, this, and this is the. The best options, for example. And this saves you time and you can really, like, for a coach, it's really nice to select your team after data as well. But of course, the human being has to fit as well. Like it doesn't bring anything if the data is nice, but you come in and you cannot work with the guy because he thinks he, he's better than the others, for example. So it's always like a, a combination. But it's definitely a good thing for like a club for Mitchell, and they showed like this Moneyball experience and they showed like, it is working or it can work, but if it works for every club you, you never know, then everybody would do it, I guess.
Speaker 7:Yeah. Yeah. There's a, the television question. So what's it like, I, I analyze your analysis sometimes. So I've got the, you know, I've, I'm getting, I'm looking at data feeds, primitive ones, whereas you are doing it live in live. What's that like? What, just take us inside that, that world.
Jamie Carragher:No, I, I love that. Especially, you know, the show I think you turn on to Monday Night Football is probably the Yeah. Biggest data analysis show around football. Probably. I would, I would imagine anywhere in the world. Really. Yeah. And so we pride ourselves on that. And all we are trying to give is the viewer is what we think, and, and we know that the players are. In the clubs. So that's the sort of data we're trying to give. But as you said now, I think with social media, I think over the last four to five years, I think even supporters now of clubs understand sort of the business side of clubs and what to spend and the type of money that they're bringing in, the type of players they're trying to buy and what they're trying to do and the sort of model that club as well. Now every fan wants their club to spend lots of money, but I think there's a lot of supporters now up and down the country who look at teams like a B Brentford or a Brighton and how they go about things, uh, data wise and probably one club actually in the state that we're in right now, there's a lot of disgruntled, I think Westham fans right now and probably a lot of that is they feel that maybe at times their club isn't run or at maybe at the, the top of the table in terms of the data analytics. And we see teams like Brighton, uh, and Brentford who are. Not as big a club as, as Westham, but overachiever more compared to their club. Uh, so I think fans are a lot more aware of that right now. Uh, yeah, we, we try and provide that in terms of the analysis on the pitch as well within the game. But I think the level of understanding of the game now as well for, I think from supporters, I think they're probably a lot more educated, maybe because there's more football on tv. There's shows like Monday night football, social media as well. So yeah, it's, it's a great platform for us. And as I said, hopefully it gives, you know, people home. A little bit of understanding of maybe what, you know, a team meeting would be like on a Monday morning after a game at the weekend.
Speaker 7:Someone sometimes the sort of the data and I'm wondering what the most valuable jobs are going to be at a football club. Sometimes a data analyst job, it's a sort of back page story when, you know, one go, one is poached by another club.
Jamie Carragher:Well it always felt when a manager left the club. The most important guy he would bring with him would always be his number two, his assistant manager. That was okay, this guy always has to come with him now. It almost seems like the data analyst he brings with him. Yeah. He is almost as important as, you know, the guy who's, who coaches with him or is his general manager. So yeah, I think that, you know, the data analysis now is, is a huge part of the game. And there's no doubt when a, when a coach leaves a, a job goes to a new job, that guy is a big part of that team.
Speaker 7:Nicola, why can't other clubs just copy Brentford Brighton and the other models? What, what, just, I, I want to push us into the forward, into the future in terms of this AI question. What does it mean when you are laying that into this question?
Speaker 10:Well, it it, it's complex. I mean, I, I don't, I'm not sure why. I mean, you've got four minutes, 20 seconds. Okay. I'll try to start with it. No. Um, um, I think today there's still a lot of human aspects. Like AI is still a tool that is enhancing what the humans will do and how you use your data. Of course. There's data and there is insights and you need to use those insights to really make decisions. Um, what if we're looking ahead into the future, what Sport Radar has started to do is building this so-called foundation model. There is a technical name, which is the same technology that you use to build charge GPT or, or Gemini. And basically, instead of predicting the next word when they're giving you those answers, where you use these models to predict what's gonna happen next in an action, right? Using the tracking data and, uh, the, to do that the same way, the Charge G PT two, where you need to understand the world. This system needs to understand the game, which is the big next step. And understanding the game really means like, almost like a chess engine. That's maybe the best, easiest way to explain it. It's like a chess engine. Understand chess, like chess is super simple. There's just eight things and moves one at a time. But there, at some point you can really start evaluating how every move, every step in the field, every pass, everything. Like, what is the impact? What's gonna, what's the, like, how it improves this specific action a little bit? Or even just the mad presence. I can imagine that messy being in the field, not moving already attracts attention of three people, right? Just because merely being there, even, I don't know, sitting. And, um, um, this system eventually will understand all of these things and will probably also start coming up with new tactics. What's the best way to react to this lineup? What's the best way to play given these players? Or what is the best player to hire that is now in, I don't know, high school that will fit or will have a career trajectory similar to this other one? But again, um, this will tell you on average, uh, things okay. Yeah. Yeah. It'll, it'll never decide how you're gonna score a goal. What magic, what act of crazy dexterity you're gonna invent. That's the beauty of sport. Like how you're gonna be able to, you know, solve a situation that even to that system sounded unsolvable. And you do like, you know, or someone makes a bad mistake at the wrong time. That's, and still that's gonna be the, the beauty of, uh, you know, how unexpected the game is. Even if you can predict statistically how things are gonna go, you never know what the reality is going to be. There's, so
Speaker 7:what, what's the role of a manager in that environment? Let's get, let you know, if we sort of push that forward. LOP was a, was a huge influence on you. He feels like someone who's got enormous emotional intelligence. He feels like someone who's not just the data bit of it, but that bit of the emotional part becomes more important when every, when everything is being modeled.
Speaker 10:Yes, absolutely.
Speaker 7:We are looking at a different
Speaker 10:layer. I can, I can't predict a hundred years ahead for sure, but for now, these models are never gonna be able to understand the psych, the human safety. And, uh, I believe that emotional intelligence plus the tool, plus the understanding of what these tools are telling you. Are what's gonna make the difference. Like you still need players to be bonded together. You know, uh, Eric was telling us stories like, you know, when club did the right thing at the right time and, uh, that's like still super valuable and it's gonna continue to be that.
Speaker 7:It's quite a nice conclusion to, to have that human beings might still be relevant,
Speaker 10:you know? So
Jamie Carragher:thank God
Speaker 7:we still need Jamie Carri. We still need Monday Night Football.
Jamie Carragher:I'm sure about that.
Speaker 7:Um, very finally, CBS Gazzo, the, the, that program pointed at America, it feels different to Monday Night Football. What is the difference, what brief do you get for that program?'cause there's a question about what America's relationship to football and how that's gonna be. Obviously you've got the World
Jamie Carragher:Cup next year. It's probably the, the complete opposite end of the scale to sort of Monday night football. And that's why I enjoy doing sort of one on a Monday and I can go to the other one on, on the Tuesday and Wednesday. Uh. You just mentioned Monday Night Football really data really about analysis and numbers. Whereas this, as I said, is, is, is not taking the game too serious, really. And that's, that's what's worked for us. I don't think that would work on English tv. I think it works, uh, because it's the Champions League and not every fan watching as teams in the Champions League. Uh, so it's not as tribal, uh, it's going out obviously in, uh, in America. It's, it's not really affected by, by the tone of Atonement is, uh, is Europe and, uh, and yet it just seems to work. We have a, you know, a nice sort of brand of, you know, different people bringing different things to it. But I, I would say the producers on that show are always taking risks, always sort of pushing us as close as we can get to the line and hopefully not crossing at the times. And they. Listen, whether we like it or not, social media plays a big part in, in all of our lives, and, and they really hit home on that in terms of, it feels like
Speaker 7:it's built for social media. Yes.
Jamie Carragher:That that's exactly what it is. So, you know, everyone in this country knows of it, but it's not obviously on, uh, on TV over here. But people see it just because of social media clips. And as I said, whether you like it or not, it's, it's very powerful social media in terms of getting messages across or, you know, getting stuff out there. Uh, and certainly from our point of view. Yeah.
Speaker 7:Perfect. Right. Listen, we have come to the, you've gotta get a plane. I wanna thank the group. Round of applause, please for our panel. Thank you. Thanks.
Richard Gillis, Unofficial Partner:I just wanna go around the table, just say who you are, um, what you do all day, what's your job?
Darren Small:I'm Darren School. I'm the Senior Vice President for Managed Trading Services within Sportradar.
Richard Gillis, Unofficial Partner:Okay. I'm gonna ask what that means in a minute.
Darren Small:Okay, great.
Nikolaus Beier:my name is Nikki Bayer and I'm the SVP Marketing Services and sportradar.
Nicolo D'Ercole:Okay. Hello, I'm Nicola Dela. And I'm EVP for technology and AI at Sport Radar. Yes.
Richard Gillis, Unofficial Partner:So can you just explain what happened today? I was trying to work out whether people were being optimistic or pessimistic. About the state of the marketplace. What was your sense,
define marketplace soccer, or, well, you define it for me. What are the marketplaces? Well, I mean, if we talk about generally, I guess soccer and, and the, popularity about soccer, because in the end, the event was about, it was about soccer. It was largely optimistic or, or at least, certainly not pessimistic. At least that was my impression. I mean, obviously there are things. That is, changing this world as, the world generally is changing when we talk about, technological developments like ai for instance. So I think yeah, from that perspective, sure there is influences, um, there's developments, but I think generally it's a positive one. Yeah.
Richard Gillis, Unofficial Partner:The AI question nicolo is, is one that it's just coming up. It's all over the place, not just today, but just in every conversation that you have about business, any type of business. And you are at the sharp end of that. What, how are you sort of dealing with that?'cause we talked about it on our panel with Jamie Carragher and Eric Derham, but there is a sense that we're only scraping. We can only scrape. I mean, I asked you something about the future of AI and I had gave you about four minutes to answer. Yeah, yeah. No, I will take less this time. No kidding.
Nicolo D'Ercole:Um, the. I thought what was really great today was like that the two football player, former football players, are now involved in media or fan engagement in the us. They really love the impact that technology is having on the sport. They're not opposed to it as like players in the, in this kind of market. And so, uh, this was really great to see. They were super aware of the technology advancements of how it can help the sport, the performance, the coaching, how it can help, uh. The media aspects of it and the fan engagement aspects of it. So this was really great to see.
Richard Gillis, Unofficial Partner:Who is gonna win and who's not gonna win in the terms of ai, the race that we're in, and I keep coming back to whether or not it's to do with the quality of the data that exists in the first place. Because I was talking to someone from the travel industry the other day and they were saying a lot of people were just putting bots on top of what is a pretty crappy data set and just hope, you know, fingers crossed and hoping for the best. I can sense that that might be, there'll be people around the sports industry are saying, the CEO wants. A really flashy ai. You know, he's, she has heard about AI and wants to have this interface and wants to have the, the bells and whistles, but we haven't spent the last 20 years getting our data in order.
Darren Small:I think that's the opposite for Sportradar particularly. Yeah. I mean, it's been, it's core to our product, we've. Spent a lot of time structuring and forming our databases to make sure that the data is good and it can be read and it can be interrogated a lot. Everything we've done until now has always been about delivering. High quality, repeatable data to the end user so that they're able to use it in whatever process they want. So what we are pretty proud and pretty, pretty confident with the, the fact that the data that we have it and the way we continue to collect it will be able to support us in the need around that AI and that pursuit.
Nicolo D'Ercole:Yeah, for sure. Of course, there's always improvements in terms we have to do, breaking silos, making all of the data that we have compatible to be accessible by ai, which is. Things we're working towards to, I will also want to say that many companies just use models, the textual models, like the chat gpt of the world to build experiences, chat bots or yeah, content generation. Of course we also do that, but what we're doing that as well, of course, other companies in other kind of markets are doing is actually also build different kind of models, like the ones that. Learn about the game. And these are not, they use similar technologies, similar architectures, but they're completely different. And, uh, how do you make sure that the bets where the odds we're providing to bookmakers are like ideal to optimize for profits, et cetera, or how you really understand the game in a way that almost start to resemble the best humans in, in understanding of the dynamics of the game. That's really what we're trying to achieve. And, uh, so we're going a little bit beyond just, uh, giving some data to, an existing model from, uh, Google or Open AI or Tropic, and then just make it, talk about it.
Richard Gillis, Unofficial Partner:Well, no, what's gonna happen when this is more, I guess for the retail and the, the sort of betting the sports books. Question and it's a differentiation question about what the difference is at that end.'cause you guys have, are sort of back a stage from that. As I understand it, you've, you are the sort of plumbing between the rights holders that you're collecting data on one side and presenting it to the market. On the other.
Darren Small:But we, that's exactly, that's a really good question.'cause that's exactly what we're trying to do with everything we're doing. Like from a, from a sports betting and odds perspective, from where I, where I sit, we are doing a lot of personalization to the, to the odds offer through AI services. We are changing the way that customers can integrate their product.
Richard Gillis, Unofficial Partner:That. Because at the moment, I mean, and you just put me right, because again, my assumption is, I mean, I'm using the travel analogy, but I probably won't need to go to a travel agent anymore. I'll just go to chat or I'll go to some. Ai. And so my in, it's the interface question in terms of how I am gonna be engaging if I want to have a bet, am I gonna be then sort of making a choice between the brands that are presented to me, or am I just going to go via a large language model? I'll
try to answer that from a, from a marketing perspective. Right. So I think the, generally one hypothesis on how generally the internet Yeah. Will develop into is, is essentially, a Google 2.0 where you only have one interface and whatever you want to have, you just put it in there. That's one hypothesis. I don't really believe in that because I think in the end it will be very, very hard to kind of. Have everything you need in basically just, a line of, of a website where you just put in something. Right. Um, so I think there will be some, some differentiation. There will be different types of, websites. There will still be content available. I think the key question is how will user behavior change and that we already see that. Yeah. So I think, um, if I'm not mistaken, jet GPT, is already like what the sixth biggest website in the world, the fifth biggest web website in the world, or something like that. So there is already a lot of people using, AI generate models. The question is, where will it end? Yeah. And I think from a, from a betting perspective, I think we'll see. Definitely offerings where, you know, people will not go to a betting website anymore, but simply interact with an ai, I'm sure we'll also see people that will still want to go to their betting website because it's just a different way of consuming content. Yeah.
Nicolo D'Ercole:Um, for sure there's gonna be a new generation of people that are used to chatbots, uh, that will probably start their, uh, engagement with betting and having fun during a game through chatbots. It's possible and for sure it's already happening and it'll continue to happen. I also deeply believe in optimized user interfaces. We've seen this in, in my career in many places. We still see that if you wanna. And what does that mean? Optimize. You know, when you're booking a, a hotel, you really still love to go to a website that is specialized on that. And as all the s and whistle to make you really find what's perfect for you. And or when you're looking for restaurant, when you're using your bank account, et cetera. Some simple operations, the chat bot will help you. Or when you are starting and you don't know how to move, but once you become an expert or you want to engage in a, you know, stronger way, uh, optimize website, something that is able to surface the content in the right way will probably continue to win over a generic chatbot.
Richard Gillis, Unofficial Partner:The idea of, of convergence of media betting, where are we in that?'cause obviously life score are on there and that's a story that they've been pushing for for the last period
Darren Small:well, if we look historically, it's always been a, something that's been attempted and with mixed results. Yeah. So if we look to Sky Bet historically, yeah. It took a long time for them to get that connection between the betting and the Sky brand. Yeah. And they had to become separate companies again for it to really work. Um, if we looked at ESPN BET in the US. They've just reversed it out now and they're, well, I
Richard Gillis, Unofficial Partner:can I ask you about, it's quite interesting and whether that's about,'cause you can't get a better, bigger sports brand than ESPN and if that fails on that, in that experiment, is it, is it about brand or product, I guess is the question and is it there's, or is it something specific to the American market about the duopoly at the center of it? I,
I can only share my, my view on that. Right. I think it's, I think it's a combination of, of all of those things that just because you are. A media brand doesn't necessarily mean you're a betting brand. Yeah. Um, obviously you need to get the product right, you need to get the marketing right. These brands tend to advertise a lot on their own kind of properties, but. You know, obviously their properties don't cover a hundred percent of the market, so they miss out on certain, players that they cannot, um, reach. I don't think that there is a one answer to that. Yeah. Also, for this particular case, I'm, I'm not aware of this one thing. I think there can be use cases where it, where it might work. We also, in spot order, we have some kind of products that. Where we're trying to help, bookmakers to get the better integration to media products. Right? So the question is also, does it have to be, uh. Really the same brand or is it just like a, a stronger corporation? but clearly betting is becoming much more of an entertainment product and one of the panels, I dunno who, who said that, I think it was on my panel, uh, where somebody said, I'm, I'm, I'm thankful that there is more influence from the US markets on how they see sports. I generally agree, maybe not in all aspects. There are some that, you know, might be different in, in Europe, but generally speaking, when it comes to, sports is entertainment. and as such, betting is also entertainment. That's the way going
Richard Gillis, Unofficial Partner:forwards. So these new products, sports formats and new leagues that are popping up. So for example, I was listening to about TGL the golf. Yes. Yeah. The new golf product. You know, the PGA tours anti lve position, although they don't. Like me calling it that, but that, that's essentially what it is. It's a golf, it's a short form golf. And I was listening to, George Pine, who is brewing sports, sports private equity investor, and I was quite interested in how, when he was asked what he liked about TGL, how high betting was in the, his analysis of the market for that. And he sees that product. As a betting being central
Darren Small:to that. Yeah, I, I wouldn't disagree, and actually, if you look to that product, it is highly technical. So actually the data that you can get out of TGL particularly instant information around the, the speed of the board, the trajectory where it's gonna land, proximity to the pin, which translates to fast, quick odds. Quick, uh, consumability from the end user. So I wouldn't disagree that, I don't think it's gonna sit up nicely against a, sports betting, uh, product for sure.
Richard Gillis, Unofficial Partner:There's a question which sort of builds on that slightly, which is, is complexity then one of the characteristics of a good betting sport?
Darren Small:No, when I, when I say complexity, it wasn't complexity around the offer to the consumer.'cause actually that narrative needs to be pretty straightforward and consumer, they have to understand. Right. What, how they get into that, the complexity around that data and the ability to be able to extract that from a, let's call it real sport. So when you are, you've got the PGA tour on, that's very hard to, to extract and to tell that story in the same way because you've got 72 players off after a cut. Yeah. Playing out on a, on a huge field of play from different holes and playing different times and effectively building to a different story point. Whereas with the TGL, it is all there. But you can extract all of those really complex data points and then transfer them into that information pretty
Richard Gillis, Unofficial Partner:quickly. I mean, I guess one, the question is, if we were to start a sport. New format today, what would you do if you said, right, okay. One of the priorities is to is to put betting as at the center of it.
I'll answer your question or try to answer your question a bit. Um, but maybe just one, one sentence on that. The global iGaming mark to, uh, market to my knowledge is, that's casino and, and sports betting combined. It's, it's around about, uh, hundreds. Uh, billion euros, right? So that's big market when you look at the, at, at opportunities there. So obviously sports are looking into that market. And then let's just say out of, I forget which league it was. That almost 20% of the sponsoring money is coming from, sports betting somehow in, in sponsoring. So I think it's just natural that when you. When you think about a product, you think about betting, you think about sponsoring, and you think about like, how can you monetize, how can you make it an attractive betting product, right? I think that's one thing. The other thing is when something is a betting product, typically it, it has changes quick. Quick changes, uh, things in life that you can, that you can bet on. Right. Um, so for instance, you know, table tennis for instance, a sport that is kind of, I guess compared to, to other sports in terms of popularity, there's not an awful lot of people, you know, watching it. You would be surprised on how many people are actually playing it. but betting is, it's much more relevant than it is elsewhere. And that's because it's, it's always on, right? That's one thing. And the other thing is. You know, there is a lot of interactions, right? There's a lot of points played. So I think that's, that's part of it. Right? And I would also, and I'm not the golf person, so No, but
Darren Small:I'd like to add to that because that's a really important point. Yeah.'cause I think if you ask a thousand people, who's the best table tennis player in the world, I don't think anyone would be able to tell you. Right. But if you ask them who's the best driver in F1, you're probably gonna get a decent response to that. Mm-hmm. But F1 doesn't turn into a good betting product. It doesn't even compete with, with table tennis in terms of its interest globally and volume, but it's as a sport, it's very interesting. So having one doesn't necessarily translate to having the other is, I dunno, where we're gonna under make our next billion on the new sport. This is what I'm idea,
Nicolo D'Ercole:but generally you want something that has some unexpected situation that can happen, something that can change suddenly, uh, in a very unexpected way. You want also to be able to. Really from day zero to extract what Darren was saying, to extract all the possible data that in a way or another can influence odds computation. So like you need to be able to extract those and uh, as automated as possible so that you can immediately push it and generate very efficient odds. and then when you have unexpected moments, it drives emotions, it drives sudden wins or losses. And it drives, I guess, also profits for, uh, for the leagues and for the bookmakers. So that's, Uh, an ideal combination
Darren Small:would be, yeah. Sale gps a really good example of that. That data is phenomenal. Like just, I, I watch the data rather than the, stuff happening on the screen as a, as a trader, but it's a very niche sport, so it's very hard to transfer that to a lot of people. But they've done exactly that. They've started with a huge investment in how they extract data from their sport. Yeah. And then now they're trying to, move that forward and get more engagement from tv, media and sports betting itself.
Richard Gillis, Unofficial Partner:Yeah. Yeah. I just, I've got a question for you before you go. Is, we had Sam Sadie from live score, and the x ai question in terms of they've done a deal to basically you use rock in some way. Can you just explain what they're doing and whether that's, are they early to that? Are we gonna see more of that sort of thing? Because I think it's sort of getting sentiment at scale, I think is one of the
Nicolo D'Ercole:Yes, exactly. I think there's many attempts getting, uh, understanding of sentiments or getting more data. Uh, also connection with new kind of markets the goal is all, you fetch more data, you try to understand more, and you can, both get more engagement, et cetera. I think these are still very early experiments though.
Richard Gillis, Unofficial Partner:And do we have to pick a horse in terms of these foundational models that are emerging? Do we all have to make a decision?
Nicolo D'Ercole:No. I, I, I think right now, not, not for sure. there's been this theory right at the beginning, about two years ago, that if one model was ahead of all of the others, at some point you would get to this singularity moments rapid acceleration when, because the, the one that first reaches this breaking point where it starts self-improving and then it becomes so much better than the others, that it dominates the world, right? This hasn't happened. We see that they're very close to each other. We also see open source model being. 3, 4, 6 months behind state of the art closed model. So I think competition is very good. All of these companies are spending a lot of money into improving their models and uh, I think we all benefit out of this. And uh, I think for sure right now it is not the time to just pick one and we're not doing it sport radar. I think things are still very much open to
Richard Gillis, Unofficial Partner:change. Yeah. And one of the things I'm not. Are better but one of the reasons it was mentioned earlier about the level of, complexity that's quite often a barrier to me in terms of I'm, I don't engage because I, I sort of don't know the world and I never really sort of dunno about odds. And it feels like the more tech becomes more central to it. The less I'm gonna be able to understand. the sort of simplicity of having a bet, who's gonna win? Who's not?
Darren Small:That's shifted. I can't agree more. I think that's the, that's the role of the operator and, and technology to, to still allow the recreational customer to still be able to engage and understand a narrative that they wanna build into. It's always been the thing of like, how do we get people to bet on different markets? Well, unless you can tell a story around that. I think. Dan alluded to the fact they had the quickest player on the pitch at one point. Like how do you even measure it and how do, how do you qualify whether that's gonna be, uh, he's gonna run it at 12.9 or like, it's very difficult. So from, as a consumer, how you, you then give up on it, right? Well, it
Richard Gillis, Unofficial Partner:starts to look a bit like sort of financial trading, which is another area I'm not, is not my strong point. So the more that. Bit comes in, which is exciting to a certain number of people, but to actually, to a layman, it's can be off-putting.
I mean, when you look at the, at the US right, they have this thing called prediction markets where, you know, in the end it's a, kind of betting, but also not right, like regulatory, from a regulatory point of view, it's a bit unclear what it, what it actually is, but in, in the end it's, it's, it's something like that. Uh, and this comes from financial providers, right? So there's, there is, um, Robin Hood, for instance, is offering some of these, um, prediction markets. So yes, it's getting closer. but in the end, when it comes to what is relevant, in the end, maybe if you wanna build a sports, uh, that is perfectly made for betting, probably you would not choose soccer in the first place. Right. For, because some of the things that we just mentioned, you know, soccer doesn't qualify and seals. by far the most bet on sports. So those are the emotions, right? Yeah, exactly. Emotions. That's my point. Exactly. And, it's the fans, right? It's just much more than that. So coming back to your question, what type of sports would you try to kind of found, if you find a new one, then, then probably I would try to find one that a lot of people like. Right. And I think then a lot of other questions, will be answered. There is a good one
Darren Small:called football.
Look, I, you know, as a, I I, I made the joke already as a German speaker. I don't really understand this kind of challenge of like soccer and football. For us, it's a, we have a German name for it, right. And I don't care what, what name I use in English, to be very honest.
Nicolo D'Ercole:from a technology perspective, I think that the role that really we need to fulfill is to, you know, these sports, especially football, drives the most emotions the top events live every year, always sport events. Yeah. And, When you're able to basically connect that emotion to the right person at the right moment with the right offer, that's where you get the engagement that brings people to maybe also engage with betting, but, uh, this is what you need to, to connect. So as a new market, a new type of betting, et cetera, needs to be connected from the emotion at the right time, et cetera. And if you're able to explain it with the right technology, which is not just ai, it's also about user interface, it's also about story making. Uh, then, You'll succeed. Okay, listen, thank you very much. Enjoyed that.