FIFA Data Scientists Explain Match Momentum

Intro

0:00Hello internet. This is your captain speaking Lester Narre. Today we have a very special interview episode to end our World Cup coverage. It has been a phenomenal six weeks. We've done several segments covering the intersection of science and the world's beautiful game. And we will be joined by two guests today who are members of FIFA's data science team to discuss one of the new visualizations that has been featured very prominently in the 2026 World Cup match momentum. And over the course of the conversation, we'll get to learn a

0:41little bit more about what does it mean to be a data scientist at FIFA? How does it fit into the larger organization? What is match momentum? How did they come up with it? How do they use all of the complex and different options or data to derive and ultimately get the graphic on our screens as we watch the world's best team compete for the World Cup? And the guests joining us today will be Juan Busouso, who is a senior football data scientist based out of Zurich, Switzerland, alongside Aaron Aur, who is team lead for football performance analysis out of the United

1:24Kingdom. As always, we are going to talk about the science from the ground up today because this is from first principles.

1:44>> [music]

Meet FIFA’s data science and performance analysis teams

1:47>> Juan, Aaron, thank you so much uh for joining us today. I'm super excited about the opportunity to get to have the chance to talk to both of you. Um, for those who are fans of the pod, we covered many aspects of the World Cup over the last couple of weeks. And we ended up putting out a short explainer on the match momentum visualization, which was many people found very fascinating, but also raised a lot of questions about how you could actually make something like that happen. So, we are very blessed today to have two members of the data science team over at FIFA to help us walk through and better understand how the match momentum visualization works and how that

2:28integrates with the rest of the graphics and broadcast team. So before we dive in, gentlemen, um I just wanted you guys to see if you could tell us a little bit about your role within FIFA and how it sort of works inside the larger ecosystem where there's a lot of moving parts again, graphics, broadcast, live, it's global, it's not just the World Cup and maybe we can start uh uh with with you and go to Aaron. >> Cool. Thank you for having us. It's a great chance to also share with the rest of the world what we do. I mean other than the world cup I think that it's nice to have a chance to explain a bit further the details of the or the inner workings of of our job. Um so yeah I'm

3:10data scientist at FIFA and my job mostly basically relates to trying to convert the principles of football into some statistical and mathematical component or algorithm that allows us to extract objective insights from the match. And for that we have different data sources and we have a whole development process that we discuss with professionals in different aspects of football in order to come up with the most suitable and trying to bring the let's say most helpful insights to the teams, the fans and the rest of the audience that we

3:51have. >> So >> thanks Juan. Um, so yeah, so my role is slightly different. So I look after the performance analysis team here at FIFA. And I probably would say in terms of analysis, whether that be data or video, we're probably the closest to the grass and the coaches and the technical staff that we have. So on a tournament by tournament basis, we always bring a group of football experts in um to work alongside us. So in the World Cup, we have Jurgen Klinsman for example. Um, we have Michael O'Neal from Northern Ireland. We have Tobin Heath with us who are working, living and breathing this this every day with us to learn as much as we possibly can. But one of the most

4:32fascinating things for us is and biggest challenges as you've already experienced, Lester, is is how we translate into that into information that can be understood on the most basic level. Right? So that's where we then work with uh infotainment or uh stadium entertainment we we call it or or TV broadcasting for example and every year every few months we go through the process of identifying potentially new metrics potentially new new stories to tell within the game right and then the challenge becomes okay how do we graphically represent that so I work extremely closely with with Juan for example we work in the same team um

5:14ultimately And we then come together to go okay what do we have within the data? We have a a huge amount of data that both tracking event scal um as you can imagine for for all of our competitions. And then it's about okay well we know we use the data for this and we can tell stories uh around the data in this way that may be understood by a data scientist or maybe understood by a coach but how do we bring those two together to really tell a story to the audience and that's where I guess momentum has come in and played quite a big kind of TV role over the last six weeks during the World Cup.

What problem Match Momentum is designed to solve

5:50That's so fascinating and and and makes total sense. I think you know coming from a background in you know software from more of a tech perspective but having been a footballer myself seeing kind of the intersection of these two worlds is uh I continue to find fascinating as data has become more and more integrated into the game you know as over the last you know decade or two. And so if we kind of dig in here, you know, with maybe the first question, which is, you know, given that answer Aaron you just brought up about storytelling, right, through data and analytics, what, you know, what problem are you trying to solve with the concept

6:30of of match momentum and kind of what motivated the development around it? >> Sure. So I'm happy to to give a bash at this. Um, look, we want to be able to tell the most honest and truest story of a match that's happening, right? And we know that each team has their own style that they want to play. We know that teams have nuances and variations of the things that they do on the pitch. [snorts] We can measure them in fairly basic ways by counting things, right? We can measure them based on positionally where players are positioning themselves. and commentators for example and experts on TV will identify these anyway but actually what is it that we

7:12can use to really try and identify and show that okay maybe this team doesn't have the ball but when they do have the ball they're creating a lot of threat and they are becoming really dangerous right and that's where you know myself and Juan work together really closely on trying to take knowledge from the experts about what does it look like what does it feel like um with regards to momentum, with regards to threat, identify what that potentially looks like in the data and then represent that graphically. So I know obviously previously on the pod you talked about um another kind of uh uh uh data set or data company um that uh had their version and our approach was was

7:54slightly differently which I'll I'll let Juan explain in a minute. But one of the key and most important things for us to us was it's not necessarily just about having the ball in the final third. And that's probably what most people think about momentum. You can use two extreme examples. These are the two examples I always use. Why is it always that Atletico Madrid win games when they don't dominate the ball? >> Whereas because they create threat in the right moments, right? As an extreme example. We saw that in this tournament with Carbo Verde. Why do they for instance create great chances without having a lot of the ball for instance against Spain who completely dominate the ball right and although our model um will identify naturally that Spain have

8:35a lot of threat because they do um that isn't to say that at any no point other than when Spain don't have the ball that then for instance Cabo Verde or in lots of cases Atletico Madrid have the ball right so that was a a huge important piece for us to try and overcome um that was beyond the basic kind of looking way of looking at it where it's just if you have the ball in the final third therefore you control the greatest amount of chances that is true in most parts but it isn't always true and how do we make sure that we can represent as close to the truth as possible Juan

From threat to momentum

9:11>> yeah I think that you explained quite well the principle I think that the the idea was to have a metric that can tell a story and include the most contextual information about football that we can find and we have in our data sets let's say. Um so yeah momentum was derived from another metric that is called threat and this metric you could think of it like a a bit of a layer cake with different layers. One of them is like a kinetic pitch control that takes into acceleration the acceleration and speed of the players and the direction they're moving in order to calculate on the pitch where the control from the

9:53different teams is. Then on top of that we have another layer pertaining the ball and this is also with the speed direction and height of the ball to see uh you calculate the trajectory and then you can know if a player would intercept the ball if it would go out of bounds or the ball would go to a teammate and then create more threat. Then we have another layer that is basically the pitch she would say danger underneath which is basically not just how close you are to the opponent's goal frame but also how the setting is of the opponent you know like the distance between the defenders and the

10:33goal frame the goalkeeper and the goal frame the attackers and the defenders and all these interactions which is quite rich and very dynamic football that I think that this is one of the things that makes it so interesting is that there is so much context and so much happening at the same time that brings u these challenges for us to represent the metrics but also makes it interesting and then we have another layer on top of that that is basically the side of the players looking at how clear is the view of the goal frame. So if they have a direct view or there are some players in between and how far you know because a player on your face will block much more than a player 20 m away.

11:14And then we bring all of this together and we have a thread calculation for each frame of the of the match. And then momentum basically what it does is feeds on this thread anal metric and depending on the amount of attacks, the length of the attacks and the strength of these attacks or the threat of these attacks will spike or go down, right? like the more that you see the more frequent the longer the let's say the more threatening these attacks are the metric will go up and then when the team basically subsidize a bit then starts to slow down and so on so it's a very very contextual metric that in a very short

11:55glimpse of time allows you to have the context of the or the history of the match and I think that that was the entire purpose of this try to tell a story in one graph of what has been happening until this moment in very much.

Tracking the match at 50 Hz

12:09>> This is this is fascinating. So if I'm if I'm understanding you both correctly, there's a there's an initial there's almost like two layers to this, right? The initial layer is taking you know multiple pieces of context which is being identified as the threat threat metric and that's being taken every frame meaning basically every some time period you know 500 milliseconds a second what it might be >> it's 50 Hz the tracking data that we have. So 50 Hz. Okay. Incredible. And then so you use that as sort of the base and then that change over time and sort of basically a little bit of an algorithm on top of that is how we then create what we sort of define as momentum which is a combination of all

12:50these contextual layers combined. Yeah. Exactly. And how they are sort of eb and flow over a time period. That is that is very interesting. And I think one of the things um Juan especially you brought up there as well is um that there are so many points by which the data can be captured, right? So can you guys actually talk a little bit more about between passes, player movements, other match events which have to be tracked in real time which is an incredible processing challenge independent of just the actual data analysis challenge. But is it you know computer vision? Is it optical sensor data? like how does this sort of uh mesh of sensors really work

13:33together? >> So I'll give you for for momentum first and foremost it's purely tracking data. There is no event data in it whatsoever.

FIFA’s four major data sources

13:42Okay. >> Um which I think again is it's really cool in its own reason right because all the only information that we know from that data is who has the ball at every frame. Outside of that we just have the player location uh 50 times a second. Um but when it comes to the other kind of data ecosystem or the full data e ecosystem that we have um we have a joint venture with Hawkeye for example. So when it comes to TV what you will see is all of our data and our data model that we've built over the last couple of years um which is a lot different to most other providers for example but then that's automated. So that is

14:22automated through a number of um computer vision processes and algorithms to identify a lot of machine learning has gone into that to identify what uh the events actually look like and then to automatically categorize them. Um so Juan I don't know whether you want to give a little bit more detail on that process. >> Yeah so as I mentioned we have I would say four different sources of data. One is the tracking data from the players that as he mentioned comes every 20 milliseconds and provides a lot of context and information uh just by

15:02looking at how the dynamic of the players is on the page. Then we have the event data that it's also we have a version live and then we have a version postmatch that it's uh basically scrapped and cleaner and following what we have developed which is the FIFA football language which is what Aaron hinted before and this data is extremely precise to the frame what the players make contact with the ball or perform a particular action and very detailed also of how These events are basically not only if it was a left or right foot, but what kind of pass, who was a receiver, how and etc. So each of

15:44the events has its own set of traits and characteristics. We also have a lot of limb tracking data that it's also used for other components in football like the AR for example. And then we also have the ball data which is very very high resolution. uh it's 500 me um frames per second and we get a lot of information from it. It's a very complex data set because it has a lot of um let's say components but uh but yeah it's very enriching as well not only the ball position but also like the rotational accelerometers etc. So, so

16:26it's so those are the the main data sets, but as Aaron mentioned for momentum, we focus mostly on the I mean only on the tracking data because that's where we get all that context of the players. What are they doing? How are they running, interacting, and so >> that's that's very I mean there there's so many interesting pieces here because I think we're sort of there's sort of two separate conversations here, right? which is the entire pipeline of data that informs a variety of different end products real time and postmatch uh is quite vast. Um but within that context the the match momentum feature is actually a very small just a very narrow

17:08set of of the available context there. um which I it's so interesting and and

Why Match Momentum uses player tracking

17:16so when you know I kind of want to dig in a little bit about this idea of momentum and why is it that uh given the vast array of data that's available the having a confidence level that that minimum data set like is the correct approach like can you help me understand the philosophy behind that a little bit if that makes sense. I'm going to come back at you for a second because I think if we simplify it for a moment, we have all of this data which gives us loads of different other opportunities. Um but the reality is is those are very new things, right? So the ball data is very new, the limb tracking is very new.

17:58Um, it's very heavy in terms of its its size and its processing rate, which is also that's not an issue in in many ways. But the big thing for us is the most telling when it comes to momentum and dynamics of the game actually is not how fast the ball spins or the location of the ball or where it goes to. That is a uh an outcome of what the players decide to do. Now the next iteration may be that limb tracking supports movement. It supports body position. It supports body orientation. But the reality is is the dynamics are all in players movements, which positions they hold, the spacing between each other, the

18:39spacing between them and the opposition, their reaction, their timing, the real, you know, dynamics of of of the game. So that's why we're so confident that just with this single data source that we get the majority of what we need to really understand how we define momentum, right? >> No, that that makes a lot of sense. And so maybe another interesting just this question to kind of touch on this is you brought it up earlier where simple possession like the idea of simple possession as a value um is unique and

Momentum versus possession

19:13distinct from how momentum is defined here and quantitatively speaking when we say momentum as unique from possession like what are we really like I just want to dig deeper on that what are we really saying in the difference there in the storytelling Juan, do you want to go or I'm happy to go? >> Go. I I'll I'll jump later. >> Sure. So, quantitatively, what we're saying is that we already have a good understanding of what good looks like, let's say, right? Um because we have, you know, a lot of data that shows us what good looks like. So, we work

19:54backwards from what good looks like, right? And we look we work backwards or in a sense that just because you don't have the ball or just because you're not close to the goal necessarily in this moment that you do not have a potential threat on the opposition. Right? So quantitatively you have a really high line. you have uh a player moving at an acceleration pace of X, you know, which we know is getting closer to goal um at a certain rate which we know causes uh a level of threat that you also see a dispersion of the other players. So quantitively we know that all of these

20:36movements are creating space and space is what ultimately um creates the greatest amount of threat. And then when you take space creation closer to goal at a speed, that is when you get the most threatening moments. Forget about the ball for a second. That is that is ultimately what you then have. So when you are then without the ball, you're sat in a really deep position, for example, and you have multiple players going and pressing the opposition who yes, we have a one to say that that opposition team are in uh in possession of the ball. we quantitatively know the speed at which they are closing that space down and therefore reducing the threat of opposition but also increasing

21:16their own threat should they then get the ball all of a sudden okay bang there you go right so if you if you don't take all of those things into consideration what you just have is you have possessionbased metrics which is not what momentum is momentum team having a momentum isn't a possessionbased metric

What surprised the team during development

21:39And so on a followup, you know, as you guys have been going through this development process, right, around kind of honing in, you know, to really make sure that the match momentum reflects the flow of the game, you know, as you went through that process, was there anything surprising or something that you didn't expect as you worked through that process? Yeah, Juan, I'll give you some of the surprising things that we found. >> Um, to be honest, I think that the we when we were developing these principles like it was very I would say for me interesting to see I

22:20my background is in biology. So I come from a place where there are no boxes basically. So everything is in a distribution. Everything is kind of there are probabilities. So uh this is a bit the for me the surprise that in football like uh bringing this into football and trying to you know transform the the box concept into these uh probabilities or or basically continuous distributions uh generated a lot of interesting situations outcomes during the validation process where the there will be a high let's say threat value or high momentum and and the

23:00validators would go and check and then they would be like, "Oh, true. Now it makes sense." But it would be like uh something that sometimes situations would pop up that were not perceived before. And when you actually look at

Why an offside player can still create threat

23:14that and because of what Aaron said that you have all this context with the tracking data of creating space and the moving players then all these things come to to life and all the things come to let's say resurface and so I think that there was a lot of situations in that sense and also in the other sense I would say that it took some tweaking in order to see okay so the this moment was considered let's say more threatening that what the metric actually was what was happening. So again having the discussions and trying to incorporate okay what do you see footballistically here and how can I transform that into a mathematical model

23:55that basically reflects the perception. So so >> and I think one of the other one of the other things that I think we both found was quite cool was uh actually identifying the value of being offside, right? That was a huge discussion. So, you know, you're holding a position offside. Well, technically you receive the ball and you are not threatening in that moment. But actually holding your position offside for a certain amount of period of time does hold some weight to the value of threat because again this this how smart players are in terms of using the offside to then create space for themselves to then come on side receive the ball and then all of a sudden the threat goes up. So, you know,

24:36taking some of those things into consideration was, you know, was a huge thing to understand and overcome and, you know, discuss that with football experts in terms of, you know, what what they're coaching their players when they're talking about how do they use the offside. So, there's there's so many fascinating things that just understanding space and and valuing space was uh and movement was was just fascinating in itself. And I think we've seen even in the discussions around the games, even as we've gotten deeper um into the knockout stages, these discussions from, you know, football fans when it passes the eye test, when you see what looks like

25:17and feels like to be, oh, the shifts in the game and the teams that are able to identify spaces in ways that you sometimes be at home or like how did you know X see Y in this fashion or in this way? Um and and it it's just it's really fascinating to hear you discuss what is really a blend of sort of the art of football, right, and the science of data and finding a middle ground between those two. Uh because it is, you know, um there's such a long storyried history, right, in this sport. Um and there sort of are es and flows and changes tactically. you know, you could, you know, [clears throat] there's eras

25:57where the way in which the game is played changes. And I think it we're sort of in a very fascinating era right now for the rise of so many of these different um analytics such as match momentum. I mean, just looking at it as an observer on TV at home, for example, the momentum like it passes the eye test when we look at it from home, right? and and and so there's clearly the validation process and the work that you guys have done has gotten to a place where folks who have been, you know, football watchers of any league, you know, for some period of time can kind of intuitively see that there's this this feels has this feeling that's that's correct. I will I will ask just a

26:38few more questions here. In that same vein, um there are aspects of the game, you know, that are new that it I would

Hydration breaks and tactical resets

26:49be interesting to get your take on how you may have seen it affect match momentum. Most uh prominent of which is the introduction of hydration breaks during uh the World Cup specifically because correct me if I'm wrong, the the match momentum has existed prior to the World Cup. It just happened to be featuring prominently at the World Cup. Is that correct? So, did you notice, you know, as you've been looking through, has there been any differences between the non-hydration break data sets versus the hydration break data sets as it relates to match momentum or does it not really m or is it not really the right way to think about this method? >> I think yeah, no, there there are some

27:31signs. Um, I would say we haven't fully analyzed it. It's one of the questions that we've been asked to analyze and we will do so. But at the moment, I wouldn't say necessarily that momentum necessarily reflects any big changes for sure. The reasons why we're talking about them because of the tactical decisions the coaches are making as a result of having those those um those hydration breaks. Um but it's really hard especially for instance when a team already has momentum and they further increase their momentum right what does that actually um what does that actually mean and how do you

28:12actually measure that versus when you see a momentum switch as a result of um as a result of the the hydration break. So I think first we have to really understand what we're trying to use momentum to measure in that context >> before we then can go okay well this is this is a consequence or this is what's happened as a result of of of the hydration break on momentum. I think it's it's actually the hydration breaks are more complex than we actually probably realize um just watching it and what it does to to teams both psychologically, tactically um and also

28:55the state of the game that you're in when when that hydration break comes, right? Um there are so many different scenarios that you can use those high duration breaks to then overcome and I wouldn't say match momentum is the single thing that necessarily will reflect that. That's that's fascinating and and it it is um you know as we see sort of the tacticians in managers uh it's been interesting to see how managers have utilized the introduction of that and how quickly sort of you know strategy around using those to approach the games differently and having that opportunity to have a reset

29:36again whether it's psychologically whether it's tactically and so I I would be interested interested to see how that continues to evolve both from a how do you how do you look at it from an analytics perspective and what story is it telling um and and you know does it continue to exist in the same format you know or does this does this become something that's perceived after the data is a little bit more digested that it impact on the game is outsized in a way that's maybe not intended but has become the reality in its implementation >> um >> yeah I I think there's no indication that we will see uh hydration breaks

30:17again in the near future um because of the way that we measure the reasons why we have a hydration break, right? It's it's pretty complex that the measurement that the calculation for the reasons why we have them. Um so I think the best way for us to actually look at it is in our next competition, right? So you understanding it once our next competitions have as h when our next competition has happened actually looking back what impact did they have because the other thing that we obviously had coming into the tournament where we didn't have hydration breaks or you know we did have them in the club world cup but only on certain matches there wasn't a clear kind of principle

30:59around tactically how we can use them. So, you know, we've got to maybe a stage where everybody has some form of tactical decision to make during or uses that opportunity to make a tactical decision. Um, and now we'll go back to not having that opportunity or the same type of opportunity. So, I think it's probably in that way. Previously, the tactical decisions were based on substitutions, right? Now, you've got an extra layer that you can use in the current tournament to to have some to make some tactical changes.

The future of FIFA football analytics

31:33I want to end on sort of a forward look here because I think you you guys really helped I think us understand some of the philosophy behind what is the story that match momentum is trying to tell the layers that are used to structure it as independent from threat as an example or the wider tool set and data sets that are available which again the like data processing aspects of make my head hurt just even thinking about it with that number of different sources and doing it in real time. So that that's a a whole separate conversation. But as we look forward, you know, and I think Erin, you were just mentioning this, you know, there

32:14are a variety of new sources of data, um the the the limb tracking, you know, the introduction of ball tracking. the part of the mandate for the team is to continue to find ways to tell different stories about the game using data. Do you guys have any thoughts about as you look forward now with match momentum and you know seeming to be a success other things you're thinking about um if you have brainstorms or ideas kind of on the dart board or just even sort of a wish list of as we look forward this would be great if we could start to do these types of things. >> Should I go I'll let you go first. >> Okay. So um I think that one important

32:55pillar that we are working on and we we have already some work is just that we we want to further develop things before sharing with the rest of the world is the analysis of the physical data and trying to develop metrics that reflect a bit more the physical demands of the the game. I think that in football there is a I would say a conservative [clears throat] approach that has been applied for a certain period of time which is good and functional

Measuring player energy and physical effort

33:24[clears throat] but I think that it leaves out of the table a lot of the effort that the players are putting on the pitch and sometimes because reaching a particular sprit threshold is not how the particular context of the match allows you to perform then this performance performance is undervalued or sometimes not included in the effort but players do a lot of effort. So I would say that there is a [clears throat] new wave of metrics or that would come in this direction in order to try to reflect what the actual physical effort or approximate more with a more

34:05appropriate proxy the physical efforts that the players are putting on the pitch and how much work they are actually doing. And sometimes we are not perceiving it because you only get or in your head the players that are like running at top speed and you're like wow that was a fantastic run. But there is a lot of work in the very small short distances that they are doing and that that's what we want to try to reflect and bring the audiences to to basically to value as well. So for example, Lester, one of the things that we've been working on um is actually the value of the energy that players contribute and how that actually

34:46links with the threat that they then support with the team. So for example to use Jan's example that we see a lot of the times that uh people talking about okay maybe Kian Mbappé speed or OS speed or you know these players who are super super quick who are extremely dangerous we know this right uh or it may be the total volume that a midfielder runs. Everybody talks about 12k for a for a player, right? Uh a World Cup uh match for example, but the reality is of what the 12K for the left center midfielder versus the right center midfielder looks completely different um based on their

35:27role and the actually the moments that they're putting their all of their energy into that is completely different. Or you have vice versa is a center back. K maybe only runs 10K, but actually he puts in more energy and effort than somebody that does run 12K, right? And being able to okay, we have this information now. Myself are really confident that we've really got our heads around this and we've come up with some really nice metrics to to to tell this story to maybe the scientific audience and be able to measure that in a way that makes sense. But now it's that case of okay, how do we bring that to life for for coaches, for fans, for TV, for example. But I think for us that will be the next frontier. And you know what you may see in the women's world

36:08cup next year is is a lot more information around energy that players are putting down on the pitch which I think is really fascinating and cool in itself. Right. >> If as a former number six uh who was doing who's doing 12k every game I I >> 12k fair play. >> Yes. I can appreciate um you know your point just about >> um there are moments of explosiveness there are moments of top how frequently are you reaching top speed so just because you have the same total distance how you're managing each component part of that total distance is a very different thing um and that's such a really a important point here and I'm

36:50really looking forward to seeing what you all do with that because I think there's you know especially because we live in the world of connected devices, streaming, uh, mobile application. There's a variety of end destinations for this data that may not necessarily be in the broadcast, but can be a part of this larger ecosystem of how people engage >> with with the game. Um, and there's a lot of different opportunities and contexts by which to provide that. I know there's sort of a culture of we want to watch the game, we don't want all the extra stuff, but I think there are there there's a time and place for everything and and have >> that changes based on geographical audiences as well. For example, the US audience this year has been craving it

37:32because they're so used to it when you look at the NFL or you look at the NHL or the NBA, right? So I think with every region that we go to with the World Cup, the demands and the appetite for this information changes and naturally as the years go on, people's appetite for this information is constantly increasing anyway. Right.

Growing demand for deeper football data

37:51>> 100 100%. Which is as you can see from the level of engagement on the short we put out on match momentum. I mean people were very curious not only about it in general but understanding how it works. And I think going into next year's World Cup, which again we, you know, now that we have an open line of communication, would love to continue the discussion as you continue to put some of these concepts out because I think there is an audience that is hungry for, >> you know, a a bridge between a general audience and a little bit more of a technical understanding of the work that gets, you know, done behind the scenes here. And I'm I'm grateful to both you, Erin, and Juan for for joining us today

38:31to have this discussion. I mean, it's the beautiful game is continuing to grow. Um, it's already the world's most popular sport. It will continue to be so. And I'm looking forward to you and your teams continuing to push the envelope to help us better understand and tell different types of stories about this game that means so much to so many people on the planet. >> No, it's been an absolute pleasure, Lester. Thank you for having us first and foremost and uh yeah, enjoy the remainder of the games. >> I said guns. Thanks and also I really appreciate the the platform to also be able to share with the rest of the world as well like the integrate work and

39:13concepts that go behind the the things that we try in a simple line try to show but there is a lot of thought and process behind it.

Final thoughts

39:21>> Absolutely. We both we be having both of you back here here hopefully soon. Thank you gentlemen so much for your time today. >> Thanks Esther. [music]