FIFA Data Scientists Explain Match Momentum (EP 49)

In this special interview episode, Lester Nare speaks with Juan Busso, Senior Football Data Scientist at FIFA, and Arron Ackerman, FIFA’s Team Lead for Football Performance Analysis, about the data science behind the Match Momentum visualization featured throughout the 2026 World Cup. What does “momentum” actually mean in football—and how can it be measured without reducing the game to possession or shots? Juan and Arron explain how FIFA translates football principles into mathematical models, validates those models with coaches and technical experts, and turns complex tracking data into a graphic that fans can understand at a glance. We break down the underlying “threat” model, including kinetic pitch control, player speed and acceleration, ball trajectories, defensive spacing, distance to goal, sight lines, and the creation of space. Match Momentum is calculated from player-tracking data captured 50 times per second, allowing the model to recognize when a team is becoming dangerous even without dominating possession. The conversation also covers FIFA’s wider data ecosystem—including event data, skeletal tracking, and the connected match ball—why offside positioning can still create threat, whether hydration breaks alter momentum, and the next generation of football analytics focused on player energy and physical effort. Guests Juan Busso — Senior Football Data Scientist, FIFA Arron Ackerman — Team Lead, Football Performance Analysis, FIFA Support the show Donate: FFPod.com/donate Follow: @FFPod on X / Instagram / TikTok / Facebook

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lester Nare?

  Hello, Internet. This is your captain speaking, Lester Nare. 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 little bit more about what does it mean to be a data scientist at FIFA?

• 0:45Lester Nare?

  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 for 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 Busso, who is a senior football data scientist based out of Zurich, Switzerland, alongside Aaron Ackerman, who is team lead for football performance analysis out of the United Kingdom. As always, we're going to talk about the science from the ground up today because this

• 1:29Lester Nare?

  is from First Principles.

• 1:47Lester Nare?

  Juan, Aaron, thank you so much for joining us today. Super excited about the opportunity to get to have the chance to talk to both of you. For those who are fans of the pod, we've 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

• 2:10Lester Nare?

  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 integrates with the rest of the graphics and broadcast team. So before we dive in, gentlemen, 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 with you Juan and go to Aaron.

• 2:52Speaker 01

  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 our job. And so, yeah, I'm a data 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

• 3:36Speaker 01

  professionals in different aspects of football in order to come up with the most suitable and trying to bring the, let's say, the most helpful insights to the teams, the fans and the rest of the audience that we have.

• 3:54Speaker 01

  So Aaron.

• 3:55Speaker 02

  Thanks Juan. So yeah, so my role's 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 to work alongside us. So in the World Cup, we have Jürgen Klinsmann, for example, we have Michael O'Neill 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.

• 4:31Speaker 02

  But one of the most fascinating things for us is, and biggest challenges, as you've already experienced Leicester, 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 infotainment or 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 comes, okay, how do we graphically represent that? So I work extremely closely with with Juan, for example, we work in the same team.

• 5:13Speaker 02

  Ultimately, and we then come together to go, okay, what do we have within the data? We have a a huge amount of data, both tracking, event, skeletal. 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.

• 5:33Speaker 02

  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.

• 5:50Lester Nare?

  That'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, I continue to find fascinating as data has become more and more integrated into the game. You know, 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 of of match momentum and kind of what motivated the development around it?

• 6:35Speaker 02

  Sure. So I'm happy to to give a bash at this. 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. 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 can 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,

• 7:19Speaker 02

  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 with regards to momentum, with regards to threats? 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 another kind of

• 7:47Speaker 02

  data set or data company that have their version. And our approach was was slightly differently, which I'll let Juan explain in a minute. But one of the key and most important things 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? Was because they create threat in the right moments, right? As an extreme example, we saw that in this tournament with Cabo 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?

• 8:31Speaker 02

  And although our model will identify naturally that Spain have a lot of threat, because they do. And 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, Atlético Madrid have the ball, right? So that was a huge, important piece for us to try and overcome. And 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?

• 9:11Speaker 01

  Yeah, I think that you explained quite well there. 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. And so, yeah, momentum was derived from another metric that is called threat. And this metric, you could think of it like 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 different teams is.

• 9:54Speaker 01

  Then on top of that, we have another layer pertaining the ball. And this is also with the speed, direction, height of the ball to see, 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, we'd 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 goal frame, the goalkeeper and the goal frame, the attackers and the defenders, and all

• 10:37Speaker 01

  these interactions, which is quite rich and very dynamic. Football, 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. 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 sight of the players looking at how clear is the view of the call 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 meters away. And then we bring all of this together and we have a threat calculation for each frame of the of the match.

• 11:21Speaker 01

  And then momentum, basically what it does is feeds on this threat and a 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 subsides a bit, then it starts to slow down and so. So it's a very, very contextual metric that in a very short glimpse 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.

• 12:03Speaker 01

  Try to tell a story in one graph of what has been happening until this moment in the match.

• 12:09Lester Nare?

  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.

• 12:33Speaker 02

  It's 50 hertz, the tracking data that we have, so.

• 12:35Lester Nare?

  50 hertz, 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 these contextual layers combined.

• 12:52Speaker 02

  Yeah, exactly.

• 12:53Lester Nare?

  And how they are sort of ebb and flow over a time period. That is. That is very interesting. I think one of the things Juan especially you brought up there as well is

• 13:06Lester Nare?

  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 mesh of sensors really work together?

• 13:34Speaker 02

  So I'll give you for momentum first and foremost, it's purely tracking data. There is no event data in it whatsoever, which I think again, 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 50 times a second.

• 13:58Speaker 02

  But when it comes to the other kind of data ecosystem or the full data ecosystem that we have, 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, which is a lot different to most other providers, for example. But then that's automated. So that is automated through a number of computer vision processes and algorithms to identify, a lot of machine learning has gone into that, to identify what the events actually look like and then to automatically categorize them.

• 14:37Speaker 02

  So Juan, I don't know whether you want to give a little bit more detail on that process.

• 14:40Speaker 01

  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 you mentioned, comes every 20 milliseconds and provides a lot of context and information

• 15:01Speaker 01

  just by looking at how the dynamic of the players is on the pitch. Then we have the event data that it's also

• 15:10Speaker 01

  We have a version live and then we have a version post-match that it's 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 made contact with the ball or performed 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 the receiver, how, etc. So each of the 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,

• 15:54Speaker 01

  like VR for example. And then we also have the ball data, which is very, very high resolution. It's 500 frames per second and we get a lot of information from it. It's a very complex dataset because it has a lot of,

• 16:16Speaker 01

  let's say, components, but it's very enriching as well. Not only the ball position, but also like the rotational accelerometers, etc. So it's, so those are the the main datasets. 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?

• 16:44Lester Nare?

  That's, that's, that's very, I mean, 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 post-match is quite vast. But within that context, the the match momentum feature is actually a very small, just a very narrow set of of the available context there.

• 17:13Lester Nare?

  Which it's so interesting and so when, you know, I kind of want to dig in a little bit about this idea of momentum. And why is it that? Given the vast array of data that's available, 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?

• 17:36Speaker 02

  Sure. I'm gonna 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. But the reality is, is those are very new things, right? So the ball data is very new, the limb tracking is very new. It's very heavy in terms of its its size and its processing rate, which is also, that's not an issue 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.

• 18:19Speaker 02

  That is a 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 spacing 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?

• 18:58Lester Nare?

  No, that that makes a lot of sense. And so maybe another interesting, 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, is unique and distinct 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 again. What are we really saying in the difference there in the storytelling?

• 19:30Speaker 02

  Juan, do you want to go or I'm happy to go?

• 19:33Speaker 01

  Go, I'll jump later.

• 19:36Speaker 02

  Sure, so quantitatively, what we're saying is that we already have a good understanding of what good looks like, let's say, right? And because we have, you know, a lot of data that shows us what good looks like. So we work backwards 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.

• 20:16Speaker 02

  You have a player moving at an acceleration pace of X, you know, which we know is getting closer to goal at a certain rate, which we know causes a level of threat that you also see a dispersion of the other player. So quantitatively, we know that all of these movements are creating space and space is what ultimately 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,

• 20:59Speaker 02

  and you have multiple players going and pressing the opposition who, yes, we have a one to say that that opposition team are 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 their 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 possession based metrics, which is not what momentum is. Momentum, team having a momentum isn't a possession based metric.

• 21:39Lester Nare?

  And so on a follow up, 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?

• 22:02Speaker 02

  Yeah, Juan, I'll give you some of the surprising things that we found.

• 22:07Speaker 01

  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, my 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 this is a bit for me to surprise that in football, like bringing this into football and trying to, you know, transform the box concept into these probabilities or basically continuous distributions, generated a lot

• 22:48Speaker 01

  of interesting situations, outcomes during the validation process where the, there would be a high, let's say, threat value or high momentum, and then the validators would go and check and then they would be like, oh, true, now it makes sense, but it would be like, something that sometimes situations would pop up that were not perceived before, and when you actually look at that, 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

• 23:33Speaker 01

  say that it took some tweaking in order to see, okay, so this moment was considered, let's say, more threatening than 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 that basically reflects the perception, so.

• 23:59Speaker 02

  And I think one of the other, Juan, one of the other things that I think we both found was quite cool was 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 onside, receive the ball, and then all of a sudden the threat goes up. So, you know, taking some of those things into consideration was, you know, was a huge thing to understand and overcome and, you know, discuss that with

• 24:44Speaker 02

  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, and movement was, was just fascinating in itself.

• 25:03Lester Nare?

  And I think we've seen even in the discussions around the games, even as we've gotten deeper into the knockout stages, these discussions from, you know, football fans, when it passes the eye test, when you see what looks like and 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 are like, how did, you know, X see Y in this fashion or in this way? And 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. Because it is, you know.

• 25:48Lester Nare?

  There's such a long storied history, right, in this sport. And there's sort of our ebbs and flows and changes tactically. You know, you could, you know, there's eras where the way in which the game is played changes. And I think it's we're sort of in a very fascinating era right now for the rise of so many of these different 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

• 26:32Lester Nare?

  see that there's this this feels has this feeling that's that's correct. I will, I will ask just a few more questions here. In that same vein, there are aspects of the game, you know, that are new, that it would be interesting to get your take on how you may have seen it affect match momentum, most prominent of which is the introduction of hydration breaks during 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?

• 27:12Lester Nare?

  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 matter? Or is it not really the right way to think about this?

• 27:27Speaker 02

  I think, yeah, no, there, there are some signs. 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 are because of the tactical decisions the coaches are making as a result of having those, those, those hydration breaks. But it's really hard, especially for instance, when a team already has momentum and they further increase their momentum, right?

• 28:08Speaker 02

  What does that actually, what does that actually mean? And how do you actually measure that versus when you see a momentum switch as a result of, as a result of 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 realise, just watching it. And what it does to, to teams, both psychologically, tactically,

• 28:54Speaker 02

  and also the state of the game that you're in when, when that hydration break comes, right? And there are so many different scenarios that you can use those hydration breaks to then overcome. And I wouldn't say match momentum is the single thing that necessarily will reflect that.

• 29:11Lester Nare?

  That's, that's fascinating. And, and it, it is, um, you know, as we see sort of the tacticians in managers, it, 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 again, whether it's psychologically, whether it's tactically. And so I, I would be interested to see how that continues to evolve, both from, how do you, how do you look at it from an analytics perspective? And what story is it telling? And, and, you know, does it continue to exist in the same format?

• 29:55Lester Nare?

  You know, or does this, does this become something that's perceived after the data is a little bit more digested? That its impact on the game is outsized in a way that's. Maybe not intended, but has become the reality in its implementation.

• 30:10Speaker 02

  Yeah, I think, I think there's no indication that we will see hydration breaks again in the near future because of the way that we measure the reasons why we have hydration break, right? It's, it's pretty complex, the, the measurement, the, the calculation for the reasons why we have them.

• 30:32Speaker 02

  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, 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 around 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. And, and now we'll go back to not having that opportunity, or the same type of opportunity.

• 31:17Speaker 02

  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.

• 31:33Lester Nare?

  I 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 toolset and data sets that are available, which again, the like data processing aspects of this 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, Aaron, you were just mentioning this, you know,

• 32:14Lester Nare?

  there are a variety of new sources of data, 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, if you have brainstorms or ideas kind of on the dartboard, 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.

• 32:49Speaker 01

  Should I go?

• 32:49Speaker 02

  Juan, I'll let you go first.

• 32:51Speaker 01

  Okay, so, I think that one important pillar that we are working on, and we have already some work, it's just that we wanna 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 game. I think that in football, there is a, I would say, a conservative approach that has been applied for a certain period of time, which is good and functional, but I think that it leaves out of the table a lot of the effort that the players are putting on the pitch.

• 33:32Speaker 01

  And sometimes, because reaching a particular sprint threshold is not how the particular context of the match allows you to perform, then this performance is undervalued or sometimes not included in the effort, but players do a lot of it. So, I would say that there is a new wave of metrics that would come in this direction in order to try to reflect what the actual physical effort, or approximate more with a more appropriate 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

• 34:17Speaker 01

  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's what we wanna try to reflect and bring the audiences to, basically to value as much.

• 34:34Speaker 02

  So, for example, Lester, one of the things that we've been working on is actually the value of the energy that players contribute, and how that actually links with the threat that they then support with the team. So, for example, to use Juan's example there... We see a lot of the times that people talking about, okay, maybe Kylian Mbappe's speed, or Lissay's speed, or you know, these players who are super, super quick, who are extremely dangerous. We know this, right? Or it may be the total volume that a midfielder runs. Everybody talks about 12K for a player, right? A World Cup match, for example. But

• 35:18Speaker 02

  the reality is of what the 12K for the left centre midfielder versus the right centre midfielder looks completely different, based on their role and the actually the moments that they're putting all of their energy into that is completely different. Or you have vice versa, is a centre back, okay, 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 and Juan are really confident that we've really got our heads around this and we've come up with some really nice metrics 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 coaches, for fans, for TV, for example?

• 36:03Speaker 02

  But I think for us, that will be the next frontier. And you know, what you may see in the Women's World Cup next year 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?

• 36:18Lester Nare?

  If, as a former number six, who was doing, who's doing 12K every game, I

• 36:24Speaker 02

  12K, fair play.

• 36:25Lester Nare?

  Yeah, so I can appreciate, you know, your point just about, 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. And that's such a really

• 36:48Lester Nare?

  Important point here and I'm really looking forward to seeing what you all do with that because I think there's, you know, especially because we live in a world of connected devices, streaming, mobile applications. 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 the game. 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.

• 37:24Speaker 02

  Yeah, and that changes based on geographical audiences as well. For example, the US audience this year has been craving it because 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?

• 37:51Lester Nare?

  Hundred, hundred percent, 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, 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 grateful to both you, Aaron and Juan for, for joining us today to have this discussion. I mean, it's, the beautiful game is continuing to grow.

• 38:36Lester Nare?

  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.

• 38:52Speaker 02

  No, it's been an absolute pleasure, Lester. Thank you for having us first and foremost. And yeah, enjoy the remainder of the games.

• 39:00Speaker 01

  Thanks. And also, I really appreciate the platform to also be able to share with the rest of the world as well, like the intricate work and concepts that go behind the things that we try in a simple line, try to show, but there is a lot of thought and process behind it.

• 39:20Lester Nare?

  Absolutely. We'll both, we'll be having both of you back here, here hopefully soon. Thank you, gentlemen, so much for your time today.

• 39:27Speaker 02

  Thanks, Lester.