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Bloomberg Audio Studios, Podcasts, Radio, News. Hello and welcome to another episode of the OddLots Podcast. I'm Joe Wiesenthal. And I'm Tracy Alleway. Tracy, I have a question.
I know you spent a lot of your youth overseas. Why you? I'm here. Did you ever go to many baseball games again? Yeah, so I was in Chicago for a few years, so I went to the Cubs games and then I was in Japan, and the baseball scene in Japan is amazing.
Like the best vibes of a live sports event that I've ever witnessed during counting. I was just talking to someone about this last night. I've always wanted to go to a Japanese baseball game. Highly recommend. This actually, like, if we ever do a live show in Tokyo, let's try to schedule it during baseball season.
Because I sort of think that's a bucket list thing. But anyway, the reason I asked this question is I have this really vague memory as a child, going to see Detroit Tigers games with my grandfather, like probably when I was like, maybe these memories are probably from one I was younger than like six or five. But there used to be a non-trivial number of people who would go to the games and they would keep score and they would write down every single hit bat. I'm pretty sure I've seen that, not in person, but maybe movies or something like that. It doesn't really happen anymore.
Like I never see it. There may be a few like old-timers who still has a hobby or habit do that. But it was like a non-insignificant number of people. And it's interesting to me, you know, I've been to a couple of soccer games this year. There's no equivalent way you could do that, right?
Because it's like baseball is filled with all of these discrete events. Picture, who's the picture? Who is the better? Hit, not a hit, single, not a strikeout, walk, etc. Like, what would even be the equivalent of soccer?
So this has been a long-running debate in soccer. And I remember when Moneyball came out and sports analytics became a big thing, especially for baseball because as you point out, it's these sort of discrete events that have a lot of statistics embedded in them. A lot of people were saying that soccer, you could never use data analytics in the same way for soccer. Like it's too chaotic, it's too fluid, there's too many variables, there's not enough goals. That complaint comes up a lot whenever we talk about, are we going to say soccer or football by the way?
You know what, let's just say soccer. Okay, all right. I'll try. We can say football. I actually don't feel strongly about this one.
But that said, we do see soccer analytics on the rise, right? Now we get all these stories about tiny clubs that are using data to source specific players in very Moneyballs, not always. We obviously have prediction markets because people are doing a lot of sports spending. And so all the analytics seem to be becoming more important. And I will just say, I've read this crazy stat right before we came on from the law firm Morgan Lewis.
And they were saying in the 2026 FIFA World Cup, match-based data is going to be something like, so 104 matches generating more than 90 petabytes of data, which is a 45-fold increase over the volume produced in the last World Cup in 2022. You know, this raises a question. And I know we're going to get to this in the conversation. And it almost is like a philosophical question, which is, okay, we see the game of soccer is like very fluid, right? There's no, we just said there's fewer discrete events.
Like in theory is something that's fluid, a series of like microscopic discrete events. You know, could you get a million frames per second and actually turn it into discrete events? Like that is sort of like an interesting question. I mean, a parallel that I think of in this conversation is chess is like all discrete events. And that was seen as like a very difficult thing to crack within like over 25 years ago.
That's like, yeah, of course. They cracked it. But then you would think, okay, well, like what is the opposite of chess, which would be language? And you say, okay, that's fluid. That's all over the place.
And yet computer seems to be understanding language pretty good. My inner Luddite says there must still be a secret like unmodal level. Thank you. Talk when it comes to like football, the beautiful game. But you're right that technology may prove me wrong very quickly.
Well, as you mentioned, you know, soccer analytics growing and obviously the interest now is, for obvious reasons, there's all the betting on the World Cup. People talk about the XG of like, I don't know, a situation or a player, the expected goals. Now we have body posing analytics as well, which is my mind. And when our producer, Kale introduced me, I hadn't seen them before earlier this year, like the momentum charts that show like, you know, how dominant a team is. And then you give a moment, you see building waves and stuff like that.
So there clearly is a lot of work being done. I don't know how much it works. I don't know how much like momentum charts consistently predict who is going to be the winner. But just this question of like the ability to model deeply fluid things, the beautiful game, like it's an art, right? Like can computers actually model this?
And then like if they can, what does that say about the ability to model a bunch of other things? Strikes me is just like a very relevant question right now, because it's the World Cup, but also relevant question in general about markets and finance. Exactly. So I'm curious to know like how it works. Yeah, the moneyball revolution, where we are in the arc with soccer.
Anyway, I'm really excited to say we really do have two perfect guests because they do sit right in this space and also the intersection of everything that we were talking about. We're going to be speaking with yours. Becker is a soccer analytics consultant, a professional soccer analytics consultant for the last decade, as well as Mike Tracy, he's the head of risk at Apex FinTech Solutions. He used to be a volatility arbitrage trader at peak six. And he also does soccer analytics for the club FC.
So literally the two perfect guests to talk about some of these questions that are rising right now. So Mike and yours, thank you both so much for coming on the podcast. Thank you for having us. That's a great introduction. Yeah, thank you.
You know, Mike, let me start with you a question. A lot of people I think have for good reason, an intuitive feel that like sports analytics and trading markets are adjacent ideas that like we know this. We know that a lot of the prop jobs and market makers do both and so forth. And how would you articulate based on your career, this sort of overlap between the skills and techniques you've developed as an arbitrage trader, a volatility trader, and the overlap of skills that they're like, okay, this thing that we'll get into that we call soccer analytics. Well, soccer is unique because I'd say every aspect of the game is distribution.
Okay. When you look at on pitch, the performance by team, when you look at performance by an individual player, when you look at the seasonal outcomes and this unique component, that is promotion relegation. So your finances are highly variant year over year. And so there's a lot of overlap between volatility trading and working in soccer in that you are making highly levered bets on often imperfect information that's not necessarily predictive like other sports such as baseball. Can I ask a very basic question, which is what is the point of soccer analytics?
So if we go back to the markets analogy, we talk about price discovery, right? Like you're trying to find the right price for a particular asset. With soccer, are you trying to price players, improve the training, make more successful predictive bets? I imagine it's a bunch of different things. Yeah, I'd say everything.
I think you said what are the things you're trying to do? And I think from an investor operator perspective, it's what are you trying to not do? You're not trying to get relegated and you're not trying to spend 30 million pounds on a player that is going to be terrible and you'll have to get rid of them two years. Yours, what are you talking from your perspective? I mentioned your soccer analytics pro actually are a producer, by the way, just put in the chat the Bloomberg style guy just football.
Yeah, we should just switch to let's stay in company style. Your football analytics pro, Tracy has like, what is the point? Is it more on the team side of things in terms of identifying talent, etc? Or is it more on, I guess, the sort of the betting side? What is the problem you and your professional capacity are trying to solve?
So it highly depends on the organization. Right? So my organization might want to make good hires, make sure that they don't lose millions of pounds. Some organizations might use soccer analytics to try and improve their strategy, but it might look at ingame data and try to figure out if there's some optimization that they can make based on where the past should go or where players should be, what place house make more sense or what yields more expected goals, if you will. And then I guess the third one is entertainment.
There are a lot of apps out there, websites out there that provide fans with some sort of information and even back in the 90s, they would have overlays on the TV where they show number of corners, number of the other parts and potential percentage. I remember that. It doesn't mean anything, but it's interesting. So you can only imagine that you can go much deeper with that and people will still be engaged. Well, actually, just say more on that.
You said it doesn't mean anything. Is that something that has always been understood or is this something in 2026? We could say that a few of these stats that they like to put on the TV were just extremely like low signal data points. Yes. So when I say they don't mean anything, I don't think those were necessarily predictors of the outcome of the match.
Corners might because it's an indicator of which team has the overhand in the game, but I'm quite sure most of the others, like possession percentage, don't mean all that much. We're trying to look at game outcomes. Wait, so we touched on this in the intro, but the perceived wisdom in the early 2000s was that soccer was far too complicated to be given the baseball, moneyball style treatment. What actually changed to get us to this point where we're not just talking about things like expected goals, but we're also talking about body movements and posturing and things like that. Baseball had moneyball in 2003 and soccer had two things in the 2010s.
So in 2013, Chris Anderson and David Sally published this book called The Numbers Game that distilled soccer down to more of a weakest link game. And then the other thing that happened was, I would say we had a bit of this revolution on Twitter, which you kind of spoke about recently, Joe, where ideas were incubated. And I give Michael Kaylee a lot of credit for this. He's deep down the pod, but every Saturday and the Premier League, you'd have six matches and then you'd wait 30 minutes and Kaylee would just post a stream of every expected goals chart and it started to stimulate this discussion of does this represent what should have happened, what should have been the outcome? And I think it started to lead us down this path of where is XG flawed? It only registers when you have a shot. And so there's much more to the game than simply
which shots occur. There's possession and the threat of each possession. There's match momentum and there's changing styles. So it's evolved from on-ball data to tracking data to now we're going to that deeper layer of body pose and what movements can prevent or create opportunities to score. I think what goes hand in hand with that is when I started 10 years ago, soccer football was perceived as too complex. There were 22 players there was a ball. People
were doing interesting analytics, research and applied research already in basketball and what was said was well, there's five players on each team. So it's a lot less complex. There's more games and we have more data and there's more scoring. So you could derive metrics a lot easier. And I think the evolution in just how AI was applied in data availability. So going from
like Mike said only on-ball events where we know which player made the pass, which player made the shot. But we always had to say well we don't know where all the other players are because those are not recorded. And now we have the ability to make highly complicated artificial internal nets with this particular tracking data where at 10 frames per second or 25 frames per second we know where all the players are and we know where the ball is. And then additionally at some point we will get full body pose. We know where the whole skeleton of all players are basically. That sort of went
hand in hand and it felt like it wasn't inevitable but also the game is just more complex. So it needed more compute and needed more data and more knowledge. There are some market stories where you want every detail. Joe and I have made quite a few podcasts on that basis. But sometimes you've only got 10 minutes and just want to know what's moving markets fast. That's the Barclays Brief podcast.
Every week experts from Barclays Markets and Research get you up to speed on what's happening and what to watch next. Concise, focus and brief. Search Barclays Brief wherever you get your podcasts. Tracy, Mike mentioned that you know just looking at a shot on clothes. So I get to tell you so much.
For example, it doesn't tell you if the referees are going to revisit a call made a minute before the shot halfway on the other half of the screen and take away the goal. This was going to be my question which we were talking about before the podcast recording. But like how do you factor in let's say the occasional randomness of the game and perhaps some erratic trying to be diplomatic here. A erratic decision making by referees and footballing bodies. I think it's controlled in controllables. That's a fixed parameter within the game is that uncertainty
that you can't control and it's hard to predict. There is human error obviously and it's impossible to isolate when that's going to happen in a match and how it will. You just sort of play the game. You also don't look at individual events per se. You might from an analysis perspective but if you're building a basic predictive model for football outcomes you might just take all the scoreboard results from the last 10 years and then all those things cancel out. Like if one
team has a red card somewhere you wouldn't even know from these models but you can build some interesting let's say rudimentary predictive models with just the outcomes of games. Out of curiosity has var changed football analytics at all or presented new opportunities for data. That presents new opportunities for data because that is an event that happens. If a player is fractionally offside because his hand is ahead of the last offender that doesn't take away from the fact that he still got into a good opportunity position, possess the ball turned instruct the ball in the net and often that data is nullified because of the var but in theory it's something that you should consider in your data set. Within the raw data itself there's tons of data that you could add or censor out. You know yours mentioned red cards and a lot of our models we censor that
out of our data set. There's an infamous match from three years ago between Chelsea and Tottenham where Tottenham went down to nine men and their response was to play a very high line meaning they put all of their defenders up near midfield and tried to catch Chelsea off sides and as one would expect Chelsea proceeded to score multiple goals later in the match and when player in particular Nicholas Jackson had three goals in that one match and when you look at his seasonal outputs but that entire season that three goals was probably around 20% of his total goals. So when something like that happens when you have a regular game state it is wise to sort of manipulate your data and remove that to give you a full picture of what does this game look like at an equal game state. Oh that's interesting so it's not like that game in particular was a rich fountain of data it's important to sort of like recognize that the data from this particular game is not going to be particularly predictive
about other games and therefore you know the guy who scores three goals of that match is probably not going to continue to score. Three goals again for the rest of it. It was a rich fountain of bad data. Okay. So at the end of the season when you see people analyzing the player they often analyze their season and what do they do per 90 minutes of football and so you have this highly skewed data set by some really poor data and so there's a lot of data mining involved in the process of building a model when you evaluate a team and individual players. So here's a question I have
and you're talking about using neural networks and it's like eventually like you know we'll have the compute to like have the position of every player's body flash maybe you know hundreds of images frames per second and so forth and then you have feed it all into a model and then we learn something about who's more likely to win. But one of the things that happens in a lot of like other domains and here I'm thinking about like chess or go for example is that you can have these models that are extraordinary but they don't speak English or they don't speak any human language and so the transmission of like what was learned from these events is something usable by say a coach who's thinking about strategy or a general manager who's thinking about player selection. Talk to us about when you think about these machine learning models that can't really communicate their findings in any way in language that humans can understand how you sort of bridge that gap to where this is useful information for a team or manager. This is generally be understood I think in the last couple of years or the last eight years as one the biggest problem you can build these models these neural nets already exist. The main thing is the translation like you said from
Molal Quiz to Coach and I think in the last couple of years most things have found is that they need an expert analyst and data analyst to do this conversion or this translation step where the coach isn't fed the data directly. The coach is fed just the information that the analyst finds from the data and that could be in the end that generally still boils down to having video clips so you can use the data to find video and then show the video to the coach which then from bottom up you have this approach. That makes sense but the part about okay we're going to use the data to find video so all of this makes sense you have the data specialist who translates you have the videos so that there's something tangible but talk to us about that specific step where the data analyst sees some sort of model output and then is able to use that to find the relevant clip to show the coach something potentially instructive because that seems like the hard part to me.
Yeah so the model output could be many things it could be outputs from a classification model that says in this given 10 seconds or 15 or 22nd window this team played in this sort of build up or they have this type of structure it could also be a little bit more advanced where you can simply say well in this instance we had a high probability of conceding a goal and that could be just from an extracted goal shot if you're looking at only a 10-bit you could also have model outputs from something we call an expected possession value model or an expected trend level where you measure the chance that the team is going to score in let's say the next 30 seconds or the next possession and then you can find this fight there and either measure when your team is likely to concede or measure when the other team is likely to score and you can use those kind of signals to boil it down to video. Yeah this is something I wanted to ask actually so you mentioned speed just then like what is the actual latency that we're talking about since we're using all these markets like are we talking about an insight that's actionable within seconds like you're going to sub a player on after your model spits something out like live during the game or is it more realistically that you're reviewing the model in the analytics after a game and sort of tweaking I guess when things have
calmed down. Veryly most of this does not happen live so most of it happens either pre-match or post-match but you can still do it there's enough live data to make these instances the worst one. Yeah I'd say most of those types of adjustments based on data typically happen at halftime or if you're in the World Cup during a high duration break and in the derivatives world I always think of expected outcome versus realized outcome so what is the market implying what do you expect and then what is actually happening and that is sort of the nexus of how teams prepare for matches they come up with their own expected outcome how is our opposition going to play how are we going to play and then that live in game is your realized outcome and so you're receiving that data and it's being transmitted to analysts who can then communicate it down to the bench to discuss with the manager who can then make those changes at that halftime. It's interesting the game of two halves Joe is now the game of four quarters offering up more opportunities to make model based adjustments. That's right we have we used to have
two discrete events in a game in the first half of the second and now we have at least four so I guess that creates more data as well as maybe more ad revenue as well as more ad revenue. You know obviously in baseball at least according to Michael Lewis right that there was this period where you know you had the old time scouts and they're like oh this guy has good hustle right oh this guy is like good heart there was like no data behind any of it he may just sort of had the you know the swagger of someone who looked like maybe a star player and then it's like oh no but he looked at his like on base percentage or that's or his vorp or whatever and then they get promoted but it was like a culture thing. Has there been a similar cultural clash within sort of soccer scouting where what the data says about what constitutes a player does not map to traditional intuitions? I think a lot of clubs look at it from two perspectives. I don't think there's this old school scouts versus the data guy okay. It's a very collaborative. I think what a lot of clubs do is they have an individual scout who
will go watch a player and give his assessment and his rating and then they will have their own internal model with data and they will look at the deltas between those two different models and if something seems off you often have collaboration between the data person and the scout and they figure out who's right who's wrong and then to your point about hustle let's call it yeah there are Swiss army knife ways to quantify that in soccer in certain instances if you look at game states say the game state you know game state is essentially zero plus one minus one plus two minus two plus three minus three and so say you're in a plus three minus three game state and win probability for one team is 98 percent you can manipulate the data and only look at how players are performing in that game state you're out of the game are you still competing do you still care are you in the right position oh now that may not align with the old school scout saying you know this guy has grit but again there's
there's Swiss army knife ways to give some type of indication you know I'd say soccer data creates questions it doesn't give us answers wait just to better understand this yeah can you give us like an analytics framework if you were trying to judge the best this is a loaded question but it comes every discussion but you're trying to judge the best soccer player either of all time or currently like what would the analytics framework for that actually look like jubelingham okay okay like really that's a great question like walk us through what the math says about jubelingham and how you would derive that well jubelingham can play four or five different positions right most players they have the number nine and their role is is number nine but if you think of the game as this book with different chapters within the story jubelingham can perform whatever task he needs to perform at every single chapter throughout the book and he does it at the highest level he could play any position on the pitch besides goalkeeper and just sorry like how is this established like someone could say oh this guy they say this is a baseball toe he's a good all-around player whatever
we can say but like what is the data that actually establishes that jubelingham can play at high levels in a wide range of position how do you derive that conclusion quantitatively so from a data lens we think of two fronts in possession and out of possession okay so in possession is how you're progressing the ball into breaking areas now just creating high probability opportunities then out of possession is how are you preventing a team from moving the ball into threatening areas it's a very tricky question sports because you're trying to quantify the value of an event that does not happen so let's say joe you have the ball out on the wing tracy you're right in front of the box jubelingham is always moving into that passing lane he's right in between you guys at the right moment and we have the ability to to quantify the value of those movements and the closure of these lanes as players move out on the pitch and then you'll obviously get to the next phase where it's how do they do this with their feet we started talking about this sort of translation from what
the model says to a coach or something like that and that still seems tricky for better someone who's betting on sports that might be totally irrelevant like they're just like here the model says this this team is better the line doesn't match up with this they're forget about on this team i don't know why the model says my model says this team is better but it does so the translation is unnecessary if you're just betting like are we at the stage where we get close to the stage where you could for example feed a model the first five minutes of a game scoreless zero zero and tell the players like oh this looks this looks like a competitive match it's gonna be good both sides but models are able to detect something that we can't articulate that says oh no this team is playing and even though it looks like a tie game and a competitive one actually for reasons that we can't put into english put into language this team looks like they're gonna win the game seven they have a 70 percent chance is that a thing or is that a phenomenon or is that a realistic thing to expect there are you game with probability models yeah which start with just a pre-game team strength so both teams have some value perhaps you can think of an elo rating okay um and they boil down to a win a draw and a loss percentage and then those percentages can in-game
be updated but they won't swing all that much because you have this prior information so maybe after after the first minute one team has some x-e or maybe after the first five minutes let's say something has created some high probability chances and the team might be the underdog team this was highly unexpected i guess since the the elo rating set that the other team would be fdo friend so you can you can slightly update your beliefs there i don't think it's like changing the needle or moving the needle all that much given that it's just five minutes of information but you can definitely update your beliefs throughout the game given a chances created expected possession value as we just talked about or momentum or something in between depending on the data you have available to you mike i wanted to ask you given your involvement with austin fc we know that there are obviously differences between major league soccer and european leagues and some of those are you know things like salary caps designated players roster rules lack of relegation lack of yeah that's a big one does that actually make soccer analytics and mls like a little bit cleaner in some ways in the sense that like in the european leagues the money is again
trying to be diplomatic here but it's very free flowing there's a bit of rule stretching going on at times when it comes to salary restrictions and things like that like compare mls analytics versus european analytics for us so in european analytics you have essentially your recruitment and then first team analysis in mls you have recruitment first team analysis but you have this third vector that i call portfolio management right this cap structure in the mls is put in place with the intention of creating parity and as you mentioned it's highly complicated the best way for you to understand it would be like me saying tracy i'm gonna give you 10 million dollars you can spend two million dollars on the video seven million dollars on walmart and one million dollars on a speculative biotech socket and so you have to think of each player from a relative value perspective based on where they slot in your cap structure has that worked i mean with mls so it's like i understand you have this new league that's you know you don't want some really rich
team to win all the time and then i don't know only miami or wins and then fans lose interest in the rest of the country i'm just i don't know what the actual i think my understanding is Austin in particular is a very big fan base but um has that worked in practice to sort of create an equal level of like fan affinity that's geographically distributed well at Austin we're really nine months into our project here and we just had some turnover with our sporting department and so i would say that we have yet to integrate and improve this portfolio management theory and the impact on success and points on the table but as a market participant when i read these rules i like my brand immediately goes to the markets and and portfolio allocation okay so we have to watch austin fc as a test case for the portfolio management thesis in football wait just to be clear so this portfolio management thesis which you obviously have a strong intuition for having traded volatility for a long time this is the framework that you're bringing to your work in Austin yeah correct every player has a designated slot so a player that could come in
as a dp could be terrible but if he's gonna be with the dp a designated player oh okay but that same player if he is going to be a senior minimum salary player could be in the top percentile of talent for that specific slot and then within the league itself you know you have different roster construction strategies you can either have three designated players or you could up for two designated players and four u22 players and the way the league works is they have a they have a salary cap but it's it's more of a salary cap charge so whilst you know messy maybe making over 20 million dollars in salary his salary cap charge as a designated player is going to be much lower than that it could be 750 000 wait i don't understand yeah it's confusing there's a salary cap charged based on these roster designations so every slot has a dollar charge associated with it so
you have to work within that framework of what is the charge for each player and your finite amount of capital you're allowed to spend so the designated players are the ones you're allowed to pay more got it because this is the legacy of like la galaxy you're getting david beckham and wanting to pay him lots and lots and lots of money this is helpful okay so one of the criticisms of modern football i guess is that it's dominated by the wealthiest clubs like whoever has the most money can buy the best players certainly in europe and so the big just kind of get bigger and i could certainly see an argument where if analytics becomes more important to actually playing the game then whoever has the most resources the most cute the most engineers i guess nowadays is gonna have an edge here but on the other hand we have had technology before that has a sort of democratizing effect we have yeah all of that and i think there have been some instances of the smaller clubs actually like using this technology to perform better but which way are we gonna go is it the big get bigger or maybe we see some smaller clubs level the playing field here i hope it's the latter yeah same so
you mentioned uh open source models actually build open source software to help these smaller clubs i guess it's also helping the bigger clubs but it allows them to build these cross neural nets and these uh expected possession of your models and then also load this these tracking data uh districting data which has been a big task just in general because of the data structure and the data size and software that i work on helps clubs any club basically get started so i hope by doing that it will help uh smaller clubs help data providers i guess provide better insights to level of playing fields but i'm not sure not sure that it is working out necessarily because getting the knowledge like we've talked about before actually distilling the knowledge from the data is still i think one of the one of the problem next oh yeah so this reminds me i wanted to ask as well like who actually owns the data here where does the data come from it depends and i think some of it is a grey area there are a lot of data providers some have licenses some don't yeah it's a specific question mark it's on some instances i'll tell you a story about when i first started building models for AFC born that back in the primarily in 2016 i had no idea where i could find
the data and so i went on up work and i posted an ad and i just said i need a developer who has worked with european football betters and a gentleman named Dimitri Ukraine replied to me and said yeah i've worked with many professional betters and i said give me all the data that you could find and so it was very skunk works but but Dimitri was able to find a way for me to get access to all of the on-ball data across the world at the time to start building my models what is the data so it's like okay you find some data provider or Dimitri and Ukraine collect data is this numerical data is this a series of frames like what can when you say okay you need to go out and get the data what form are you getting it what does that mean like what does it look like so it's very throughout the years but the most prevalent generic data out there is is on-ball data okay and on-ball data if you're just up on your excel hat you think about what it looks like in an excel spreadsheet has every single event tagged so you have it just goes down a series of events pass pass pass
dribble shot goal pass pass dribble tackle it has the event it has the player or players involved it has the xy coordinate on a pitch and it has the time and so when you have these raw data points you can conditionally put together a mosaic of of what is happening on the pitch because you can measure the events the speed and the location tracking data is is a bit more nuanced i'll let yours touch on that so you can you can still imagine it an excel spreadsheet but i don't think your excel spreadsheet would like it very much if you try to load in this data it's basically a player identifier a team identifier and then x and y coordinates for all players at 10 or 25 frames per second so that means i guess 25 rows for a single frame we never really touched let's say the raw data in the sense that we don't get the pictures of the game and then try to figure out our self where the but according to our with the coordinates are so there are a lot of data providers out there that
either put cameras in the stadium or use the broadcast footage to extract this data so they they will have different models one of the models would be first identify where all the the pitch markings are so they have a way to to understand like where all the players are relative to the pitch markings then they know where all the players are you can convert all of that into coordinates they know where the goals are obviously and then you'll get that in a single file for single game some providers might give you one file per minute they might give you one file per half and that's just the raw data with the identifiers and the coordinates that you might get an additional file that has all the metadata that says this identifier belongs to this player decided to fire belongs to this team if you're lucky and you're checking that you get an identifier that says which team is actually on the ball because that's highly relevant but sometimes it's not included and you have to figure it out yourself like calculating the distance to the ball for each player and then obviously you have skeletal data which is i guess 27 times more dense or maybe even more because you have 27 coordinates one for each body post points or body points so you might have one for your left shoulder for the tip of your nose for your left ear for your right ear for your right foot for your ankle and that gets into the millions of millions of data points so when you talk to ball into an introduction about these
petabytes of data i assume it's going to be mostly skeletal data because dead day guys incredibly rich how do players actually feel about all of this because you know if people were watching me do my job and monitoring like my neck movements they are that's the thing they are interesting all right but no one's modeling like what i'm doing with my hands or my feet at all hours of this particular recording like i would have mixed feelings about it right like do you have any color on how players actually feel about i guess the rise of a statistical analysis in football i think they find it useful i think something that New York has worked on this specifically is is i-site what can they see and what they cannot see and so when you look at the data and you think about opportunity cost decision making and you make a sub-optimal decision when you go and talk to the player they might just simply tell you i couldn't see it and then you move on to you move on to the next so it's it's very complex i find most players to embrace the data there's curiosity around it but you know they likewise know the
limitations you know we started this talk about oh soccer is fluid it's a beautiful game it's an art i tell you okay but like people actually did say this about chess 30 or 40 years ago and it was actually some people who held out the belief computers will never be able to beat humans at chess because it is an art and it almost seems hilarious that like this view held on for as long as it did but it turns out that no chess is just a calculation problem and when you have enough data and compute you can solve the game kind of is soccer in the end like is it just a series of lots and lots of discrete events that our eyes are not capable of but at the end of the day with sufficient compute and data collection is it just like chess and that it's just a lot of like real binary decisions that can all be summed up and is this there for how life is when you work with this data as long as i have and with this tracking data specifically in the beginning it seems very overwhelming because it's just an infinite stream of coordinates and so i throw with it a little bit in the beginning i was wondering what i should do with this how are you doing to model any of this but i totally agree with you you can discretize this so you can just bit this but i did into let's say very minor events where we use the onball event data and if we align
that with the positional tracking data we might know every single moment where a player may surpass and then you might know the next moment where a player makes a reception so that could be discretizing to one event that might last two and a half seconds or three seconds the next step would then be the player receives the ball they make a normal action that also lasts two and a half seconds that would then be your next discretized events and then you have all these small events which are micro movements by players counter movements by defenders and if you go these let's say sequences one at a time you can still make aggregated metrics from this but using all the tracking data that you have a good disposal but it's actually still understandable for you so you might say well this player make this many dribbles and again the team this much in terms of added value disforming and you can do the reverse for defenders where you can say well this defender was always close to the ball so he was hoping not to concede a goal and if you go back to the analysis part where you want your video analysts to look at this you can also do this just discretize it discretizing it like this will help significantly so i have one more question which is it is obviously world cup season which means
it is also a sell side analyst publishing world cup prediction notes and research season and in my experience they tend not to be very good like often they will publish that england or the usa are going to win whatever world cup and is the numeris or just predictor pan that not to my knowledge but you know they often get it wrong if we think about statistical modeling data i mean the sell side firms they should be pretty good at this and yet do you have a take on on why they seem to struggle with soccer predictions every four years i don't like honestly for for them i have no idea what what data they're using whether they're using an elo model whether no mura has on ball event data tracking data there's different ways that you can come up with these predictions but i think you know i like to stay stay in your lane and i think sell side analysts should stick to sell side analyzing it's a big step in age old problem if you have a good model you wouldn't publish it you would just be to focus right there's uh there's often criticism of people who publish things for a living like in yours thank you so much for coming on odd lots learned a lot there and i really appreciate
your time and enjoy the rest of the world cup thank you thank you Tracy i have to admit i find it a little depressing that probably most things in life are probably just computation thanks you know what i'm saying it's like a lot of things that like that there's something called art and beauty and something it's probably just computers all the way down binary events that can be chunked and analyzed by microchips oh you know the question i would have asked yeah but we ran out of time was the idea of like good arts law which is like once you have a measure like yeah because you see this criticism of sports analytics which is like players start focusing on their stats coaches are focusing on the player's stats and then they get the players with the good stats and then they just focus on improving your stats more but the stats don't necessarily translate into like wins all the time yeah you know i was thinking something that Mike said in the beginning which is that like a particular like an english premier league team there's multiple things they could be optimizing for so they could be optimizing for profit right it could be optimizing for avoiding relegation
they could be optimizing for avoiding relegation they could be optimizing for wins those are all distinct things but you see this when a sport gets over optimized it didn't come up a good example it was like basketball when i was younger it was like the game was fun because there were lots of slam dunks and then everyone realized that three-point attempts were not being taken enough it's just suddenly the game is dominated by three pointers which may be a better way to play but it's not necessarily a more fun fan experience than watching a dunk so you think like okay could the game get better formally but it becomes lesser entertaining there's a lot of criticism yeah i think that's a possibility because people are already talking about convergence and like actual football play style totally and you see this like you know there was a lot of criticism in the like people are very critical of how parable it played right like play to just survive into penalty kicks then hope that the variance of the penalty kick period allows you to be friends but it's like no that's like the the game theory optimal or just the game optimal play if you're considered to be the weaker team so it does feel like there's all different things you know again going just to the core of the question stands like what are you solving for solving for solving for winning is very different from solving for profit is very different from solving for a gambler and there are different
answers to each one yeah you win but no one's paying for it or happy about it this is plausible although for now people are paying crazy amounts of money still to go see soccer games so all right so we leave it there let's leave it there this has been another episode of the all-thoughts podcast i'm Tracy all the way you can follow me at Tracy all the way and i'm Joe Wiesenthal you can follow me at the stalwart follow our producers kerman rud regas at kerman rman dash open at a dashpot kill brooks at kelbrooks and kevin lasano at kevin lizano and from our odd lot's content go to bloomburg.com slash odd lots of the daily newsletter and all of our episodes and you can chat about all these topics 24-7 in our discord discord.gg slash odd lots and if you enjoy odd lots if you like it when we talk about stochastic soccer modeling then please leave us a positive review on your favorite podcast platform and remember if you are a bloomberg subscriber you can listen to all of our episodes absolutely ad-free all you need to do is find the bloomberg channel on apple podcasts and follow the instructions there thanks for listening here are five reasons to subscribe to bloomberg news now number five
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