StarTalk Radio - #ICYMI - Moneyball 2.0 – Soccer Edition

Episode Date: December 6, 2018

In case you missed this episode on the Playing with Science channel… Hosts Gary O’Reilly and Chuck Nice enter the world of big-data analytics and its rising influence on the game of soccer on the world stage alongside Dan Altman from North Yard Analytics and Howard Hamilton from Soccermetrics.Image Credit: Soccermetrics, with data by DataFactory LA. Subscribe to SiriusXM Podcasts+ on Apple Podcasts to listen to new episodes ad-free and a whole week early.

I'm Gary O'Reilly and I'm Chuck Nice and this is Playing With Science. Today's a show about football or soccer if you prefer. A look not through the eyes of a player or a fan but through the cascading waterfall of zeros and ones that data analysts love to hide behind. Yes, and soccer, or football if you prefer, has long been considered Moneyball 2.0, a sport at the elite level that's ripe for a data dump and seismic shift in the thinking on how all sport is viewed, conceived and enjoyed. Yes, and joining us to pull back the curtain and reveal all is one of, if not one of the world's leading experts, analytics, Dan Altman.

The founder of North Yard Analytics, a man whose opinion and thoughts are sought far and wide. Yes, and later we'll also hear from Dr. Howard Hamilton, the founder and CEO of Soccer Metrics Research. Yes, but first, Dan Altman. Dan, welcome to Playing With Science. Thanks very much. Great to be here. Thank you. Well, let's say as people listening know exactly why you're here. Sports analyst and professor of economics at NYU Stern School of Business. The list goes on. Author of Outrageous Fortunes, The 12 Surprising Trends That Will Reshape the Global Economy. Fascinating. I think I might need that book. Oh, yeah. We all need that book.

Founder of Northyard Analytics and leading sports data consultancy. So have I done you justice, sir? Yeah, you've even mentioned things from my former life as an economist. So more than justice. Good man. All right. So here we go. Let's take a little trip back down memory lane. When did data analytics, and I use this pun intentionally, kick in for football, and who drove it? Oh, I see what you did there. Thank you very much. Well, some people would argue that it was Charles Reap after the Second World War, who was standing on the sidelines out in the cold, taking things down on a notepad, who really got things going. But I don't think that it had the same sort of traction that it did, let's say, in baseball a few decades ago with the emergence of Bill James and his acolytes.

decade, I would say that sports analytics, as we know it in other sports, especially in the United States, has started to percolate into football in the great reaches of Europe and possibly even South America. What is the typical kind of data? I mean, we know we can all look at who won the game because of the number of goals scored. But what is the typical type of data that is being used and who actually is capturing this? So there are two major types of data. One is match data that comes from the events that happen on the ball. And you can get several companies that will provide you with a feed live or after the match of up to, let's say, 2,000 events that occur, every pass, every tackle, every shot. And they'll tell you a little bit about where it happened as well as when it happened and some specifics of how it happened, but really just on the ball data. So with that kind of data, you know the narrative of the match, but you don't know what all

22 players and referees in the ball were doing at every moment. For that, you need tracking data, which is provided by another set of companies quite often. For that, you need tracking data, which is provided by another set of companies quite often. And they will be tracking every moving object on the pitch for, let's say, 30 frames per second. So it generates a huge amount of data just for one match, more perhaps for one match than you would get for a whole season of the event data. And the key is to try and meld those things together so that you know what everybody's doing and you know the context as well. Wow, that sounds insane. I mean, you know, when you talk about that much data, which, by the way, could only exist in this day and age, if you think about it. I mean, you need a huge amount of computing power to make that happen. But let me ask you this. How is it used?

But let me ask you this. How is it used? I mean, how can you even make heads or tails of that much data? And does it do anything for player evaluation things we can do and implement within a soccer club are growing because there's greater understanding of these things among the coaching staff, the scouts, etc., the amount of things that we can do is growing even faster. So the gap between what we can implement and what we can do is actually widening, even as we get better at using the data, which is a weird thing. But we use most commonly the match data for something like scouting because we can get that same type of match data from dozens of leagues around the world. The tracking data are much more difficult to come by and they're often recorded in

different formats in different leagues. So for scouting, if you want to look at everybody using the same benchmarks all around the world, a player from Brazil or a player from Croatia, you're really confined to using the match data. When it comes to tracking data, you're much more often trying to examine your own players or your own team's performance because you are collecting that data inside your own stadium. Sometimes you might be able to get it for other clubs in your league, but it's tougher to get it further afield. tougher to get it further afield. So if, as we've said, goal scored the ultimate stat in any data collection for a football game, what are the main areas you find coaches, players, rather than the fans outside? And I don't want to exclude fans because there's no professional football without fans. What do you find are the most important metrics

that people are always focusing on? Well, it's interesting because there's just a huge amount of diversity in what the coaches want to hear. With one club I was working with, I went from an assistant coach because typically I'll be working with the assistant coach on a day-to-day basis while the manager or the head coach is dealing with the players. An assistant coach who just wanted to see a plot of all the goals scored,

let's say, from corners from the whole league. And I didn't know really what that was meant to describe. Oh, I think I do. That's what we were going to do. Yeah. Yeah. But I mean, you know, we're not playing the whole league every weekend. We're playing a specific club. But that's what he wanted to see. And then the next guy who came in, because as you know, in European football, the managers and coaches can change quite frequently. The next guy who came in said, I want to see the expected goal networks for every club that we're going to play based on the opponents that were most similar to us in playing style measured by these technical attributes. You've got to be kidding me. That was a request? On a Wednesday.

That was a request. Only on a Wednesday. a request on a Wednesday that was a request yeah only only on a Wednesday yeah what's interesting me the assistant coach who wanted all of the goals scored directly from a corner kick yeah my imagination because I used to be a defender I'm now thinking how do I set up to negate vulnerability from the broad spectrum of what I'd be looking to face. I would then be looking on a game-by-game basis at the opponent and how they set themselves up for set plays and corner kicks. But just as an overarching,

should I be going man-to-man? Should I be playing zonal? Or should I go hybrid to best solve the most things that I would be facing? Just when you said that my little cobweb so I think that's that's a totally valid point for two situations one is when you're trying to set up for the whole season you know how should you be training on a day-to-day basis yeah the second is for scouting if I'm looking for a center back I want the kind of guy who's going to be

well placed well positioned to deal with those threats. But this particular coach wanted to see that chart every week. And, you know, you're only adding a couple goals to it every week, so it would be pretty much the same chart every week. Right, exactly. In which case, I've got no thought to offer on the back of my mind every week indeed. week indeed so how how does it how productive is this data avalanche when it comes to analyzing individual players and then you say right this is our player or this group of players are our intended targets how successful are they with a identification and then be bringing them to say, I'm guessing a European league and then being a success within the clubs that you've worked with? Well, I'll give you a prime example.

I worked until recently with DC United, which is part of an ownership group that I was serving as head of strategy for. And we had a signing that made some news over the summer. A guy named Wayne Rooney, England's all-time leading scorer, came over after spending a season at his original hometown club, Everton, another blue team, Everton. Before that, he was with Manchester United for many years. We're going to spend a huge amount of money for an MLS team to bring him to the United States. And we were going to just get one player rather than signing, let's say, a few young prospects from Latin America or something.

So it was a risky proposition in some senses. And, of course, we wanted to be able to predict how he was going to perform in his new league. Well, one of the things that I've worked on for many years is creating league adjustments so that we can look at a player's performance in one league and try and predict how well he'll play in another league. And we looked at him at a couple different positions. And as far as his attacking output, he was exactly where we thought he would be coming to MLS. And that prediction was based on thousands of matches, dozens of players who've moved not just between MLS to the Premier League to MLS. Well, then now you have a link from France all the way to MLS, right? Yeah. So we can make lots of connections

to try and see how players might perform if they go from one league to another. And in this case, it was spot on. And as you saw, there was good reason to sign him. Wow. If you're not familiar with the Wayne Rooney story, Chuck, and I won't give the whole Bourne to now, just if you look at his time at DC United in MLS, a big fanfare, a major coup for the club.

So congratulations to DC United for that. They go into a brand new stadium, Howdy Stadium in DC, and the question mark remains against wayne rooney have you bought into him after the end after all the good stuff has gone what i'm interested in is what metrics you used if you can give any of your ipo a or give us an understanding of what you would focus on that made you believe this guy still had enough left in the tank to get people off their seats and into the stadium? So there are a lot of different statistics that get bandied around these days in soccer. But I preferred and I started out this way, probably because of my background as an economist, I prefer to create a holistic model of the game. So I have one model of winning, actually two models that I use side by side,

and every action gets a value within that model. And so we try to see if we combine all the actions that a player is going to contribute, how are they going to stack up? What's their overall contribution to winning going to be? And does that express itself in a numeric value? Is that what happens? Yes. Okay, go ahead. Yes. And then we adjust that numeric value based on the league where we think they're going

to play. So, you know, Rooney in the Premier League, one of the toughest leagues in the world, if not the toughest, was going to come to MLS, which, as Sean Harvey, who's the chief executive of the English Football League, said at a conference I attended two days ago, is probably somewhere between the second and third tiers in England. So he's not just going from the first tier in England to the second, but he's maybe going from the bottom to the bottom of the second or maybe the top of the third tier. It's quite a big jump. Did you feel that assessment was slightly harsh? or maybe the top of the third tier. It's quite a big jump. Did you feel that assessment was slightly harsh? Actually, my statistics agree with it. So I didn't think so. And that actually

brings up an interesting point. You know, we could see that Rooney was going to be making this big jump. And so even as a guy who looked past his prime, perhaps in the Premier League, he was going to kill it in MLS. But, you know, that adjustment used to be smaller. It used to be that MLS was sort of closer to mid-table championship, which is the second tier in England. And you got to ask yourself, well, MLS has been getting better. Why is the gap between MLS and the English leagues growing? And it's because the English leagues are just getting better faster. I mean, there's so much money in the championship right now through the parachute payments from the Premier League and all of these owners pouring money into their clubs to try and get to the Premier League that the quality is just going up really fast. Dan, I'm just going to explain something for Chuck.

So this gets quantified with a dollar sign in front of it. Yeah. of it yeah if you are relegated or so finishing the bottom three and dan knows this in the premier league right and you get relegated into what is now the championship the second tier of english soccer football you will you will earn more money than the team that wins the uefa champions league wow fact so you're in the bottom of yeah you relegated, but you will have earned more television money than the winner of the Champions League. That's why this acceleration with English soccer is so, so quick. Wow. I mean, I'm in the right place without giving too many numbers.

That is the principle behind this. Is that right, Dan? Yeah. And, you know, the thing is that the Premier League keeps growing mostly because of the foreign business that they're doing. The price of the TV rights for the UK has sort of topped out, but the price of the foreign broadcast rights keeps growing. Yeah. I mean, they're here. They're on Saturday morning here. That's for sure. I mean, it is the most valuable sporting property on the planet. Wow. Without a doubt.

What I'm interested in, I've got to focus on Wayne Rooney, not because it's Wayne Rooney, but how you quantify it. Because Wayne has something you can't, to my mind, and I'm up for being taken to school for this one and learning, how do you quantify his mental attitude towards the game of football? Great question. So the two models that I told you about already are what I call mechanistic models. As I said, they assign a value to every action and we can observe each action and we attach a value to it. I have another model, which I would call an agnostic model, which is sort of like a

plus minus in hockey, or we have adjusted plus minus in the NBA, where you're looking at the overall contribution, but you're not saying how the athlete does it. The patron saint of this in the NBA was a guy named Shane Battier. And there was a big article by Michael Lewis in the New York Times Magazine saying, why do Battier's teams win even though it doesn't score a ton of points? Yes. So why is that? I read that article, but go ahead. I want you to say. So he makes these intangible contributions. He does things that we don't necessarily track in this mechanistic data. And so I have a very complicated formula based on something called

a Shapley value, which comes from a Nobel laureate, Lloyd Shapley in the 60s, and try to figure out this overall contribution. And we don't know how we do it. If you take that overall contribution and subtract the mechanistic contribution, what's left are the intangibles. And that's how we measure them. Interesting. So you're able to assign a value to something that is intangible itself yeah you look at the whole and you take away the stuff you can measure and what's left that's freaking fascinating dude i can't that is amazing so well back in the day and it's it's way back right and we're going to break and up just before we do i'll throw this in we did all of that by standing or sitting in a seat watching we didn't have numbers we didn't have any laptops we would know that as

a fact right i'm gonna leave you with that yeah because we are gonna take a break and are you gonna have to hold that hold that thought right oh it's killing me hopefully not no right we're gonna take a break more from dan alt who, I'll be honest with you, this is as close as I get to nerding out. It's fabulous. I love it. And we'll be back very, very shortly. Welcome back to Playing With Science.

While we were away, Chuck's head exploded. Yeah. But we've managed to bring it back together again. There'll be some pieces missing. I'll look under the exploded. Yeah. But we've managed to bring it back together again. There'll be some pieces missing. I'll look under the table. Right. We are in the company of Dan Altman

who is just outrageously thoughtful on the game of football, a.k.a. soccer, and how you analyze it, not as a coach, but as a data analyst. How you see things numerically, how you can quantify what is sometimes

to people unquantifiable. Yes. And bring it to the table. Chuck, let's go again. I was losing my mind because I was like, you know, what Dan has done is just brilliant. You know, what I call it is filling the negative space, right, Dan? You're filling the negative space. And, you know, so what you said, Gary, was we used to stand on the sideline and basically you were making judgments. So now here's what I want to say. So what you've done essentially is, and here's what I'm going to ask you, Dan, because this is your model, so you're going to have to be really honest here. With judgments, there are always biases that must be overcome. When you, Dan, build a model, and not just you, I'm talking the collective you, all of you analysts.

When you build a model, how do you not build in the biases to that model so that the model itself is kind of mimicking the judgments that may be erroneous, like what Gary was talking about. Yeah. Yeah. So there are always biases for a programmer or whoever's designing a model, mathematician. With one of these agnostic models, we try to cut out those biases because we don't actually say what's important, what's not. We let the model tell us. Okay. And machine learning is really involved in that whole theme as well. But what I do is really complementary to the guy standing on the sideline. It's not a substitute. And you're much more powerful when you get both of those working together. Now, both of those are going to be wrong

sometimes. I mean, coaches and scouts don't like to admit it, but they are wrong sometimes too. It's not just the statistical models, which are always going to be wrong sometimes. I mean, coaches and scouts don't like to admit it, but they are wrong sometimes too. It's not just the statistical models, which are always going to be wrong sometimes. The key is to be wrong in different ways. Because if you're wrong in different ways, when you put those two things together, you can come up with a really good decision algorithm. I was working with a club in the Premier League, and that's exactly what we did. We had a filter based on all of these metrics that we had coming out of the models. And then when we got a list of players from that filter, we would have a video analyst look at

them. If the video analyst thought the player was promising, then we'd have a human scout go to a game and watch the players. And our hit rate going down from that original filter was about 70%, which is great. And it gives us plenty of options as recruiters. But when I was working with a team in MLS, we took it even a step further. And we said, you know, we want to make sure that we're going to bring in players from this filter that the coach is going to like right away. So we gave the coach a questionnaire and we said, here are all the attributes that we're measuring. Tell us how important they are, either very important, somewhat important, or not at all important. And if you said very important, we gave a hundred percent weight to that. If he said somewhat important, we gave 50%.

If he said not important, we gave it zero. And then we had a new formula to look for the players in leagues around the world. Then the hit rate started hitting the roof because we would show him guys that were spotted by this bespoke metric and he loved them. I love that. Can you tell us which the premier league and mls club is or would that get you a rap across the knuckles i have some non-disclosure agreements all right i thought i might i might have guessed they were there but you don't mind me asking so the thing is i spent four days in turin talking with the good people of juventus this was back in 98.

And I remember sitting in there, and you all know who this is, Roberto Bettiga, the Silver Fox, legendary Italian striker. And I remember sitting in his office with the translator and going through, how do you approach your transfers, your signings? And he said, we do not take the coach knocking on the door and say I need a striker I want a goalkeeper I need a winger I need a midfield player holding attacking whichever it is

what we do is we sit down as a group and then we decide the thoughts of the group and the underlying message is this signing has to be of benefit to Juventus Football Club. What we bring in has to have a value while it's here, but then be a value to us with a plus plus once it leaves. And there will always be something that comes in that breaks that up, but invariably, that was their model. It was a very interesting thought process. Is that thought process in play with more clubs now? Or as you said, does the weight land in different areas because of the different thinking that's in place? If you had a process like that, Juventusus process and you didn't have any data analysts,

you would still be ahead of a lot of clubs that do have data analysts and no process. Wow. Wow. Because there are some clubs that don't have regular recruiting meetings, not weekly, not monthly. They don't have a recruiting committee. They don't have this filtration via video analysts and scouts. A coach can come in a couple of weeks before the transfer window opens, put down a list maybe of two names or three names and say, I want these instead of alternatives of 10 at each position. And this happens even at clubs at the highest level. And if you do that, you're just taking on a huge amount of risk. Dan, you just broke my heart.

Yeah. We were talking about progress and a wonderment in bringing your thinking and your like in that part of the game to my passion for football. And then when you tell me there are still clubs anchored into way back into the 20th century. It's heartbreaking to hear that, but part of me is not surprised. They'll change when they lose. That's how it works. Oh, by the way.

All right. So let's, I mean, apart from the recruitment side of it, because it does have such a presence as we've highlighted and most obviously with someone like Wayne Rooney, which our American listeners will equate to because of his standout performances this season. What other areas can you take the area of analytics to? For instance, our team. You're on our team, and we have this group of players, but we think we can get more of them out of them.

But how? What areas do you feel data analytics can move into that would allow us to achieve even more? Well, tactics is a big area. And every club I've worked with, we've done pre-match opposition analysis, and we do post-match analysis as well to see what's gone right and what's gone wrong. The pre-match packages can be very comprehensive. They can be tailored to each opponent as well. We could look at how their back four distribute the ball. We could look at who the most important nodes in their passing networks are. We can look at how they set up for corners, where the completions are. We can map all of their set pieces. We can do a bunch of stuff like that. But we can do it for individual players as well.

We can do a bunch of stuff like that, but we can do it for individual players as well. For example, the club I worked with recently, Swansea City, sent Jordan Ayew, a Ghanaian striker, on loan to Crystal Palace, one of your former clubs, Gary, this season. Thank you, yes. And it was obviously a benefit to both clubs to have him succeed, right? Because Swansea wanted to see his value increase and Palace obviously wanted him to do well. wanted to see his value increase and palace obviously wanted him to do well so i gave their ownership an owner's manual for jordan iu to show which situations he'd been most successful in where he'd created the highest quality chances where he had the best quality touches and there was a really coherent narrative about how he moved across the pitch and what his options were at each point along the pitch and what other players needed to be in different positions for him to have the maximum effectiveness.

And I think that can be a very powerful tool. It's interesting because Crystal Palace have American co-owners who own sports franchises here in the USA. So they're more than aware of this type of thinking. And it's interesting. You just gave them the roadmap to get more out of one of their players. And that is so granular that I'm wondering, will we see at some point, right now you have teams as your client, will there come a point where your clients are actually agents who are able to take your information on that granular level and increase

the value of the players when they go into negotiations. Are we going to see that happening? Interesting. Yeah, I think we certainly will. It's now the case that there are enough data providers that agents can get access to the same quality of data as the clubs do. And we saw something like this happen in hockey. I remember a couple of years ago, there was a big contract negotiation for a player. I believe it was with the Toronto Maple Leafs. And, you know, the club knew that this was their guy.

This was the guy they wanted. They had done all the analytics. And then the agent comes in with a huge binder with all the same numbers. He knew just how good his player was. He wasn't going to take a dollar less. That's the guy I want as my agent. Exactly. I mean, okay. We're talking about them and they're not in the room. So let's invite them in the players. Uh, some do, some don't, am I right? Engage in the analytical process. Some will tell you they've watched it because the analysts will provide a video package for, say, a fullback. Who has a, all right, we're going to play Paris Saint-Germain.

So he's a one-on-one with Neymar Jr. So they'll produce a package on Neymar tracking and mapping. Not that if you're going to play against Neymar, you are fully focused, because if you're not, you're going to get smashed to pieces. Oh, by the way. But you take that sort of one-on-one confrontation in a game. How many players engage and how many players just don't care?

I mean, there's a huge variety. We saw it in the NFL recently. There was a player to whom the team started sending blank tapes to see if he was really watching the video. He would say, oh, yeah, I watched those blitzes. Yeah, that's awesome. They found that out pretty quick. But, you know, there's a variety. I think that it depends on the culture and it depends on the players that you're going to bring in. It starts with the head coach and filters on down from there at Bolton. For example, when Sam Allardyce was the coach, they had an incredibly intense data culture. They made the players buy into it. They made sure the players they brought in would,

they got down to, in terms of granularity, Chuck, they got down to where the players were sitting in the video room during the sessions and then testing their comprehension. Unbelievable. Unbelievable, man. It's there. It's so crazy. When someone, I mean, Big Sam has a reputation and that's the reputation. Some people love him, some people don't. But what he did was embrace this thinking

and the more outlier type of thoughts back in the mid-90s that no one else wanted to know about. You know, what did we say about, oh, about oh no man that's voodoo yeah wasn't that what sugar ray leonard said to us right leonard said it was back then that was all the compute all this sort of thing that we're discussing whoa voodoo stuff man he wasn't having it and it's interesting that kind of echo still vibrates today and i'd be surprised all right here's a question do you think there's room for a data poor player in elite football a data poor player do you say correct yes one who doesn't pay attention i mean if if you're a player who's talented and you work very hard

you shouldn't need to pay attention because the coaches are going to take the insights on board and then convey them to you. Right. You know, there's some players who are going to be able to consume that stuff directly. And there are some players who may need an interpreter, just like there are some coaches who may need an interpreter. And that's fine, because if they still have ability, you need to be able to incorporate that. Now, how about betting?

You can't help but monetize this can you yeah all right it's got it's got it's like what do you do and can predictive analytics actually help the better and then are you going to have guys that are just getting into this so that they can lay money on the line and perhaps make some dough already have yeah really there are there are plenty of guys who have yeah and and what's interesting is some of those guys have gone on to buy clubs as well get out yeah matthew benham at brentford and matillion and tony bloom at brighton another one of your former clubs yeah i know them i know them these these are guys who made their money uh in betting on football and sometimes other sports

and now they're running their clubs with an analytics bent. I've wasted my life. No, you haven't. I have wasted my life. No, you haven't. That's what I'm actually looking to do too. I have a group of investors I'm putting together and we want to buy a club because we think one of the best ways to monetize my analytics platform is to take a club from a lower division in Europe and get it promoted to a higher division where it's going to have a lot more value. Well, something tells me you're going to be damn successful, sir. Oh, I'll tell you what, Dan, we must have another conversation. That won't be recorded. I'd love to have that conversation. It has been a pleasure, sir. Hey, Dan, man, thanks so much. What a fascinating conversation. It has been a pleasure, sir. Hey, Dan, man. Thanks so much. What a fascinating conversation.

Yeah, and thank you for bringing your mind to my game. I don't own it. It's been my pleasure. Thank you for your game. Oh, on the shoulders of giants as we all stand.

Thank you. Dan Altman. Yeah, Dan Altman. Who knew you'd like soccer only because you would bet on it and get lots of money? Yeah, damn right, buddy. Let me tell you something.

I'm changing my whole career. I'm changing it all. I'm going right into sports betting, and I'm using analytics, and I'm buying a soccer team. Or should I say a football club? I'm going to play you a Pink Floyd track called Money. That might just dampen your thoughts for a while. Right, we're going to take that break.

Fabulous Dan Altman has been our guest, and when we come back, we'll have the fabulous Dr. Howard Hamilton, another data analyst for football. Can't get enough, can we? Back in a minute. Welcome back to Playing With Science. This is my running around in my own backyard, nerding out like a true geek.

We have spoke to Dan Altman from Northyard Analytics, and I can say now we have the privilege and pleasure to speak to another sports analyst, Dr. Howard Hamilton, founder and CEO of Soccer Metrics Research. What a great name, too. All right, let's get straight into it. Tell us about Soccer Metrics, how it it came to be and where it is headed right now so soccer metrics is a data analytics firm that performs quantitative analysis

on data that the football industry uses every day that the football use football industry generates and uses every day from team performance, player performance, team and player valuation, operational statistics related to the performance of leagues, the performance of teams at the front office. the front office. If it involves data of any sort, I'm interested in analyzing it and generating actionable information. So it's not just focusing on the game, the players, the outcome, and why. It might be things into the front office. So for things like ticket sales, other areas like that, Have I got that wrong? Yeah, ticket sales is part of it. There was one project I did very early on where I was looking at the sensitivity of ticket sales to the current record of a sports team.

And there was some interest in that. I presented that work at Penn Wharton about five, six years ago. And there was some interest from StubHub on that. It was using StubHub's data. The front office work more relates toward a benchmarking analysis. There was a professor out of the UK named Bill Gerard. He's worked with Billy Bean on a number of projects from Oakland A's to AZ, Alcoa.

And he created a benchmarking analysis of the performance that teams got relative to the amount of financial resources they were putting in. Okay, let's take a step back. The amount of points that took to win, the amount of payroll that took to win a match or a league point. And I apply that to major league soccer. All right. Okay, let's take a step back. Not a big one.

You mentioned the B word, the Billy, Billy Bean. soccer all right okay let's take a step back not not a big one you mentioned okay you mentioned the b word the billy billy bean um az alkmaar chuck is a professional team in the dutch league right at a division and i'm not mistaken billy has joined a group that's bought a football club in England, Barnsley in Yorkshire. Is football money 2.0 as we've been thinking it is? Is it ripe for the kind of money ball scenario that we saw the Oakland days and what is now prevalent through Major League Baseball? I think you've seen it in a number of places. You know, Matthew Benham and his clubs from North Jalan to Brentford have employed a moneyball type approach. They had a head of football strategy, Ted Knutson,

who starred Statsbomb, has done a lot of work with Matt, Matthew Benham. Um, Barnsley, I'm not really aware, aware of, we don't have to focus on that. Let's come, let's come out of that scenario. Um, but I would say to, to answer your question, I, I would say that it's, it's right for it, but it's a very gradual process right now. I think there's still a lot of cultural resistance to it. Yeah. Not totally unfounded in my view, but I think that there will be some cultural resistance to that.

So let's switch gears for a second because I want to ask you about some of the stuff that you've done. I was looking at your website and some of the things where you did an analysis of effective playing time in the World Cup for different referees and players. I mean, I'm sorry, and teams with those particular officials. What do you do with that as a coach? Like, do you actually prepare in your game plan for the officials that will be officiating? And do you plan on your teams being more rested or more taxed? Why do you do something like that? It's interesting, and I'm interested to hear your answer to that, because as a former player, whoever is the referee on the game

can not always make a big difference. It's interesting that you talk about that analysis. And I've applied that analysis to various competitions from Major League Soccer to the English Premier League to Japan's J-League and to the World Cup. And what I found out was that the identity of the referee really doesn't make that much of a difference as to the amount of effective playing time. It's the two teams that make more of a difference.

For example, going back a few years in the Premier League, any match involving Stoke City would almost always have an effective playing time of less than 55 minutes, which is well below the Premier League average. and I remember sharing that with some people in the Premier League and without my communicating which team was associated with the least amount of effective playing time they all said I'm sure Stoke City is at the bottom and they were I think where the referee is important is in the amount of, say, time between stoppages. Because that's where the time between stoppages, time between fouls, because fouls are those events that the referee is actively involved in stopping play.

And there's a lot of matches where, again, that's a function of the two teams that are involved. But the referee has some input in that as well. Yeah, so I think the referee matters more as to the amount of flow in the match as opposed to the amount of effective time that is actually being some of the work you did and thank you for explaining that um some of the work you did regarding the world cup we've just seen in the summer in russia um and this this caught my eye um so please explain with as regards the passing networks that are produced and provided by a number of sites, yours included. Please explain to Chuck, myself, and probably our listeners, what is an eigenvector centrality?

Because I'll be honest with you, Howard, it sounds as if you stole that from an episode of Star Trek. Ooh, I like it now. You know what I mean? I can't get to eigenvectors. It's straight out of Gene Roddenberry's textbook, that one, for sure. So please explain. She'll never hold cotton. She'll never hold.... She'll never hold.

That is the worst Scottish accent ever. That's not a Scottish accent. That's actually Scotty from Star Trek. See? See? See, he got it. Still not good. Sorry, Howard, please.

The eigenvector centralita. See, now that's a Sean Connery Scottish accent. Of course it's Sean Connery. Yes. See, please, we've interrupted his answer. Go ahead. In network theory, where you have these nodes, and I'll just make it really specific to passing networks. So networks consist of nodes

and edges. Your nodes are the players, and your edges are the passes between each player. You can represent this in various ways. You can represent those nodes and edges in terms of a matrix, just some numerical representation of this network. Now, the eigenvector is, this is real scientific math, I'll generalize it.

No, no, be as deep as you wish. Seriously, be as deep as you wish. It's a representation of that metric, of that matrix. And centrality is the relative importance of each node to the network. And eigenvector centrality is one specific type of measure of importance of a node, in our case a player, to the passing network. So the reason why I used an eigenvector centrality was that it represented the player who was most important to the success or the structure of that network.

If that player wasn't present in the passing network, in the passing network, that passing network would look significantly different. And yes, there's some natural variation from match to match, but even in the World Cup, where you have a minimum of three matches, some teams play four up to match, but even the World Cup, where you have a minimum of three matches, some teams play four up to seven,

you can still identify those players who have a consistent impact on the success of their passing network. South Korea is one example. Through Son. Yeah, Son. Son was by far the most important player of the passing network which is extremely unusual because he's a forward player yeah because son's a forward player interesting so what i mean it's funny i would watch the game and then what

what someone like howard would do was quantify it numerically quantify it in um if you've seen ever seen a passing network it's a diagram diagram. Yeah. And you can then visualize. It looks like 30 NFL plays layered on top of one another. Yeah. So, and then what happens is, from the analytics point of view, if you realize South Korea are using Son as one of these points, these nodes, in an advanced area, you then structure your team to disable that to happen

while they are trying to construct every way to enable Sun to do his thing. So it's quite interesting the way I would look at that. But what Howard does, and he's the like that's out there, they show you in a visual form that's easily condensable. Interesting. Let me ask you this. Yeah. Okay.

So France won the World Cup, right? Right. Right. And so everybody was just like, wow, France won the World Cup. Wow. Okay. Because, you know, that's. Congratulations.

Yeah. Congratulations. And by the way, Johnny, Johnny Bontemps is in our control room. He's our producer of this show and he is, he, congratulations. Yeah, congratulations. And by the way, Johnny Bontemps is in our control room. He's our producer of this show, and he is French. So, you know, I'm sure he's very happy. He's like a thumbs up right now. But here's what I want to know. Was France, from an analytics standpoint, the best team to win the World Cup?

Because everybody was like, whoa, France won the World Cup. Wow. But from an analytics standpoint, should they have won the World Cup? Because everybody was like, whoa, France won the World Cup. Wow. But from an analytics standpoint, should they have won the World Cup? I think France are the most consistent team at the World Cup. I think this World Cup was very inconsistent on

a number of levels, which may be entertaining for us as fans. I think this was one of the most unpredictable World Cups in a while. Germany not making it on the group stage for the first time in 80 years. You know, Spain, you know, Spain falling to stage that they did. You know, England going all the way to the semifinal. A team, you know, they were very, you know, Argentina imploding, Brazil not making it past the four finals.

I read Brazil's stats that you produced, and Brazil's stats were as spectacular as their failure. Yeah, yeah, totally. goal figures, not just on expected goals scored, but also expected goals allowed, Brazil was way out in front in both statistical categories. I think if Neymar had advanced to the finals, I think he would almost certainly have been seen as the best player in the tournament. But their conversion rate was abysmal, which was part of the reason why they didn't advance very far. If you looked at that match against Belgium, you could see that.

I think France, France are a good team. They're a really solid team. They had the best. I'm sorry, Dr dr hamilton they would kind of take issue with that uh you say they're a good team they would say we're the best effing team because we won the world cup so the thing is i mean they're totally they're totally allowed to say that so it's interesting it's interesting here chuck 16.3%. Doesn't sound a lot, does it? We always talk about 100%. That was France's conversion rate

from chances created to chances converted into goals. And the team that's won the World Cup has less than 20% conversion rate. Is that because everyone is useless or, Doctor, is the game of football that we know and love proving to be a lot harder than people give it credit for? And that's why the score isn't 110 versus 103. It's possibly 1-0 or 2-1. I think it's a higher scoring league or your defenses are garbage.

if it's a higher scoring league or your defenses are garbage. So to have a conversion rate of 15% or 18% or more is really fantastic for a team, especially at an international level where you don't get a lot of chances to score. If you're converting at greater than a 12% clip, you're putting yourself in a really good position to go really far in a tournament and ultimately win it. Okay. Well, there you go. There you have it. Dr. Howard Hamilton. Basically, here's the Johnny.

Johnny, our producer, is looking through the glass here. Johnny, what everybody seems to be saying is France got lucky, so enjoy it. It's not going to happen again. Okay. So, Dr. Hamilton, thank you so much. You know what? It just might. It just might. You never know. to be saying is uh france got lucky so enjoy it it's not going to happen again okay so dr hamilton thank you so much you know what it just might oh he just might you never know um can happen um i'm joking what a pleasure thank you so much for sharing and uh the new word of the day is eigenvector eigenvector eigenvector oh right okay

this is the first time he's done it in the show. He's got to get it out of his system. All righty. Thank you so much to Dr. Howard Hamilton from Soccer Metrics. Been a pleasure to talk to him. So, Chuck, we have learned a lot. Yeah, it's been. And we have still only scratched the surface.

And we have learned there are some silly names attached to football that have nothing to do with the game itself, but make it all the more interesting. Eigenvector. Eigenvector. All right. Kleibenflaben. Yes.

Right, well, I recover from Chuck's outburst. I'd like to say I've been Gary O'Reilly. And I've been Chuck Nice. Reilly! Yes, he has. And I've been chugged nice. Grainy! Yes, he has. And this has been Playing With Science and my little

nerd geek fest on football, which I have thoroughly enjoyed. I hope you have too and we look forward to your company very, very soon.