Well, thank you for that excellent introduction, it's going to be hard to compete with that.
So this is my question then and will the mathematician ever manage a football team?
And I probably mean a little bit more than that because maybe a mathematician has managed an amateur football team,
but will a mathematician at some point manage a Premier League team?
That's my question. So let's start with our prejudice because I think a lot of my title plays on those prejudices.
This is a football manager. Now this one in particular,
who I didn't like for many reasons because I'm a Liverpool fan and he has a style which is probably the antithesis of mathematics.
It's all about getting the players to be as passionate as possible and disregarding
any kind of statistics which say that he's failing and he just keeps going and going,
and he uses this sort of least mathematical approach to football.
I think that might be. That's my prejudice. I'm going to own up.
This is my prejudice. So we'll put that against another person who we might have some prejudices about.
And this is Andrew Wiles, of course. And when we think of mathematicians, we think of a very logical structure to everything,
very carefully thinking through and answering questions in a very, very precise type of way.
And so our prejudices really tell us that Moreno and Wiles are somehow polar opposites.
So how can we expect to unite these two and have a mathematician manage a football team?
Well, I've got some suggestions or I'm going to work towards some suggestions.
This is a starting point. This is another type of managerial style that we've heard about.
This is Pep Guardiola and Guardiola. He's certainly passionate about the game,
but he likes to sort of portray himself more as the kind of intellectual the man who's opening
up space for his team and planning carefully about tactical decisions against the opposition.
So there is this kind of idea also of the football manager as the intellectual who's trying to solve a problem.
And then there's also a type of mathematician, not the pure mathematician, but there's also the type of applied mathematician.
And this brings us to Philip, who's sitting up there in the back row and this is Philip Hare, and he's very kindly.
This is this is the team he's been playing for.
He's very kindly instructed the rest of the team exactly how you should stand when you have a photograph.
So he's got his managerial style here, and he's got most of them seem to have listened to his advice.
And Philip is just one of several mathematicians who are accomplished football players.
I don't know what league it was. You played in, Philip, can you tell us?
OK. But we actually have another professor at Oxford who has played at a much higher level.
So this is Ruth Baker, who's also a professor of applied of applied mathematics mathematical biology,
and she wears the number eight shirt or has worn the number eight shot for Oxford.
So this must be a vast was Ruth. OK, this must be a varsity match, and this was the one you won.
I take it. Yeah. Did you get the ball in that run? Can you remember what happened?
So it is possible to be a mathematician and to be a successful football player.
You have been you were described to me earlier today as the most successful football player in the whole maths institute.
So very impressive.
And I'm going to describe a little bit about my own background, so how I have gone from being a mathematician to becoming more involved in football,
I thought I'd start with a picture of myself when I was a kid. I'm I'm the one.
This one has this cute one. That's my little brother, Colin, and this is me.
I think I must have been about eight or nine when this photograph was taken.
And unfortunately, I wasn't any good at football.
I liked having the strap on and I thought I looked very good in it.
But I found out that I wasn't particularly talented football player.
But what? I was much better at with mathematics, and I have this picture of myself a few years later when I, as Alan, said,
I worked here as a Royal Society Research Fellow and I was particularly interested
in one type of mathematics how you could apply maths to understand the real world.
And a lot of my research here, I was only half time based here at the maths department.
I was also based at the zoology department, and I work together with people who were studying locusts who were studying ants and pigeons.
Think of a picture of the pigeons here. These are actually pigeons from from just outside Oxford.
This Mr Ribeiro, who was one of my biologist colleagues who took these photographs and I studied
how they interact and how they produce different types of collective behaviour.
And oh yes, this gets onto the next picture.
And I needed to put this in because I was at Wolfson College and this is a picture of my daughter blowing bubbles in Wolfson College.
I just wanted to have that in there as well. And then I became a professor in Uppsala.
And then it turned out that my son Henrik, who's this one here, he started playing a lot of football,
and it turned out that he had a lot more talent in the game than I did.
And they needed people to manage or manage his team.
So I ended up partly with a bunch of other dads who were, like, overly enthusiastic, trying to relive their own failed dreams through their children.
We started training as a team and I'm still actually training his team.
He's now 14 years old and he's still still plays football on a regular basis, despite me embarrassing him and talks like this.
He's still involved with that. And then I ended up because I became interested in football again, and I'd always had that in the background.
I ended up writing the book Soccer Mattox, thinking about how we can use maths to explain different parts of football.
And in the book, I look at game theory for strategy. I look at randomness in the game.
I look at passing networks, all aspects of mathematics that I knew.
I tried to break down into the into the soccer maths textbook, but this was mainly an academic.
That was part of it was how you might apply it in reality, but it was mainly an academic exercise.
And then I, as you'd get asked to do, you get asked to do kind of talks when you write a book like this?
I got asked to do a TED talk. And in that TED talk, or it was a TED talk, you should make a distinction here.
But in that talk, I put my hand up to with the telephone and told Jürgen Klopp Being a Liverpool fan,
I told you again, Klopp, Well, you know, you can ring me any time. I'm I'll help Liverpool get promoted or win the Champions League or whatever.
And he didn't call. But strangely, actually, quite a few other clubs did call me and I have had a visit in Uppsala from two Premier League clubs,
they made me write non-disclosure agreements. I can't say which.
And I've also visited a few clubs here in the UK, and another club that called me is the club Hammarby and in Stockholm.
And they were very open and they said that I can actually go and work their work together
with the coaches and with the players and help them develop their play with analytics.
And so now I'm actually working part time together with a big football club trying to improve their game with mathematics.
So that's a kind of brief history of me. But what I'm now going to answer is I don't think I'm quite there.
I'm not actually managing Hammarby. I'm a long way from there.
But what I want to get at is the basis of why applied mathematics is useful to to a football team.
Why? What is it that applied mathematicians have got that allows them to get somewhere and in helping a football team?
And I think I've been told that when you do a talk like this, you should like be extremely clear what your messages.
And so I've just written down precisely what my messages and I'm going to repeat it at the end of the talk.
This is precisely what an applied mathematician has to has to give.
We base what we say on data. When we talk about something, we collect data and we we base what we see on data.
We formulate models to understand that data.
It's a essential part of everything that we do, that we have a model which describes how we think the data works and how we understand the data.
And then we fit our models to the data in order to make predictions of the future.
So that's the rules that we follow, is applied mathematicians.
What I'm going to spend the rest of the talk doing is explaining how we put this into practise exactly in football.
And hopefully by the end of our persuade you that well may be applied, mathematicians can be trusted to run a football team.
So let me get to the get to the base of this idea, right?
Let's start with some goals. So I'm going to ask you a question then out of these two goals, which do you think is the best goal?
So let's put our hands up hands up for the top goal.
Is that the best goal? And who thinks that the bottom one is the best goal?
OK, so we've got a I think, a slight majority for the top, the top goal now, the top goal is a very long distance shot.
There is a mistake by the goalkeeper here left in the end. I don't like this goal.
Obviously, being a Liverpool fan, this was the reason we didn't win the Champions League last season.
And but the Boston one is a very, very messy goal.
There's a lot of sort of things. There's a claim there by the goalkeeper that was a foul.
It clearly wasn't a foul and the ball bumps into the into the back of the net.
And so it's a much more messy type of goal. Well, what does maths have to say about this?
Well, what we do is we start with this idea. We start by looking at the data.
And now here is some data over lots and lots of goals in football matches, not just one these this is actually all in a season of La Liga.
This was the season before Neymar went to PSG.
All of these little dots are shots that have been taken by these players, Lionel Messi, Neymar and Luis Suarez, so this yellow is a shot from here.
These are long distance shots and if there's a black circle around it, then that means that it was a goal.
So this is where the goals were scored from. And the important takeaway message from this is that if you see where the shots are, where are the goals?
Nearly all of the goals are very close to the goal mouth, even namely as close.
Messi has to, which are slightly long distance,
but almost every goal scored by Barcelona in La Liga is scored very close to the goal and that was exactly like Manhas goal at the bottom.
And not like Gareth Bale's goal at the top. So typically, goals scored from much, much nearer to the goal.
They're not scored at long distance. And this isn't just true of Barcelona.
There are teams which shoot from a longer distance. This is for the same season.
This is Real Madrid and Gareth Bale who scored the video.
The goal in the video I showed you. You see, he's got two long, long distance goals.
Cristiano Ronaldo, he has shot. I think it's 60 times from outside the box.
And he scored twice.
So when he scores these goals and you often see them as the highlights on on TV afterwards, he's actually scores only one out of 30 efforts.
And you often see a YouTube compilation of all of the goals that he's scored.
Well, they should really have in these YouTube compilations is all of the misses
that he's made because he misses a lot of the time where Ronaldo scores a lot.
It's actually very close to the goal. Most of his goals come from very nearby.
And that's a that's our data.
So we've got our data. Next step is the model. And I want to start, and I think this is always what applied mathematicians tried to do.
We start with the simplest possible model and then we build from there.
And I want to start with the idea of this angle and the angle is how much of the face of the goal can you see?
So if you're looking if if we take this podium, for example,
the people who are sitting straight on at that podium can see it much more clearly than the people who are sitting there.
There's a much wider angle. The people who are sitting at the back, they have a narrower angle than the people who are sitting at the front.
And so our basic idea is this angle to the goalposts is going to be the model of the probability that you can score.
Bale's shot is a long way out. So here's a little angle. Money shot is very close in, so it has a very large angle.
And so the quality of a chance in football can be assessed by the angle to the goalmouth here.
And then you have this nice property that, well, you can go around,
as I mentioned that people sitting out there looking at this thing have a very narrow angle, even though they're closer to me.
But people sitting up there, they might have a similar somewhere around there. They would have a similar angle.
And it's actually a circle which stretches out from the goal. All of those points on the circle are that this is a segment of the circle here,
and there's a circle that goes all the way round, and all of those points are on the circle.
And so now we can define basically this.
This is the essential bit of applied mathematics and applied mathematicians are always going on about how many parameters they have in their model.
Here we just have a one parameter model. This angle, which defines the probability of scoring.
A little bit of trigonometry. This is something I did by hand, you can actually work out from the X y coordinates of the shot.
You can work out the angle. I won't go through the calculation, but you end up finding that these circles all have exactly the same angle.
And now the next thing we're going to do is we're going to actually take data into this, and what we do is we do a thing called logistic regression,
where we take all of the shots that have been taken in LaLiga on the Premier League
and we calculate the angle for them and we calculate using logistic regression.
We calculate the probability of them scoring a goal as a function of this angle and by computer fitting, I get these two parameters three point five,
four times the angle theta and then this constant here, and they can then tell us the probability of scoring from different angles.
It turns out the one parameter model isn't perfect. It turns out you do need to actually put in the X coordinate to get the best fit to this data.
But this roughly gives a universal rule for scoring in football that there is a 30 percent chance here, a 50 percent chance here.
And then there's a seven percent chance out in this in this circle, and that's roughly true across a wide range of leagues.
And if you have if you have looked at things like football analytics, which has lots of this is the basis of what's called the expected goals model.
Basically, this probability gives the probability on an average day of football that you'll score a goal in these different situations.
So it's called the expected goals model.
And it works very well, it turns out the teams that have the best expected goals in the long term, they do well.
And so this is an example. There's an online app if you want to go in and look at your team's expected goals,
it works for the Premier League, for the last few seasons, the Champions League and so on.
You can go into our 12 football analytics app and you can actually look at the shots that the players have got.
And here you see why Liverpool won the Champions League. Mané took so many shots from very close to the goal.
Lots of them were misses, but lots of them with goals and the same thing with Mohamed Salah.
So they've specialised in getting extremely close to the goal before they shoot,
getting inside those circles and producing a lot of goals as a result.
I've got something similar for an elder. Now I've got I've got one more thing here for Philip, who's a Leeds United fan.
If you use this measure, Leeds United's last season they failed to qualify for the Premiership.
But it turns out when I calculated or when Opta calculated the expected goals for Leeds United,
it turns out they were extremely unlucky if it wasn't for randomness.
Leeds United would have won the Championship last season.
If you look at this chart, they scored more expected goals had better chances than any other team.
They conceded fewer chances than any other team. But they still weren't promoted.
So there's a lot of unfair randomness in football that we can't really get away from.
But we can get an over overall underlying idea of how good teams are, and Leeds United are actually doing very well this season again.
So I think that they because they're good in this measure, they will eventually if they can keep it up and get promoted.
OK, so I want to go back to some of our pledges.
This is because I think what's very interesting when you've got a statistical tool like this or a mathematical tool for understanding the world,
how can you use it? Well, one way you can use it is the following. So this is Raheem Sterling, who plays for Manchester City.
And this is the type of headline that you see about Raheem Sterling in certain newspapers.
This is the headline from the Daily Mail. Manchester City star Raheem Sterling earns two hundred thousand a week but takes an £80 easyJet flight,
and apparently this is somehow really bad behaviour on the part of Raheem Sterling.
How dare you take an easyJet flight?
But even worse, the son found out that Raheem Sterling he hired a private jet and headed out for two holidays in a week.
So Raheem Sterling can't win if he goes on a cheap easyJet flight.
That's really bad. You know, cheap football player. He earns so much money, but if he hires a private jet, then that's something.
There's something wrong about that. And what you'll often hear is this sort of justification of why they say bad things about Raheem Sterling
is that he's not playing very well or he's not doing all he can for the team that he plays for.
I'm Vinnie. Jones came out and said if Raheem Sterling didn't have pace, he would be playing for Exeter and Vinnie Jones.
So despite the fact they got to the semi the semi-final of the World Cup with Raheem Sterling playing in every match,
Vinnie Jones was worried that he was wasting too many chances, so Raheem was to blame.
And so you get these types of headlines about players all the time. And.
What a collie or another person who's interested in football analytics.
Bobby Gardner did, is he actually study this statistically?
Is Raheem Sterling wasting lots of chances like Vinnie Jones says, and it turns out he is.
And if you compare is expected goals with his actual goals, he's spot on.
So the chances that he gets, he converts as an average striker or a top level striker would.
So you have people who are overperform, for example, Harry Kane of overperformance.
Mohamed Salah over performs. But Raheem Sterling plays out on the left.
He isn't their top striker. That's actually a Guerra who's up there on the on the list.
So Raheem Sterling is actually just slightly above average in conversion, so you can actually do that statistical test and show that.
And we find actually this season that Raheem Sterling is the top striker who has the best chances,
and he's scored quite a lot of them if we do the statistics.
But even if you don't believe any of the statistics, I now have the final proof that Raheem Sterling actually is a good striker.
I take it back. Vinnie Jones admits he was wrong about Raheem Sterling after Manchester City put in a fantastic display against Southampton.
And so that's a lot of the time. What we're up against this idea that, yeah, there's this idea that it's all about some sort of passion.
And it's not just to do with with statistics, but there's often statistics underlying why certain players are the best players.
And I would say Raheem Sterling is the best. Statistically, he's the best England player.
We have just now. And this goes on, so it's another example, this is Paul Pogba and the son's Old Trafford correspondent,
and he said that it basically compared Paul Pogba to Roy Keane and poor shows and said that he
wasn't doing his job for the team and he should try harder and do better for Manchester United.
And this type of thing is slightly more difficult to test because it's easy when it comes to goals.
But how do you test a midfield player? Paul Pogba plays deep in midfield.
He plays sometimes assists, but he doesn't always score all of the goals.
So how do you test this?
Well, a solution to this was suggested by Sarah Rudds, who has not sure if she's a mathematician, but she has a strong applied mathematics background.
And her idea was to use something called a mark off chain to assess, assess midfielders.
And the idea is you basically build up a model of that in a typical game of football.
What's the percentage chance that a ball in midfield goes out to the wing?
What's the chance that it goes into the box?
What's the chance that it goes into the goal and you can build up an overall model, assuming that passes in the past don't affect the future.
You can build up an overall model of the quality of different types of passes.
And that framework is something that we've worked with quite a bit and developed.
Again, coming back to the idea of modelling data,
the idea is that we use all of the ball actions in the group and we group them into possession chains,
so each one of these these messy lines here is a possession chain ending in a shot.
So it might be a sequence of passes dribbles.
The ball might be lost for a few seconds, but then regained, says a chain of a possession landing in the shot.
Then what we do is we find the probability that a certain path in this chain ended in a shot and this is really extending.
The expected goal was model over the entire pitch. So we're looking at all the passes.
What was the chance that they ended in the shot? Then we work out.
Was it a goal? If you have all of these past coordinates in the shot and then we did a similar thing,
but in reverse, we did a similar thing for dribbles and then we do the reverse for tackles.
So if you prevent a attempt on goal by the opposition, then you can actually evaluate how dangerous that type of attempt was.
And this allows us to build up a model of the whole game of football,
assuming that everything is memory loss that we go from just from one place to the other, not remembering the sequence that they were in before.
And that could then find us we actually could find that Paul Pogba was one of the most important players at the World Cup.
He made a lot of passes from deep here in midfield.
And these are multiple evaluated is extremely valuable.
If you can pass the ball from here to here, then you're going to be able to launch a very quick counterattack counterattack.
And so he makes a lot of passes that are of extremely high value.
And we made a ranking actually for last season based on this measure.
The green one is the attack value, the red one is defence and the blue one is scoring goals.
And I just wanted I wanted to really rub it into Manchester United fans about who the best players were.
So I use this as an excuse. I just ran Liverpool and Manchester United players last season, ranked according to our Markov chain model.
And it's interesting. See that Virgil van Dijk, who won European Player of the Year, came out top mainly because of his defensive contributions.
But then in second place you have Paul Pogba and he does everything.
He produces lots of effective passes. He defends very well and he has lots of very good shots and goals.
Then you have a whole row of Liverpool players all the way down to market share.
And so we can actually measure the scale of the players and this is what I like to think of.
If you are, I'm going to come to Barcelona now where I went to visit.
But Barcelona were for a while very interested in signing Paul Pogba and I don't think that they think, well, Pogba doesn't have as much passion,
is keen, does what they're thinking in their mind is Pogba passes the ball up from midfield in a much more effective way than any other player.
So now I'm going to come to my own contribution and how what I've been doing and in football, as I said before,
I think we've gone over that a lot of my work was on collective animal behaviour, trying to understand the movements of animals.
And then when I started writing Soccer Matic's, I became very interested in exactly this type of movement on the pitch.
So if you watched Barcelona, maybe on our show, this is the Barcelona team of 20, 10 11.
Lionel Messi, you see him. But what you also see is some very, very nice, ticky tack of passing that they did during that time.
And what we could do is we could break down those types of passes.
By we used something called a de looney triangulation, and this is where you connect the nearest neighbours,
the most direct neighbours, the neighbours who have a border with each other, whether with a line, and that gives you a triangulation.
And what you see is that message opens up a lot of passing to John Vitol example.
He has an open past in the East. He has one hair out to the right.
And if they do this, if they stand in this way where they're kind of maximally distant from other players around them,
they're still only triangulation opens up automatically. And so this is called a veterinary diagram.
And it turns out that a lot of what Barcelona do is that they stand in a way which puts their opposition on the edges of the veterinary diagram,
which means that they're maximally distant from them at any given point.
And as a result, this triangulation opens up where they can actually pass the ball very effectively to each other.
So there's a kind of mathematical principle underlying the Tic TAC of football that Barcelona have been playing,
and I was very excited about this and I wrote a lot about it in schematics.
And then I was lucky enough to be invited down to Barcelona and see this is Messi up close and personal, so I got to see Messi really near in action.
I didn't get to meet him, unfortunately. I was probably at one point I was at the training group.
I was at this training ground, so I was probably a few hundred metres away from Messi when he was training and 100 metres away.
But I didn't get the introduction, unfortunately, but I did get to meet the data scientists who work at Barcelona and they actually have a team.
It's a little bit like a sort of university department there with all the sports science they do,
it's really impressive placed to see what they're trying to do.
And I met this guy here. This is Javier Fernandez, and I was, of course, very pleased with all my glowingly triangulation and border noy diagrams,
and I gave a presentation there and told them all about it. And they were like, Yeah, yeah, of course we know all of that.
They're there. And I was very surprised to hear that they were they already use that type of technique.
And what Javier had developed was an even better technique for measuring basically who's going to come to the ball first.
So here is a here is Barcelona playing in a purple and the green areas show where a Barcelona player will come to first.
The red area show where the opposition players will come to first.
And this they track for every match and they're able to see how the team controls space.
And the whole of Barcelona's game is built up about how you can control space.
And Javier is sitting there after the match looking well.
Have they controlled space in the way that they said they would control space and
he can actually give feedback and so on about how they're controlling space?
And so I took a lot of what I learnt to Barcelona,
and now now I have a research project together with them and wanted to put that into work in Hammarby.
As I said, this started with me phoning virtually, phoning you by the way, Juergen, you know, you can actually still come and contact me.
I'm I am a Liverpool fan, so I would rather be working for you.
So yeah. But so they started with me making this and Hammarby did ring me up, and they were local team in Stockholm,
so it was much more easy for me to start working with them and their coach, Stefan Bellbottom.
He was very open to this type of this type of collaboration, and now I often go to the trainings.
We have development things here.
And what's most fun is that we have a feedback cycle where we can present what we've learnt to the players and show the mathematical models.
And they say whether they think that this works or that we can actually give them feedback on their performance.
And I'm going to give a few examples of this.
One is exactly as I said, this was Javier's method that he's been developing, we've we've been developing along that idea.
This is a goal that we scored recently. Where the green team here.
So you're cheering for the green team now, right? OK, so if the blue team score is bad, green team need to score.
So oh good. We've got the ball back. This is Mojo Jankovic shoes our best ranked player just now, according to our system.
Alex Kuczynski, who played for Fulham. Both of them play for Fulham at one point.
Get the ball out to Nico. Nico gets it back into the middle and Moyo, who got the ball back in the first place.
He manages to score a goal and is very happy. And what I like about this goal in particular and now going to do the same thing,
but analyse it from this top down view and I'll just let it play to start with so you can get a feeling for it.
So this is the opposition. It's stones foul attacking. They take the shot, the ball comes back.
Moyo is twenty two. He collects it. Nico opens up space here.
Alex opens up space here. Nico still got this lovely space here.
All the time, keeps it, keeps it keeps that. Then the attacking players come in and score a goal and so we can actually
see how they're controlling and using space in the build up for those goals.
These points are very important because one thing we talked about a lot was defence.
You need to come back and defend. Even as an attacking midfielder, Moyo is a player who always wants to run up and score goals.
But if he's standing around here,
then we actually when when the shot comes in and it's controlled by the defender or it's headed away by the defender,
we control a lot of the area outside our box and they don't control that.
And so if you think of the ball is just sort of bouncing out a random direction,
we're going to control a lot of the places where it could balance out, too.
And here's another where a few seconds later, you see Alex controls the space in front of him.
Alex is No. 20. He's running in this direction.
Nico is number 40 running in this direction, and they've started to control the attacking space on their way out.
And so you can actually get an idea about what spaces we're controlling.
Another thing you can do is you can look at past probabilities. This is when Alex sent the pass out to Nico.
This is basically 100 percent success rate expected from our model.
We calculate all the possible passes he could make, and we could say Alex could have tried to play the ball through to Moyo straight away.
We have a 60 percent on that, but he has a 98 percent chance if he passes through to Nico.
Then a few seconds later, we have this situation, and then we found out that now Nico has a 65 percent chance to Moyo.
He could have also taken the post further away, but if he takes the post further away,
then he's got a worse angle for the striker that so the best decision is to take the 65 percent chance which he takes.
And on this score. And so we can use this type of technique basically to improve the positioning.
This is a project together with Fran Peralta, who did.
This is a master's thesis. I've been I've become incredibly popular in my university when it comes to writing master's thesis.
Like, I get a lot of requests to do this and I know that most of them do.
I do manage to find the time for them because it's so many exciting cycling projects to do in maths and football.
But Fran wrote a master's thesis doing this and what our idea was.
We can actually feedback and improve the performance of play, and I feel I take another example from Barcelona.
I think this is a lovely. So what do you think is the most important?
Maybe I'll have? This is a sort of test. What's the most important thing that happens in this sequence?
Have I got any suggestions, anyone? So the girl, the that guy in the left is very important.
He runs and gets the ball. Is this the one? You mean it's going to spread the cost?
Him here. Yes, he's very he gets the bullets, so he's very important, but I think that the absolute most important thing is this run here.
Because what that run does is it opens up space for the for the player on the left so he can run through onto it,
and you can actually see that in this picture. So here is the top down view again,
the ball is coming from here in the past is going to there and you can see as the player as the player here moves inwards,
it opens up a green space for an elbow on the left. And so it's the movement actually opens up for the other players.
And we can actually simulate how the football players move.
And this idea comes from a lot of what we worked on with animal behaviour.
We can simulate one or two seconds into the future because when animals try and avoid colliding with each other,
they tend to anticipate where other animals are moving to and move into open space.
And so we can anticipate what happens into the future.
We can say to the players, you can anticipate what's going to happen into the future, where should you move optimally in such situations?
And we can simulate that. This is an example of a right back. He was quite a long way from the play.
We have an optimal position. He should move out here. In fact, he moves quite centrally, so he doesn't really move in an optimal position.
But what we actually find about the attacking midfielder here is he did move in a perfectly optimal way.
So his run where he if you look at the run, it starts, he runs like this and then he stops like that,
and you could classify that as being quite lazy that you then kind of, yeah, OK.
But he actually, the way he stops when we calculated it in the model was optimal.
He had the best stopping position. And this is the sort of thing that we can use with the players, and we've been doing this,
how maybe we're basically starting doing this, maybe just now where we can give them feedback.
Now I have to. Yeah, so this was this was a very nice example where we got the ball back.
You shouldn't shoot from there, actually, but so we'll forget, we'll forget about the shot.
We talked about that, too. What I'd like to point out here is that the press play here, so they've got the ball and we organised.
So we've got man marking very nearby.
But we also control all of the space so we can measure how we're managing to press them and how we're controlling the space around the team.
And we could calculate with this was their actual position is a shadow picture, the shadow dot and their real position.
And we could actually show the players afterwards that you were pretty much optimally positioned in that type of press.
I'll take the question afterwards.
There's lots we can do, this was this was this was one of my favourites, because can I say another thing about football players?
They're so sweet, like you see them out on the pitch and they're like, really angry and angry, and I should have been a foul referee.
But like, when you meet them, they're like the nicest people you left me.
They're really sweet and mild mannered. And David's, you know, it's OK if you tell me about this or something.
And this is an example. So this this what I was just showing here. This is Emad Khalili.
He played the following pass. And it went out there and it didn't get to the player.
And if you want to show this one more time, here we go.
If you look at Moyo, there is like really annoyed with Armand because he wanted the ball and Imat didn't pass into.
And so afterwards, Imad asked me, You know, did I make the right decision in making this pass?
And you could actually see that given the percentages. There was a very small margin that he could get it to.
Moyo And though the board didn't quite get to their left back, it was the right decision to play that pass.
So you can actually use the model to evaluate how you're playing after after the game.
Right. That's a lot in my experience and how it's been working out in using this type of mathematics, this type of applied mathematics.
And now I come back to my major point, what is it that applied mathematicians have got in all of these examples?
We start with data. We start building up models and then we try and fit our models to data.
And that's basically how we cycle through in everything that we do, all of the projects that we study.
And that's really what I think makes applied mathematicians quite quite special.
And we have got one very special applied mathematician here, and I learnt I myself learnt a lot of this art from from Philip.
And I had a room of Philip that you have actually written to Premier League clubs and said you've lost him or, you know, you've sacked your manager,
you need a new manager and you have a collection of you don't have any any except unless you have a collection of rejection letters.
Is that correct? They're not rejection letters. They're considered, Oh, they're considering it, right?
Well, I think if anyone would like to pose the question we can ask, ask Philip about the contents and some of these letters afterwards.
But I think unfortunately, Philip, I mean, we're here to celebrate your 40th birthday.
Was it way? Unfortunately, I think it might be too late for you because one thing I've learnt,
for example, Stefan Billboard, who's the manager of Hammarby, is quite an analytic person.
Actually, I think Marino is a bit more analytic than I gave him credit for at the start.
And certainly, Claudia is an analytic person. Plus, it's an incredibly difficult job.
So we've probably got quite a lot of academics here in the room. And you know how it is when you get your paper rejected,
how horrible it is for your fellow students who've written spent so much time working on it and so on.
Think about being a manager and how you have to go into work after losing a match and get everyone.
You've got twenty two students to take care of.
In this case, 20 to 30 students and lift up their morals is a really, really difficult job to keep going.
I've got a lot of respect now for being a football manager, but I do think.
That there is this future, I think that there will be some point in the future where these types of mathematicians like Bob and Sarah and Javier,
there is a chance that they might be managing football clubs or be very close to being an important decision making roles within those clubs.
So I do think there's this inspiring possibility for a future where an applied mathematician can make a difference at a football club.
And I think that was all I have to say. Thank you very much.