July 18, 2024 • 41 mins
Sarah Rudd is the co-founder and CEO of the soccer analytics company src | ftbl (It’s pronounced “Source Football.”) Sarah’s problem is this: How do you model a sport as fluid and complex as soccer and translate the analytical insights from the model into meaningful changes on the pitch?
This is the third and final episode of our series about people who are working at the frontiers of technology to help elite athletes perform better.
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Speaker 1 (00:08):
Pushkin. Around twenty ten, Sarah Rudd was rooting for this
British soccer team. It was a team called Blackpool. It
had been her great grandfather's favorite team. She was really
into it, but she started getting annoyed by this one player.
(00:30):
His name was Charlie Adam.
Speaker 2 (00:32):
He just loved to take shots from really long distance
and it drove me crazy. Every possession just kind of
ended with him taking like these random shots and it's like,
what a waste?
Speaker 3 (00:44):
What are you doing.
Speaker 1 (00:45):
Sarah was living in Seattle working for Microsoft as a
software engineer, but she really wanted to get into sports analytics. Basically,
she wanted to do moneyball, but for soccer. And then
she discovered that this sports data company called stat DNA
was hosting a contest, a contest where people could use
the company's soccer tracking data to generate analytical insights. And
(01:10):
she thought this could be my chance, This could be
my chance to break into the industry, and also at
the same time that that guy, Charlie Adam really should
not be taking those long shots.
Speaker 2 (01:22):
I'm a big fan of searching for inspiration elsewhere, so
I started just looking around and seeing like, well, what
have other people done?
Speaker 3 (01:30):
In this space, and it turns out for soccer not much.
Speaker 2 (01:35):
But there was some really interesting papers in other sports,
and the one that caught my eye was by a
gentleman named Keith Goldner. He had done something using NFL
data where he looked at kind of this, if the
ball is on the thirty five yard line, it's second
and ten with you know, X amount of time on.
Speaker 3 (01:53):
The clock, like how likely are you to score?
Speaker 2 (01:56):
And you know, I thought that was really interesting and
I thought, well, isn't that kind of similar to Charlie
Adam having the ball forty yards out?
Speaker 1 (02:04):
So Sarah took this methodology it's called a Markov chain
just fyi, and she applied it to soccer. She built
a model that let you look at a moment when
a player had the ball in a given location on
the field, and the model could evaluate what the player
did at that moment. Specifically, the model let you ask,
(02:25):
did the choice the player made passing forward, passing to
the side, shooting whatever. Did this choice the player made
increase or decrease the probability of the team scoring a
goal on that possession?
Speaker 2 (02:41):
And yeah, it really hadn't been done in soccer before,
which was kind of yeah, why it was kind of
a revolutionary paper.
Speaker 3 (02:50):
I hate to use that term, but.
Speaker 1 (02:52):
Like I'm saying it listed, I'm saying that listed five
point thirty eight had like a list of the ten
big papers and figuring out this kind of thing, and
yours was the first out on the list, so it
was chronological to be fair, but still big first is
a big deal.
Speaker 2 (03:05):
Yeah, and you know it was cool because I was
kind of like, oh, well, I'm just doing what this
guy in the other football is doing.
Speaker 3 (03:12):
But yeah, it was pretty impactful.
Speaker 1 (03:14):
And by the way, what did you find out about
Charlie Adam?
Speaker 2 (03:18):
Yeah? Not good to shoot from my hearts out was
what I found out, which was really like soul soothing
for me, or it was like I was right.
Speaker 1 (03:34):
I'm Jacob Goldstein and this is what's your problem. Today
we have the third and final episode in our series
of interviews with people who are working at the frontiers
of technology to help a lead athletes perform better. My
guest today is Sarah Rudd. She's the co founder and
CEO at Source Football, a soccer analytics company that works
(03:55):
with professional soccer teams in the US, the UK, and Europe.
There are different to frame the core problem that Sarah
is trying to solve for today's show, I'm going to
go with a problem that generalizes way beyond sports. The
problem is this, how do you translate analytical insights into
meaningful changes in the real world. In our conversation, Sarah
(04:18):
and I talked about analytics and the challenge of getting
people to change their behavior, change the way they make
decisions based on analytics. But before we got to all that,
we talked a little bit more about that first paper
that Sarah wrote to prove that Charlie Adams should stop
shooting from forty yards out. Sarah told me that paper
also led to some less obvious insights.
Speaker 2 (04:41):
Yeah, I think like one of the first ones was
that crossing from kind of like wide situations, which was
real really still very popular. So if you think of
like a player coming down the wings and then they're
going to kick the ball high in the air and
then hopefully a teammate is going to head it into
the goal.
Speaker 3 (04:57):
That is also like pretty low value.
Speaker 2 (05:00):
Oh interesting, yeah, because you have like a really high
chance of like turning the ball over from that.
Speaker 1 (05:06):
And was that finding contrary to the sort of conventional
wisdom of the time.
Speaker 2 (05:11):
Yeah, at that time, it was at that time there
was still a lot of teams using that tactic, and
I think, you know, going back, like people were starting
to just kind of intuitively be like, maybe there's a
better way. So it's not like it kind of like
broke soccer or football, but it just kind of I
(05:32):
think reinforced some of the intuitions that people had where
it's like, maybe there's a better way to do this.
Speaker 1 (05:36):
And just to be clear, in the time since then,
in the whatever fifteen years or so since then, have
analytics shown clearly that that's a bad call and has
that style of play decreased as a result.
Speaker 2 (05:48):
Yeah, And you know, now it's actually seen as like
a sign of like ooh, the offense is struggling, like
we're resorting to this really low probability tactic.
Speaker 1 (05:57):
So okay, so this paper you write, you hired by
this analytics company, and then the analytics company gets acquired
by Arsenal, the famous London soccer team, and you wind
up working at Arsenal for many years. And one of
the things I've heard you talk about about that that time,
(06:17):
and that clearly is a big important theme that goes
beyond soccer is trying to figure out how to get
the people who make real world decisions to actually listen
to you, to you and the analytics people.
Speaker 2 (06:31):
Yeah, it's it's really hard because like if you you know,
remove sports from it, just getting anybody to change their
opinion based on facts or evidence is really really difficult.
And now you're talking about people who are in incredibly
stressful jobs where they can lose their job really based
on like.
Speaker 3 (06:51):
Some random occurrence that happens over the weekend.
Speaker 2 (06:54):
Right, Like, right, the result doesn't go other way, you're fired,
Good luck to you.
Speaker 1 (06:58):
Which is not the way analytics works. Right, Like, the
way analytics works is you need a large end, You
need to do the thing one hundred times, and then
sixty times it'll go the way the analytics says it.
Forty times it won't. That's just the nature of the
probabilistic world we live in.
Speaker 2 (07:13):
Yeah, and you know, like analytics is also all about
separating process from outcomes, and yet these decisions are still
being made on outcomes. So I have a lot of
empathy for kind of people who are in this situation.
Speaker 1 (07:25):
And a coach will get fired more likely if they
do the thing that the analytics says that's contrary to
the sort of conventional wisdom in the sport. Right. It's
like why in American football coaches for a long time
didn't go for it enough on fourth down, right when
the analytic clearly said they should, But everybody would get
pissed at them if they went for it and didn't
get it. Now that's change right because of analytics. Interestingly, although,
(07:48):
was it this last Super Bowl where the coach kept
going for it on fourth down and not getting it
and people are like, he did it too much? I
was like, no, he didn't. Just because it didn't work
out doesn't mean it was the wrong decision.
Speaker 2 (07:58):
Yeah, And so you know, this just kind of goes
back to like you need to have kind of top
top decision makers saying like, yeah, it's okay if you're
going to do something that looks a little bit unconventional.
Speaker 3 (08:08):
Yeah, we believe in it, we trust it.
Speaker 1 (08:11):
So okay. So we've talked about why it's hard. Did
you figure anything out about how to get people to
change their decision?
Speaker 2 (08:16):
I would say the best thing would be building trust
through a common language, And so for us, the common
language was video, so we could build a model but
like until we could show them the model outputs on
video and walk them through it and say, this is
what the model sees, this is what the model predicts.
What do you think, like, walk me through it. And
(08:39):
so then it became kind of like these collaborative, iterative
model building processes.
Speaker 1 (08:44):
It's sort of a version of having an idea and
then convincing your boss that that idea is actually their idea,
and once they think it's their idea, then they'll do it.
Speaker 3 (08:52):
It sounds like that, like maybe a little bit less cynical,
but yeah.
Speaker 1 (08:56):
Yeah, yeah. So so eventually you left Arsenal and you
started source Football, this company that you're running. Now, what
what led you to make that leap?
Speaker 2 (09:05):
So, you know, part of starting this company was that,
you know, I wanted to learn and to experience as
much as possible. But the other part is that we
felt like there's a huge need for clubs to get
help in terms of getting started on this analytics journey
where you know, a lot of them just don't even
know where to begin.
Speaker 3 (09:26):
They don't know what's good what's bad.
Speaker 2 (09:28):
So our kind of main value add is coming in
helping them get set up, helping them get started and
then once that happens, doing all of the hard work,
hard thinking that's impossible to get done within a football club.
If anyone has ever been in a training ground, Like
I'm sure it's the same across all sports, but they're chaotic,
(09:51):
they're noisy, they're loud, Like they're not good places to
do deep testosterone. Yeah, a lot of a lot of
really loud music, like humping. You know, like if your
office is anywhere near the gym, like forget it. Like
you got to wait until everybody goes home before you
can actually like have a clear thought. So you know,
(10:11):
that was that was one of the things that we realized,
is like you can't work on solving these really hard
problems and like football is really hard to analyze in
terms of analytics, and like you just you can't do
it within a club.
Speaker 3 (10:24):
So that's why we want it to be a little
bit outside.
Speaker 2 (10:26):
And then kind of our our long term vision is
actually like you know, we're using this consulting business to
kind of fund the development of our intelligence platform, and
then the ideas eventually to look for like a set
of investors or maybe an ownership group and you know,
take control of a club and run it in kind
of like the modern progressive way that we think they
(10:47):
should be run.
Speaker 1 (10:48):
So what you really want to do is basically buy
a soccer team and run it smarter than everybody else.
Is that what you're telling me? Yeah, of course, tell
me more about the big gream. We'll do the pieces.
But I'm curious, Like that's a big, audacious dream and
it's fun tell me about it.
Speaker 2 (11:03):
Yeah, I mean, I think you know, one of the
things that probably everybody in analytics has experienced is that
unless you're kind of like the key decision maker, it's
always going to be kind of difficult to influence decisions
because there's always going to be somebody that has, you know,
kind of their own perspective and everything. And you know,
at Arsenal, we were really lucky because we had like
(11:25):
a lot of resources. But what I see at a
lot of clubs is that you know, they only hire
one or two people. The work that they do is good,
but like there's a limit to what two human beings
can do, and so like a lot of what they
produce doesn't necessarily like match the gut, and so it's
hard to get this buy in. And so then they
(11:46):
just say like, well, okay, thanks for that. I'm only
going to listen to you if I agree with it.
Speaker 3 (11:50):
If I disagree with it, I'm going to go with
my gut, and so it just becomes difficult.
Speaker 2 (11:55):
But I think also, like there's so many issues within
football clubs that go beyond just analytics or you know,
making the right decision on players. I mean, there's so
much in terms of like building good, good cultures, you know,
kind of making sure that everything is kind of set
up in like a professional way, because if you think
about it, clubs are kind of run by people who've
(12:17):
never worked outside of football.
Speaker 3 (12:19):
But I think there's a lot of.
Speaker 2 (12:20):
Lessons that you can take from working outside of sports
bring them into football, and we've seen it in every
other major American sport.
Speaker 3 (12:30):
Not to be like super ruthless.
Speaker 2 (12:32):
And say like we got to maximize profit, maximize profit,
because I think like they are these weird social institutions
that have a lot of meaning to a lot of people,
and so you obviously want to respect that. And you
also have these amazing platforms to bring positive change into
the world, and so you want to take advantage of
that as well.
Speaker 3 (12:50):
But like, there certainly can be a.
Speaker 2 (12:51):
Lot more kind of professionalism in them than what we
experience at a lot of places.
Speaker 1 (12:58):
So, now you have this company and you want to
use it eventually to take over the world. Correct, before
you use your company to take over the world, Like,
what are the what are the services you're selling to
teams to clubs?
Speaker 2 (13:11):
Yeah, I mean, so clubs are in really different situations.
And then also, like you know, it's not like the
US where like every single major League baseball team is
like loaded with cash. Typically in European leagues, like you're
going to have several divisions, so like you're talking about
anywhere from like a single a baseball team to like
(13:34):
a Major League baseball team, Like there's huge difference in resources.
Speaker 1 (13:38):
So like orders of magnitude in terms of revenue, how
much presumably they want to spend on your services, et cetera.
Speaker 2 (13:46):
Yeah exactly, And so like the main area where we
can help them is really recruitment, so helping them kind
of find the best players. So unlike American sports where
you kind of like trade players and draft them, everything
here is kind of like an open market where you
can buy and sell the contracts of these players.
Speaker 3 (14:04):
And so if you're a small team, and you have
a really really.
Speaker 2 (14:07):
Good player, like you can just sell him keep that cash,
or you can invest it back into your club.
Speaker 1 (14:13):
Huh. This is this is really echoes of Moneyball. I mean,
I'm sure I don't know if you're tired of hearing
about Moneyball. That book came about twenty years ago, but
like that was the basic idea there, Right, it was
better scouting essentially, right, Like you had these scouts who
were sort of using their guts and these kind of
conventional wisdom heuristics, and then you had what I'm sure
(14:33):
now seem like primitive analytics coming in and basically doing
a better job of predicting player success, right at some level.
Speaker 3 (14:41):
Yeah, exactly.
Speaker 2 (14:42):
And so you know, I hate to say that, like
soccer is twenty years behind baseball, but like in a lot.
Speaker 3 (14:47):
Of ways we are. And so like this is still
like the main area of.
Speaker 2 (14:52):
Value add for a lot of clubs because you're competing
in this open market with people with varying amounts of knowledge.
So sometimes that knowledge is just hey, like we had
scouts at that game, We've seen this player once, like
this is our opinion of him. And then you have
the more sophisticated teams that are like, Okay, well, we
have a database of you know, six hundred thousand matches
(15:14):
in the world. We know the fifty best players in
every league at every position, we know how much they're worth.
So we help teams kind of go from like the
former to the latter and just be a little bit
more sophisticated in terms of the amount of information that
they have.
Speaker 1 (15:30):
I mean, it's sort of crass to put it this way,
but it's really like pricing assets right, like the players.
And again I realized this is kind of dehumanizing. I'm sorry,
but it is analogous to people valuing a stock or
valuing anything they might buy. Right, And the better you
can model the value of the asset, the better you're
(15:52):
going to be at finding mispriced assets. Right. You want
to you want to find bargains. You want to get
the most you can and uh for your dollar.
Speaker 2 (16:02):
Yeah, absolutely, And so you know, there's a lot of
techniques that we can take from other industries because it's
not that different from it. Where it gets hard is
that you have to have those assets work well together.
Speaker 1 (16:14):
They are in fact human beings.
Speaker 3 (16:16):
Yes, that is you know the.
Speaker 2 (16:18):
Kind of joke where it's like, well, the problem with
football clubs is that it's full of human beings.
Speaker 1 (16:23):
So let's talk about what you can model, Like, what
are you good at modeling in this context, like in
helping in scouting, essentially in helping clubs value, you know,
players they might acquire.
Speaker 2 (16:35):
Yeah, so we're pretty good at modeling everything that happens
when a player has the ball, and unfortunately in football,
that's like a very very small portion of the gaming.
Speaker 3 (16:46):
Yeah. And so there was this really cool.
Speaker 2 (16:48):
Development in the last four or five years where a
number of companies have come out with a data source
that's basically taking the video feeds from TV and turning
into tracking data. So basically they're able to track the
location of every player that's on screen.
Speaker 3 (17:07):
So obviously you don't know all the players, and.
Speaker 2 (17:10):
Then you know, what we've learned is that really what's
going on off screen tends to not be as relevant
and the location of those players isn't as relevant. And
so now all of a sudden, you can start doing
modeling on what people are doing off the ball and
use it for recruitment. Because prior to this, similar to
the NBA, there were cameras in all the stadiums and
(17:32):
you would get this full data set, but only for
your league, and so you couldn't really use it for
a recruitment.
Speaker 3 (17:39):
And so this has been like a really big change.
Speaker 2 (17:41):
So you know, we can get much better views into
what is a player doing physically, what are they doing
defensively in terms of cutting off passing lanes, things like that.
There's still a lot we're not good at with that,
but what it's also done is allowed us to say, well,
if we're going to move a player from this league
to that league, how different is it physically? How do
(18:04):
we think they're going to adapt? And so this has
really opened up a lot of things. So if we
want to talk about like risk adjusted pricing of assets,
now we're starting to be able to quantify a little
bit like what's the risk of bringing in somebody from
a really really different environment into this one versus one
that's more similar, Or.
Speaker 1 (18:25):
So you should apply a larger discount when you're bringing
a player from a very different league, presumably because you're
taking more risk.
Speaker 2 (18:33):
Yes, yes, and if not, then it's not an undervalued
asset and maybe walk away.
Speaker 1 (18:39):
So tell me more, like, is the output you have
just here is what you should pay for each of
these players, Like you have whatever ten thousand players or something,
and you put a dollar value on each one, or
like what's the output?
Speaker 3 (18:55):
Yeah, I mean I wish it was that simple.
Speaker 2 (18:57):
We're still like pretty far from putting it all together
there and saying like, by this player at this price.
You know, a lot of the difficulties is that there's
no good data set on pricing information.
Speaker 1 (19:10):
So then what do you just have a relative like
kind of a stack rank.
Speaker 2 (19:14):
I mean, there are like estimates, so in the in
the media, they'll say like, oh, this guy went for
ten million, but then a different media source will say
this guy went for fifteen million, because like the selling
club wants to report a higher price and the buying
club wants for lower price.
Speaker 3 (19:31):
So there's real no truth.
Speaker 2 (19:33):
And then salaries for players are not public for most leagues.
Speaker 1 (19:39):
So okay, so that's a very important variable that you
don't really have access to. That's a problem. So what
is the output of your model? Then?
Speaker 2 (19:47):
Yeah, I mean, so we we kind of do like
a stack ranking and like a you know, projected like
what do we think this player would do in this situation?
Speaker 3 (19:56):
But like the error bars on these things are like
are pretty big.
Speaker 2 (19:59):
So we're still kind of in the like subjective realm
of like based on these factors, we think this or
we think that, and then you know a lot of
the like the markets change every year in football as well.
So even if we had like a really precise model
that said this guy should be worth five million, well
he should he would be worth five million last year.
Speaker 1 (20:20):
Yeah, I appreciate your candor you know, so the market changes.
You don't have pricing data, there's big uncertainty even on
the outputs you do have, Like all of this seems
so what is the use of what you're doing? How
is it valuable to people?
Speaker 2 (20:39):
That's the point is that we're competing with people who
you know, have gone to maybe three or four games
watch this guy in you know, unknown conditions. The human
brain is like not conditions to be like very objective
and so like, you know, there's a host of like
(21:00):
very known biases that like people can can fall for.
And so really what we're trying to do is just
give you like a much more fair and even view
of a player, taking into account a lot of you
know the factors that these scouts are trying to account
for as well, but just doing it more objectively, more rigorously,
(21:23):
you know, over a longer period of time.
Speaker 1 (21:25):
It seems from what you've been saying, like soccer is
behind certainly baseball and perhaps other sports in analytics. I
mean your first paper, you were following somebody who had
written a paper in American football, and you're like, oh,
what if we do that for soccer? Is it true
that soccer is behind?
Speaker 3 (21:41):
And if so, why, Yeah, it's true.
Speaker 2 (21:44):
And I think, you know, the debate used to be like, well,
are we making any progress? I think we've made a
ton of progress, but we're still really far behind other sports.
And so one answer is that we also don't get
the funding that other sports get. So so the level
of investment just isn't there. And so if we were
behind fifteen years ago, we're certainly not keeping pace, you know.
(22:08):
And then I think there's other structural issues with it.
So I love to use this image that somebody else made,
but it shows the relative size of a basketball court
to a soccer pitch, And basically a basketball court can
fit into like the little tiny penalty area on a
soccer pitch, and so you know, the distances and spacing
(22:30):
of players is so much more variable in soccer, So
you can't say, like, oh, ten meters is a good
distance between me and a teammate, because it depends on
the situation that's happening where on the pitch, like is
it a transitional moment, is it kind of a more
controlled moment, And so there's a lot of complexities like that.
Speaker 3 (22:48):
You know, the game doesn't stop.
Speaker 2 (22:49):
There aren't a lot of these set pieces where it's
like we have a very choreographed idea of what we
want to do, and then you know, the sad reality
is that most leagues only play thirty eight matches a season,
and so you never see a team in the same
context twice.
Speaker 3 (23:07):
You either play them at home or away and then that's.
Speaker 2 (23:10):
It, or maybe you play them in a cup game,
which is like a really different environment, and so all
of these things just kind of like add up and
it's like, well, we have fewer resources, it's much more complex, Like,
of course we're behind on.
Speaker 1 (23:24):
This still to come on the show, the one big
strategic change that Sarah really really wants someone in soccer
to try and why nobody has tried it yet. So
(23:46):
we talked a lot about essentially scouting, evaluating players as
kind of one of the one of the big things
you do. What else do you do?
Speaker 2 (23:56):
Yeah, I mean, so there's a number of different services
that we provide, so you know, it could be doing
like retrospectives of how did the team play this weekend,
or maybe some kind of I guess in US terms
they would call it advanced scouting, but delivering kind of
like a data profile on their upcoming opponent and then
going into like on field stuff, like we're doing a
(24:19):
lot of research now around you know, various things in
terms of you know, how can we maximize set pieces?
Speaker 1 (24:26):
A set piece is like a play analogous to sort
of a play.
Speaker 3 (24:30):
Yeah, so, you know, football is really fluid.
Speaker 2 (24:33):
It never really stops except for these certain moments that
are called dead balls and when a dead ball.
Speaker 3 (24:41):
Happens and kind of like the attacking area when you.
Speaker 1 (24:44):
Have like a throw in or a penalty kick or something.
Speaker 2 (24:46):
Yeah, I mean, penalties are pretty pretty straightforward. They're kind
of exceptional. It's like we'll just kick it into the goal.
But corners corner kicks are kind of like the most
common one because they all happen from the same location.
They happen fairly frequently. You can analyze them, you can
prepare for them. So you have this set piece opportunity,
(25:07):
and then it's you know, what's the strategy we're going
to use to maximize it.
Speaker 3 (25:12):
So a lot of research into that.
Speaker 1 (25:14):
Has analytics, in a general way changed the way people
take corners.
Speaker 2 (25:19):
Yeah, I mean, I think it has influenced how much
time they spend thinking about it and preparing for it.
Speaker 3 (25:27):
So that's another one.
Speaker 2 (25:28):
Ted Kinnutsen, who's the CEO of Statsbomb, he's been shouting
this from up high for the longest, but set pieces
for a long time were really valuable, but teams would
only train them for like ten minutes a week.
Speaker 1 (25:43):
It's an interesting level of analytics. That's an analytical claim, right,
It's like, your training time is measurable and you should
be allocating the right proportion of it to the right things.
And he's essentially arguing that you're underfunding in time corners. Yeah.
Speaker 2 (25:59):
Yeah, the number of goals scored from corners or conceded
from corners was not at all proportional to the amount
of time spent training them. And so like that's been
a big focus on teams lately. And you know, I
still don't think they spend enough time training it. But
now you'll see, you know, certain clubs even hiring set
(26:19):
piece specialists.
Speaker 3 (26:20):
So they'll have a member of coaching staff whose job.
Speaker 2 (26:23):
Whose only job is really kind of like thinking about
these things and helping prepare the players for it.
Speaker 1 (26:28):
If that person basically the corner kick coach, I.
Speaker 2 (26:32):
Mean they you know, there is a guy who's like
a throwing coach as well, so you can even have
like some specialties to them.
Speaker 1 (26:39):
But like guy dreams of being the corner kick.
Speaker 3 (26:41):
Coach, Yeah, gets promotion too.
Speaker 1 (26:44):
So I mean just thinking more generally, like it seemed
well other sports, my US centric sports knowledge, which itself
is somewhat limited. Like clearly analytics have changed. You know football,
you see people going for it on fourth down. More basketball,
obviously the demise of the mid range shot, right, somebody
(27:06):
just realized analytically some years ago, like don't shoot from
whatever twelve feet out right, shoot from under the basket
or take a three. And in baseball there was the shift,
like there are these big, big changes in the way
the games look because of analytics. Is there anything analogous
in soccer?
Speaker 2 (27:22):
Yeah, probably, Like the earliest one was that the distance
from which people are shooting has changed.
Speaker 1 (27:27):
Huh.
Speaker 3 (27:28):
So you're not going.
Speaker 2 (27:29):
To see a Charlie Adam taking a shot from forty
yards too often. And lets you know, there's like a oh,
the goalkeepers off his line is out of position. I
can get it in there, but because of you.
Speaker 3 (27:40):
You know, I can't.
Speaker 2 (27:41):
I can't take credit for it. It feels like, you know,
the invention of calculus, where there were a lot of
people kind of coming to this conclusion.
Speaker 1 (27:49):
So you're saying, you might not be Newton, but if
you're not Newton, your liibnitz.
Speaker 2 (27:53):
Yes, okay, fair enough, I'll take that. Yeah, that's probably
like the earliest one. And then again, like you can't
prove causality, but there's been a shift to this tactic
of teams putting a lot of pressure up high. So
if they have the ball in the attacking area of
the pitch and they lose it, they immediately put pressure
(28:15):
on the other team to try to win it back
as quickly as possible.
Speaker 1 (28:18):
Uh huh, kind of a full court press.
Speaker 3 (28:20):
Yeah, exactly.
Speaker 2 (28:21):
And so I don't know if the origin of that
is based in analytics, but like analytics will tell you, yeah,
you're more likely to score a goal if you win
the ball back up pie.
Speaker 3 (28:31):
So that's another one.
Speaker 1 (28:32):
Huh, why do you think people didn't do it before?
Is it not intuitively obvious that that's good?
Speaker 2 (28:37):
Yeah, because if you don't want to be tired, well yeah,
so one, it makes you tired and there is like
a risk of injury. Trying to kind of like make
the guys super fit to do this. But two, kind
of going back to like the fourth down analogy, if
you don't win the ball back, then you've committed so
many players upfield that you're going to concede kind of.
Speaker 3 (28:58):
Like silly looking up.
Speaker 1 (29:00):
It's risky.
Speaker 3 (29:01):
It's risky. Yeah, it's a calculated.
Speaker 1 (29:02):
Risk, but you expected value is positive, but the variance
is high.
Speaker 3 (29:06):
Exactly what's what's what are.
Speaker 1 (29:08):
Some interesting frontier problems? I mean they're sort of getting
people to listen to you, which we've talked about, but
just on the analytics side, Like, what are you trying
to figure out on on the analytics side, what's a
big problem You're trying to solve.
Speaker 2 (29:23):
One area that we haven't really exploited yet, but I'm
curious about.
Speaker 3 (29:27):
But we always have this problem that like we.
Speaker 2 (29:29):
Can't always learn from the data because everybody's doing the
same thing.
Speaker 1 (29:34):
You can't really do an ab test because everybody does a.
Speaker 3 (29:38):
Yeah exactly, and so it's really frustrating.
Speaker 2 (29:40):
I mean, like the biggest example would be substitution patterns.
Everybody makes roughly the same type of substitution at the
same time.
Speaker 1 (29:49):
And presumably it's not optimal, right, it's just conventional wisdom.
You're like, there could be a whole better world that
nobody's ever tried.
Speaker 2 (29:56):
Yeah, exactly, And so I think, you know, with generative AI,
like now you're kind of talking about, like what can
we do really smart, really realistic simulations on these.
Speaker 1 (30:05):
Sorts of things synthetic data.
Speaker 3 (30:07):
Yeah, yeah, that's what we're hoping.
Speaker 1 (30:09):
Oh so are you trying to figure out a better
way to do substitutions? Yes, yeah, you feel like you
got it? Do you feel like you have one in
your pocket? Or you're not quite ready to say.
Speaker 2 (30:20):
I mean, we have some theories, we haven't proven them,
but like we also need somebody to say, like, yeah,
go ahead, like mess with our substitution patterns.
Speaker 3 (30:27):
We feel comfortable with us.
Speaker 1 (30:28):
H It is amazing how I mean, like if you
think of the shift in baseball or whatever, like a
game can just exist for one hundred years and there
can be a way better way to do it, and
nobody ever does it just because they don't have the
imagination or the courage.
Speaker 3 (30:46):
Yeah, it's it's.
Speaker 2 (30:46):
Wild and I think, you know, that sort of thing,
like I hope in soccer is less common because it's
more of like an adversarial game, but it.
Speaker 1 (30:55):
Maps to the world more generally, right, Like I think
a lot of it is just fear, like you won't
get in trouble if you do the thing everybody else
did and you have a bad outcome, like you see
it in you know, in finance certainly, right, Like a
financial advisors just exhibit herd behavior because the way the
incentives are structured, Like if you do what everybody does
(31:16):
and you lose money, you're like, I was just doing
what everybody else did.
Speaker 3 (31:20):
Yeah, yeah, and it's it's really frustrating.
Speaker 2 (31:23):
And so you know, I think that again goes back
to like why we want to get control of a club,
because you know, I think there is a lot of
ways that we can optimize things, and if we're only
reporting to ourselves, then it's like, well, you know, I'm
not going to fire myself just because the theory didn't
(31:43):
work out.
Speaker 1 (31:44):
Yes, it's hard to get to a big enough end though, right,
it's hard to get to a big enough samplicized there
just aren't that many games. So let's talk about the
sort of happy outcome for you. Say it's whatever, five
years from now and you have found capital, You've found
somebody who wants to essentially bet on you, which is,
if I understand correctly, what would have to happen, and
(32:06):
you are, you know, with your financy financierer partners running
a club. What's that look like?
Speaker 2 (32:15):
Yeah, I mean I think it would look really really different.
I think that the typical profile of who would be
working in that club would be quite different.
Speaker 3 (32:24):
So I think the.
Speaker 2 (32:25):
First thing is, yeah, nerdier for sure, fresh are It's
hard because like, you know, I've played a lot of
sports growing up, and so it's like, well, you know,
I'm not that nerdy. And then it's like, oh yeah, yeah,
but yeah, I mean I think it would look nerdier,
but I think it would be a lot more like
(32:45):
just a totally different mindset. Really a lot more people
who are like, yeah, let's do things differently, Let's have
the courage to try new things, and then let's have
kind of the thinking power behind it to not just
like let's try random things, but let's try really well
thought out, creative but courageous things.
Speaker 1 (33:06):
When you talk about, like, you know, being bold and
creative and different, is there some particular thing you have
in mind? Is there's like one thing where you're like
that one thing I wish somebody would just try it.
Speaker 2 (33:19):
I mean, right now, it's probably the substitution thing, because
that's like the lowest hanging fruit, and it like really
really drives me nuts.
Speaker 1 (33:27):
What's your secret theory? What do you think would be better?
Speaker 3 (33:29):
Oh, I think you should do early.
Speaker 2 (33:31):
I think you should kind of treat it like line
changes in hockey. So I mean, like right now, what
they do is they typically try to bring on maybe
like a fast player late in the game that you
know would theoretically be running against tired legs.
Speaker 3 (33:46):
But the problem is that like it's.
Speaker 2 (33:47):
Too late, and so you know, our ideas basically like
bring on people who like know that they only have
thirty minutes, run as hard as you can, as crazy
as you can, get that one goal advantage, your two
goal advantage, and then adapts.
Speaker 1 (34:02):
And nobody has tried that. That doesn't seem crazy, Like
there's one hundred soccer teams, all you need is one
verson to try it. It's like a free idea.
Speaker 3 (34:11):
I know, I don't.
Speaker 2 (34:12):
Well, you know, if we see like a change in
substitution patterns after this content, yeah, that's right. Yeah, but
you know, like the crazy thing is there used to
only be three substitutions in soccer, and so you always
wanted to keep at least one in case a player
gets injured, so that really leaves you with two. But
during COVID, they upped it to five, and so now
it's like, well you.
Speaker 1 (34:32):
Have that has persisted. Yeah, so they've almost doubled the
number of substitutions allowed. Has the strategy in using them changed?
Speaker 2 (34:41):
No, it's like very slightly changed, And a lot of
coaches don't even take advantage of all substitutes.
Speaker 1 (34:50):
Why do you think nobody has, you know, dramatically significantly
changed their strategy around substitutions even though the rule has
changed so significant.
Speaker 3 (34:59):
One, I mean, I guess there's like two explanations.
Speaker 2 (35:02):
The easiest one is like, well, it's always been the
way that we've done it, so.
Speaker 3 (35:06):
You know why change?
Speaker 2 (35:08):
I think part of what's driving that though, is that
the way that rosters are constructed hasn't changed. And so
you know, depending on like the finances of a team,
you might not have as many like quality players to
go that deep onto the bench. But you know, if
this is your strategy, then you can change how you recruit,
(35:29):
you can change the profile of that squad composition.
Speaker 1 (35:32):
The second answer is interesting, right, because it requires you
to think more systematically. It's like, oh, the rules are different,
so therefore we should build a different team, which is
kind of next level.
Speaker 2 (35:42):
Yeah, and yeah, that's that's not typically how teams operate.
Speaker 1 (35:50):
We'll be back in a minute with the lightning round.
We're gonna finish with the lightning round. Okay, soccer or football?
I say football, frieser chips, chips, Cookies are biscuits.
Speaker 3 (36:12):
So my husband is from India and he will kill
me if I say cookies.
Speaker 1 (36:15):
But cookies are there other britishisms. You sign your emails cheers.
Speaker 3 (36:20):
Sometimes I hate doing it. Mate is another one?
Speaker 1 (36:24):
Mate?
Speaker 3 (36:25):
Yeah, yeah, because that's what.
Speaker 1 (36:27):
You call everything brilliant. I love talking to British people
because they say everything I say is brilliant, but they
don't mean it. The way I think.
Speaker 3 (36:32):
Yeah, it's brilliant, bloody. Yeah, just little things.
Speaker 1 (36:36):
By British guy once said blind I loved that.
Speaker 2 (36:40):
Yeah, I've not not done.
Speaker 3 (36:43):
That, but yeah.
Speaker 2 (36:43):
I think when I was little, my brother and I
used to antagonize my dad until he would like yell
at us in British slang, and that was like success
for us.
Speaker 1 (36:53):
Give me your impression of your dad yelling at your
British slang.
Speaker 3 (36:56):
It would just say, like, oh, bugger off, the.
Speaker 1 (36:58):
Bugger off is good. What's one thing you learned working
at Microsoft?
Speaker 3 (37:06):
It's hard to get things done in large companies.
Speaker 1 (37:10):
Do you have any tips for getting things done in
large companies? Or is it just leave?
Speaker 2 (37:15):
I mean my answer was leave, But yeah, I mean
I think there's there's certain behaviors that maybe I don't
necessarily possess or like love, but like aggressive, loud people
tend to get things done.
Speaker 1 (37:28):
There were there in the Balmer area era. That's very
Steve Balmer vibes. Who's the most underrated player you've ever seen?
Speaker 2 (37:41):
Ooh, it might be this guy named Manu Tregueros. He
plays for a club in Spain called Viriao, and I
think he's brilliant he was kind of born at a
time when Spain had like loads of really really talented
players in his position, so he never got called up
the national team or anything like that.
Speaker 3 (38:02):
But his running style is like a little bit nerdy,
Like he runs very.
Speaker 2 (38:05):
Much on his toes, and you know, he doesn't look
like an athlete. He actually is like a like while
he was playing professionally, he was doing his master's in education,
so he was like a student teacher.
Speaker 1 (38:18):
Like, it's just it's just that you love him. It's
not that he's an amazing player. Is that I love him?
Speaker 2 (38:22):
I love him, but like he was also an amazing player,
but I think like having this air of like nerdiness
around him kind of kept him a little bit under
the radar.
Speaker 1 (38:33):
Who's the most overrated player you've ever seen?
Speaker 2 (38:36):
Ooh, that's a yeah, that's a really hard one.
Speaker 1 (38:45):
Are you afraid of getting in trouble? Is there a
name in your mind and you just don't want to
say it because you don't want to antagonize anybody.
Speaker 3 (38:51):
Yeah, definitely, there's some of that.
Speaker 2 (38:53):
And then there's like a little bit of like hindsight bias,
where it's like there were guys who were overrated at.
Speaker 3 (38:58):
The moment and then like kind of crumbled and failed.
Speaker 2 (39:02):
So it's like, well, if I say like Marlon Shamack,
who was a kind of like a notoriously disastrous signing
for Arsenal, everyone would say like.
Speaker 1 (39:11):
Oh, yeah, too easy. Yeah, what's one piece of advice
you have for women working in male dominated fields?
Speaker 3 (39:21):
Oh, that's a really good one.
Speaker 2 (39:26):
It would probably be that just you belong and know
that you belong, and don't let people try to make
you feel that you don't. And one of the ways
that you can do that is talk to other women.
When I got started fifteen years ago, like there weren't
any other women. Now there's loads of women working in
all different sports and all different sorts of roles. And
(39:51):
a couple of years ago there's conference started called Women
in Sports Data. This year it's could be held in
Philadelphia September seventh, I think. But it's like a great
place to connect with people because to me, I find
it really powerful to be in like a gymnasium with
like a room literally full of women who are interested
in sports and data and technology. And yeah, just I
(40:15):
don't know, it's good for your mental health, it's good
for you know, your self esteem and everything. But yeah,
just know that that you belong, that you can do it,
and that yeah, nobody should tell you otherwise.
Speaker 1 (40:29):
So Brad Pitt played Billy Bean and Moneyball. Who's going
to play you in Moneyball too? Don't call it soccer?
Speaker 2 (40:37):
Oh yeah, I have I have no idea. I don't
watch a lot of movies, So, like, I couldn't even
name an actress right now. Maybe maybe Kiera Knightley because
she was in Bendettlake Beckham, So I'll go with Kiera Knightley.
Speaker 1 (40:54):
Sarah Rudd is the co founder and CEO of Source Football.
Today's show was produced by Gabriel Hunter Cheng. It was
edited by Lyddy jeene Kott and engineered by Sarah Brugeer.
You can email us at Problem at Pushkin dot Fm.
I'm Jacob Oldstein and we'll be back next week with
another episode of What's Your Problem
Pushkin. Around twenty ten, Sarah Rudd was rooting for this
British soccer team. It was a team called Blackpool. It
had been her great grandfather's favorite team. She was really
into it, but she started getting annoyed by this one player.
(00:30):
His name was Charlie Adam.
Speaker 2 (00:32):
He just loved to take shots from really long distance
and it drove me crazy. Every possession just kind of
ended with him taking like these random shots and it's like,
what a waste?
Speaker 3 (00:44):
What are you doing.
Speaker 1 (00:45):
Sarah was living in Seattle working for Microsoft as a
software engineer, but she really wanted to get into sports analytics. Basically,
she wanted to do moneyball, but for soccer. And then
she discovered that this sports data company called stat DNA
was hosting a contest, a contest where people could use
the company's soccer tracking data to generate analytical insights. And
(01:10):
she thought this could be my chance, This could be
my chance to break into the industry, and also at
the same time that that guy, Charlie Adam really should
not be taking those long shots.
Speaker 2 (01:22):
I'm a big fan of searching for inspiration elsewhere, so
I started just looking around and seeing like, well, what
have other people done?
Speaker 3 (01:30):
In this space, and it turns out for soccer not much.
Speaker 2 (01:35):
But there was some really interesting papers in other sports,
and the one that caught my eye was by a
gentleman named Keith Goldner. He had done something using NFL
data where he looked at kind of this, if the
ball is on the thirty five yard line, it's second
and ten with you know, X amount of time on.
Speaker 3 (01:53):
The clock, like how likely are you to score?
Speaker 2 (01:56):
And you know, I thought that was really interesting and
I thought, well, isn't that kind of similar to Charlie
Adam having the ball forty yards out?
Speaker 1 (02:04):
So Sarah took this methodology it's called a Markov chain
just fyi, and she applied it to soccer. She built
a model that let you look at a moment when
a player had the ball in a given location on
the field, and the model could evaluate what the player
did at that moment. Specifically, the model let you ask,
(02:25):
did the choice the player made passing forward, passing to
the side, shooting whatever. Did this choice the player made
increase or decrease the probability of the team scoring a
goal on that possession?
Speaker 2 (02:41):
And yeah, it really hadn't been done in soccer before,
which was kind of yeah, why it was kind of
a revolutionary paper.
Speaker 3 (02:50):
I hate to use that term, but.
Speaker 1 (02:52):
Like I'm saying it listed, I'm saying that listed five
point thirty eight had like a list of the ten
big papers and figuring out this kind of thing, and
yours was the first out on the list, so it
was chronological to be fair, but still big first is
a big deal.
Speaker 2 (03:05):
Yeah, and you know it was cool because I was
kind of like, oh, well, I'm just doing what this
guy in the other football is doing.
Speaker 3 (03:12):
But yeah, it was pretty impactful.
Speaker 1 (03:14):
And by the way, what did you find out about
Charlie Adam?
Speaker 2 (03:18):
Yeah? Not good to shoot from my hearts out was
what I found out, which was really like soul soothing
for me, or it was like I was right.
Speaker 1 (03:34):
I'm Jacob Goldstein and this is what's your problem. Today
we have the third and final episode in our series
of interviews with people who are working at the frontiers
of technology to help a lead athletes perform better. My
guest today is Sarah Rudd. She's the co founder and
CEO at Source Football, a soccer analytics company that works
(03:55):
with professional soccer teams in the US, the UK, and Europe.
There are different to frame the core problem that Sarah
is trying to solve for today's show, I'm going to
go with a problem that generalizes way beyond sports. The
problem is this, how do you translate analytical insights into
meaningful changes in the real world. In our conversation, Sarah
(04:18):
and I talked about analytics and the challenge of getting
people to change their behavior, change the way they make
decisions based on analytics. But before we got to all that,
we talked a little bit more about that first paper
that Sarah wrote to prove that Charlie Adams should stop
shooting from forty yards out. Sarah told me that paper
also led to some less obvious insights.
Speaker 2 (04:41):
Yeah, I think like one of the first ones was
that crossing from kind of like wide situations, which was
real really still very popular. So if you think of
like a player coming down the wings and then they're
going to kick the ball high in the air and
then hopefully a teammate is going to head it into
the goal.
Speaker 3 (04:57):
That is also like pretty low value.
Speaker 2 (05:00):
Oh interesting, yeah, because you have like a really high
chance of like turning the ball over from that.
Speaker 1 (05:06):
And was that finding contrary to the sort of conventional
wisdom of the time.
Speaker 2 (05:11):
Yeah, at that time, it was at that time there
was still a lot of teams using that tactic, and
I think, you know, going back, like people were starting
to just kind of intuitively be like, maybe there's a
better way. So it's not like it kind of like
broke soccer or football, but it just kind of I
(05:32):
think reinforced some of the intuitions that people had where
it's like, maybe there's a better way to do this.
Speaker 1 (05:36):
And just to be clear, in the time since then,
in the whatever fifteen years or so since then, have
analytics shown clearly that that's a bad call and has
that style of play decreased as a result.
Speaker 2 (05:48):
Yeah, And you know, now it's actually seen as like
a sign of like ooh, the offense is struggling, like
we're resorting to this really low probability tactic.
Speaker 1 (05:57):
So okay, so this paper you write, you hired by
this analytics company, and then the analytics company gets acquired
by Arsenal, the famous London soccer team, and you wind
up working at Arsenal for many years. And one of
the things I've heard you talk about about that that time,
(06:17):
and that clearly is a big important theme that goes
beyond soccer is trying to figure out how to get
the people who make real world decisions to actually listen
to you, to you and the analytics people.
Speaker 2 (06:31):
Yeah, it's it's really hard because like if you you know,
remove sports from it, just getting anybody to change their
opinion based on facts or evidence is really really difficult.
And now you're talking about people who are in incredibly
stressful jobs where they can lose their job really based
on like.
Speaker 3 (06:51):
Some random occurrence that happens over the weekend.
Speaker 2 (06:54):
Right, Like, right, the result doesn't go other way, you're fired,
Good luck to you.
Speaker 1 (06:58):
Which is not the way analytics works. Right, Like, the
way analytics works is you need a large end, You
need to do the thing one hundred times, and then
sixty times it'll go the way the analytics says it.
Forty times it won't. That's just the nature of the
probabilistic world we live in.
Speaker 2 (07:13):
Yeah, and you know, like analytics is also all about
separating process from outcomes, and yet these decisions are still
being made on outcomes. So I have a lot of
empathy for kind of people who are in this situation.
Speaker 1 (07:25):
And a coach will get fired more likely if they
do the thing that the analytics says that's contrary to
the sort of conventional wisdom in the sport. Right. It's
like why in American football coaches for a long time
didn't go for it enough on fourth down, right when
the analytic clearly said they should, But everybody would get
pissed at them if they went for it and didn't
get it. Now that's change right because of analytics. Interestingly, although,
(07:48):
was it this last Super Bowl where the coach kept
going for it on fourth down and not getting it
and people are like, he did it too much? I
was like, no, he didn't. Just because it didn't work
out doesn't mean it was the wrong decision.
Speaker 2 (07:58):
Yeah, And so you know, this just kind of goes
back to like you need to have kind of top
top decision makers saying like, yeah, it's okay if you're
going to do something that looks a little bit unconventional.
Speaker 3 (08:08):
Yeah, we believe in it, we trust it.
Speaker 1 (08:11):
So okay. So we've talked about why it's hard. Did
you figure anything out about how to get people to
change their decision?
Speaker 2 (08:16):
I would say the best thing would be building trust
through a common language, And so for us, the common
language was video, so we could build a model but
like until we could show them the model outputs on
video and walk them through it and say, this is
what the model sees, this is what the model predicts.
What do you think, like, walk me through it. And
(08:39):
so then it became kind of like these collaborative, iterative
model building processes.
Speaker 1 (08:44):
It's sort of a version of having an idea and
then convincing your boss that that idea is actually their idea,
and once they think it's their idea, then they'll do it.
Speaker 3 (08:52):
It sounds like that, like maybe a little bit less cynical,
but yeah.
Speaker 1 (08:56):
Yeah, yeah. So so eventually you left Arsenal and you
started source Football, this company that you're running. Now, what
what led you to make that leap?
Speaker 2 (09:05):
So, you know, part of starting this company was that,
you know, I wanted to learn and to experience as
much as possible. But the other part is that we
felt like there's a huge need for clubs to get
help in terms of getting started on this analytics journey
where you know, a lot of them just don't even
know where to begin.
Speaker 3 (09:26):
They don't know what's good what's bad.
Speaker 2 (09:28):
So our kind of main value add is coming in
helping them get set up, helping them get started and
then once that happens, doing all of the hard work,
hard thinking that's impossible to get done within a football club.
If anyone has ever been in a training ground, Like
I'm sure it's the same across all sports, but they're chaotic,
(09:51):
they're noisy, they're loud, Like they're not good places to
do deep testosterone. Yeah, a lot of a lot of
really loud music, like humping. You know, like if your
office is anywhere near the gym, like forget it. Like
you got to wait until everybody goes home before you
can actually like have a clear thought. So you know,
(10:11):
that was that was one of the things that we realized,
is like you can't work on solving these really hard
problems and like football is really hard to analyze in
terms of analytics, and like you just you can't do
it within a club.
Speaker 3 (10:24):
So that's why we want it to be a little
bit outside.
Speaker 2 (10:26):
And then kind of our our long term vision is
actually like you know, we're using this consulting business to
kind of fund the development of our intelligence platform, and
then the ideas eventually to look for like a set
of investors or maybe an ownership group and you know,
take control of a club and run it in kind
of like the modern progressive way that we think they
(10:47):
should be run.
Speaker 1 (10:48):
So what you really want to do is basically buy
a soccer team and run it smarter than everybody else.
Is that what you're telling me? Yeah, of course, tell
me more about the big gream. We'll do the pieces.
But I'm curious, Like that's a big, audacious dream and
it's fun tell me about it.
Speaker 2 (11:03):
Yeah, I mean, I think you know, one of the
things that probably everybody in analytics has experienced is that
unless you're kind of like the key decision maker, it's
always going to be kind of difficult to influence decisions
because there's always going to be somebody that has, you know,
kind of their own perspective and everything. And you know,
at Arsenal, we were really lucky because we had like
(11:25):
a lot of resources. But what I see at a
lot of clubs is that you know, they only hire
one or two people. The work that they do is good,
but like there's a limit to what two human beings
can do, and so like a lot of what they
produce doesn't necessarily like match the gut, and so it's
hard to get this buy in. And so then they
(11:46):
just say like, well, okay, thanks for that. I'm only
going to listen to you if I agree with it.
Speaker 3 (11:50):
If I disagree with it, I'm going to go with
my gut, and so it just becomes difficult.
Speaker 2 (11:55):
But I think also, like there's so many issues within
football clubs that go beyond just analytics or you know,
making the right decision on players. I mean, there's so
much in terms of like building good, good cultures, you know,
kind of making sure that everything is kind of set
up in like a professional way, because if you think
about it, clubs are kind of run by people who've
(12:17):
never worked outside of football.
Speaker 3 (12:19):
But I think there's a lot of.
Speaker 2 (12:20):
Lessons that you can take from working outside of sports
bring them into football, and we've seen it in every
other major American sport.
Speaker 3 (12:30):
Not to be like super ruthless.
Speaker 2 (12:32):
And say like we got to maximize profit, maximize profit,
because I think like they are these weird social institutions
that have a lot of meaning to a lot of people,
and so you obviously want to respect that. And you
also have these amazing platforms to bring positive change into
the world, and so you want to take advantage of
that as well.
Speaker 3 (12:50):
But like, there certainly can be a.
Speaker 2 (12:51):
Lot more kind of professionalism in them than what we
experience at a lot of places.
Speaker 1 (12:58):
So, now you have this company and you want to
use it eventually to take over the world. Correct, before
you use your company to take over the world, Like,
what are the what are the services you're selling to
teams to clubs?
Speaker 2 (13:11):
Yeah, I mean, so clubs are in really different situations.
And then also, like you know, it's not like the
US where like every single major League baseball team is
like loaded with cash. Typically in European leagues, like you're
going to have several divisions, so like you're talking about
anywhere from like a single a baseball team to like
(13:34):
a Major League baseball team, Like there's huge difference in resources.
Speaker 1 (13:38):
So like orders of magnitude in terms of revenue, how
much presumably they want to spend on your services, et cetera.
Speaker 2 (13:46):
Yeah exactly, And so like the main area where we
can help them is really recruitment, so helping them kind
of find the best players. So unlike American sports where
you kind of like trade players and draft them, everything
here is kind of like an open market where you
can buy and sell the contracts of these players.
Speaker 3 (14:04):
And so if you're a small team, and you have
a really really.
Speaker 2 (14:07):
Good player, like you can just sell him keep that cash,
or you can invest it back into your club.
Speaker 1 (14:13):
Huh. This is this is really echoes of Moneyball. I mean,
I'm sure I don't know if you're tired of hearing
about Moneyball. That book came about twenty years ago, but
like that was the basic idea there, Right, it was
better scouting essentially, right, Like you had these scouts who
were sort of using their guts and these kind of
conventional wisdom heuristics, and then you had what I'm sure
(14:33):
now seem like primitive analytics coming in and basically doing
a better job of predicting player success, right at some level.
Speaker 3 (14:41):
Yeah, exactly.
Speaker 2 (14:42):
And so you know, I hate to say that, like
soccer is twenty years behind baseball, but like in a lot.
Speaker 3 (14:47):
Of ways we are. And so like this is still
like the main area of.
Speaker 2 (14:52):
Value add for a lot of clubs because you're competing
in this open market with people with varying amounts of knowledge.
So sometimes that knowledge is just hey, like we had
scouts at that game, We've seen this player once, like
this is our opinion of him. And then you have
the more sophisticated teams that are like, Okay, well, we
have a database of you know, six hundred thousand matches
(15:14):
in the world. We know the fifty best players in
every league at every position, we know how much they're worth.
So we help teams kind of go from like the
former to the latter and just be a little bit
more sophisticated in terms of the amount of information that
they have.
Speaker 1 (15:30):
I mean, it's sort of crass to put it this way,
but it's really like pricing assets right, like the players.
And again I realized this is kind of dehumanizing. I'm sorry,
but it is analogous to people valuing a stock or
valuing anything they might buy. Right, And the better you
can model the value of the asset, the better you're
(15:52):
going to be at finding mispriced assets. Right. You want
to you want to find bargains. You want to get
the most you can and uh for your dollar.
Speaker 2 (16:02):
Yeah, absolutely, And so you know, there's a lot of
techniques that we can take from other industries because it's
not that different from it. Where it gets hard is
that you have to have those assets work well together.
Speaker 1 (16:14):
They are in fact human beings.
Speaker 3 (16:16):
Yes, that is you know the.
Speaker 2 (16:18):
Kind of joke where it's like, well, the problem with
football clubs is that it's full of human beings.
Speaker 1 (16:23):
So let's talk about what you can model, Like, what
are you good at modeling in this context, like in
helping in scouting, essentially in helping clubs value, you know,
players they might acquire.
Speaker 2 (16:35):
Yeah, so we're pretty good at modeling everything that happens
when a player has the ball, and unfortunately in football,
that's like a very very small portion of the gaming.
Speaker 3 (16:46):
Yeah. And so there was this really cool.
Speaker 2 (16:48):
Development in the last four or five years where a
number of companies have come out with a data source
that's basically taking the video feeds from TV and turning
into tracking data. So basically they're able to track the
location of every player that's on screen.
Speaker 3 (17:07):
So obviously you don't know all the players, and.
Speaker 2 (17:10):
Then you know, what we've learned is that really what's
going on off screen tends to not be as relevant
and the location of those players isn't as relevant. And
so now all of a sudden, you can start doing
modeling on what people are doing off the ball and
use it for recruitment. Because prior to this, similar to
the NBA, there were cameras in all the stadiums and
(17:32):
you would get this full data set, but only for
your league, and so you couldn't really use it for
a recruitment.
Speaker 3 (17:39):
And so this has been like a really big change.
Speaker 2 (17:41):
So you know, we can get much better views into
what is a player doing physically, what are they doing
defensively in terms of cutting off passing lanes, things like that.
There's still a lot we're not good at with that,
but what it's also done is allowed us to say, well,
if we're going to move a player from this league
to that league, how different is it physically? How do
(18:04):
we think they're going to adapt? And so this has
really opened up a lot of things. So if we
want to talk about like risk adjusted pricing of assets,
now we're starting to be able to quantify a little
bit like what's the risk of bringing in somebody from
a really really different environment into this one versus one
that's more similar, Or.
Speaker 1 (18:25):
So you should apply a larger discount when you're bringing
a player from a very different league, presumably because you're
taking more risk.
Speaker 2 (18:33):
Yes, yes, and if not, then it's not an undervalued
asset and maybe walk away.
Speaker 1 (18:39):
So tell me more, like, is the output you have
just here is what you should pay for each of
these players, Like you have whatever ten thousand players or something,
and you put a dollar value on each one, or
like what's the output?
Speaker 3 (18:55):
Yeah, I mean I wish it was that simple.
Speaker 2 (18:57):
We're still like pretty far from putting it all together
there and saying like, by this player at this price.
You know, a lot of the difficulties is that there's
no good data set on pricing information.
Speaker 1 (19:10):
So then what do you just have a relative like
kind of a stack rank.
Speaker 2 (19:14):
I mean, there are like estimates, so in the in
the media, they'll say like, oh, this guy went for
ten million, but then a different media source will say
this guy went for fifteen million, because like the selling
club wants to report a higher price and the buying
club wants for lower price.
Speaker 3 (19:31):
So there's real no truth.
Speaker 2 (19:33):
And then salaries for players are not public for most leagues.
Speaker 1 (19:39):
So okay, so that's a very important variable that you
don't really have access to. That's a problem. So what
is the output of your model? Then?
Speaker 2 (19:47):
Yeah, I mean, so we we kind of do like
a stack ranking and like a you know, projected like
what do we think this player would do in this situation?
Speaker 3 (19:56):
But like the error bars on these things are like
are pretty big.
Speaker 2 (19:59):
So we're still kind of in the like subjective realm
of like based on these factors, we think this or
we think that, and then you know a lot of
the like the markets change every year in football as well.
So even if we had like a really precise model
that said this guy should be worth five million, well
he should he would be worth five million last year.
Speaker 1 (20:20):
Yeah, I appreciate your candor you know, so the market changes.
You don't have pricing data, there's big uncertainty even on
the outputs you do have, Like all of this seems
so what is the use of what you're doing? How
is it valuable to people?
Speaker 2 (20:39):
That's the point is that we're competing with people who
you know, have gone to maybe three or four games
watch this guy in you know, unknown conditions. The human
brain is like not conditions to be like very objective
and so like, you know, there's a host of like
(21:00):
very known biases that like people can can fall for.
And so really what we're trying to do is just
give you like a much more fair and even view
of a player, taking into account a lot of you
know the factors that these scouts are trying to account
for as well, but just doing it more objectively, more rigorously,
(21:23):
you know, over a longer period of time.
Speaker 1 (21:25):
It seems from what you've been saying, like soccer is
behind certainly baseball and perhaps other sports in analytics. I
mean your first paper, you were following somebody who had
written a paper in American football, and you're like, oh,
what if we do that for soccer? Is it true
that soccer is behind?
Speaker 3 (21:41):
And if so, why, Yeah, it's true.
Speaker 2 (21:44):
And I think, you know, the debate used to be like, well,
are we making any progress? I think we've made a
ton of progress, but we're still really far behind other sports.
And so one answer is that we also don't get
the funding that other sports get. So so the level
of investment just isn't there. And so if we were
behind fifteen years ago, we're certainly not keeping pace, you know.
(22:08):
And then I think there's other structural issues with it.
So I love to use this image that somebody else made,
but it shows the relative size of a basketball court
to a soccer pitch, And basically a basketball court can
fit into like the little tiny penalty area on a
soccer pitch, and so you know, the distances and spacing
(22:30):
of players is so much more variable in soccer, So
you can't say, like, oh, ten meters is a good
distance between me and a teammate, because it depends on
the situation that's happening where on the pitch, like is
it a transitional moment, is it kind of a more
controlled moment, And so there's a lot of complexities like that.
Speaker 3 (22:48):
You know, the game doesn't stop.
Speaker 2 (22:49):
There aren't a lot of these set pieces where it's
like we have a very choreographed idea of what we
want to do, and then you know, the sad reality
is that most leagues only play thirty eight matches a season,
and so you never see a team in the same
context twice.
Speaker 3 (23:07):
You either play them at home or away and then that's.
Speaker 2 (23:10):
It, or maybe you play them in a cup game,
which is like a really different environment, and so all
of these things just kind of like add up and
it's like, well, we have fewer resources, it's much more complex, Like,
of course we're behind on.
Speaker 1 (23:24):
This still to come on the show, the one big
strategic change that Sarah really really wants someone in soccer
to try and why nobody has tried it yet. So
(23:46):
we talked a lot about essentially scouting, evaluating players as
kind of one of the one of the big things
you do. What else do you do?
Speaker 2 (23:56):
Yeah, I mean, so there's a number of different services
that we provide, so you know, it could be doing
like retrospectives of how did the team play this weekend,
or maybe some kind of I guess in US terms
they would call it advanced scouting, but delivering kind of
like a data profile on their upcoming opponent and then
going into like on field stuff, like we're doing a
(24:19):
lot of research now around you know, various things in
terms of you know, how can we maximize set pieces?
Speaker 1 (24:26):
A set piece is like a play analogous to sort
of a play.
Speaker 3 (24:30):
Yeah, so, you know, football is really fluid.
Speaker 2 (24:33):
It never really stops except for these certain moments that
are called dead balls and when a dead ball.
Speaker 3 (24:41):
Happens and kind of like the attacking area when you.
Speaker 1 (24:44):
Have like a throw in or a penalty kick or something.
Speaker 2 (24:46):
Yeah, I mean, penalties are pretty pretty straightforward. They're kind
of exceptional. It's like we'll just kick it into the goal.
But corners corner kicks are kind of like the most
common one because they all happen from the same location.
They happen fairly frequently. You can analyze them, you can
prepare for them. So you have this set piece opportunity,
(25:07):
and then it's you know, what's the strategy we're going
to use to maximize it.
Speaker 3 (25:12):
So a lot of research into that.
Speaker 1 (25:14):
Has analytics, in a general way changed the way people
take corners.
Speaker 2 (25:19):
Yeah, I mean, I think it has influenced how much
time they spend thinking about it and preparing for it.
Speaker 3 (25:27):
So that's another one.
Speaker 2 (25:28):
Ted Kinnutsen, who's the CEO of Statsbomb, he's been shouting
this from up high for the longest, but set pieces
for a long time were really valuable, but teams would
only train them for like ten minutes a week.
Speaker 1 (25:43):
It's an interesting level of analytics. That's an analytical claim, right,
It's like, your training time is measurable and you should
be allocating the right proportion of it to the right things.
And he's essentially arguing that you're underfunding in time corners. Yeah.
Speaker 2 (25:59):
Yeah, the number of goals scored from corners or conceded
from corners was not at all proportional to the amount
of time spent training them. And so like that's been
a big focus on teams lately. And you know, I
still don't think they spend enough time training it. But
now you'll see, you know, certain clubs even hiring set
(26:19):
piece specialists.
Speaker 3 (26:20):
So they'll have a member of coaching staff whose job.
Speaker 2 (26:23):
Whose only job is really kind of like thinking about
these things and helping prepare the players for it.
Speaker 1 (26:28):
If that person basically the corner kick coach, I.
Speaker 2 (26:32):
Mean they you know, there is a guy who's like
a throwing coach as well, so you can even have
like some specialties to them.
Speaker 1 (26:39):
But like guy dreams of being the corner kick.
Speaker 3 (26:41):
Coach, Yeah, gets promotion too.
Speaker 1 (26:44):
So I mean just thinking more generally, like it seemed
well other sports, my US centric sports knowledge, which itself
is somewhat limited. Like clearly analytics have changed. You know football,
you see people going for it on fourth down. More basketball,
obviously the demise of the mid range shot, right, somebody
(27:06):
just realized analytically some years ago, like don't shoot from
whatever twelve feet out right, shoot from under the basket
or take a three. And in baseball there was the shift,
like there are these big, big changes in the way
the games look because of analytics. Is there anything analogous
in soccer?
Speaker 2 (27:22):
Yeah, probably, Like the earliest one was that the distance
from which people are shooting has changed.
Speaker 1 (27:27):
Huh.
Speaker 3 (27:28):
So you're not going.
Speaker 2 (27:29):
To see a Charlie Adam taking a shot from forty
yards too often. And lets you know, there's like a oh,
the goalkeepers off his line is out of position. I
can get it in there, but because of you.
Speaker 3 (27:40):
You know, I can't.
Speaker 2 (27:41):
I can't take credit for it. It feels like, you know,
the invention of calculus, where there were a lot of
people kind of coming to this conclusion.
Speaker 1 (27:49):
So you're saying, you might not be Newton, but if
you're not Newton, your liibnitz.
Speaker 2 (27:53):
Yes, okay, fair enough, I'll take that. Yeah, that's probably
like the earliest one. And then again, like you can't
prove causality, but there's been a shift to this tactic
of teams putting a lot of pressure up high. So
if they have the ball in the attacking area of
the pitch and they lose it, they immediately put pressure
(28:15):
on the other team to try to win it back
as quickly as possible.
Speaker 1 (28:18):
Uh huh, kind of a full court press.
Speaker 3 (28:20):
Yeah, exactly.
Speaker 2 (28:21):
And so I don't know if the origin of that
is based in analytics, but like analytics will tell you, yeah,
you're more likely to score a goal if you win
the ball back up pie.
Speaker 3 (28:31):
So that's another one.
Speaker 1 (28:32):
Huh, why do you think people didn't do it before?
Is it not intuitively obvious that that's good?
Speaker 2 (28:37):
Yeah, because if you don't want to be tired, well yeah,
so one, it makes you tired and there is like
a risk of injury. Trying to kind of like make
the guys super fit to do this. But two, kind
of going back to like the fourth down analogy, if
you don't win the ball back, then you've committed so
many players upfield that you're going to concede kind of.
Speaker 3 (28:58):
Like silly looking up.
Speaker 1 (29:00):
It's risky.
Speaker 3 (29:01):
It's risky. Yeah, it's a calculated.
Speaker 1 (29:02):
Risk, but you expected value is positive, but the variance
is high.
Speaker 3 (29:06):
Exactly what's what's what are.
Speaker 1 (29:08):
Some interesting frontier problems? I mean they're sort of getting
people to listen to you, which we've talked about, but
just on the analytics side, Like, what are you trying
to figure out on on the analytics side, what's a
big problem You're trying to solve.
Speaker 2 (29:23):
One area that we haven't really exploited yet, but I'm
curious about.
Speaker 3 (29:27):
But we always have this problem that like we.
Speaker 2 (29:29):
Can't always learn from the data because everybody's doing the
same thing.
Speaker 1 (29:34):
You can't really do an ab test because everybody does a.
Speaker 3 (29:38):
Yeah exactly, and so it's really frustrating.
Speaker 2 (29:40):
I mean, like the biggest example would be substitution patterns.
Everybody makes roughly the same type of substitution at the
same time.
Speaker 1 (29:49):
And presumably it's not optimal, right, it's just conventional wisdom.
You're like, there could be a whole better world that
nobody's ever tried.
Speaker 2 (29:56):
Yeah, exactly, And so I think, you know, with generative AI,
like now you're kind of talking about, like what can
we do really smart, really realistic simulations on these.
Speaker 1 (30:05):
Sorts of things synthetic data.
Speaker 3 (30:07):
Yeah, yeah, that's what we're hoping.
Speaker 1 (30:09):
Oh so are you trying to figure out a better
way to do substitutions? Yes, yeah, you feel like you
got it? Do you feel like you have one in
your pocket? Or you're not quite ready to say.
Speaker 2 (30:20):
I mean, we have some theories, we haven't proven them,
but like we also need somebody to say, like, yeah,
go ahead, like mess with our substitution patterns.
Speaker 3 (30:27):
We feel comfortable with us.
Speaker 1 (30:28):
H It is amazing how I mean, like if you
think of the shift in baseball or whatever, like a
game can just exist for one hundred years and there
can be a way better way to do it, and
nobody ever does it just because they don't have the
imagination or the courage.
Speaker 3 (30:46):
Yeah, it's it's.
Speaker 2 (30:46):
Wild and I think, you know, that sort of thing,
like I hope in soccer is less common because it's
more of like an adversarial game, but it.
Speaker 1 (30:55):
Maps to the world more generally, right, Like I think
a lot of it is just fear, like you won't
get in trouble if you do the thing everybody else
did and you have a bad outcome, like you see
it in you know, in finance certainly, right, Like a
financial advisors just exhibit herd behavior because the way the
incentives are structured, Like if you do what everybody does
(31:16):
and you lose money, you're like, I was just doing
what everybody else did.
Speaker 3 (31:20):
Yeah, yeah, and it's it's really frustrating.
Speaker 2 (31:23):
And so you know, I think that again goes back
to like why we want to get control of a club,
because you know, I think there is a lot of
ways that we can optimize things, and if we're only
reporting to ourselves, then it's like, well, you know, I'm
not going to fire myself just because the theory didn't
(31:43):
work out.
Speaker 1 (31:44):
Yes, it's hard to get to a big enough end though, right,
it's hard to get to a big enough samplicized there
just aren't that many games. So let's talk about the
sort of happy outcome for you. Say it's whatever, five
years from now and you have found capital, You've found
somebody who wants to essentially bet on you, which is,
if I understand correctly, what would have to happen, and
(32:06):
you are, you know, with your financy financierer partners running
a club. What's that look like?
Speaker 2 (32:15):
Yeah, I mean I think it would look really really different.
I think that the typical profile of who would be
working in that club would be quite different.
Speaker 3 (32:24):
So I think the.
Speaker 2 (32:25):
First thing is, yeah, nerdier for sure, fresh are It's
hard because like, you know, I've played a lot of
sports growing up, and so it's like, well, you know,
I'm not that nerdy. And then it's like, oh yeah, yeah,
but yeah, I mean I think it would look nerdier,
but I think it would be a lot more like
(32:45):
just a totally different mindset. Really a lot more people
who are like, yeah, let's do things differently, Let's have
the courage to try new things, and then let's have
kind of the thinking power behind it to not just
like let's try random things, but let's try really well
thought out, creative but courageous things.
Speaker 1 (33:06):
When you talk about, like, you know, being bold and
creative and different, is there some particular thing you have
in mind? Is there's like one thing where you're like
that one thing I wish somebody would just try it.
Speaker 2 (33:19):
I mean, right now, it's probably the substitution thing, because
that's like the lowest hanging fruit, and it like really
really drives me nuts.
Speaker 1 (33:27):
What's your secret theory? What do you think would be better?
Speaker 3 (33:29):
Oh, I think you should do early.
Speaker 2 (33:31):
I think you should kind of treat it like line
changes in hockey. So I mean, like right now, what
they do is they typically try to bring on maybe
like a fast player late in the game that you
know would theoretically be running against tired legs.
Speaker 3 (33:46):
But the problem is that like it's.
Speaker 2 (33:47):
Too late, and so you know, our ideas basically like
bring on people who like know that they only have
thirty minutes, run as hard as you can, as crazy
as you can, get that one goal advantage, your two
goal advantage, and then adapts.
Speaker 1 (34:02):
And nobody has tried that. That doesn't seem crazy, Like
there's one hundred soccer teams, all you need is one
verson to try it. It's like a free idea.
Speaker 3 (34:11):
I know, I don't.
Speaker 2 (34:12):
Well, you know, if we see like a change in
substitution patterns after this content, yeah, that's right. Yeah, but
you know, like the crazy thing is there used to
only be three substitutions in soccer, and so you always
wanted to keep at least one in case a player
gets injured, so that really leaves you with two. But
during COVID, they upped it to five, and so now
it's like, well you.
Speaker 1 (34:32):
Have that has persisted. Yeah, so they've almost doubled the
number of substitutions allowed. Has the strategy in using them changed?
Speaker 2 (34:41):
No, it's like very slightly changed, And a lot of
coaches don't even take advantage of all substitutes.
Speaker 1 (34:50):
Why do you think nobody has, you know, dramatically significantly
changed their strategy around substitutions even though the rule has
changed so significant.
Speaker 3 (34:59):
One, I mean, I guess there's like two explanations.
Speaker 2 (35:02):
The easiest one is like, well, it's always been the
way that we've done it, so.
Speaker 3 (35:06):
You know why change?
Speaker 2 (35:08):
I think part of what's driving that though, is that
the way that rosters are constructed hasn't changed. And so
you know, depending on like the finances of a team,
you might not have as many like quality players to
go that deep onto the bench. But you know, if
this is your strategy, then you can change how you recruit,
(35:29):
you can change the profile of that squad composition.
Speaker 1 (35:32):
The second answer is interesting, right, because it requires you
to think more systematically. It's like, oh, the rules are different,
so therefore we should build a different team, which is
kind of next level.
Speaker 2 (35:42):
Yeah, and yeah, that's that's not typically how teams operate.
Speaker 1 (35:50):
We'll be back in a minute with the lightning round.
We're gonna finish with the lightning round. Okay, soccer or football?
I say football, frieser chips, chips, Cookies are biscuits.
Speaker 3 (36:12):
So my husband is from India and he will kill
me if I say cookies.
Speaker 1 (36:15):
But cookies are there other britishisms. You sign your emails cheers.
Speaker 3 (36:20):
Sometimes I hate doing it. Mate is another one?
Speaker 1 (36:24):
Mate?
Speaker 3 (36:25):
Yeah, yeah, because that's what.
Speaker 1 (36:27):
You call everything brilliant. I love talking to British people
because they say everything I say is brilliant, but they
don't mean it. The way I think.
Speaker 3 (36:32):
Yeah, it's brilliant, bloody. Yeah, just little things.
Speaker 1 (36:36):
By British guy once said blind I loved that.
Speaker 2 (36:40):
Yeah, I've not not done.
Speaker 3 (36:43):
That, but yeah.
Speaker 2 (36:43):
I think when I was little, my brother and I
used to antagonize my dad until he would like yell
at us in British slang, and that was like success
for us.
Speaker 1 (36:53):
Give me your impression of your dad yelling at your
British slang.
Speaker 3 (36:56):
It would just say, like, oh, bugger off, the.
Speaker 1 (36:58):
Bugger off is good. What's one thing you learned working
at Microsoft?
Speaker 3 (37:06):
It's hard to get things done in large companies.
Speaker 1 (37:10):
Do you have any tips for getting things done in
large companies? Or is it just leave?
Speaker 2 (37:15):
I mean my answer was leave, But yeah, I mean
I think there's there's certain behaviors that maybe I don't
necessarily possess or like love, but like aggressive, loud people
tend to get things done.
Speaker 1 (37:28):
There were there in the Balmer area era. That's very
Steve Balmer vibes. Who's the most underrated player you've ever seen?
Speaker 2 (37:41):
Ooh, it might be this guy named Manu Tregueros. He
plays for a club in Spain called Viriao, and I
think he's brilliant he was kind of born at a
time when Spain had like loads of really really talented
players in his position, so he never got called up
the national team or anything like that.
Speaker 3 (38:02):
But his running style is like a little bit nerdy,
Like he runs very.
Speaker 2 (38:05):
Much on his toes, and you know, he doesn't look
like an athlete. He actually is like a like while
he was playing professionally, he was doing his master's in education,
so he was like a student teacher.
Speaker 1 (38:18):
Like, it's just it's just that you love him. It's
not that he's an amazing player. Is that I love him?
Speaker 2 (38:22):
I love him, but like he was also an amazing player,
but I think like having this air of like nerdiness
around him kind of kept him a little bit under
the radar.
Speaker 1 (38:33):
Who's the most overrated player you've ever seen?
Speaker 2 (38:36):
Ooh, that's a yeah, that's a really hard one.
Speaker 1 (38:45):
Are you afraid of getting in trouble? Is there a
name in your mind and you just don't want to
say it because you don't want to antagonize anybody.
Speaker 3 (38:51):
Yeah, definitely, there's some of that.
Speaker 2 (38:53):
And then there's like a little bit of like hindsight bias,
where it's like there were guys who were overrated at.
Speaker 3 (38:58):
The moment and then like kind of crumbled and failed.
Speaker 2 (39:02):
So it's like, well, if I say like Marlon Shamack,
who was a kind of like a notoriously disastrous signing
for Arsenal, everyone would say like.
Speaker 1 (39:11):
Oh, yeah, too easy. Yeah, what's one piece of advice
you have for women working in male dominated fields?
Speaker 3 (39:21):
Oh, that's a really good one.
Speaker 2 (39:26):
It would probably be that just you belong and know
that you belong, and don't let people try to make
you feel that you don't. And one of the ways
that you can do that is talk to other women.
When I got started fifteen years ago, like there weren't
any other women. Now there's loads of women working in
all different sports and all different sorts of roles. And
(39:51):
a couple of years ago there's conference started called Women
in Sports Data. This year it's could be held in
Philadelphia September seventh, I think. But it's like a great
place to connect with people because to me, I find
it really powerful to be in like a gymnasium with
like a room literally full of women who are interested
in sports and data and technology. And yeah, just I
(40:15):
don't know, it's good for your mental health, it's good
for you know, your self esteem and everything. But yeah,
just know that that you belong, that you can do it,
and that yeah, nobody should tell you otherwise.
Speaker 1 (40:29):
So Brad Pitt played Billy Bean and Moneyball. Who's going
to play you in Moneyball too? Don't call it soccer?
Speaker 2 (40:37):
Oh yeah, I have I have no idea. I don't
watch a lot of movies, So, like, I couldn't even
name an actress right now. Maybe maybe Kiera Knightley because
she was in Bendettlake Beckham, So I'll go with Kiera Knightley.
Speaker 1 (40:54):
Sarah Rudd is the co founder and CEO of Source Football.
Today's show was produced by Gabriel Hunter Cheng. It was
edited by Lyddy jeene Kott and engineered by Sarah Brugeer.
You can email us at Problem at Pushkin dot Fm.
I'm Jacob Oldstein and we'll be back next week with
another episode of What's Your Problem
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