April 2, 2024 • 34 mins
All modern sports are inundated with a seemingly endless stream of stats and data. As with everything else, F1 takes it to another level. With hundreds of sensors collecting all information possible, the F1 data fans are spoiled with endless possibilities. To better understand some of those possibilities we spoke with Tareq Nazlawy, founder of Trace - a Digital Assets Venture Studio, and Sean Kelly, F1's unofficial chief statistician. Will Michael overcome his aversion, join the ranks of the data nerds, and start coding his own CFD prediction models? Have no idea what we are talking about? Put your geek cap on and smash that play button.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:03):
Tony, Michael, this past week, while you were resting, I
was hard at work crunching the numbers.
Speaker 2 (00:11):
Did you come up with anything interesting?
Speaker 3 (00:13):
Yes?
Speaker 1 (00:14):
Actually, okay, looking at the breakdown here, it looks like
last season, Yohai intervened an average of two point seven
times an episode for what constituted an average of three
point two minutes of airtime per episode. I see, and
looking at the data for this current season, which we're
just getting started with, really, but so far that number
has been up a staggering four point seven interventions per episode,
(00:38):
which represents a whopping airtime of three point eight minutes
per episode. I don't know about you, but I find
this very troublingling.
Speaker 4 (00:47):
I'm just not sure it's referred to as airtime anymore. Michael.
It's a podcast. It might be a bit arcade.
Speaker 1 (00:53):
You should feel lucky that I'm calling at interventions and
not interruptions. Interventions is very general.
Speaker 2 (01:00):
You were just cooled, dog, kay. I'm just gonna put
that out there.
Speaker 1 (01:03):
Oh Ship for My Heart podcast one on one Studios
and Sports Illustrated Studios. This is choosing sides.
Speaker 5 (01:15):
Yes, one wow, wow, Tony, I see.
Speaker 1 (01:26):
For today's episode, it's titled the data analyst. And I
have to be honest with you right out of the gate,
I'm not a big data numbers person. For me, it's
about heart, guts, that's sports, all that stuff. Also, every
sport has statistics and data. You ever look at the
back of a baseball card, So.
Speaker 2 (01:45):
Yes, every sport has statistics and data. But for example,
how many sensors does a baseball bat actually have?
Speaker 1 (01:51):
When do you think of it? I can think of
the gun that measures the miles per hour of the fastball,
so that the sensor that's one.
Speaker 2 (02:01):
Well, here's the thing. In a modern Formula one car,
you're looking at roughly two hundred and fifty or three
hundred sensors and they generate. These two hundred and fifty
three hundred sensors generate an immense amount of data, and
all of that data is being streamed back to the
team and it's constantly being processed and analyzed. And also
it's not just on the car, Michael, the tires have
these data sensors as well. The driver's gloves have sensors.
(02:24):
The F one tracks have sensors.
Speaker 1 (02:26):
The driver's gloves, yes, have sensors on them.
Speaker 2 (02:30):
Gonna come back to that also from a safety and
security reason.
Speaker 1 (02:33):
Okay, well, what are they sensing? All these sensors sense.
No matter how many meetings you have, you didn't think
you'd come up with that one.
Speaker 2 (02:44):
Okay, So to give you a few examples, they're sensing
things like lap time, tire pressure, breake pressure, frottile position,
fuel flow, the speed of the wind and how it's
going over the car, which sounds very poetic, break temperatures,
engine temperate, which is basically any temperature you can think of.
And to your point about the driver's gloves, Yes, they
(03:05):
have sensors and they monitor things like your heart rates.
Speaker 1 (03:07):
I's gonna say all of those other things are just
measuring the car exactly when we've talked about the driver.
Speaker 2 (03:12):
Yet, And that's what where the gloves come in. And
it's not the only part of the sensors, but these
sensors in the little fingertips of the glove are sensing
their heart rate. There's also an oximeter that measures the
amount of oxygen in the blood. And I'm just naming
a few examples right now. All of these sensors, this
is what's wild, is transmitted back in real time and
it generates around three terabytes of data for each car
(03:35):
during each race.
Speaker 1 (03:36):
Wow, that means nothing to me.
Speaker 2 (03:37):
Imagine stacking three hundred thousand Webster dictionaries. That would create
like a fifteen kilometer high stack of data. If you
printed out all the data that that's generating. I'm not
sure that helps you.
Speaker 1 (03:54):
It's a shitload, is what it is.
Speaker 2 (03:56):
There you get. That's another way of saying how much
data be Well.
Speaker 1 (04:00):
I'm also now thinking once you have the data, Yeah,
the printed Webster dictionaries, I know that was a metaphor,
but what do you do with it?
Speaker 2 (04:09):
Where do you go out in the wild now and say,
do you know.
Speaker 1 (04:13):
Out? Do they have to like storting caves underneath rivers
to keep it cool?
Speaker 2 (04:17):
Okay, so this is actually a really cool question because
the data centers have become somewhat like an integral part
of Formula one racing because the teams need not only
a reliable way to store, but also analyze the data
from their cars during the race. And more or less
everything has been moved to the cloud these days. So
it used to be that you would have local storages,
(04:37):
but now everything's in the cloud and there's some backup
in storage spaces as well, and I think it's time
for a fun fact. Great, the real time data that
we're getting right now from each race is combined with
over seventy years of a historical race data so that
they can look back and compare the data that they're
picking up at each race, and they have seventy years
(04:59):
of historical data look back on.
Speaker 1 (05:00):
So they've re censored all the races.
Speaker 2 (05:03):
So they've migrated Formula one's extensive library of seventy years
of images, audio video, and they've put all of that
into the cloud so that the data that they're capturing
today can be compared to that historical data. I cannot
imagine the amount of work that went into to taking
seventy years of footage data, all of that and putting
(05:26):
it into cloud, but that's something that they've done. And
apparently it's over one hundred and fifty thousand hours of content.
Speaker 4 (05:31):
Just sorry, just doing my thing here crunching the numbers.
Consuming one hundred and fifty thousand hours of content would
take you about six two hundred and fifty days, or
a little over seventeen years. That's, of course, without any
stoppage time to eat, sleep.
Speaker 1 (05:52):
Yeah, yeah, Jo has intervened inter intervened way over four
points seven times.
Speaker 2 (06:00):
It's also another point it's actually worth mentioning. So remember
in the episode that we looked at the sponsorships and
the business of Formula one. So in addition to Formula
one having these incredible tech partners, the teams also have
these partners to help them store all of this data.
So we're talking Oracle, Aws, Salesforce, Dell. Technology is cognizant.
(06:21):
That's why we're in this space right now of tech
sponsors versus the non tech sponsors that we might have
had fifty years ago.
Speaker 1 (06:28):
I was wondering, when you start thinking about this data
me and you if you give us data, yeah, I
don't know. I mean maybe you know more than I do,
but I don't know what to do with data. So
now I need experts to help me know what to
do with the data exactly. So that makes sense what
you're saying that you're partnering now with tech. It makes
me wonder if this is where all sports are headed.
(06:51):
I honestly think so you know, is one of the
managers in the baseball dugout going to be more data
driven than the guide chewing tobacco, you know, giving signals
to steal. Second is going to be a little kid
in a computer going like, well, technically, you know.
Speaker 2 (07:07):
What's also going to be interesting is do we want
data helping us make sense about how decisions are taken
into sport.
Speaker 1 (07:14):
I'll tell you no, yes, O froze.
Speaker 4 (07:18):
Yeah, just for a second. Do you have a bunch
of tabs open? Maybe?
Speaker 3 (07:21):
I do?
Speaker 4 (07:21):
Sometimes it helps if you can.
Speaker 3 (07:22):
Just give me half an hour and I'll close all
these tabs.
Speaker 2 (07:25):
I'm hearing a lot of clicks there, Tarrek, all.
Speaker 3 (07:27):
Very apropos, I assure you so.
Speaker 2 (07:29):
To help us today, we've recruited two first rate F
one data notes. First up, Tarek Naslwi.
Speaker 3 (07:36):
Been looking forward to this all day.
Speaker 2 (07:37):
Tarek is the creator of a digital platform called Trace,
a way for fans to document their fandom through data.
Speaker 1 (07:44):
Open here we go. Oh wow, that's really beautiful and cool.
Every race leaves a trace. This is hard to describe,
but it's available at trace dot. Fan traces are pieces
of art made from race data and it's like mathematical
and it almost looks like if every lap of a
Formula one race left a shadow and they were all
(08:07):
on top of each other. It forms this very cool,
fast looking, almost evil shape, very on brand for everyone.
This is cool, and a man.
Speaker 2 (08:16):
Who's an absolute legend and an integral part of Formula one.
Speaker 6 (08:20):
All the tentacles are grown way in there.
Speaker 2 (08:23):
And a real, walking, talking encyclopedia of facts and stats.
Speaker 6 (08:26):
Hello, folks, I'm Sean Kelly, and if you've watched a
Formula one broadcast anywhere in the world in the last
say two decades, you won't know me, but you will
know my work because I am essentially the chief statistician
to the F one Broadcaster's. All the facts and figures
you hear in a Formula one broadcast, whether they be
in the graphics, whether they be spoken by the likes
(08:46):
of David Croft or Martin Brundle or Alex Jakes or whomever,
I am chiefly responsible for all of that content. So,
depending on where you stand on the whole statistics spectrum,
either you're welcome or I'm sorry.
Speaker 2 (09:01):
Just to give you a sense of the genius of Sean,
I'm going to have to play you this really short
clip here, and to give you a bit of context.
We were still in the small talk portion of our
interview here, just getting to know each other and sharing
how we got into the spot.
Speaker 6 (09:16):
Can you remember the first race.
Speaker 2 (09:18):
Yes, sper font Gauchamp, only because it was Michael Schumacher's
first Yeah, first chair.
Speaker 6 (09:25):
Is nearly won it mantl how the voltage regulator go
out on his card, was in the lead, and then
the lady was leading, the engine failed and then somehow
after all that and jordanily ended up winning, and then
his engine failed and then we ended up with a
McLaren one too. Like after all this chaos, we ended
up with the most boring result possible. I went from
that race so bored that day. I was like, how
did we end up with that result?
Speaker 2 (09:45):
I need our listeners to understand that Sean had no
idea that ninety one was my first race, ninety one
in spa funkle Sean, and that you were able to
just have all of these facts.
Speaker 6 (09:58):
Berta had the fast slap in that race, I think,
which is the only time he ever set one in
a Formula one race.
Speaker 1 (10:03):
That's insane.
Speaker 2 (10:04):
Your brain is fascinating.
Speaker 1 (10:07):
I live with it.
Speaker 6 (10:08):
It's like it's not my constant roommate.
Speaker 3 (10:10):
All this data, all.
Speaker 1 (10:11):
Right, So this is a little hard for me to
wrap my hair around. Can you give me an example
of how data is used by a team to gain
an advantage? Like, okay, great, George Russell's heart rate is high,
is the blood is oxygenated? His lips are chapped? How
we got to win the fucking race? What does that
help us with?
Speaker 2 (10:27):
Okay, so number one, that's more or less a fun fact.
The data is encrypted and it's sent to the teams
via radio frequencies, and the frequency is due to like
little antenna that's mounted on pointing right now, but that's
mounted on the top of a car. And what's also
crazy is this happens in like fractions of a second.
It's also interesting to see that the data transferred is
(10:50):
faster when the races are in Europe than when the
races are abroad, even if it's like maybe mini mediseconds.
Speaker 1 (10:55):
Because it's kind of go under the ocean and down
through the cable and up to the sky and then
down to the antenna.
Speaker 4 (10:59):
Whoa.
Speaker 2 (11:00):
And sometimes when you're racing in the middle of a city,
like it's harder to get the frequency out it it's insane.
But to answer your question, all the data is transferred
so in real time to the pitball to the factory
and also the FA so there's a lot of people
receiving this data, and then it can be used for
things like comparing your lap time. You overlap your lap
time with other driver's lap time to see where people are,
(11:20):
you know, breaking breaking earlier, breaking later.
Speaker 3 (11:23):
If you ever hear one of the race engineers talk
about you need to go easy on the curbs, it'll
be because they're sensing vibrations which potentially get to a dangerous
level causing resonance and potential damage with car components, the
engine itself probably has I don't know, dozens and dozens
of sensors on that and actually saw that with Lando
this weekend. His car went into some kind of safety
(11:46):
mode where the engine was protecting itself from damage back
you know, pressure, temperature, vibration, travel as well in the
suspension on the cars. Anything which can be sensed will
be sensed.
Speaker 2 (12:02):
Basically, with all this data, the engineers and the driver
get a much better picture of where the driver is
losing time, where his opportunities to win some time, and
where he can make up that time in the race.
Speaker 1 (12:13):
But I can't get my competitors data.
Speaker 3 (12:15):
Can I the on track data that actually I believe
Formula one owns I think is visible to pretty much everybody.
So like on track data, like you can see what
other cars are doing and infer, for example, about your
pace for the race relative to others' pace, But for example,
you don't see what fuel loads they had in their
car or how fast they're consuming that. And that's data
(12:37):
is like proprietary to the team. So there's a mix
of data that I think everybody sees and then a
lot of the data that only the team sees.
Speaker 2 (12:46):
But and this is the joy of a lot of
data analysts and the f one data analyst fans, is
a lot of the data is actually also open to
the public.
Speaker 1 (12:54):
That's I'm going to ask, like can fans get the data?
Speaker 2 (12:57):
Yes?
Speaker 1 (12:57):
Can I get the data?
Speaker 2 (12:58):
Yes?
Speaker 6 (12:59):
Not all of it, some of it, the vast majority
of it is available to everybody on FORX, if you
go to the Forest's website, which is a subscription part
of the Autosport website, yeah, it's all there. It's a
complicated website to operate, and there's a lot of easter eggs.
You've got to keep clicking around to find the information
you want to find. But a great deal of the
raw numbers can be derived from the publicly accessible part
(13:22):
of forex if you subscribe to it.
Speaker 1 (13:25):
You know, football turn into a bunch of like drunk
bros at the bar with their laptops open because of
Fantasy football doing the stats and the receivers and I,
you know, I grumble about that, but this is like
whole new level.
Speaker 2 (13:42):
This is a whole new level and perfect for those
nerdy fans who just wants that to numbers. The other
really interesting thing is it used to be back in
the day that the teams would create their own sensors
and set up the own antennas and all of that,
and then the teams kind of realize, hey, wait a minute,
we're not in a race to create the best sensors
(14:03):
and the best technology. Hence why today all of that
is mandated by the FIA.
Speaker 1 (14:06):
What a unique time in history for F one because
tech is at its most profound ever ever. Yeah, so
it's pretty interesting.
Speaker 2 (14:17):
And it's it's at its most profound. And what's really
interesting is behind closed doors, a lot of the things
that are developed inside of the factories of the F
one teams then spill over into the rest of the world.
Glaxo Smith Klein, one of the big farmer companies in
the world, looked at how I think it was McClaren
or Williams did their pit stops, and we're just like, huh,
there's something we can learn you about how we bottle toothpaste.
(14:40):
And so they figured out how to bottle toothpaste faster,
which meant they could create more bottles of toothpaste and
sell more bottles of toothpaste.
Speaker 1 (14:46):
And I was like, genius, is it true that Max
for Stapping keeps us coffee warm in that coffee cup?
Because I heard that that that's how the car industry
keeps our coffee worm. Now what No, But my point
is sorry for the bad joke, everybody, but the point
is much like the military was like leading the world
(15:07):
in research and development and velcro and all this other shit,
wender bread, all this stuff spilled over to us plebeians.
F one is doing that as.
Speaker 2 (15:17):
Well, exact same. The FIA produces like this fifty or
sixty page guide at the end of every year where
it just highlights all of the callshit that's developed behind
closed doors, but then spills over whether it's taking care
of newborn babies, they've used the same system that they
used to No, but it's true. Like the thing that
the drivers are satin to keep them really protected, They've
(15:38):
developed the same mechanism for newborn babies that have maybe
have issues or that need to be transported by helicopter
or Apple to make sure that the babies don't move.
And I'm just like, this is genius.
Speaker 1 (15:47):
That's so cool. We have to take quick break and
we'll be right back. Hey, we're back. Okay, where were.
Speaker 2 (15:57):
We pulling on that thread of what the fan the
lens at which the fans go to, So you mentioned
your baseball fans. Some especially nerdy fans, will actually write
up all kinds of different software that pulls the publicly
available data from all the different relevant sources and then
it lets them play around with it on their own.
Speaker 3 (16:15):
Like there are some engineers or ex engineers who are
sharing on social images of how like the new parts
change the flow of a car and what that does
to the aerodynamic forces on a car.
Speaker 1 (16:24):
Okay, hold on CFD, Yeah, come on, Derek, what's CFD?
Speaker 3 (16:29):
CFD Yeah. CFD is computational fluid dynamics. So if you
ever see pictures of like simulations of how a of
how an airflow will go over a surface. Those simulations
are done in a computer, which are also then leads
to what people decide to test in wind tunnels. Right,
this is basically the rocket science part of the Formula
(16:49):
one stuff. There are ways for fans to access that
kind of you know, storytelling and information like you go
look in the Box Box Club app and in the
Explore section there there's a whole bunch of people who
are basically taking images and overlaying things to do with
what forces are happening on the car. And you know,
I studied engineering at Cambridge for four years and even
(17:11):
some of that information is like pretty close to the
kinds of stuff you would study if you specialize in
fluid dynamics and mechanics and things like that. So it's
fantastic there are creators that want to expose that level
of technical understanding of what's going on in the car
as well. Of course, the number of people that want
to hang out with you and talk about it gets
(17:32):
smaller and smaller as you go down. But don't let
it stop for you. Don't let it stop for you
find your tribe sit in the rabbit hole where however
deep you want to go.
Speaker 2 (17:40):
If you just head over, for example, to get her Michael,
and you look at the Python code for fast F
one like you will find if you know, if after
this episode you get really into the data, that's where
you should go.
Speaker 1 (17:51):
In my life, no one has ever said this sentence
to me. Just head over to getthub and look for
the Python code for fast F one. Take that statistic sheets,
you know, I don't know, like men go out ice
fishing for six hours on a Sunday, you know, and
it's like how much It's like, how much do you
(18:12):
hate your family if you're But I'm like, well, if
you're coating for F one, you know, we all got
our stuff. Man, we did.
Speaker 4 (18:18):
What they need is they need a GitHub code to
tell them.
Speaker 1 (18:22):
Where the fish are. Yeah, fun fact, they don't even
care about the fish. It's just being out there.
Speaker 2 (18:34):
Moving swiftly.
Speaker 3 (18:35):
On.
Speaker 1 (18:35):
Let me ask you this, Tony. I noticed watching the
races that drivers do a thing called track walk. Is
that what it's called?
Speaker 2 (18:42):
Yeah?
Speaker 1 (18:43):
Okay? And they literally just walk around the entire track.
Given this conversation we've been having, am I understanding how
the vast amount of data they have about every little detail.
What data are these drivers picking up from just walking
around the track.
Speaker 2 (18:57):
They will look over if the track has changed, the layout,
huts changed, look up identifying potential issues or challenges that
the driver might have over the weekend, and more. It
depends on the driver and the race engineer and the
relationship that they have. But more often than not, it's
like a two way conversation about going back and forth
about what they're I imagine it's like a nice reset
for the team of like this is okay, now it's
race time. Let's go.
Speaker 1 (19:18):
It must feel so fast when they drive it, yeah,
because they were just like last time I was walking.
Speaker 2 (19:22):
This fun fact of this year, I think they banned
bicycles on tracks. These tracks are pretty long, and so
some of the drivers would be like, oh, we're just
going to cycle around the track. They banned that because
too many drivers were having like bicycle accidents.
Speaker 1 (19:38):
Just can't slow it down. They can't just slow it,
just walk, use the two legs that God gave you.
Speaker 4 (19:43):
Is there an element of like superstition to this, like
a kind of bit of a ritual.
Speaker 2 (19:49):
There's so much superstition in sport.
Speaker 1 (19:51):
I feel like the higher the stakes the more superstition.
And I would think when F one there's high stakes,
because there's life can be on the line. Do these
drivers pray? Are they're religious?
Speaker 2 (20:04):
Pierre Gasley, for example, always does the sign of the Cross.
Speaker 1 (20:08):
Because there's a beautiful contradictory here in data and faith,
because they don't always go hand in hand, and often
times the highest scientific scientists are not as keen to
spiritual faith. And yet also every athlete I've ever known,
especially the high levels, they there's a lot of faith there.
Speaker 3 (20:29):
I can tell you what I think about this, which
is that you can be an extremely technically oriented person
and believe in science, believe in science being subscribed to
science as a way of describing how the universe works,
But answers to why things are the way they are
or how they came to be that way are not
answered by data that you can record, least of all
(20:51):
on the F one car. So I wouldn't fall into
the trap of thinking that if you are into data
or science, that you don't have the capacity or the
predilection for any kind of spiritual beliefs. And at least
that's my personal take on things. And yeah, don't underestimate
any capacity of any human for being able to operate
at that dimension.
Speaker 2 (21:16):
How did we get here?
Speaker 1 (21:17):
I don't know, but that's what's fun about podcasting. So Okay,
you know I was going to ask you this, but
I've been excited to ask you this. Okay, too much data,
at some point, they're overthinking it. There's too many data points.
If you were to analyze everything, you would never sleep.
You would just be a robot of data. And then
you couldn't even drive. Put the keys on the car.
(21:39):
I know these car, These cars don't have keys. But
is there such thing as too much data?
Speaker 3 (21:44):
Yeah, there is such a thing as too much data?
There certainly is. I mean, you can argue, what harm
does it do if it doesn't take time or money
or to collect it or store it. But it does.
Data is only as good as the rates that you
can process it and the insights that you can glean
from it. There certainly is. I mean, I would say
most corporations suffer from the challenge of not being able
(22:05):
to manipulate and distill insight from the data that they
collect as a business. I know certainly that's been my experience.
So I think there is. Yeah, there is such a
thing but you know, you first have to have the
raw ingredient in order to sort of cook with it.
So I think collecting data and figuring out what to
do with that. You know, collecting it is always the
first thing, and you make an investment decision about what
(22:26):
you want to spend time collecting.
Speaker 2 (22:28):
So yes, there is such a thing as too much data.
But also know, if there's data to collect, they're going
to collect it.
Speaker 1 (22:33):
They must think it's useful. Yeah, you know, I mean
does max verst ap and collect more data?
Speaker 3 (22:40):
My hypothesis would be no, I think that's really more
of a function of the car than it is it
is the driver. He might be generating less insight, but
I don't know if he's if he's generating less data.
There's a difference between data, information and insight. Right is
the raw ingredient. Right information is when somebody puts that
(23:05):
information on a chart or a screen, like what you
see on the pitwall. Insight is the ability to read
that and draw a new conclusion which leads you to
some kind of decision or to twist or stick with
what you're currently doing. So when we think about that,
like most people think data and they think charts, well,
I think data. I think bits and bytes, information and
(23:27):
insight are what data turns into. So that might be
why I might have answered your question a little bit literally.
Speaker 1 (23:34):
I forget what it was. It was an NPR and
and know it's a different competitor. But they did a
story on how you can hack into someone's computer and
control their car. Now, me and you are car. So
if they have all this data, can they control the car?
Can the engineer control the car? Who's controlling the car?
Is the driver just a pretty face?
Speaker 3 (23:55):
Some drivers are pretty faces. I won't necessarily reveal who
I think is pretty than others, but I mean, I
think the clear answer is no. You know, if you
sort of think about like that, the car is something
which is being configured to create the maximum opportunity for performance.
You still have to extract that performance, and you cannot
(24:19):
program that in a way which beats a human yet
at least right, and so the ability for a human
to interact with the physical world and to be able
to control that car with all of the parameters that
they have, which is you know, steering input, acceleration input,
break input, which basically the three basic inputs and the
(24:41):
perfect way at the perfect moments and the perfect combination
on a car which is basically designed to operate on
an absolute knife's edge. If you and I got in
the car, it would be a very different outcome. I mean,
actually there wouldn't be a lot of space for you
and I to get in the same car. But like
you get my point, which is, if you're a driving
by the way, I have my synric behind me, so
(25:02):
I do have a go at this right And I
do think that there is such a thing as good driver.
It's like there's no way around it. That's why the
competition to be one of the twenty on the grid
is just that intense. I'm a fan believer that you
can't take the driver out of the car and expect
anything special to happen.
Speaker 1 (25:21):
We got to take a short break. Hey, we're back
to Michael.
Speaker 2 (25:26):
Now. Is all of this data hitting you? Are you
feeling and enrich? Do you fashion yourself to be a
data fan? Where's your head up?
Speaker 1 (25:36):
I'm thinking calculating. I'm thinking. My knee jerk reaction to
the word data is that it's a four letter word.
Speaker 2 (25:46):
Michael doesn't like four letter words.
Speaker 1 (25:48):
Yeah, and I don't enjoy how society is so data
driven right now, because I'm you're taking the feel out
of things. It's not everything in life is just a
one in a zero. But I'm more intrigued now that
we've talked about it.
Speaker 2 (26:05):
I love it.
Speaker 1 (26:06):
If I have multiple screens up watching Formula one, I'm
not gonna want data. I'm gonna want different driver's helmet
cams and the stadium shot. I mean, I'm gonna want
those visuals.
Speaker 2 (26:18):
You want the athletes, yeah, or just.
Speaker 1 (26:20):
The machine, the rocket going off, you know. But there
are these nerds that want to just check out torque
revolutions or whatever, but have it. I mean, that's cool.
I'm happy for them.
Speaker 2 (26:32):
By the way, is the new name of the podcasts.
Speaker 3 (26:37):
If anybody's listening to this thing that feels like they're
not a data driven fan and that this isn't for
them because they don't feel like they were never into
math when they grew up, or they're not like particularly
technical in their orientation or something like that, I guess.
I guess all I would say is like, you don't
(26:58):
have to label yourself a data driven fan to understand
that there's another layer of story that's going on in
mosor spot and you know, don't think of the data,
which is something which is kind of scary, which makes
this for other people, especially for technically oriented oriented males.
I think there's a lot more than meets the eye,
and you don't have to dig that deep to find it.
(27:19):
And I think that's to me what the beauty of
this sport and what got me hooked on it. If
you imagine I'm doing this with my hands now, right,
I'm creating this sort of picture of this kind of funnel,
right of like you know, whide at the top and
narrow at the bottom. For me, the data driven part
of fandom and the stories which really unfold during a
race or during a season are kind of in the
(27:40):
middle of that funnel. And if you're a person a
fan that loves to live in the rabbit hole, that
loves to understand what's going on beneath the surface, then
you can't really do that without some way of capturing
what's going on and then using that to replay and
tell a story. Because you're I mean, I'm sure, Tony,
you've spoken to a people who were like, it's just
(28:01):
a bunch of cars going around in circles, isn't it.
And then some of those. These are the people like
right at the top of this funnel, right then there's
the people who got into Drive to Survive and they're like, oh,
there's so much more than just cars go around a track.
There's narratives and stories and characters and drama and whatever.
And then as you get drawn into the sport, and
for me that was through gaming. Actually, like I got
(28:23):
I went from Drive to Survive to playing F one,
the co Masters game. Religiously, this is when I still
had time to play video games. And then I started
to understand what's going on from the seat of a
driver or of a team because you experience that, and
then when you see the race again, you're like, well,
all the story. Like the broadcast, it's such a narrow
(28:44):
window on all of these events that are going on,
like tire graining, pit stop times, which overtakes the happening, overcuts,
undercuts fuel consumption. You start to see way more than
is shown on the screen. And to me, that's the
real fun of it. It's almost like you're decoding a
live event as is happening, and then after the fact
(29:07):
you get to chew on it for that many more days,
which is a super fan, It's like, that's all you
want to do, right, is just engage more and more
deeply with the thing that you love. And without the
way to capture data and use that to replay to
tell the stories, I think it would be a less
compelling experience. From my perspective, what I'm.
Speaker 2 (29:23):
Hearing from you is you like the idea that data
could make you better, but you don't want data to
be the driving force behind the athlete.
Speaker 1 (29:31):
No, I don't want to be so judgmentally against data.
It's just not what tickles my fancy in sport. But
it is absolutely remarkable how this sport consumes data.
Speaker 2 (29:43):
Here's another thing for you to think about. Why might
be fundamentally different or exponentially using big words here, but
exponentially more useful in a sport like Formula one because
the teams have such limited times with the cars on.
Every time you take a Formula one car, it's mandated,
it's restricted. They can't, no one can. A driver can't
(30:06):
just wake up one they and go I'm just gonna
go and take the car up for a run and
practice if you they can't. So because it's so limited
with how they can practice with the cars, I think
that's why every ounce of data is picked up and
collected and analyzed and useful. A baseball person makes sense,
and baseball person, a baseball athlete, a golfer can pick
up a pair of clubs and swing, you know, practice
(30:27):
in their own time. I actually can't think of another
sport where you can't practice at will.
Speaker 1 (30:34):
I remember talking about that last season. It's very fascinating.
So I think you can hit a tennis ball for
as many hours as you're allowed to, as many hours
as you physically want to. But that can't be the case.
Speaker 2 (30:43):
Yeah, And I remember Kobe talking about this with being
a basketball player. Of just like I get up five
hours before everyone else, and so I have five hours
of training on everyone else every single day. Formula one
drivers do not have that. Hence why I think that
data plays such a big role in Formula one.
Speaker 1 (30:58):
Also, it's mind boggling to think that I was buying
shoes today on the internet and it was frustrating me
because I would add it to my cart and then
check out, and it would there'd be like a gap
in uh loading loading, you know, and I'd be like,
why is there a gap? It's a new computer edition
everything was fine, but it's like it's a little too strong.
And then to think that this their way they're racing
(31:20):
is happening in data like instantaneously yep, and it's crazy
and that probably gets into our logistic episode. That's wild
to me that that can even happen.
Speaker 2 (31:29):
And they're still pushing the limits to figure out how
they can get the data uploaded to HQ faster than
what it is now when we're talking zero point zero ones,
you know, we're always pushing the limit.
Speaker 1 (31:39):
Fall boots, if that's what you're asking, fall Boots. We
still get like a month left in the fall here.
Speaker 4 (31:45):
So Michael, shit is that? YOHI Again, our team here
at Choosing Size HQ, we've been crunching the numbers and
unfortunately it does seem that you're going off script about
twenty five percent more than than than management would like.
So that I've got a request here. They're asking if
(32:05):
you can just try to be a little more vigilant.
Speaker 7 (32:08):
You see. Uh, this has been Choosing Sides f one,
a production of Sports Illustrated Studios, iHeart Podcast and one
(32:29):
oh one Studio podcast.
Speaker 4 (32:32):
The show is hosted by Michael Costa and Tony Cowan Brown.
This episode was edited, scored, and sound designed by senior
producer Johai may Thad scott Stone is the executive producer
and head of audio, and Daniel Wexman is Director of
podcast Development and production Manager at one on one Studios.
(32:54):
At iHeart podcast Sean ty Tone is our executive producer.
And a special thank you to Michelle Newman, David Glasser,
and David Hoodkin from One o one Studios. For more
shows from iHeart Podcasts, go visit the iHeartRadio app, Apple
Podcasts or wherever you get your podcasts, and whatever you do,
(33:17):
don't forget to rate us and tell your friends. It
really does mean a lot.
Speaker 2 (33:27):
Michael, yess what's coming up next week?
Speaker 1 (33:31):
Is it?
Speaker 2 (33:32):
Yes?
Speaker 1 (33:32):
It is finally, Yes.
Speaker 2 (33:35):
Yes, it's the turn of the tireheads.
Speaker 1 (33:40):
Yeah. I'm not sure why I'm so excited about tires,
but I am time.
Speaker 2 (33:45):
For talking rubber tires, screeching, the whole shebang.
Speaker 1 (33:49):
It seems very important. It seems so so so important.
The S and the M and the H and they
all get the same amount of tires or whatever. And
when I take my car into the MC, they say
the most important thing is tires and breaks next on
Torque Revolution. YOHI could you send us off with some
screeching tire noises or something
Speaker 4 (34:12):
Such a cliche
Tony, Michael, this past week, while you were resting, I
was hard at work crunching the numbers.
Speaker 2 (00:11):
Did you come up with anything interesting?
Speaker 3 (00:13):
Yes?
Speaker 1 (00:14):
Actually, okay, looking at the breakdown here, it looks like
last season, Yohai intervened an average of two point seven
times an episode for what constituted an average of three
point two minutes of airtime per episode. I see, and
looking at the data for this current season, which we're
just getting started with, really, but so far that number
has been up a staggering four point seven interventions per episode,
(00:38):
which represents a whopping airtime of three point eight minutes
per episode. I don't know about you, but I find
this very troublingling.
Speaker 4 (00:47):
I'm just not sure it's referred to as airtime anymore. Michael.
It's a podcast. It might be a bit arcade.
Speaker 1 (00:53):
You should feel lucky that I'm calling at interventions and
not interruptions. Interventions is very general.
Speaker 2 (01:00):
You were just cooled, dog, kay. I'm just gonna put
that out there.
Speaker 1 (01:03):
Oh Ship for My Heart podcast one on one Studios
and Sports Illustrated Studios. This is choosing sides.
Speaker 5 (01:15):
Yes, one wow, wow, Tony, I see.
Speaker 1 (01:26):
For today's episode, it's titled the data analyst. And I
have to be honest with you right out of the gate,
I'm not a big data numbers person. For me, it's
about heart, guts, that's sports, all that stuff. Also, every
sport has statistics and data. You ever look at the
back of a baseball card, So.
Speaker 2 (01:45):
Yes, every sport has statistics and data. But for example,
how many sensors does a baseball bat actually have?
Speaker 1 (01:51):
When do you think of it? I can think of
the gun that measures the miles per hour of the fastball,
so that the sensor that's one.
Speaker 2 (02:01):
Well, here's the thing. In a modern Formula one car,
you're looking at roughly two hundred and fifty or three
hundred sensors and they generate. These two hundred and fifty
three hundred sensors generate an immense amount of data, and
all of that data is being streamed back to the
team and it's constantly being processed and analyzed. And also
it's not just on the car, Michael, the tires have
these data sensors as well. The driver's gloves have sensors.
(02:24):
The F one tracks have sensors.
Speaker 1 (02:26):
The driver's gloves, yes, have sensors on them.
Speaker 2 (02:30):
Gonna come back to that also from a safety and
security reason.
Speaker 1 (02:33):
Okay, well, what are they sensing? All these sensors sense.
No matter how many meetings you have, you didn't think
you'd come up with that one.
Speaker 2 (02:44):
Okay, So to give you a few examples, they're sensing
things like lap time, tire pressure, breake pressure, frottile position,
fuel flow, the speed of the wind and how it's
going over the car, which sounds very poetic, break temperatures,
engine temperate, which is basically any temperature you can think of.
And to your point about the driver's gloves, Yes, they
(03:05):
have sensors and they monitor things like your heart rates.
Speaker 1 (03:07):
I's gonna say all of those other things are just
measuring the car exactly when we've talked about the driver.
Speaker 2 (03:12):
Yet, And that's what where the gloves come in. And
it's not the only part of the sensors, but these
sensors in the little fingertips of the glove are sensing
their heart rate. There's also an oximeter that measures the
amount of oxygen in the blood. And I'm just naming
a few examples right now. All of these sensors, this
is what's wild, is transmitted back in real time and
it generates around three terabytes of data for each car
(03:35):
during each race.
Speaker 1 (03:36):
Wow, that means nothing to me.
Speaker 2 (03:37):
Imagine stacking three hundred thousand Webster dictionaries. That would create
like a fifteen kilometer high stack of data. If you
printed out all the data that that's generating. I'm not
sure that helps you.
Speaker 1 (03:54):
It's a shitload, is what it is.
Speaker 2 (03:56):
There you get. That's another way of saying how much
data be Well.
Speaker 1 (04:00):
I'm also now thinking once you have the data, Yeah,
the printed Webster dictionaries, I know that was a metaphor,
but what do you do with it?
Speaker 2 (04:09):
Where do you go out in the wild now and say,
do you know.
Speaker 1 (04:13):
Out? Do they have to like storting caves underneath rivers
to keep it cool?
Speaker 2 (04:17):
Okay, so this is actually a really cool question because
the data centers have become somewhat like an integral part
of Formula one racing because the teams need not only
a reliable way to store, but also analyze the data
from their cars during the race. And more or less
everything has been moved to the cloud these days. So
it used to be that you would have local storages,
(04:37):
but now everything's in the cloud and there's some backup
in storage spaces as well, and I think it's time
for a fun fact. Great, the real time data that
we're getting right now from each race is combined with
over seventy years of a historical race data so that
they can look back and compare the data that they're
picking up at each race, and they have seventy years
(04:59):
of historical data look back on.
Speaker 1 (05:00):
So they've re censored all the races.
Speaker 2 (05:03):
So they've migrated Formula one's extensive library of seventy years
of images, audio video, and they've put all of that
into the cloud so that the data that they're capturing
today can be compared to that historical data. I cannot
imagine the amount of work that went into to taking
seventy years of footage data, all of that and putting
(05:26):
it into cloud, but that's something that they've done. And
apparently it's over one hundred and fifty thousand hours of content.
Speaker 4 (05:31):
Just sorry, just doing my thing here crunching the numbers.
Consuming one hundred and fifty thousand hours of content would
take you about six two hundred and fifty days, or
a little over seventeen years. That's, of course, without any
stoppage time to eat, sleep.
Speaker 1 (05:52):
Yeah, yeah, Jo has intervened inter intervened way over four
points seven times.
Speaker 2 (06:00):
It's also another point it's actually worth mentioning. So remember
in the episode that we looked at the sponsorships and
the business of Formula one. So in addition to Formula
one having these incredible tech partners, the teams also have
these partners to help them store all of this data.
So we're talking Oracle, Aws, Salesforce, Dell. Technology is cognizant.
(06:21):
That's why we're in this space right now of tech
sponsors versus the non tech sponsors that we might have
had fifty years ago.
Speaker 1 (06:28):
I was wondering, when you start thinking about this data
me and you if you give us data, yeah, I
don't know. I mean maybe you know more than I do,
but I don't know what to do with data. So
now I need experts to help me know what to
do with the data exactly. So that makes sense what
you're saying that you're partnering now with tech. It makes
me wonder if this is where all sports are headed.
(06:51):
I honestly think so you know, is one of the
managers in the baseball dugout going to be more data
driven than the guide chewing tobacco, you know, giving signals
to steal. Second is going to be a little kid
in a computer going like, well, technically, you know.
Speaker 2 (07:07):
What's also going to be interesting is do we want
data helping us make sense about how decisions are taken
into sport.
Speaker 1 (07:14):
I'll tell you no, yes, O froze.
Speaker 4 (07:18):
Yeah, just for a second. Do you have a bunch
of tabs open? Maybe?
Speaker 3 (07:21):
I do?
Speaker 4 (07:21):
Sometimes it helps if you can.
Speaker 3 (07:22):
Just give me half an hour and I'll close all
these tabs.
Speaker 2 (07:25):
I'm hearing a lot of clicks there, Tarrek, all.
Speaker 3 (07:27):
Very apropos, I assure you so.
Speaker 2 (07:29):
To help us today, we've recruited two first rate F
one data notes. First up, Tarek Naslwi.
Speaker 3 (07:36):
Been looking forward to this all day.
Speaker 2 (07:37):
Tarek is the creator of a digital platform called Trace,
a way for fans to document their fandom through data.
Speaker 1 (07:44):
Open here we go. Oh wow, that's really beautiful and cool.
Every race leaves a trace. This is hard to describe,
but it's available at trace dot. Fan traces are pieces
of art made from race data and it's like mathematical
and it almost looks like if every lap of a
Formula one race left a shadow and they were all
(08:07):
on top of each other. It forms this very cool,
fast looking, almost evil shape, very on brand for everyone.
This is cool, and a man.
Speaker 2 (08:16):
Who's an absolute legend and an integral part of Formula one.
Speaker 6 (08:20):
All the tentacles are grown way in there.
Speaker 2 (08:23):
And a real, walking, talking encyclopedia of facts and stats.
Speaker 6 (08:26):
Hello, folks, I'm Sean Kelly, and if you've watched a
Formula one broadcast anywhere in the world in the last
say two decades, you won't know me, but you will
know my work because I am essentially the chief statistician
to the F one Broadcaster's. All the facts and figures
you hear in a Formula one broadcast, whether they be
in the graphics, whether they be spoken by the likes
(08:46):
of David Croft or Martin Brundle or Alex Jakes or whomever,
I am chiefly responsible for all of that content. So,
depending on where you stand on the whole statistics spectrum,
either you're welcome or I'm sorry.
Speaker 2 (09:01):
Just to give you a sense of the genius of Sean,
I'm going to have to play you this really short
clip here, and to give you a bit of context.
We were still in the small talk portion of our
interview here, just getting to know each other and sharing
how we got into the spot.
Speaker 6 (09:16):
Can you remember the first race.
Speaker 2 (09:18):
Yes, sper font Gauchamp, only because it was Michael Schumacher's
first Yeah, first chair.
Speaker 6 (09:25):
Is nearly won it mantl how the voltage regulator go
out on his card, was in the lead, and then
the lady was leading, the engine failed and then somehow
after all that and jordanily ended up winning, and then
his engine failed and then we ended up with a
McLaren one too. Like after all this chaos, we ended
up with the most boring result possible. I went from
that race so bored that day. I was like, how
did we end up with that result?
Speaker 2 (09:45):
I need our listeners to understand that Sean had no
idea that ninety one was my first race, ninety one
in spa funkle Sean, and that you were able to
just have all of these facts.
Speaker 6 (09:58):
Berta had the fast slap in that race, I think,
which is the only time he ever set one in
a Formula one race.
Speaker 1 (10:03):
That's insane.
Speaker 2 (10:04):
Your brain is fascinating.
Speaker 1 (10:07):
I live with it.
Speaker 6 (10:08):
It's like it's not my constant roommate.
Speaker 3 (10:10):
All this data, all.
Speaker 1 (10:11):
Right, So this is a little hard for me to
wrap my hair around. Can you give me an example
of how data is used by a team to gain
an advantage? Like, okay, great, George Russell's heart rate is high,
is the blood is oxygenated? His lips are chapped? How
we got to win the fucking race? What does that
help us with?
Speaker 2 (10:27):
Okay, so number one, that's more or less a fun fact.
The data is encrypted and it's sent to the teams
via radio frequencies, and the frequency is due to like
little antenna that's mounted on pointing right now, but that's
mounted on the top of a car. And what's also
crazy is this happens in like fractions of a second.
It's also interesting to see that the data transferred is
(10:50):
faster when the races are in Europe than when the
races are abroad, even if it's like maybe mini mediseconds.
Speaker 1 (10:55):
Because it's kind of go under the ocean and down
through the cable and up to the sky and then
down to the antenna.
Speaker 4 (10:59):
Whoa.
Speaker 2 (11:00):
And sometimes when you're racing in the middle of a city,
like it's harder to get the frequency out it it's insane.
But to answer your question, all the data is transferred
so in real time to the pitball to the factory
and also the FA so there's a lot of people
receiving this data, and then it can be used for
things like comparing your lap time. You overlap your lap
time with other driver's lap time to see where people are,
(11:20):
you know, breaking breaking earlier, breaking later.
Speaker 3 (11:23):
If you ever hear one of the race engineers talk
about you need to go easy on the curbs, it'll
be because they're sensing vibrations which potentially get to a dangerous
level causing resonance and potential damage with car components, the
engine itself probably has I don't know, dozens and dozens
of sensors on that and actually saw that with Lando
this weekend. His car went into some kind of safety
(11:46):
mode where the engine was protecting itself from damage back
you know, pressure, temperature, vibration, travel as well in the
suspension on the cars. Anything which can be sensed will
be sensed.
Speaker 2 (12:02):
Basically, with all this data, the engineers and the driver
get a much better picture of where the driver is
losing time, where his opportunities to win some time, and
where he can make up that time in the race.
Speaker 1 (12:13):
But I can't get my competitors data.
Speaker 3 (12:15):
Can I the on track data that actually I believe
Formula one owns I think is visible to pretty much everybody.
So like on track data, like you can see what
other cars are doing and infer, for example, about your
pace for the race relative to others' pace, But for example,
you don't see what fuel loads they had in their
car or how fast they're consuming that. And that's data
(12:37):
is like proprietary to the team. So there's a mix
of data that I think everybody sees and then a
lot of the data that only the team sees.
Speaker 2 (12:46):
But and this is the joy of a lot of
data analysts and the f one data analyst fans, is
a lot of the data is actually also open to
the public.
Speaker 1 (12:54):
That's I'm going to ask, like can fans get the data?
Speaker 2 (12:57):
Yes?
Speaker 1 (12:57):
Can I get the data?
Speaker 2 (12:58):
Yes?
Speaker 6 (12:59):
Not all of it, some of it, the vast majority
of it is available to everybody on FORX, if you
go to the Forest's website, which is a subscription part
of the Autosport website, yeah, it's all there. It's a
complicated website to operate, and there's a lot of easter eggs.
You've got to keep clicking around to find the information
you want to find. But a great deal of the
raw numbers can be derived from the publicly accessible part
(13:22):
of forex if you subscribe to it.
Speaker 1 (13:25):
You know, football turn into a bunch of like drunk
bros at the bar with their laptops open because of
Fantasy football doing the stats and the receivers and I,
you know, I grumble about that, but this is like
whole new level.
Speaker 2 (13:42):
This is a whole new level and perfect for those
nerdy fans who just wants that to numbers. The other
really interesting thing is it used to be back in
the day that the teams would create their own sensors
and set up the own antennas and all of that,
and then the teams kind of realize, hey, wait a minute,
we're not in a race to create the best sensors
(14:03):
and the best technology. Hence why today all of that
is mandated by the FIA.
Speaker 1 (14:06):
What a unique time in history for F one because
tech is at its most profound ever ever. Yeah, so
it's pretty interesting.
Speaker 2 (14:17):
And it's it's at its most profound. And what's really
interesting is behind closed doors, a lot of the things
that are developed inside of the factories of the F
one teams then spill over into the rest of the world.
Glaxo Smith Klein, one of the big farmer companies in
the world, looked at how I think it was McClaren
or Williams did their pit stops, and we're just like, huh,
there's something we can learn you about how we bottle toothpaste.
(14:40):
And so they figured out how to bottle toothpaste faster,
which meant they could create more bottles of toothpaste and
sell more bottles of toothpaste.
Speaker 1 (14:46):
And I was like, genius, is it true that Max
for Stapping keeps us coffee warm in that coffee cup?
Because I heard that that that's how the car industry
keeps our coffee worm. Now what No, But my point
is sorry for the bad joke, everybody, but the point
is much like the military was like leading the world
(15:07):
in research and development and velcro and all this other shit,
wender bread, all this stuff spilled over to us plebeians.
F one is doing that as.
Speaker 2 (15:17):
Well, exact same. The FIA produces like this fifty or
sixty page guide at the end of every year where
it just highlights all of the callshit that's developed behind
closed doors, but then spills over whether it's taking care
of newborn babies, they've used the same system that they
used to No, but it's true. Like the thing that
the drivers are satin to keep them really protected, They've
(15:38):
developed the same mechanism for newborn babies that have maybe
have issues or that need to be transported by helicopter
or Apple to make sure that the babies don't move.
And I'm just like, this is genius.
Speaker 1 (15:47):
That's so cool. We have to take quick break and
we'll be right back. Hey, we're back. Okay, where were.
Speaker 2 (15:57):
We pulling on that thread of what the fan the
lens at which the fans go to, So you mentioned
your baseball fans. Some especially nerdy fans, will actually write
up all kinds of different software that pulls the publicly
available data from all the different relevant sources and then
it lets them play around with it on their own.
Speaker 3 (16:15):
Like there are some engineers or ex engineers who are
sharing on social images of how like the new parts
change the flow of a car and what that does
to the aerodynamic forces on a car.
Speaker 1 (16:24):
Okay, hold on CFD, Yeah, come on, Derek, what's CFD?
Speaker 3 (16:29):
CFD Yeah. CFD is computational fluid dynamics. So if you
ever see pictures of like simulations of how a of
how an airflow will go over a surface. Those simulations
are done in a computer, which are also then leads
to what people decide to test in wind tunnels. Right,
this is basically the rocket science part of the Formula
(16:49):
one stuff. There are ways for fans to access that
kind of you know, storytelling and information like you go
look in the Box Box Club app and in the
Explore section there there's a whole bunch of people who
are basically taking images and overlaying things to do with
what forces are happening on the car. And you know,
I studied engineering at Cambridge for four years and even
(17:11):
some of that information is like pretty close to the
kinds of stuff you would study if you specialize in
fluid dynamics and mechanics and things like that. So it's
fantastic there are creators that want to expose that level
of technical understanding of what's going on in the car
as well. Of course, the number of people that want
to hang out with you and talk about it gets
(17:32):
smaller and smaller as you go down. But don't let
it stop for you. Don't let it stop for you
find your tribe sit in the rabbit hole where however
deep you want to go.
Speaker 2 (17:40):
If you just head over, for example, to get her Michael,
and you look at the Python code for fast F
one like you will find if you know, if after
this episode you get really into the data, that's where
you should go.
Speaker 1 (17:51):
In my life, no one has ever said this sentence
to me. Just head over to getthub and look for
the Python code for fast F one. Take that statistic sheets,
you know, I don't know, like men go out ice
fishing for six hours on a Sunday, you know, and
it's like how much It's like, how much do you
(18:12):
hate your family if you're But I'm like, well, if
you're coating for F one, you know, we all got
our stuff. Man, we did.
Speaker 4 (18:18):
What they need is they need a GitHub code to
tell them.
Speaker 1 (18:22):
Where the fish are. Yeah, fun fact, they don't even
care about the fish. It's just being out there.
Speaker 2 (18:34):
Moving swiftly.
Speaker 3 (18:35):
On.
Speaker 1 (18:35):
Let me ask you this, Tony. I noticed watching the
races that drivers do a thing called track walk. Is
that what it's called?
Speaker 2 (18:42):
Yeah?
Speaker 1 (18:43):
Okay? And they literally just walk around the entire track.
Given this conversation we've been having, am I understanding how
the vast amount of data they have about every little detail.
What data are these drivers picking up from just walking
around the track.
Speaker 2 (18:57):
They will look over if the track has changed, the layout,
huts changed, look up identifying potential issues or challenges that
the driver might have over the weekend, and more. It
depends on the driver and the race engineer and the
relationship that they have. But more often than not, it's
like a two way conversation about going back and forth
about what they're I imagine it's like a nice reset
for the team of like this is okay, now it's
race time. Let's go.
Speaker 1 (19:18):
It must feel so fast when they drive it, yeah,
because they were just like last time I was walking.
Speaker 2 (19:22):
This fun fact of this year, I think they banned
bicycles on tracks. These tracks are pretty long, and so
some of the drivers would be like, oh, we're just
going to cycle around the track. They banned that because
too many drivers were having like bicycle accidents.
Speaker 1 (19:38):
Just can't slow it down. They can't just slow it,
just walk, use the two legs that God gave you.
Speaker 4 (19:43):
Is there an element of like superstition to this, like
a kind of bit of a ritual.
Speaker 2 (19:49):
There's so much superstition in sport.
Speaker 1 (19:51):
I feel like the higher the stakes the more superstition.
And I would think when F one there's high stakes,
because there's life can be on the line. Do these
drivers pray? Are they're religious?
Speaker 2 (20:04):
Pierre Gasley, for example, always does the sign of the Cross.
Speaker 1 (20:08):
Because there's a beautiful contradictory here in data and faith,
because they don't always go hand in hand, and often
times the highest scientific scientists are not as keen to
spiritual faith. And yet also every athlete I've ever known,
especially the high levels, they there's a lot of faith there.
Speaker 3 (20:29):
I can tell you what I think about this, which
is that you can be an extremely technically oriented person
and believe in science, believe in science being subscribed to
science as a way of describing how the universe works,
But answers to why things are the way they are
or how they came to be that way are not
answered by data that you can record, least of all
(20:51):
on the F one car. So I wouldn't fall into
the trap of thinking that if you are into data
or science, that you don't have the capacity or the
predilection for any kind of spiritual beliefs. And at least
that's my personal take on things. And yeah, don't underestimate
any capacity of any human for being able to operate
at that dimension.
Speaker 2 (21:16):
How did we get here?
Speaker 1 (21:17):
I don't know, but that's what's fun about podcasting. So Okay,
you know I was going to ask you this, but
I've been excited to ask you this. Okay, too much data,
at some point, they're overthinking it. There's too many data points.
If you were to analyze everything, you would never sleep.
You would just be a robot of data. And then
you couldn't even drive. Put the keys on the car.
(21:39):
I know these car, These cars don't have keys. But
is there such thing as too much data?
Speaker 3 (21:44):
Yeah, there is such a thing as too much data?
There certainly is. I mean, you can argue, what harm
does it do if it doesn't take time or money
or to collect it or store it. But it does.
Data is only as good as the rates that you
can process it and the insights that you can glean
from it. There certainly is. I mean, I would say
most corporations suffer from the challenge of not being able
(22:05):
to manipulate and distill insight from the data that they
collect as a business. I know certainly that's been my experience.
So I think there is. Yeah, there is such a
thing but you know, you first have to have the
raw ingredient in order to sort of cook with it.
So I think collecting data and figuring out what to
do with that. You know, collecting it is always the
first thing, and you make an investment decision about what
(22:26):
you want to spend time collecting.
Speaker 2 (22:28):
So yes, there is such a thing as too much data.
But also know, if there's data to collect, they're going
to collect it.
Speaker 1 (22:33):
They must think it's useful. Yeah, you know, I mean
does max verst ap and collect more data?
Speaker 3 (22:40):
My hypothesis would be no, I think that's really more
of a function of the car than it is it
is the driver. He might be generating less insight, but
I don't know if he's if he's generating less data.
There's a difference between data, information and insight. Right is
the raw ingredient. Right information is when somebody puts that
(23:05):
information on a chart or a screen, like what you
see on the pitwall. Insight is the ability to read
that and draw a new conclusion which leads you to
some kind of decision or to twist or stick with
what you're currently doing. So when we think about that,
like most people think data and they think charts, well,
I think data. I think bits and bytes, information and
(23:27):
insight are what data turns into. So that might be
why I might have answered your question a little bit literally.
Speaker 1 (23:34):
I forget what it was. It was an NPR and
and know it's a different competitor. But they did a
story on how you can hack into someone's computer and
control their car. Now, me and you are car. So
if they have all this data, can they control the car?
Can the engineer control the car? Who's controlling the car?
Is the driver just a pretty face?
Speaker 3 (23:55):
Some drivers are pretty faces. I won't necessarily reveal who
I think is pretty than others, but I mean, I
think the clear answer is no. You know, if you
sort of think about like that, the car is something
which is being configured to create the maximum opportunity for performance.
You still have to extract that performance, and you cannot
(24:19):
program that in a way which beats a human yet
at least right, and so the ability for a human
to interact with the physical world and to be able
to control that car with all of the parameters that
they have, which is you know, steering input, acceleration input,
break input, which basically the three basic inputs and the
(24:41):
perfect way at the perfect moments and the perfect combination
on a car which is basically designed to operate on
an absolute knife's edge. If you and I got in
the car, it would be a very different outcome. I mean,
actually there wouldn't be a lot of space for you
and I to get in the same car. But like
you get my point, which is, if you're a driving
by the way, I have my synric behind me, so
(25:02):
I do have a go at this right And I
do think that there is such a thing as good driver.
It's like there's no way around it. That's why the
competition to be one of the twenty on the grid
is just that intense. I'm a fan believer that you
can't take the driver out of the car and expect
anything special to happen.
Speaker 1 (25:21):
We got to take a short break. Hey, we're back
to Michael.
Speaker 2 (25:26):
Now. Is all of this data hitting you? Are you
feeling and enrich? Do you fashion yourself to be a
data fan? Where's your head up?
Speaker 1 (25:36):
I'm thinking calculating. I'm thinking. My knee jerk reaction to
the word data is that it's a four letter word.
Speaker 2 (25:46):
Michael doesn't like four letter words.
Speaker 1 (25:48):
Yeah, and I don't enjoy how society is so data
driven right now, because I'm you're taking the feel out
of things. It's not everything in life is just a
one in a zero. But I'm more intrigued now that
we've talked about it.
Speaker 2 (26:05):
I love it.
Speaker 1 (26:06):
If I have multiple screens up watching Formula one, I'm
not gonna want data. I'm gonna want different driver's helmet
cams and the stadium shot. I mean, I'm gonna want
those visuals.
Speaker 2 (26:18):
You want the athletes, yeah, or just.
Speaker 1 (26:20):
The machine, the rocket going off, you know. But there
are these nerds that want to just check out torque
revolutions or whatever, but have it. I mean, that's cool.
I'm happy for them.
Speaker 2 (26:32):
By the way, is the new name of the podcasts.
Speaker 3 (26:37):
If anybody's listening to this thing that feels like they're
not a data driven fan and that this isn't for
them because they don't feel like they were never into
math when they grew up, or they're not like particularly
technical in their orientation or something like that, I guess.
I guess all I would say is like, you don't
(26:58):
have to label yourself a data driven fan to understand
that there's another layer of story that's going on in
mosor spot and you know, don't think of the data,
which is something which is kind of scary, which makes
this for other people, especially for technically oriented oriented males.
I think there's a lot more than meets the eye,
and you don't have to dig that deep to find it.
(27:19):
And I think that's to me what the beauty of
this sport and what got me hooked on it. If
you imagine I'm doing this with my hands now, right,
I'm creating this sort of picture of this kind of funnel,
right of like you know, whide at the top and
narrow at the bottom. For me, the data driven part
of fandom and the stories which really unfold during a
race or during a season are kind of in the
(27:40):
middle of that funnel. And if you're a person a
fan that loves to live in the rabbit hole, that
loves to understand what's going on beneath the surface, then
you can't really do that without some way of capturing
what's going on and then using that to replay and
tell a story. Because you're I mean, I'm sure, Tony,
you've spoken to a people who were like, it's just
(28:01):
a bunch of cars going around in circles, isn't it.
And then some of those. These are the people like
right at the top of this funnel, right then there's
the people who got into Drive to Survive and they're like, oh,
there's so much more than just cars go around a track.
There's narratives and stories and characters and drama and whatever.
And then as you get drawn into the sport, and
for me that was through gaming. Actually, like I got
(28:23):
I went from Drive to Survive to playing F one,
the co Masters game. Religiously, this is when I still
had time to play video games. And then I started
to understand what's going on from the seat of a
driver or of a team because you experience that, and
then when you see the race again, you're like, well,
all the story. Like the broadcast, it's such a narrow
(28:44):
window on all of these events that are going on,
like tire graining, pit stop times, which overtakes the happening, overcuts,
undercuts fuel consumption. You start to see way more than
is shown on the screen. And to me, that's the
real fun of it. It's almost like you're decoding a
live event as is happening, and then after the fact
(29:07):
you get to chew on it for that many more days,
which is a super fan, It's like, that's all you
want to do, right, is just engage more and more
deeply with the thing that you love. And without the
way to capture data and use that to replay to
tell the stories, I think it would be a less
compelling experience. From my perspective, what I'm.
Speaker 2 (29:23):
Hearing from you is you like the idea that data
could make you better, but you don't want data to
be the driving force behind the athlete.
Speaker 1 (29:31):
No, I don't want to be so judgmentally against data.
It's just not what tickles my fancy in sport. But
it is absolutely remarkable how this sport consumes data.
Speaker 2 (29:43):
Here's another thing for you to think about. Why might
be fundamentally different or exponentially using big words here, but
exponentially more useful in a sport like Formula one because
the teams have such limited times with the cars on.
Every time you take a Formula one car, it's mandated,
it's restricted. They can't, no one can. A driver can't
(30:06):
just wake up one they and go I'm just gonna
go and take the car up for a run and
practice if you they can't. So because it's so limited
with how they can practice with the cars, I think
that's why every ounce of data is picked up and
collected and analyzed and useful. A baseball person makes sense,
and baseball person, a baseball athlete, a golfer can pick
up a pair of clubs and swing, you know, practice
(30:27):
in their own time. I actually can't think of another
sport where you can't practice at will.
Speaker 1 (30:34):
I remember talking about that last season. It's very fascinating.
So I think you can hit a tennis ball for
as many hours as you're allowed to, as many hours
as you physically want to. But that can't be the case.
Speaker 2 (30:43):
Yeah, And I remember Kobe talking about this with being
a basketball player. Of just like I get up five
hours before everyone else, and so I have five hours
of training on everyone else every single day. Formula one
drivers do not have that. Hence why I think that
data plays such a big role in Formula one.
Speaker 1 (30:58):
Also, it's mind boggling to think that I was buying
shoes today on the internet and it was frustrating me
because I would add it to my cart and then
check out, and it would there'd be like a gap
in uh loading loading, you know, and I'd be like,
why is there a gap? It's a new computer edition
everything was fine, but it's like it's a little too strong.
And then to think that this their way they're racing
(31:20):
is happening in data like instantaneously yep, and it's crazy
and that probably gets into our logistic episode. That's wild
to me that that can even happen.
Speaker 2 (31:29):
And they're still pushing the limits to figure out how
they can get the data uploaded to HQ faster than
what it is now when we're talking zero point zero ones,
you know, we're always pushing the limit.
Speaker 1 (31:39):
Fall boots, if that's what you're asking, fall Boots. We
still get like a month left in the fall here.
Speaker 4 (31:45):
So Michael, shit is that? YOHI Again, our team here
at Choosing Size HQ, we've been crunching the numbers and
unfortunately it does seem that you're going off script about
twenty five percent more than than than management would like.
So that I've got a request here. They're asking if
(32:05):
you can just try to be a little more vigilant.
Speaker 7 (32:08):
You see. Uh, this has been Choosing Sides f one,
a production of Sports Illustrated Studios, iHeart Podcast and one
(32:29):
oh one Studio podcast.
Speaker 4 (32:32):
The show is hosted by Michael Costa and Tony Cowan Brown.
This episode was edited, scored, and sound designed by senior
producer Johai may Thad scott Stone is the executive producer
and head of audio, and Daniel Wexman is Director of
podcast Development and production Manager at one on one Studios.
(32:54):
At iHeart podcast Sean ty Tone is our executive producer.
And a special thank you to Michelle Newman, David Glasser,
and David Hoodkin from One o one Studios. For more
shows from iHeart Podcasts, go visit the iHeartRadio app, Apple
Podcasts or wherever you get your podcasts, and whatever you do,
(33:17):
don't forget to rate us and tell your friends. It
really does mean a lot.
Speaker 2 (33:27):
Michael, yess what's coming up next week?
Speaker 1 (33:31):
Is it?
Speaker 2 (33:32):
Yes?
Speaker 1 (33:32):
It is finally, Yes.
Speaker 2 (33:35):
Yes, it's the turn of the tireheads.
Speaker 1 (33:40):
Yeah. I'm not sure why I'm so excited about tires,
but I am time.
Speaker 2 (33:45):
For talking rubber tires, screeching, the whole shebang.
Speaker 1 (33:49):
It seems very important. It seems so so so important.
The S and the M and the H and they
all get the same amount of tires or whatever. And
when I take my car into the MC, they say
the most important thing is tires and breaks next on
Torque Revolution. YOHI could you send us off with some
screeching tire noises or something
Speaker 4 (34:12):
Such a cliche
Host
Michael Kosta
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