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Speaker 1: Tony, Michael, this past week, while you were resting, I

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Speaker 1: was hard at work crunching the numbers.

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Speaker 2: Did you come up with anything interesting?

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Speaker 3: Yes?

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Speaker 1: Actually, okay, looking at the breakdown here, it looks like

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Speaker 1: last season, Yohai intervened an average of two point seven

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Speaker 1: times an episode for what constituted an average of three

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Speaker 1: point two minutes of airtime per episode. I see, and

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Speaker 1: looking at the data for this current season, which we're

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Speaker 1: just getting started with, really, but so far that number

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Speaker 1: has been up a staggering four point seven interventions per episode,

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Speaker 1: which represents a whopping airtime of three point eight minutes

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Speaker 1: per episode. I don't know about you, but I find

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Speaker 1: this very troublingling.

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Speaker 4: I'm just not sure it's referred to as airtime anymore. Michael.

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Speaker 4: It's a podcast. It might be a bit arcade.

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Speaker 1: You should feel lucky that I'm calling at interventions and

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Speaker 1: not interruptions. Interventions is very general.

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Speaker 2: You were just cooled, dog, kay. I'm just gonna put

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Speaker 2: that out there.

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Speaker 1: Oh Ship for My Heart podcast one on one Studios

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Speaker 1: and Sports Illustrated Studios. This is choosing sides.

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Speaker 5: Yes, one wow, wow, Tony, I see.

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Speaker 1: For today's episode, it's titled the data analyst. And I

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Speaker 1: have to be honest with you right out of the gate,

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Speaker 1: I'm not a big data numbers person. For me, it's

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Speaker 1: about heart, guts, that's sports, all that stuff. Also, every

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Speaker 1: sport has statistics and data. You ever look at the

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Speaker 1: back of a baseball card, So.

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Speaker 2: Yes, every sport has statistics and data. But for example,

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Speaker 2: how many sensors does a baseball bat actually have?

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Speaker 1: When do you think of it? I can think of

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Speaker 1: the gun that measures the miles per hour of the fastball,

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Speaker 1: so that the sensor that's one.

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Speaker 2: Well, here's the thing. In a modern Formula one car,

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Speaker 2: you're looking at roughly two hundred and fifty or three

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Speaker 2: hundred sensors and they generate. These two hundred and fifty

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Speaker 2: three hundred sensors generate an immense amount of data, and

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Speaker 2: all of that data is being streamed back to the

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Speaker 2: team and it's constantly being processed and analyzed. And also

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Speaker 2: it's not just on the car, Michael, the tires have

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Speaker 2: these data sensors as well. The driver's gloves have sensors.

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Speaker 2: The F one tracks have sensors.

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Speaker 1: The driver's gloves, yes, have sensors on them.

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Speaker 2: Gonna come back to that also from a safety and

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Speaker 2: security reason.

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Speaker 1: Okay, well, what are they sensing? All these sensors sense.

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Speaker 1: No matter how many meetings you have, you didn't think

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Speaker 1: you'd come up with that one.

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Speaker 2: Okay, So to give you a few examples, they're sensing

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Speaker 2: things like lap time, tire pressure, breake pressure, frottile position,

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Speaker 2: fuel flow, the speed of the wind and how it's

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Speaker 2: going over the car, which sounds very poetic, break temperatures,

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Speaker 2: engine temperate, which is basically any temperature you can think of.

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Speaker 2: And to your point about the driver's gloves, Yes, they

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Speaker 2: have sensors and they monitor things like your heart rates.

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Speaker 1: I's gonna say all of those other things are just

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Speaker 1: measuring the car exactly when we've talked about the driver.

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Speaker 2: Yet, And that's what where the gloves come in. And

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Speaker 2: it's not the only part of the sensors, but these

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Speaker 2: sensors in the little fingertips of the glove are sensing

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Speaker 2: their heart rate. There's also an oximeter that measures the

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Speaker 2: amount of oxygen in the blood. And I'm just naming

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Speaker 2: a few examples right now. All of these sensors, this

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Speaker 2: is what's wild, is transmitted back in real time and

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Speaker 2: it generates around three terabytes of data for each car

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Speaker 2: during each race.

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Speaker 1: Wow, that means nothing to me.

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Speaker 2: Imagine stacking three hundred thousand Webster dictionaries. That would create

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Speaker 2: like a fifteen kilometer high stack of data. If you

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Speaker 2: printed out all the data that that's generating. I'm not

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Speaker 2: sure that helps you.

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Speaker 1: It's a shitload, is what it is.

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Speaker 2: There you get. That's another way of saying how much

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Speaker 2: data be Well.

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Speaker 1: I'm also now thinking once you have the data, Yeah,

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Speaker 1: the printed Webster dictionaries, I know that was a metaphor,

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Speaker 1: but what do you do with it?

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Speaker 2: Where do you go out in the wild now and say,

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Speaker 2: do you know.

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Speaker 1: Out? Do they have to like storting caves underneath rivers

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Speaker 1: to keep it cool?

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Speaker 2: Okay, so this is actually a really cool question because

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Speaker 2: the data centers have become somewhat like an integral part

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Speaker 2: of Formula one racing because the teams need not only

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Speaker 2: a reliable way to store, but also analyze the data

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Speaker 2: from their cars during the race. And more or less

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Speaker 2: everything has been moved to the cloud these days. So

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Speaker 2: it used to be that you would have local storages,

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Speaker 2: but now everything's in the cloud and there's some backup

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Speaker 2: in storage spaces as well, and I think it's time

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Speaker 2: for a fun fact. Great, the real time data that

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Speaker 2: we're getting right now from each race is combined with

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Speaker 2: over seventy years of a historical race data so that

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Speaker 2: they can look back and compare the data that they're

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Speaker 2: picking up at each race, and they have seventy years

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Speaker 2: of historical data look back on.

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Speaker 1: So they've re censored all the races.

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Speaker 2: So they've migrated Formula one's extensive library of seventy years

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Speaker 2: of images, audio video, and they've put all of that

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Speaker 2: into the cloud so that the data that they're capturing

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Speaker 2: today can be compared to that historical data. I cannot

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Speaker 2: imagine the amount of work that went into to taking

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Speaker 2: seventy years of footage data, all of that and putting

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Speaker 2: it into cloud, but that's something that they've done. And

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Speaker 2: apparently it's over one hundred and fifty thousand hours of content.

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Speaker 4: Just sorry, just doing my thing here crunching the numbers.

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Speaker 4: Consuming one hundred and fifty thousand hours of content would

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Speaker 4: take you about six two hundred and fifty days, or

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Speaker 4: a little over seventeen years. That's, of course, without any

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Speaker 4: stoppage time to eat, sleep.

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Speaker 1: Yeah, yeah, Jo has intervened inter intervened way over four

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Speaker 1: points seven times.

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Speaker 2: It's also another point it's actually worth mentioning. So remember

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Speaker 2: in the episode that we looked at the sponsorships and

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Speaker 2: the business of Formula one. So in addition to Formula

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Speaker 2: one having these incredible tech partners, the teams also have

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Speaker 2: these partners to help them store all of this data.

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Speaker 2: So we're talking Oracle, Aws, Salesforce, Dell. Technology is cognizant.

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Speaker 2: That's why we're in this space right now of tech

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Speaker 2: sponsors versus the non tech sponsors that we might have

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Speaker 2: had fifty years ago.

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Speaker 1: I was wondering, when you start thinking about this data

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Speaker 1: me and you if you give us data, yeah, I

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Speaker 1: don't know. I mean maybe you know more than I do,

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Speaker 1: but I don't know what to do with data. So

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Speaker 1: now I need experts to help me know what to

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Speaker 1: do with the data exactly. So that makes sense what

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Speaker 1: you're saying that you're partnering now with tech. It makes

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Speaker 1: me wonder if this is where all sports are headed.

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Speaker 1: I honestly think so you know, is one of the

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Speaker 1: managers in the baseball dugout going to be more data

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Speaker 1: driven than the guide chewing tobacco, you know, giving signals

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Speaker 1: to steal. Second is going to be a little kid

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Speaker 1: in a computer going like, well, technically, you know.

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Speaker 2: What's also going to be interesting is do we want

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Speaker 2: data helping us make sense about how decisions are taken

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Speaker 2: into sport.

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Speaker 1: I'll tell you no, yes, O froze.

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Speaker 4: Yeah, just for a second. Do you have a bunch

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Speaker 4: of tabs open? Maybe?

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Speaker 3: I do?

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Speaker 4: Sometimes it helps if you can.

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Speaker 3: Just give me half an hour and I'll close all

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Speaker 3: these tabs.

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Speaker 2: I'm hearing a lot of clicks there, Tarrek, all.

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Speaker 3: Very apropos, I assure you so.

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Speaker 2: To help us today, we've recruited two first rate F

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Speaker 2: one data notes. First up, Tarek Naslwi.

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Speaker 3: Been looking forward to this all day.

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Speaker 2: Tarek is the creator of a digital platform called Trace,

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Speaker 2: a way for fans to document their fandom through data.

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Speaker 1: Open here we go. Oh wow, that's really beautiful and cool.

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Speaker 1: Every race leaves a trace. This is hard to describe,

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Speaker 1: but it's available at trace dot. Fan traces are pieces

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Speaker 1: of art made from race data and it's like mathematical

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Speaker 1: and it almost looks like if every lap of a

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Speaker 1: Formula one race left a shadow and they were all

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Speaker 1: on top of each other. It forms this very cool,

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Speaker 1: fast looking, almost evil shape, very on brand for everyone.

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Speaker 1: This is cool, and a man.

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Speaker 2: Who's an absolute legend and an integral part of Formula one.

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Speaker 6: All the tentacles are grown way in there.

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Speaker 2: And a real, walking, talking encyclopedia of facts and stats.

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Speaker 6: Hello, folks, I'm Sean Kelly, and if you've watched a

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Speaker 6: Formula one broadcast anywhere in the world in the last

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Speaker 6: say two decades, you won't know me, but you will

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Speaker 6: know my work because I am essentially the chief statistician

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Speaker 6: to the F one Broadcaster's. All the facts and figures

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Speaker 6: you hear in a Formula one broadcast, whether they be

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Speaker 6: in the graphics, whether they be spoken by the likes

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Speaker 6: of David Croft or Martin Brundle or Alex Jakes or whomever,

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Speaker 6: I am chiefly responsible for all of that content. So,

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Speaker 6: depending on where you stand on the whole statistics spectrum,

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Speaker 6: either you're welcome or I'm sorry.

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Speaker 2: Just to give you a sense of the genius of Sean,

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Speaker 2: I'm going to have to play you this really short

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Speaker 2: clip here, and to give you a bit of context.

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Speaker 2: We were still in the small talk portion of our

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Speaker 2: interview here, just getting to know each other and sharing

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Speaker 2: how we got into the spot.

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Speaker 6: Can you remember the first race.

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Speaker 2: Yes, sper font Gauchamp, only because it was Michael Schumacher's

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Speaker 2: first Yeah, first chair.

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Speaker 6: Is nearly won it mantl how the voltage regulator go

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Speaker 6: out on his card, was in the lead, and then

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Speaker 6: the lady was leading, the engine failed and then somehow

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Speaker 6: after all that and jordanily ended up winning, and then

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Speaker 6: his engine failed and then we ended up with a

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Speaker 6: McLaren one too. Like after all this chaos, we ended

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Speaker 6: up with the most boring result possible. I went from

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Speaker 6: that race so bored that day. I was like, how

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Speaker 6: did we end up with that result?

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Speaker 2: I need our listeners to understand that Sean had no

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Speaker 2: idea that ninety one was my first race, ninety one

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Speaker 2: in spa funkle Sean, and that you were able to

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Speaker 2: just have all of these facts.

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Speaker 6: Berta had the fast slap in that race, I think,

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Speaker 6: which is the only time he ever set one in

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Speaker 6: a Formula one race.

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Speaker 1: That's insane.

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Speaker 2: Your brain is fascinating.

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Speaker 1: I live with it.

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Speaker 6: It's like it's not my constant roommate.

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Speaker 3: All this data, all.

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Speaker 1: Right, So this is a little hard for me to

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Speaker 1: wrap my hair around. Can you give me an example

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Speaker 1: of how data is used by a team to gain

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Speaker 1: an advantage? Like, okay, great, George Russell's heart rate is high,

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Speaker 1: is the blood is oxygenated? His lips are chapped? How

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Speaker 1: we got to win the fucking race? What does that

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Speaker 1: help us with?

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Speaker 2: Okay, so number one, that's more or less a fun fact.

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Speaker 2: The data is encrypted and it's sent to the teams

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Speaker 2: via radio frequencies, and the frequency is due to like

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Speaker 2: little antenna that's mounted on pointing right now, but that's

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Speaker 2: mounted on the top of a car. And what's also

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Speaker 2: crazy is this happens in like fractions of a second.

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Speaker 2: It's also interesting to see that the data transferred is

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Speaker 2: faster when the races are in Europe than when the

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Speaker 2: races are abroad, even if it's like maybe mini mediseconds.

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Speaker 1: Because it's kind of go under the ocean and down

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Speaker 1: through the cable and up to the sky and then

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Speaker 1: down to the antenna.

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Speaker 4: Whoa.

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Speaker 2: And sometimes when you're racing in the middle of a city,

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Speaker 2: like it's harder to get the frequency out it it's insane.

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Speaker 2: But to answer your question, all the data is transferred

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Speaker 2: so in real time to the pitball to the factory

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Speaker 2: and also the FA so there's a lot of people

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Speaker 2: receiving this data, and then it can be used for

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Speaker 2: things like comparing your lap time. You overlap your lap

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Speaker 2: time with other driver's lap time to see where people are,

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Speaker 2: you know, breaking breaking earlier, breaking later.

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Speaker 3: If you ever hear one of the race engineers talk

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Speaker 3: about you need to go easy on the curbs, it'll

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Speaker 3: be because they're sensing vibrations which potentially get to a dangerous

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Speaker 3: level causing resonance and potential damage with car components, the

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Speaker 3: engine itself probably has I don't know, dozens and dozens

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Speaker 3: of sensors on that and actually saw that with Lando

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Speaker 3: this weekend. His car went into some kind of safety

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Speaker 3: mode where the engine was protecting itself from damage back

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Speaker 3: you know, pressure, temperature, vibration, travel as well in the

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Speaker 3: suspension on the cars. Anything which can be sensed will

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Speaker 3: be sensed.

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Speaker 2: Basically, with all this data, the engineers and the driver

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Speaker 2: get a much better picture of where the driver is

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Speaker 2: losing time, where his opportunities to win some time, and

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Speaker 2: where he can make up that time in the race.

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Speaker 1: But I can't get my competitors data.

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Speaker 3: Can I the on track data that actually I believe

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Speaker 3: Formula one owns I think is visible to pretty much everybody.

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Speaker 3: So like on track data, like you can see what

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Speaker 3: other cars are doing and infer, for example, about your

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Speaker 3: pace for the race relative to others' pace, But for example,

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Speaker 3: you don't see what fuel loads they had in their

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Speaker 3: car or how fast they're consuming that. And that's data

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Speaker 3: is like proprietary to the team. So there's a mix

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Speaker 3: of data that I think everybody sees and then a

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Speaker 3: lot of the data that only the team sees.

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Speaker 2: But and this is the joy of a lot of

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Speaker 2: data analysts and the f one data analyst fans, is

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Speaker 2: a lot of the data is actually also open to

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Speaker 2: the public.

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Speaker 1: That's I'm going to ask, like can fans get the data?

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Speaker 2: Yes?

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Speaker 1: Can I get the data?

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Speaker 2: Yes?

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Speaker 6: Not all of it, some of it, the vast majority

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Speaker 6: of it is available to everybody on FORX, if you

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Speaker 6: go to the Forest's website, which is a subscription part

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Speaker 6: of the Autosport website, yeah, it's all there. It's a

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Speaker 6: complicated website to operate, and there's a lot of easter eggs.

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Speaker 6: You've got to keep clicking around to find the information

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Speaker 6: you want to find. But a great deal of the

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Speaker 6: raw numbers can be derived from the publicly accessible part

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Speaker 6: of forex if you subscribe to it.

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Speaker 1: You know, football turn into a bunch of like drunk

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Speaker 1: bros at the bar with their laptops open because of

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Speaker 1: Fantasy football doing the stats and the receivers and I,

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Speaker 1: you know, I grumble about that, but this is like

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Speaker 1: whole new level.

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Speaker 2: This is a whole new level and perfect for those

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Speaker 2: nerdy fans who just wants that to numbers. The other

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Speaker 2: really interesting thing is it used to be back in

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Speaker 2: the day that the teams would create their own sensors

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Speaker 2: and set up the own antennas and all of that,

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Speaker 2: and then the teams kind of realize, hey, wait a minute,

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Speaker 2: we're not in a race to create the best sensors

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Speaker 2: and the best technology. Hence why today all of that

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Speaker 2: is mandated by the FIA.

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Speaker 1: What a unique time in history for F one because

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Speaker 1: tech is at its most profound ever ever. Yeah, so

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Speaker 1: it's pretty interesting.

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Speaker 2: And it's it's at its most profound. And what's really

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Speaker 2: interesting is behind closed doors, a lot of the things

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Speaker 2: that are developed inside of the factories of the F

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Speaker 2: one teams then spill over into the rest of the world.

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Speaker 2: Glaxo Smith Klein, one of the big farmer companies in

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Speaker 2: the world, looked at how I think it was McClaren

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Speaker 2: or Williams did their pit stops, and we're just like, huh,

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Speaker 2: there's something we can learn you about how we bottle toothpaste.

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Speaker 2: And so they figured out how to bottle toothpaste faster,

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Speaker 2: which meant they could create more bottles of toothpaste and

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Speaker 2: sell more bottles of toothpaste.

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Speaker 1: And I was like, genius, is it true that Max

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Speaker 1: for Stapping keeps us coffee warm in that coffee cup?

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Speaker 1: Because I heard that that that's how the car industry

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Speaker 1: keeps our coffee worm. Now what No, But my point

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Speaker 1: is sorry for the bad joke, everybody, but the point

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Speaker 1: is much like the military was like leading the world

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Speaker 1: in research and development and velcro and all this other shit,

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Speaker 1: wender bread, all this stuff spilled over to us plebeians.

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Speaker 1: F one is doing that as.

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Speaker 2: Well, exact same. The FIA produces like this fifty or

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Speaker 2: sixty page guide at the end of every year where

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Speaker 2: it just highlights all of the callshit that's developed behind

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Speaker 2: closed doors, but then spills over whether it's taking care

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Speaker 2: of newborn babies, they've used the same system that they

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Speaker 2: used to No, but it's true. Like the thing that

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Speaker 2: the drivers are satin to keep them really protected, They've

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Speaker 2: developed the same mechanism for newborn babies that have maybe

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Speaker 2: have issues or that need to be transported by helicopter

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Speaker 2: or Apple to make sure that the babies don't move.

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Speaker 2: And I'm just like, this is genius.

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Speaker 1: That's so cool. We have to take quick break and

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Speaker 1: we'll be right back. Hey, we're back. Okay, where were.

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Speaker 2: We pulling on that thread of what the fan the

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Speaker 2: lens at which the fans go to, So you mentioned

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Speaker 2: your baseball fans. Some especially nerdy fans, will actually write

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Speaker 2: up all kinds of different software that pulls the publicly

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Speaker 2: available data from all the different relevant sources and then

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Speaker 2: it lets them play around with it on their own.

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Speaker 3: Like there are some engineers or ex engineers who are

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Speaker 3: sharing on social images of how like the new parts

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Speaker 3: change the flow of a car and what that does

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Speaker 3: to the aerodynamic forces on a car.

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Speaker 1: Okay, hold on CFD, Yeah, come on, Derek, what's CFD?

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Speaker 3: CFD Yeah. CFD is computational fluid dynamics. So if you

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Speaker 3: ever see pictures of like simulations of how a of

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Speaker 3: how an airflow will go over a surface. Those simulations

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Speaker 3: are done in a computer, which are also then leads

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Speaker 3: to what people decide to test in wind tunnels. Right,

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Speaker 3: this is basically the rocket science part of the Formula

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Speaker 3: one stuff. There are ways for fans to access that

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Speaker 3: kind of you know, storytelling and information like you go

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Speaker 3: look in the Box Box Club app and in the

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Speaker 3: Explore section there there's a whole bunch of people who

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Speaker 3: are basically taking images and overlaying things to do with

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Speaker 3: what forces are happening on the car. And you know,

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Speaker 3: I studied engineering at Cambridge for four years and even

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Speaker 3: some of that information is like pretty close to the

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Speaker 3: kinds of stuff you would study if you specialize in

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Speaker 3: fluid dynamics and mechanics and things like that. So it's

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Speaker 3: fantastic there are creators that want to expose that level

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Speaker 3: of technical understanding of what's going on in the car

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Speaker 3: as well. Of course, the number of people that want

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Speaker 3: to hang out with you and talk about it gets

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Speaker 3: smaller and smaller as you go down. But don't let

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Speaker 3: it stop for you. Don't let it stop for you

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Speaker 3: find your tribe sit in the rabbit hole where however

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Speaker 3: deep you want to go.

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Speaker 2: If you just head over, for example, to get her Michael,

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Speaker 2: and you look at the Python code for fast F

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Speaker 2: one like you will find if you know, if after

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Speaker 2: this episode you get really into the data, that's where

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Speaker 2: you should go.

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Speaker 1: In my life, no one has ever said this sentence

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Speaker 1: to me. Just head over to getthub and look for

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Speaker 1: the Python code for fast F one. Take that statistic sheets,

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Speaker 1: you know, I don't know, like men go out ice

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Speaker 1: fishing for six hours on a Sunday, you know, and

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Speaker 1: it's like how much It's like, how much do you

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Speaker 1: hate your family if you're But I'm like, well, if

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Speaker 1: you're coating for F one, you know, we all got

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Speaker 1: our stuff. Man, we did.

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Speaker 4: What they need is they need a GitHub code to

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Speaker 4: tell them.

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Speaker 1: Where the fish are. Yeah, fun fact, they don't even

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Speaker 1: care about the fish. It's just being out there.

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Speaker 2: Moving swiftly.

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Speaker 3: On.

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Speaker 1: Let me ask you this, Tony. I noticed watching the

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Speaker 1: races that drivers do a thing called track walk. Is

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Speaker 1: that what it's called?

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Speaker 2: Yeah?

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Speaker 1: Okay? And they literally just walk around the entire track.

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Speaker 1: Given this conversation we've been having, am I understanding how

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Speaker 1: the vast amount of data they have about every little detail.

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Speaker 1: What data are these drivers picking up from just walking

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Speaker 1: around the track.

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Speaker 2: They will look over if the track has changed, the layout,

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Speaker 2: huts changed, look up identifying potential issues or challenges that

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Speaker 2: the driver might have over the weekend, and more. It

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Speaker 2: depends on the driver and the race engineer and the

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Speaker 2: relationship that they have. But more often than not, it's

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Speaker 2: like a two way conversation about going back and forth

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Speaker 2: about what they're I imagine it's like a nice reset

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Speaker 2: for the team of like this is okay, now it's

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Speaker 2: race time. Let's go.

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Speaker 1: It must feel so fast when they drive it, yeah,

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Speaker 1: because they were just like last time I was walking.

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Speaker 2: This fun fact of this year, I think they banned

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Speaker 2: bicycles on tracks. These tracks are pretty long, and so

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Speaker 2: some of the drivers would be like, oh, we're just

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Speaker 2: going to cycle around the track. They banned that because

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Speaker 2: too many drivers were having like bicycle accidents.

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Speaker 1: Just can't slow it down. They can't just slow it,

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Speaker 1: just walk, use the two legs that God gave you.

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Speaker 4: Is there an element of like superstition to this, like

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Speaker 4: a kind of bit of a ritual.

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Speaker 2: There's so much superstition in sport.

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Speaker 1: I feel like the higher the stakes the more superstition.

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Speaker 1: And I would think when F one there's high stakes,

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Speaker 1: because there's life can be on the line. Do these

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Speaker 1: drivers pray? Are they're religious?

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Speaker 2: Pierre Gasley, for example, always does the sign of the Cross.

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Speaker 1: Because there's a beautiful contradictory here in data and faith,

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Speaker 1: because they don't always go hand in hand, and often

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Speaker 1: times the highest scientific scientists are not as keen to

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Speaker 1: spiritual faith. And yet also every athlete I've ever known,

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Speaker 1: especially the high levels, they there's a lot of faith there.

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Speaker 3: I can tell you what I think about this, which

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Speaker 3: is that you can be an extremely technically oriented person

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Speaker 3: and believe in science, believe in science being subscribed to

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Speaker 3: science as a way of describing how the universe works,

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Speaker 3: But answers to why things are the way they are

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Speaker 3: or how they came to be that way are not

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Speaker 3: answered by data that you can record, least of all

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Speaker 3: on the F one car. So I wouldn't fall into

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Speaker 3: the trap of thinking that if you are into data

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Speaker 3: or science, that you don't have the capacity or the

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Speaker 3: predilection for any kind of spiritual beliefs. And at least

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Speaker 3: that's my personal take on things. And yeah, don't underestimate

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Speaker 3: any capacity of any human for being able to operate

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Speaker 3: at that dimension.

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Speaker 2: How did we get here?

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Speaker 1: I don't know, but that's what's fun about podcasting. So Okay,

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Speaker 1: you know I was going to ask you this, but

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Speaker 1: I've been excited to ask you this. Okay, too much data,

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Speaker 1: at some point, they're overthinking it. There's too many data points.

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Speaker 1: If you were to analyze everything, you would never sleep.

443
00:21:35,560 --> 00:21:37,359
Speaker 1: You would just be a robot of data. And then

444
00:21:37,359 --> 00:21:39,600
Speaker 1: you couldn't even drive. Put the keys on the car.

445
00:21:39,600 --> 00:21:41,240
Speaker 1: I know these car, These cars don't have keys. But

446
00:21:42,040 --> 00:21:44,240
Speaker 1: is there such thing as too much data?

447
00:21:44,400 --> 00:21:46,480
Speaker 3: Yeah, there is such a thing as too much data?

448
00:21:46,640 --> 00:21:49,600
Speaker 3: There certainly is. I mean, you can argue, what harm

449
00:21:49,640 --> 00:21:51,600
Speaker 3: does it do if it doesn't take time or money

450
00:21:51,680 --> 00:21:53,399
Speaker 3: or to collect it or store it. But it does.

451
00:21:53,720 --> 00:21:56,879
Speaker 3: Data is only as good as the rates that you

452
00:21:56,880 --> 00:21:58,920
Speaker 3: can process it and the insights that you can glean

453
00:21:58,960 --> 00:22:01,640
Speaker 3: from it. There certainly is. I mean, I would say

454
00:22:01,640 --> 00:22:05,080
Speaker 3: most corporations suffer from the challenge of not being able

455
00:22:05,080 --> 00:22:07,919
Speaker 3: to manipulate and distill insight from the data that they

456
00:22:07,920 --> 00:22:10,919
Speaker 3: collect as a business. I know certainly that's been my experience.

457
00:22:11,440 --> 00:22:13,159
Speaker 3: So I think there is. Yeah, there is such a

458
00:22:13,200 --> 00:22:15,720
Speaker 3: thing but you know, you first have to have the

459
00:22:15,800 --> 00:22:17,840
Speaker 3: raw ingredient in order to sort of cook with it.

460
00:22:17,920 --> 00:22:21,000
Speaker 3: So I think collecting data and figuring out what to

461
00:22:21,040 --> 00:22:23,439
Speaker 3: do with that. You know, collecting it is always the

462
00:22:23,440 --> 00:22:26,479
Speaker 3: first thing, and you make an investment decision about what

463
00:22:26,520 --> 00:22:27,800
Speaker 3: you want to spend time collecting.

464
00:22:28,119 --> 00:22:30,359
Speaker 2: So yes, there is such a thing as too much data.

465
00:22:30,400 --> 00:22:33,080
Speaker 2: But also know, if there's data to collect, they're going

466
00:22:33,119 --> 00:22:33,679
Speaker 2: to collect it.

467
00:22:33,800 --> 00:22:37,440
Speaker 1: They must think it's useful. Yeah, you know, I mean

468
00:22:37,720 --> 00:22:40,399
Speaker 1: does max verst ap and collect more data?

469
00:22:40,600 --> 00:22:45,280
Speaker 3: My hypothesis would be no, I think that's really more

470
00:22:45,280 --> 00:22:46,879
Speaker 3: of a function of the car than it is it

471
00:22:46,920 --> 00:22:50,359
Speaker 3: is the driver. He might be generating less insight, but

472
00:22:50,560 --> 00:22:52,920
Speaker 3: I don't know if he's if he's generating less data.

473
00:22:53,119 --> 00:23:00,399
Speaker 3: There's a difference between data, information and insight. Right is

474
00:23:00,400 --> 00:23:05,320
Speaker 3: the raw ingredient. Right information is when somebody puts that

475
00:23:05,440 --> 00:23:08,919
Speaker 3: information on a chart or a screen, like what you

476
00:23:08,960 --> 00:23:12,000
Speaker 3: see on the pitwall. Insight is the ability to read

477
00:23:12,040 --> 00:23:16,280
Speaker 3: that and draw a new conclusion which leads you to

478
00:23:16,440 --> 00:23:19,639
Speaker 3: some kind of decision or to twist or stick with

479
00:23:19,680 --> 00:23:22,399
Speaker 3: what you're currently doing. So when we think about that,

480
00:23:22,520 --> 00:23:24,560
Speaker 3: like most people think data and they think charts, well,

481
00:23:24,600 --> 00:23:27,760
Speaker 3: I think data. I think bits and bytes, information and

482
00:23:27,880 --> 00:23:31,280
Speaker 3: insight are what data turns into. So that might be

483
00:23:31,320 --> 00:23:33,840
Speaker 3: why I might have answered your question a little bit literally.

484
00:23:34,640 --> 00:23:36,679
Speaker 1: I forget what it was. It was an NPR and

485
00:23:36,720 --> 00:23:38,520
Speaker 1: and know it's a different competitor. But they did a

486
00:23:38,560 --> 00:23:41,000
Speaker 1: story on how you can hack into someone's computer and

487
00:23:41,040 --> 00:23:45,040
Speaker 1: control their car. Now, me and you are car. So

488
00:23:47,040 --> 00:23:50,840
Speaker 1: if they have all this data, can they control the car?

489
00:23:50,960 --> 00:23:53,399
Speaker 1: Can the engineer control the car? Who's controlling the car?

490
00:23:53,560 --> 00:23:55,040
Speaker 1: Is the driver just a pretty face?

491
00:23:55,240 --> 00:23:58,800
Speaker 3: Some drivers are pretty faces. I won't necessarily reveal who

492
00:23:58,840 --> 00:24:02,480
Speaker 3: I think is pretty than others, but I mean, I

493
00:24:02,480 --> 00:24:04,720
Speaker 3: think the clear answer is no. You know, if you

494
00:24:04,760 --> 00:24:07,399
Speaker 3: sort of think about like that, the car is something

495
00:24:07,400 --> 00:24:14,080
Speaker 3: which is being configured to create the maximum opportunity for performance.

496
00:24:14,800 --> 00:24:19,800
Speaker 3: You still have to extract that performance, and you cannot

497
00:24:19,840 --> 00:24:23,440
Speaker 3: program that in a way which beats a human yet

498
00:24:23,720 --> 00:24:27,560
Speaker 3: at least right, and so the ability for a human

499
00:24:27,600 --> 00:24:31,000
Speaker 3: to interact with the physical world and to be able

500
00:24:31,040 --> 00:24:35,280
Speaker 3: to control that car with all of the parameters that

501
00:24:35,320 --> 00:24:38,200
Speaker 3: they have, which is you know, steering input, acceleration input,

502
00:24:38,280 --> 00:24:41,159
Speaker 3: break input, which basically the three basic inputs and the

503
00:24:41,200 --> 00:24:43,640
Speaker 3: perfect way at the perfect moments and the perfect combination

504
00:24:44,280 --> 00:24:46,679
Speaker 3: on a car which is basically designed to operate on

505
00:24:46,760 --> 00:24:49,600
Speaker 3: an absolute knife's edge. If you and I got in

506
00:24:49,600 --> 00:24:52,239
Speaker 3: the car, it would be a very different outcome. I mean,

507
00:24:52,240 --> 00:24:54,800
Speaker 3: actually there wouldn't be a lot of space for you

508
00:24:54,840 --> 00:24:56,520
Speaker 3: and I to get in the same car. But like

509
00:24:57,160 --> 00:25:00,560
Speaker 3: you get my point, which is, if you're a driving

510
00:25:00,600 --> 00:25:02,719
Speaker 3: by the way, I have my synric behind me, so

511
00:25:02,760 --> 00:25:05,000
Speaker 3: I do have a go at this right And I

512
00:25:05,040 --> 00:25:06,800
Speaker 3: do think that there is such a thing as good driver.

513
00:25:06,840 --> 00:25:10,119
Speaker 3: It's like there's no way around it. That's why the

514
00:25:10,119 --> 00:25:12,119
Speaker 3: competition to be one of the twenty on the grid

515
00:25:12,280 --> 00:25:16,800
Speaker 3: is just that intense. I'm a fan believer that you

516
00:25:17,400 --> 00:25:19,760
Speaker 3: can't take the driver out of the car and expect

517
00:25:19,840 --> 00:25:21,480
Speaker 3: anything special to happen.

518
00:25:21,800 --> 00:25:25,840
Speaker 1: We got to take a short break. Hey, we're back

519
00:25:25,960 --> 00:25:26,479
Speaker 1: to Michael.

520
00:25:26,840 --> 00:25:29,679
Speaker 2: Now. Is all of this data hitting you? Are you

521
00:25:29,760 --> 00:25:32,159
Speaker 2: feeling and enrich? Do you fashion yourself to be a

522
00:25:32,240 --> 00:25:33,560
Speaker 2: data fan? Where's your head up?

523
00:25:36,600 --> 00:25:44,000
Speaker 1: I'm thinking calculating. I'm thinking. My knee jerk reaction to

524
00:25:44,119 --> 00:25:46,200
Speaker 1: the word data is that it's a four letter word.

525
00:25:46,280 --> 00:25:48,040
Speaker 2: Michael doesn't like four letter words.

526
00:25:48,160 --> 00:25:53,159
Speaker 1: Yeah, and I don't enjoy how society is so data

527
00:25:53,400 --> 00:25:57,040
Speaker 1: driven right now, because I'm you're taking the feel out

528
00:25:57,080 --> 00:26:00,760
Speaker 1: of things. It's not everything in life is just a

529
00:26:00,840 --> 00:26:04,840
Speaker 1: one in a zero. But I'm more intrigued now that

530
00:26:04,920 --> 00:26:05,800
Speaker 1: we've talked about it.

531
00:26:05,960 --> 00:26:06,560
Speaker 2: I love it.

532
00:26:06,920 --> 00:26:10,560
Speaker 1: If I have multiple screens up watching Formula one, I'm

533
00:26:10,600 --> 00:26:15,080
Speaker 1: not gonna want data. I'm gonna want different driver's helmet

534
00:26:15,119 --> 00:26:18,040
Speaker 1: cams and the stadium shot. I mean, I'm gonna want

535
00:26:18,080 --> 00:26:18,800
Speaker 1: those visuals.

536
00:26:18,840 --> 00:26:20,800
Speaker 2: You want the athletes, yeah, or just.

537
00:26:20,760 --> 00:26:25,000
Speaker 1: The machine, the rocket going off, you know. But there

538
00:26:25,000 --> 00:26:27,800
Speaker 1: are these nerds that want to just check out torque

539
00:26:28,680 --> 00:26:31,080
Speaker 1: revolutions or whatever, but have it. I mean, that's cool.

540
00:26:31,280 --> 00:26:32,000
Speaker 1: I'm happy for them.

541
00:26:32,440 --> 00:26:37,520
Speaker 2: By the way, is the new name of the podcasts.

542
00:26:37,960 --> 00:26:41,280
Speaker 3: If anybody's listening to this thing that feels like they're

543
00:26:41,320 --> 00:26:44,439
Speaker 3: not a data driven fan and that this isn't for

544
00:26:44,520 --> 00:26:47,160
Speaker 3: them because they don't feel like they were never into

545
00:26:47,200 --> 00:26:51,160
Speaker 3: math when they grew up, or they're not like particularly

546
00:26:51,160 --> 00:26:54,719
Speaker 3: technical in their orientation or something like that, I guess.

547
00:26:54,840 --> 00:26:57,960
Speaker 3: I guess all I would say is like, you don't

548
00:26:58,040 --> 00:27:00,680
Speaker 3: have to label yourself a data driven fan to understand

549
00:27:00,680 --> 00:27:03,439
Speaker 3: that there's another layer of story that's going on in

550
00:27:03,600 --> 00:27:07,280
Speaker 3: mosor spot and you know, don't think of the data,

551
00:27:07,440 --> 00:27:09,960
Speaker 3: which is something which is kind of scary, which makes

552
00:27:09,960 --> 00:27:14,760
Speaker 3: this for other people, especially for technically oriented oriented males.

553
00:27:15,200 --> 00:27:16,880
Speaker 3: I think there's a lot more than meets the eye,

554
00:27:16,880 --> 00:27:19,000
Speaker 3: and you don't have to dig that deep to find it.

555
00:27:19,160 --> 00:27:21,760
Speaker 3: And I think that's to me what the beauty of

556
00:27:21,800 --> 00:27:24,240
Speaker 3: this sport and what got me hooked on it. If

557
00:27:24,240 --> 00:27:26,479
Speaker 3: you imagine I'm doing this with my hands now, right,

558
00:27:26,520 --> 00:27:28,800
Speaker 3: I'm creating this sort of picture of this kind of funnel,

559
00:27:29,000 --> 00:27:30,879
Speaker 3: right of like you know, whide at the top and

560
00:27:30,960 --> 00:27:34,280
Speaker 3: narrow at the bottom. For me, the data driven part

561
00:27:34,320 --> 00:27:38,199
Speaker 3: of fandom and the stories which really unfold during a

562
00:27:38,320 --> 00:27:40,960
Speaker 3: race or during a season are kind of in the

563
00:27:40,960 --> 00:27:43,159
Speaker 3: middle of that funnel. And if you're a person a

564
00:27:43,200 --> 00:27:45,879
Speaker 3: fan that loves to live in the rabbit hole, that

565
00:27:45,960 --> 00:27:49,800
Speaker 3: loves to understand what's going on beneath the surface, then

566
00:27:50,040 --> 00:27:52,760
Speaker 3: you can't really do that without some way of capturing

567
00:27:52,840 --> 00:27:56,679
Speaker 3: what's going on and then using that to replay and

568
00:27:56,680 --> 00:27:59,120
Speaker 3: tell a story. Because you're I mean, I'm sure, Tony,

569
00:27:59,160 --> 00:28:01,480
Speaker 3: you've spoken to a people who were like, it's just

570
00:28:01,520 --> 00:28:03,680
Speaker 3: a bunch of cars going around in circles, isn't it.

571
00:28:04,320 --> 00:28:06,080
Speaker 3: And then some of those. These are the people like

572
00:28:06,280 --> 00:28:08,440
Speaker 3: right at the top of this funnel, right then there's

573
00:28:08,440 --> 00:28:11,920
Speaker 3: the people who got into Drive to Survive and they're like, oh,

574
00:28:11,920 --> 00:28:14,320
Speaker 3: there's so much more than just cars go around a track.

575
00:28:14,359 --> 00:28:17,439
Speaker 3: There's narratives and stories and characters and drama and whatever.

576
00:28:18,320 --> 00:28:21,000
Speaker 3: And then as you get drawn into the sport, and

577
00:28:21,119 --> 00:28:23,480
Speaker 3: for me that was through gaming. Actually, like I got

578
00:28:23,520 --> 00:28:26,440
Speaker 3: I went from Drive to Survive to playing F one,

579
00:28:26,520 --> 00:28:30,200
Speaker 3: the co Masters game. Religiously, this is when I still

580
00:28:30,240 --> 00:28:33,960
Speaker 3: had time to play video games. And then I started

581
00:28:33,960 --> 00:28:36,359
Speaker 3: to understand what's going on from the seat of a

582
00:28:36,440 --> 00:28:39,080
Speaker 3: driver or of a team because you experience that, and

583
00:28:39,080 --> 00:28:41,760
Speaker 3: then when you see the race again, you're like, well,

584
00:28:41,760 --> 00:28:44,720
Speaker 3: all the story. Like the broadcast, it's such a narrow

585
00:28:44,760 --> 00:28:47,480
Speaker 3: window on all of these events that are going on,

586
00:28:47,760 --> 00:28:52,560
Speaker 3: like tire graining, pit stop times, which overtakes the happening, overcuts,

587
00:28:52,640 --> 00:28:56,840
Speaker 3: undercuts fuel consumption. You start to see way more than

588
00:28:56,960 --> 00:28:59,680
Speaker 3: is shown on the screen. And to me, that's the

589
00:28:59,680 --> 00:29:03,320
Speaker 3: real fun of it. It's almost like you're decoding a

590
00:29:03,360 --> 00:29:07,040
Speaker 3: live event as is happening, and then after the fact

591
00:29:07,160 --> 00:29:09,360
Speaker 3: you get to chew on it for that many more days,

592
00:29:09,360 --> 00:29:10,880
Speaker 3: which is a super fan, It's like, that's all you

593
00:29:10,920 --> 00:29:12,640
Speaker 3: want to do, right, is just engage more and more

594
00:29:12,680 --> 00:29:14,680
Speaker 3: deeply with the thing that you love. And without the

595
00:29:14,680 --> 00:29:17,720
Speaker 3: way to capture data and use that to replay to

596
00:29:17,800 --> 00:29:20,160
Speaker 3: tell the stories, I think it would be a less

597
00:29:20,920 --> 00:29:23,560
Speaker 3: compelling experience. From my perspective, what I'm.

598
00:29:23,400 --> 00:29:25,440
Speaker 2: Hearing from you is you like the idea that data

599
00:29:25,480 --> 00:29:28,080
Speaker 2: could make you better, but you don't want data to

600
00:29:28,120 --> 00:29:30,880
Speaker 2: be the driving force behind the athlete.

601
00:29:31,280 --> 00:29:34,120
Speaker 1: No, I don't want to be so judgmentally against data.

602
00:29:34,200 --> 00:29:37,200
Speaker 1: It's just not what tickles my fancy in sport. But

603
00:29:37,720 --> 00:29:43,120
Speaker 1: it is absolutely remarkable how this sport consumes data.

604
00:29:43,840 --> 00:29:47,080
Speaker 2: Here's another thing for you to think about. Why might

605
00:29:47,200 --> 00:29:51,960
Speaker 2: be fundamentally different or exponentially using big words here, but

606
00:29:52,080 --> 00:29:55,640
Speaker 2: exponentially more useful in a sport like Formula one because

607
00:29:55,720 --> 00:29:59,840
Speaker 2: the teams have such limited times with the cars on.

608
00:30:00,800 --> 00:30:03,520
Speaker 2: Every time you take a Formula one car, it's mandated,

609
00:30:03,720 --> 00:30:06,800
Speaker 2: it's restricted. They can't, no one can. A driver can't

610
00:30:06,800 --> 00:30:08,360
Speaker 2: just wake up one they and go I'm just gonna

611
00:30:08,360 --> 00:30:09,480
Speaker 2: go and take the car up for a run and

612
00:30:09,520 --> 00:30:12,520
Speaker 2: practice if you they can't. So because it's so limited

613
00:30:12,560 --> 00:30:14,840
Speaker 2: with how they can practice with the cars, I think

614
00:30:14,920 --> 00:30:17,560
Speaker 2: that's why every ounce of data is picked up and

615
00:30:17,560 --> 00:30:21,720
Speaker 2: collected and analyzed and useful. A baseball person makes sense,

616
00:30:21,760 --> 00:30:24,920
Speaker 2: and baseball person, a baseball athlete, a golfer can pick

617
00:30:25,000 --> 00:30:27,440
Speaker 2: up a pair of clubs and swing, you know, practice

618
00:30:27,440 --> 00:30:29,640
Speaker 2: in their own time. I actually can't think of another

619
00:30:29,720 --> 00:30:32,400
Speaker 2: sport where you can't practice at will.

620
00:30:34,400 --> 00:30:37,160
Speaker 1: I remember talking about that last season. It's very fascinating.

621
00:30:37,400 --> 00:30:39,000
Speaker 1: So I think you can hit a tennis ball for

622
00:30:39,040 --> 00:30:41,080
Speaker 1: as many hours as you're allowed to, as many hours

623
00:30:41,200 --> 00:30:43,200
Speaker 1: as you physically want to. But that can't be the case.

624
00:30:43,320 --> 00:30:45,520
Speaker 2: Yeah, And I remember Kobe talking about this with being

625
00:30:45,560 --> 00:30:47,760
Speaker 2: a basketball player. Of just like I get up five

626
00:30:47,800 --> 00:30:50,160
Speaker 2: hours before everyone else, and so I have five hours

627
00:30:50,160 --> 00:30:53,120
Speaker 2: of training on everyone else every single day. Formula one

628
00:30:53,200 --> 00:30:56,080
Speaker 2: drivers do not have that. Hence why I think that

629
00:30:56,200 --> 00:30:58,640
Speaker 2: data plays such a big role in Formula one.

630
00:30:58,680 --> 00:31:03,840
Speaker 1: Also, it's mind boggling to think that I was buying

631
00:31:03,920 --> 00:31:06,560
Speaker 1: shoes today on the internet and it was frustrating me

632
00:31:06,560 --> 00:31:08,160
Speaker 1: because I would add it to my cart and then

633
00:31:08,240 --> 00:31:10,120
Speaker 1: check out, and it would there'd be like a gap

634
00:31:10,400 --> 00:31:13,760
Speaker 1: in uh loading loading, you know, and I'd be like,

635
00:31:13,760 --> 00:31:15,600
Speaker 1: why is there a gap? It's a new computer edition

636
00:31:16,480 --> 00:31:18,400
Speaker 1: everything was fine, but it's like it's a little too strong.

637
00:31:18,480 --> 00:31:20,600
Speaker 1: And then to think that this their way they're racing

638
00:31:20,720 --> 00:31:24,680
Speaker 1: is happening in data like instantaneously yep, and it's crazy

639
00:31:24,720 --> 00:31:28,200
Speaker 1: and that probably gets into our logistic episode. That's wild

640
00:31:28,240 --> 00:31:29,720
Speaker 1: to me that that can even happen.

641
00:31:29,760 --> 00:31:31,600
Speaker 2: And they're still pushing the limits to figure out how

642
00:31:31,640 --> 00:31:34,720
Speaker 2: they can get the data uploaded to HQ faster than

643
00:31:34,720 --> 00:31:36,800
Speaker 2: what it is now when we're talking zero point zero ones,

644
00:31:36,840 --> 00:31:39,040
Speaker 2: you know, we're always pushing the limit.

645
00:31:39,760 --> 00:31:42,280
Speaker 1: Fall boots, if that's what you're asking, fall Boots. We

646
00:31:42,480 --> 00:31:44,360
Speaker 1: still get like a month left in the fall here.

647
00:31:45,000 --> 00:31:48,840
Speaker 4: So Michael, shit is that? YOHI Again, our team here

648
00:31:48,880 --> 00:31:53,040
Speaker 4: at Choosing Size HQ, we've been crunching the numbers and

649
00:31:53,680 --> 00:31:58,720
Speaker 4: unfortunately it does seem that you're going off script about

650
00:31:58,720 --> 00:32:01,840
Speaker 4: twenty five percent more than than than management would like.

651
00:32:02,880 --> 00:32:05,080
Speaker 4: So that I've got a request here. They're asking if

652
00:32:05,120 --> 00:32:08,360
Speaker 4: you can just try to be a little more vigilant.

653
00:32:08,920 --> 00:32:24,080
Speaker 7: You see. Uh, this has been Choosing Sides f one,

654
00:32:24,840 --> 00:32:29,040
Speaker 7: a production of Sports Illustrated Studios, iHeart Podcast and one

655
00:32:29,120 --> 00:32:30,560
Speaker 7: oh one Studio podcast.

656
00:32:32,040 --> 00:32:35,360
Speaker 4: The show is hosted by Michael Costa and Tony Cowan Brown.

657
00:32:37,000 --> 00:32:40,600
Speaker 4: This episode was edited, scored, and sound designed by senior

658
00:32:40,640 --> 00:32:46,280
Speaker 4: producer Johai may Thad scott Stone is the executive producer

659
00:32:46,600 --> 00:32:49,880
Speaker 4: and head of audio, and Daniel Wexman is Director of

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Speaker 4: podcast Development and production Manager at one on one Studios.

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Speaker 4: At iHeart podcast Sean ty Tone is our executive producer.

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Speaker 4: And a special thank you to Michelle Newman, David Glasser,

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Speaker 4: and David Hoodkin from One o one Studios. For more

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Speaker 4: shows from iHeart Podcasts, go visit the iHeartRadio app, Apple

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Speaker 4: Podcasts or wherever you get your podcasts, and whatever you do,

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Speaker 4: don't forget to rate us and tell your friends. It

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Speaker 4: really does mean a lot.

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Speaker 2: Michael, yess what's coming up next week?

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Speaker 1: Is it?

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Speaker 2: Yes?

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Speaker 1: It is finally, Yes.

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Speaker 2: Yes, it's the turn of the tireheads.

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Speaker 1: Yeah. I'm not sure why I'm so excited about tires,

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Speaker 1: but I am time.

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Speaker 2: For talking rubber tires, screeching, the whole shebang.

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Speaker 1: It seems very important. It seems so so so important.

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Speaker 1: The S and the M and the H and they

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Speaker 1: all get the same amount of tires or whatever. And

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Speaker 1: when I take my car into the MC, they say

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Speaker 1: the most important thing is tires and breaks next on

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Speaker 1: Torque Revolution. YOHI could you send us off with some

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Speaker 1: screeching tire noises or something

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Speaker 4: Such a cliche