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From PDFs to Pit Lane: Building a Real-Time Data Product for McLaren Racing
531 Plays1 day ago
In this week’s episode of the PolicyViz Podcast, I chat with Michael Gethers, former Head of Data & Strategy for the McLaren IndyCar team, about how a personal side project analyzing IndyCar timing PDFs turned into a job building real-time data tools for a professional race team. We dig into what it’s like to design data products for engineers, strategists, and drivers who need to understand information instantly while a car is on track. Michael shares how he moved from making public visualizations on Twitter to building an internal analytics application from scratch, why “pretty charts” weren’t enough for the engineers, and how user feedback shaped the product. We also talk about race strategy as a probabilistic data science problem, the difference between dashboards and data products, and what he learned about designing for cognition under extreme time pressure. If you care about dashboards, data storytelling, or building tools people truly use, this conversation is a goldmine.
Keywords: data dashboards, data product design, data visualization, motorsports analytics, race strategy, McLaren IndyCar, telemetry data, timing data, data science in sports, user centered design, dashboard design, real time analytics, D3 visualization, data engineering, analytics application
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Transcript
Introduction to Data in IndyCar Racing
00:00:12
Speaker
Welcome back to the Policy Biz Podcast. I'm your host, John Schwabisch. On this week's episode of the show, we turn our attention to data, data visualization, and dashboards in the world of racing, specifically IndyCar racing.
00:00:27
Speaker
I am joined by Michael Gethers, who worked on the McLaren IndyCar team, creating data, ingesting data, and creating all the stuff that was needed to help the team run better, winning, and more efficient races. And it's an interesting conversation to hear his path into racing and what was required and the work that went into it, basically standing up an entire data visualization, data science team in an industry that at least at the time didn't really have a lot of that.
Building Data Culture in Motorsport
00:00:55
Speaker
And so I think you're going to hear, uh, probably what a lot of us face in our day-to-day work, which is, uh, building these sorts of capabilities, building a culture around data that can help people make better and more informed decisions.
00:01:09
Speaker
So let's get over to the interview. Here's my chat with Michael Gathers, only on the Policy Biz podcast.
00:01:17
Speaker
Hey, Michael, good to see you. Good to meet you. Thanks for coming on the show. Yeah, of course. Glad to be here, John. Okay, so I'm pretty excited to chat with you for, i would say, two reasons.
00:01:27
Speaker
First, I learned of your work through the Dashboards That Deliver book. So I'm excited to talk to you just generally about dashboarding and creating things. um Also because working on a race team, like the amount of data you must have seen is just like makes my mouth water a little bit.
00:01:45
Speaker
But also my son is very much into racing, wants to get into the field and you've already connected us with some other engineers. So thank you for that in advance. But so so a couple of different reasons here.
Michael's Journey into Motorsport
00:01:56
Speaker
So I thought maybe we'd start with a little background and you were the lead of data and strategy at McLaren. And so maybe we just start like how you got to that spot and then what your role was.
00:02:06
Speaker
Yeah, so I i came into motorsport, I think, from a fairly unconventional unconventional path. And ultimately, i you know I'm very grateful for the for the opportunity that I had and the the lanes that opened up for me. But my my background really is is as a core data scientist. I studied statistics at UC Berkeley.
00:02:28
Speaker
um I worked in the Bay Area, did the tech thing in the Bay Area, i worked as an actual actually my first job out of out of college was as an actuarial analyst and then i kind of um worked at the number of different tech companies. um Most notably in San Francisco was Salesforce and then um i pivoted to some cybersecurity work after that.
00:02:49
Speaker
Um, but always, you know, since I was a kid, you know, i I went to the racetrack with my dad, specifically the Indianapolis motor speedway with my dad, um, since I was five years old. no he So he, he took me and I just loved it.
00:03:03
Speaker
You know, it was, yeah it was something that I grew up with and it was sort of a bonding thing between me and him. as we were, as I was growing up and I went to my first race when I was 10 years old and I've been to every Indy 500 since then.
00:03:17
Speaker
ah Except for the COVID year. Right. So was always kind of a part of who I was. And um because of that, actually, I just, I, and being a data nerd, as many of us are, and many of the people probably listening to this podcast are, um I just found that I was kind of drawn to finding more data about the race because racing is a sport where, you know, it's not like basketball or or football really where everything that's happening, you can see in front of you, right?
Data Analysis in Racing
00:03:53
Speaker
If you're watching a race on TV, you can see a couple of cars at a time or maybe a couple of corners at a time.
00:04:01
Speaker
um Even if you're at the race, The tracks are massive, right? You just can't see the whole thing. um And so I remember one year being at the Indy 500 with my dad and um and I just had questions about what was happening, what was going on um during the during the race that I just couldn't answer and there was no way to get that that information at the time. And so what I did after the fact was I I just kind of scoured the internet for for data. And actually IndyCar, interestingly enough, was they published public PDFs that contain some timing data, not extremely robust and not extremely easy to use. yeah They published some of that timing data. And so I just scraped that and started doing some analysis and I put it on the internet.
00:04:53
Speaker
Um, yeah so I, created it. Well, I had just had a Twitter account for myself originally, um, and started posting things there.
Engaging the Racing Community through Data
00:05:02
Speaker
through that, I actually got to know, um, one driver in particular who was J.R. Hildebrand, um,
00:05:09
Speaker
And he um was just interested in what I was doing. He happened to be sponsored by Salesforce at the time. Oh, interesting. So there was that kind of natural connection because I was working at Salesforce when I was doing this. And that was kind of my first just like in a little bit just because you know he let me he invited me to come to the track as part of the Salesforce demo, basically, that they were doing at the track. And that was kind of my first, just a little wedge in. yeah And then fast forward, you know, that that was kind of a one time thing. um
00:05:44
Speaker
Fast forward a few years and um I had just left my cybersecurity job. My wife and I were living in London. We were moving from London, we're planning to come back to the States by way of Switzerland, which is a longer story that i we don't need to get into here. But yeah.
00:06:01
Speaker
When I was in Switzerland, I was like, you know what? I really love doing that IndyCar analytics thing. so I'm going to start doing that again. um But this time I'm going to do it bigger and better. right And I wanted to treat it as a learning experience too. I'd always wanted to learn D3 because I'd never i had never known really anything about front-end web development. And it was just kind of something I had always been interested in.
00:06:25
Speaker
So um I created a new Twitter account. It was called Rose of Three. Yeah. um Which anybody who's familiar with the Indianapolis Motor Speedway, the Indianapolis 500, the cars start in 11 rows of three. That's where that comes from. So my my Twitter ah handle was Rose of Three. I created ah an accompanying website called Rose of Three dot com. And I just kind of went all in like it was my full time job, basically creating analytics content basically um around IndyCar specifically. And it was easy because um I just had so many questions. And so when you had so many questions, you just want to kind of chase after it and answer it, answer those questions. And, um you know, I it's it's kind of funny for me to look back at the evolution of of my
00:07:15
Speaker
my work there because I could see myself getting better over a period of months just because right you get better at asking the right questions. You get better at the actual technical development part of it. Um, you know, my website, I look back at the website that I made now and it's, yeah um, you know, it's clear that it was my first, it was my first website, but, um, I'm still proud of it on some level because it really took me where yeah I ended up going.
00:07:42
Speaker
Um, so through that ultimately um uh gavin ward reached out to me who um he was the soon to be team principal at aero mclaren in aero mclaren's indycar team which is the mclaren racing indycar team um in indianapolis formerly he um worked at uh red bull f1 and he worked at penske racing um in IndyCar as well.
Joining Aero McLaren's IndyCar Team
00:08:13
Speaker
He reached out and and just asked the question, you know, hey, have you ever have you ever thought about working in motorsport? And I was just kind of like, Yeah. Yeah. Got every day. Yeah. Yeah. Yeah. yeah So sort of that conversation went on for a while to to yeah actually determine what it was going to look like. But a few months later, I ended up joining the team. That was at the end of 2022. Wow. So let me ask ah a couple of questions. So between when you first played around with those PDF files and then decided to kind of go all in,
00:08:46
Speaker
Did the data that Indy was publishing, did that change fundamentally? Um, no, it did not change fundamentally, but my access changed fundamentally. okay Um, so a couple things, a couple things happened.
00:09:05
Speaker
Um, you know, I was just kind of, I was doing what I could with the, with the scraping right right of their PDF switch, um, you know, they're publicly posted PDFs. PDF parsing is not a precise science. It's better now probably than it was at the time, but I did. I spent a lot of time doing data correction basically that process. Yeah.
00:09:27
Speaker
But at one point I actually reached out to ah somebody. Actually, I can't remember if i reached out or if they reached out to me. um In any case, somebody ended up giving me better access to to some data that I could play with. And I was able to get data in in CSV format and um that made it obviously much, much easier and more accurate. And obviously that was not the data that I ended up working with on the team, but sure that is what gave me my kind of the ability to do this at ah at a greater scale than I was able to do before.
00:10:02
Speaker
I'm curious, and you may not know the answer to this, but um you know I've talked to lots of other people who do similar sorts of things, you know hockey and basketball, other sports where they they they do sort of similar things and then they get access to the to the to more of the data and they have relationships with teams or something like that.
00:10:19
Speaker
and Do you have any instinct as to why... they would be, or you would reach out to them but or they would just give you more access to the data. Like, was it, were they, at the time they just didn't have like people doing data viz and data analytics on the, on, on, I mean, there's a ton of data here and I'm just curious why, like you're this guy out there just doing this almost for fun. And they're like, Hey, yeah here's a bunch of data. Yeah, exactly. Yeah. It's just a guy. um Yeah.
00:10:46
Speaker
You know, I, I don't know the answer really i can only kind of hypothesize but i think number one what i was doing was was cool people liked it um yeah it wasn't it wasn't just for me it was for a community of people that really loved the sport and yeah um in my mind what i was doing was actually quite good for the sport because it was giving people um getting more people engaged in a different way than they were able to be engaged before. And also you might capture a new audience that they weren't able to capture before. Maybe there's the data nerd ah audience that never thought they had a home in racing, but because because all the data was was so under wraps, right?
00:11:34
Speaker
So I don't know what the actual answer is, but I think it's something something in that vein, right? Yeah. And ah yeah, i just sort of had somebody who who was excited about what I was doing and wanted to help me make it better. Yeah.
00:11:48
Speaker
I mean, that's what we all need, right? We need those cheerleaders. Yeah. Yeah. Okay. So you get to McLaren. um are you now Are you now back in Indianapolis?
00:11:59
Speaker
yeah
Role and Evolution at McLaren
00:12:00
Speaker
Yeah. You were in London, you moved back? Yeah, so you know quick quick history of my my ge like my geographical ah yeah evolution.
00:12:12
Speaker
Went to school in the Bay Area, worked in the Bay Area for a while. My now wife and I moved together to London for about five years. um Her family is actually Swiss. And so when we made the decision that we were going to come back to the US,
00:12:27
Speaker
We were like, okay, well, before we do that, let's just do a year in Switzerland before we come back. okay um We were planning to go back to California, which is where both of us um called home before we we left London. and um But then the the opportunity came up in Indianapolis and it it was, you know, yeah I had to take a shot, right? I had had to had to see what that's like. Right. Absolutely. Okay. So you're in Indianapolis, you're working for McLaren. Yeah.
00:12:54
Speaker
What is that day to day like? i mean where I mean, is the McLaren offices, are they, I've only been to the Speedway once and it was actually last summer for an IMSA race. So I'm not, okay my son's a big F1 fan and now a GT fan, but he kind of looks down on on NASCAR, especially. so um So all the racing people who are listening to this could can yell at him. um But um you're going to the office every day and like, what are you doing with with their data? I mean, are you just ingesting a ton of data all the time? Yeah, I mean, it was it was interesting. um
00:13:29
Speaker
Yeah, looking back at it, it's it's really interesting now for me, I guess. um yeah When i when i got there, it was almost like we didn't really know what was the best thing to focus on. right They had never had um somebody like me.
00:13:44
Speaker
you know I think on the F1 side, maybe they were ah a few years ahead, but not wildly ahead. um And I was really the first core data person that was brought into the IndyCar team. and um you know, a big part of that was was Gavin's vision. Right. And that's what we had talked about over a few months before I ended up joining. um So it was it was kind of like, yes, we have all of this data.
00:14:14
Speaker
Number one, how is it stored? Number two, like, is it queryable? Number three, like what is the actual output that we want from this? What do the engineers need? um There was a lot of kind of uncertainty around what that was going to look like. um And so it took for me It was a lot of trial and error, frankly.
00:14:35
Speaker
i was trying to Initially, I was trying to put together a lot of the kinds of things that I was i was developing for public consumption on on Twitter and on my website.
00:14:46
Speaker
um But I don't think you'll be surprised to to learn that that's not really what the engineers wanted and needed, right? Right. And I think they initially regarded some of what I was doing as like, oh, this is these are pretty pictures, but where's the where's the substance, right? And so it was, fortunately for me, I had i had sort of the the traditional data science background. So I was able to kind of um to kind of meet in the middle there. And and yeah and that's where i think the application that that I built and that that I built with the team there ah really kind of took hold a little bit and people started to really get some value out of it.
00:15:25
Speaker
so So were you, i mean, when when when you watch racing or you watch the F1 movie with Brad Pitt, you know, there's all these screens, people sitting on the on the on the track. um And were you primarily focused on sort of real-time analytics?
00:15:41
Speaker
Or was it more on the engineering side of, you know, they're going to test this technology? wing, this back wing, this front wing, they're going to test this other setup on the car and let's ingest all of the results of those tests and, you know, put it together or, you know, or is it all, all the above?
00:15:57
Speaker
Yeah, it was, it was probably all of the above, but I think we we tried to, we tried to shrink it a little bit at the beginning um to something that's more palatable, I guess, when you're starting from from nothing, basically, which we do.
00:16:13
Speaker
And so what we started with was was timing and scoring data. um And that's, for those that don't understand what that term is, basically any every racetrack has a number of a discrete number of timelines around the track.
00:16:31
Speaker
And when every car crosses over that timeline, it's registered.
Real-Time Race Strategy with Data
00:16:35
Speaker
So car seven crossed timeline start finish at X time, right? um And it's a bit more robust than that, but that's more or less, that's like what it is at its core. um And there's a ton of stuff that you can do with just that alone, but it it it requires a lot of kind of careful thought about how you're going to how you're going to transform that data. You can do all all kinds of things, obvious things from like, okay, how fast are we
00:17:06
Speaker
on every lap, right? That's very obvious. How fast are we in each given sector? How fast are we in each given sector if we're following one car at one second? um How fast are we if we're in heavy traffic and there's five cars within within five seconds? um Those kinds of things, you just start to build, you know you start small and just kind of build up.
00:17:29
Speaker
that capability. And then once you build up the capability to do the analysis, what we were doing was, okay, how do we present this to people in a way that is is very um digestible very quickly, right? Because that's kind of the name of the game. um The car's on track, right? And we were building an application to be used while the car was on track in addition to as a kind of data interrogation tool.
00:17:54
Speaker
after we're back in the back in the garage or back in the truck. And and you know in this scenario In both scenarios, people want data as fast as possible, but especially when the car is on track, it's kind of like, well, we need to we need to understand exactly what's going on right now. And was that a good change or was that a bad change? And um ah that's kind of where a lot of the sort of optimizations and efficiency gains had to come from that that we ended up building into the tool. um
00:18:25
Speaker
But it's not just technical efficiencies. It's also kind of cognitive efficiencies on some level. It's like, how do you get people to understand this really fast? right Which is a ah challenge that i had not I had not really been faced with before. Right.
00:18:40
Speaker
So, um I mean, I could ask a ton of questions, but um let's start with the folks. Let's start with this cognitive question. So you're so presumably you're showing, and we could just focus on on on during the race if we want just to sort of make it easy, but you're showing visuals to people who may not have seen them in this way before. Maybe they're way more visual than tabular, maybe is how they were used to them. So you know did you sit down with folks? like Did you do kind of like formal or informal like user testing to see what would be the most useful, intuitive, immediate visual that they could get? like How did you work with the team to like build something that that you know that you knew that they could use?
00:19:24
Speaker
Yeah, that's a great question because initially I didn't and I initially I was just like, I'm gonna go build what I think, what I think made, which is not the right way to go about it, um obviously, but you know you live and you learn. Yeah, um yeah for for me,
00:19:39
Speaker
It was kind of finding the, I hesitate to champions of the product that we were building, but but the people, the power users who I thought were going to use it the most and understanding what they really, what their gaps were in the tooling that they already had.
00:19:54
Speaker
They're obviously, they're you know extremely talented engineers themselves, right? They're not unfamiliar with looking at data. Right. You're not dealing with like English lit majors, right? Correct. Yeah. Yeah. so they you know they know what they know the domain 10 times better than I ever will. Right. yeah But, and they know how to look at data, but they just,
00:20:19
Speaker
you know this is a personal opinion i guess but i think it was sort of proven out as we as we continue to build the tool um the the data tools that had been built that had been built for them before which were all sort of off the shelf products that people were trying to build it was never really done in-house at least not at um the mclaren indycar team um we're not I mean, i almost wish I could show them to you they're so visually messy and they're just, there's so much data on it yeah that um it's, you know, those engineers are extremely adept at making heads and tails of it. right But for this kind of quick hit, give me a, you know, give me where are we fast, where are we slow compared to x y or Z,
00:21:10
Speaker
team or driver or, yeah you know, in this kind of scenario, it was hard to, it was hard to parse. Right. Yeah. um And so that's where, you know, through those conversations and through just observation really, because and you're you're talking about the, the, the Brad Pitt movie, the F1 movie and yeah any other sort of popular culture representation of what motorsport is, it it looks very high tech and then you see the big screens and people on the timing stands.
00:21:38
Speaker
you know I was very fortunate. I was one of those people. right i got to be yeah I was on the timing stand in the pits um and I could look at my screen and I could look at their screen. I could see what they were looking at. I was able to talk to all the engineers, the drivers.
00:21:54
Speaker
um as we were kind of in a practice session and in in races and in qualifying. um So I had that feedback loop and the feedback loop was kind of built into the job, which was which was great.
00:22:07
Speaker
right I don't think I could have built what I built without having that opportunity. like if you were just a you know if if if If you didn't actually have the exposure to what that world is like and what those needs are, it's quite,
00:22:22
Speaker
you know It's an esoteric domain, actually. Sure. It's complex and it's um it really doesn't apply anywhere else in the real world. So, well, people argue with that probably, but it's just a very unique domain.
00:22:38
Speaker
Yeah, for sure. and it's It's difficult to understand what it's like unless you've seen it. And I was fortunate that I i had the opportunity to see it and experience it. And I i think that helped me build things better. Yeah, no, for sure. So my first question is, when you are in the timing stand during a race weekend, what are you doing ah during those times? Are you adjusting the view? Are you like, what what is and what can you do quickly enough that will be useful to the rest of the team?
00:23:05
Speaker
Yeah. um Well, you know, my role on the team, you know, everybody kind of wears multiple hats. These teams are not are not huge. um Right. At least the IndyCar teams are are not are not huge.
00:23:19
Speaker
The F1 team is much bigger, but.
00:23:23
Speaker
you know, my role on the timing stand was as a race strategist, which was a a difficult role to be thrown into having never been a race strategist before or having been around yeah around that. but um,
00:23:38
Speaker
At the same time, it kind of makes a lot of sense because a lot of race strategy is is very probabilistic and it's um it's kind of, you know, what do we think the probability is if we pit now that we will beat this other car out of the pits in five laps? Or what's the probability ah right now of there being a yellow flag between now and the end of the race? And how does that influence our our strategy going forward? should we Should we conserve fuel and try to make it to the end, hoping we get a a yellow flag safety car situation? um
00:24:14
Speaker
Or should we just run as fast as we can, splash for fuel? And and if that is that the is that the most optimal way to to get to the end of this race? What's the probability that we're going to be able to pass any given car on track? Because that's quite difficult as well, right? this These aren't cars running in a vacuum. They're a bunch of cars running on track together. yeah um So this the strategy problem actually does really lend itself to um a data science approach, I think. um And so while was a difficult, you know I felt like getting thrown into the deep end a little bit there. um
00:24:50
Speaker
it was a role that I think, yeah, was not ill-fitting on some level. yeah right And so what I'm, you know, to answer your original question, what what was I doing on stand? It's multiple things, right? It's um looking at the data as it comes in, looking at the where our car is relative to other cars on track, keep keeping track of who is low on fuel, who's who's who's going to be pitting soon, who looks like they're able to pass well, who doesn't. um That's one side. That's like the very active in the moment kind of thing. But there's also this kind of passive work that's being done at the same time, which is like, what do I need to build to make this job easier? yeah
00:25:32
Speaker
Yeah, easier, right? And so both of those things, I think, were kind of happening at the same time, although the the latter was more observational than the former.
00:25:42
Speaker
So one quick question for you. What are the rules IndyCar about what each team has to share? Because to to the point you just made about tracking what other cars are doing, presumably...
00:25:56
Speaker
McLaren isn't collecting data about every car. I mean, maybe they are. And Red Bull's not like there's a data sharing situation that's going on. Yeah, so. rules Yeah, it is. Yeah. So um first of all, all of the timing data, everybody can see.
00:26:12
Speaker
So we know not just where our car is around the track. We know where everybody's cars are at all times. right um But each team also in IndyCar has to share kind of limited telemetry data.
00:26:25
Speaker
um And so that is, I'll probably get all of them. Maybe I'll miss one or two, but speed, RPM, gear, throttle, brake, steering angle.
00:26:40
Speaker
um And think there are a couple others. And IndyCar has some unique fields as well. Like, um you know, they just switched to hybrids a couple of years ago. So they got they've got hybrid deployment as a as a field as well. Anyway, there are a small handful of of fields of of telemetry signals that everybody has to send to everybody. So we can see that live actually. It's it's um at 10 hertz, which is actually lower fidelity than 10 hertz, 10 times a second. yeah
00:27:13
Speaker
Lower fidelity than then obviously what we have on ourselves. But um there's a lot that you can that you can use. And that... I have little to no understanding of of the backend side, but I assume ingesting that data and getting it as quickly and efficiently to the timing stand or to the race director or whoever, that's on your
Developing Data Infrastructure at McLaren
00:27:38
Speaker
team. That's your team's responsibility to, like, they just give it to you and then you're you're figuring out how to get it in quickly and and and efficiently.
00:27:46
Speaker
Yeah, that's right. And yeah, so IndyCar bears no responsibility for what we do with the data that they send. They just promise to send it in some form or another, basically. Hopefully not as PDFs, but yeah. Right, right. So although I have experience with that. Yeah, right. That's right. You'd have a big leg up if they were just getting out PDFs. Yeah. Yeah. Um, no, so it was, that was sort of all on us to, to build the infrastructure around that. And, you know, when I joined team,
00:28:17
Speaker
um all of that data was kind of just stored in log files on a server someplace. And you know I mentioned queryability, like none of it was queryable. You had to kind of know what you were looking for and find the right session at the right you know in in the right directory to be able to to kind of ah recreate a past session so that was a big part of what we were trying to do there was was um build up the the data infrastructure side of it because right obviously it's it's sort of foundational to everything everything that comes after it yeah um so let's talk about the the product you created um
00:28:59
Speaker
You know, again, I learned about it in the Dashboards That Deliver book, um but you've also, I think, called it a ah data product. I think in the book, they've also described it as sort of analytical app.
00:29:11
Speaker
But I'm curious about it because it doesn't strike me Well, I guess it depends on how we define dashboard, but it doesn't strike me as like a Tableau dashboard, right? Where you go in and you filter and you're select. um How do you think about the product that you created, how it differs from sort of your more standard dashboard? i mean, it's doing very different things.
00:29:36
Speaker
Yeah, it it definitely was. I mean, it it there's a lot of overlap there, right? So I'm not going to say that it's um you know completely disjoint from from all dashboards that exist in the world. They're all Tableau dashboards. But... um I didn't think of it as a dashboard. Maybe I thought of certain components of it as dashboards or dashboard-like, but what I was building fundamentally was a ah website, basically. it was ah It was a site that lived on our server that um had a back end. It had a front end. It had you know it had data flow into it. it was It was just a much bigger thing than what I think of a dashboard as being.
00:30:20
Speaker
And that's not to be reductive about dashboards or people that build dashboards at all. It's just, there was a, it was a, there was a very broad scope to what we were trying to do. yeah And it was, it was starting from literally nothing, right? We had no, we had no such tooling to do this.
00:30:37
Speaker
um That was, that had been built in house. So we were building everything up from scratch. um And so that's why I kind of, you know, I don't bristle at the term dashboard. People call it a dashboard. That's fine. um It just was never kind of how I how i thought of it. It was a product that that ah we were trying to ship and we had customers of that product and the the customers of that product were the other engineers on the team. Right. so it was Yeah, that's why I use the term that I use. um But like I said, i don't I don't get offended if anybody calls it dashboard.
00:31:15
Speaker
Would people come back to you and ask for ah different additions or filters or or bells and whistles that they kind of wanted to have?
00:31:26
Speaker
Yeah, oh yeah, all the time. yeah And that was actually one of the early kind of metrics for ah the success of the of the tool, the success of the product was to me, like I remember writing this down before we built or shipped anything, but I was like, yeah if we are getting feature requests about this, that means that people are using it and that they want more from it. So the more feature the more feature requests we get, I think the better we're doing. The better, oh, interesting.
00:31:55
Speaker
So we, yeah, there was an endless list of requests that people would submit and definitely didn't get through all of them. Right, right. We tried to get through as many as we could and we tried to prioritize the ones that were the most important. Yeah.
00:32:11
Speaker
So we've talked about the strategists and the engineers. What about the drivers? Did they ever go in and like play around and like either ask you questions specifically or did you get to watch them like use the dashboard or they're just like, where I'm driving, you just tell me the right strategy?
00:32:27
Speaker
Um, yeah, it was, uh, a little of both. I mean, the drivers have a lot, uh, a lot going on and I think it really depends on the driver, frankly. Um, so there are some drivers who are more definitely more on the analytical side of things and who want to see more of the data.
00:32:45
Speaker
And there are others that are more, um, I don't know fly by the seat of your pants kind of, kind of drivers. And so, um Yeah, definitely there were drivers that were using the tools that we had built. um In fact, I remember one sort of exciting time when basically in in between any practice run when the car would come in, the driver wouldn't necessarily get out of the car. yeah The driver would just kind of sit there while you know, we make changes to the car or we look at some data or we wait for the track conditions to be what we want them to be before we head out again. The driver doesn't pop out all the time. And so the driver always has an iPad basically that gets hooked on their steering wheel, um which which shows them a number of different things. um
00:33:36
Speaker
and And we have different views for different tools that that they can look at. but um I do remember one time or but the the first time that um the driver was looking at one of my tools in the car. I was like, oh, that's that's pretty cool. That's yeah, that is that's fun. yeah um I think most of the time the drivers would look at the tool was sort of at the behest of the of the engineers we were back in the truck. And. um you know the engineers would be looking at something and it would be more like a hey look at this kind of kind of thing right um that was generally how it went but not for every driver there were there were at least one that uh definitely more kind of data minded i thought you've got the early tom cruise the beginning of days of thunder and then the tom cruise at the end of the movie who understands a little bit about racing yeah exactly yeah yes That's very cool. That's very cool work.
Transition to Cybersecurity and Personal Reflections
00:34:30
Speaker
Before I let you go, what are you up to now? um Yeah, so now i'm I'm kind of back in the and the cybersecurity space, actually. um as As interesting as the racing life and world is, um it is not entirely conducive to raising a family. Right, yeah, a lot of travel. There's a lot of travel and a lot of commitment in the summer. And um yeah, we've got... ah we've got one at home and one on the way so um sort of back into cyber security i'm also doing um some work with infograte um if if local listeners are familiar with that um data visualization consultancy um trying to kind of you know up level their their data intelligence capability basically so that's kind of what i'm doing right now do you still have time on the side to do any uh little uh motorsports data viz just for fun here and there. I've thought about it. um you know I still have the Twitter account I still have the yeah ah followers that I had then. I'm sure i who I imagine would be interested in seeing that come back to life. I'm less excited about the platform now than I was in the past. Right. Right. Yeah. um
00:35:41
Speaker
but that yeah Whole new f one a whole new set of cars. A lot has changed. So it's about to change. Yeah. So Yeah, so i've I've thought about it. I don't know. i might I might give that a rest for a little bit. but Well, as someone who's had those two young kids, probably having the extra time is not something that's going find its way in the next few months.
00:36:01
Speaker
But maybe it's okay.
Connecting with Michael on LinkedIn
00:36:04
Speaker
Okay, last thing. If people want to learn more, either about your work at McLaren or Today in the Cyber World or the stuff you're doing for Infogrames, what's the best way to get hold of that?
00:36:16
Speaker
Yeah, you know I don't have any sort of website or anything that I can i can direct people to, but if you want to connect with me on on LinkedIn, I'm on Michael Gethers on LinkedIn. Awesome. Yeah, I'll be happy to say hey.
00:36:30
Speaker
That's great. Michael, thanks so much for coming on the show. Really interesting stuff, and good luck with the new baby. Thank you. I appreciate it. Bye. Thanks everyone for tuning into this week's episode of the show. Hope you enjoyed that. Hope you will check out other episodes that are available on Spotify, iTunes, YouTube, Zencaster, and wherever you get your podcasts. If you have a moment, please look down and click those five stars on your app to rate or review the show. I do appreciate it. Helps me get more guests on the show and helps me keep bringing this show to you every other week.
00:37:05
Speaker
So that's all we've got for this week on the show. Until next time, this has been the Policy Biz Podcast. Thanks so much for listening.