从PDF到维修区：为迈凯伦车队构建实时数据产品

欢迎回到PolicyViz播客。

Welcome back to the PolicyViz podcast.

我是您的主持人乔恩·施瓦比什。

I'm your host, Jon Schwabish.

在本期节目中，我们将关注赛车领域中的数据、数据可视化和仪表盘，特别是印地赛车。

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.

我邀请到了迈克尔·盖瑟斯，他曾效力于迈凯伦印地赛车车队，负责数据采集、处理以及创建所有有助于车队提升表现、赢得比赛和提高效率的工具。

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.

听他讲述自己如何进入赛车领域，以及当时需要做哪些工作、付出了怎样的努力，这是一次非常有趣的对话——因为在当时，这个行业中几乎还没有数据可视化和数据科学团队。

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.

所以我想你会听到许多我们在日常工作中都会面临的问题：如何构建这类能力，如何在团队中培养以数据为导向的文化，从而帮助人们做出更明智、更精准的决策。

And so I think you're gonna hear probably what a lot of us face in our day to day work, which is building these sorts of capabilities, building a culture around data that can help people make better and more informed decisions.

那么，我们马上进入访谈环节。

So let's get over to the interview.

以下是我在PolicyViz播客上与迈克尔·盖瑟斯的对话。

Here's my chat with Michael Gethers, only on the PolicyViz podcast.

嘿，迈克尔。

Hey, Michael.

很高兴见到你。

Good to see you.

很高兴认识你。

Good to meet you.

谢谢你来参加节目。

Thanks for coming on the show.

是的。

Yeah.

当然。

Of course.

很高兴来到这里，乔恩。

Glad to be here, Jon.

好的。

Okay.

所以我非常期待和你聊天，我想说有两个原因。

So I'm pretty excited to chat with you for, I would say, two reasons.

首先，我是通过《仪表盘交付书籍》了解到你的工作的。

First, I learned of your work through the dashboards that deliver book.

所以我很高兴能和你一般性地聊聊仪表盘设计和创建相关的话题。

So I'm excited to talk to you just generally about dashboarding and creating things.

另外，因为你在赛车团队工作，你接触到的数据量简直让我垂涎三尺。

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.

而且，我儿子对赛车非常着迷，也想进入这个领域，而你已经帮我们联系了几位工程师。

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.

所以提前谢谢你了。

So thank you for that in advance.

但总的来说，这里有几个不同的原因。

But, so so a couple of different reasons here.

所以我想我们或许可以从你的背景开始聊起，你曾是迈凯伦的数据与策略负责人。

So I thought maybe we'd start with a little background, and you were the lead of data and strategy at McLaren.

所以也许我们先聊聊，你是怎么走到这一步的，以及你当时的职责是什么。

And so maybe we just start, like, how you got to that spot and then what your role was.

是的。

Yeah.

我进入赛车领域的方式，可以说是非常不寻常的。

So I I came, into motorsport, I think, from a fairly unconventional, unconventional path.

最终，我非常感激自己所拥有的机会和开启的道路。

And ultimately, I, you know, I'm very grateful for the for the opportunity that I had and lanes that opened up for me.

但我的背景其实是一名核心的数据科学家。

But my background really is a core data scientist.

我在加州大学伯克利分校学习统计学。

I studied statistics at UC Berkeley.

我在硅谷工作过。

I worked in the Bay Area.

我在硅谷做过科技相关的工作。

I did the tech thing in the Bay Area.

我第一份工作其实是精算分析师，刚毕业时做的。

I worked as an actual actually, my first job out of out of college was as an actuarial analyst, then Oh.

我后来在几家不同的科技公司工作过。

I kind of worked at a number of different tech companies.

最值得一提的是在旧金山的Salesforce，之后我转行做了网络安全相关的工作。

Most notably in San Francisco was Salesforce, and then I pivoted to some cybersecurity work after that.

但从小我就一直去赛车场，尤其是五岁起就和我爸一起去印第安纳波利斯赛车场。

But always, you know, since I was a kid, you know, I went to the racetrack with my dad, specifically the Indianapolis Motor Speedway with my dad since I was five years old.

哦，原来如此。

Oh, alright.

他带我去，我就特别喜欢，你知道吗？

So he took me, and I just loved it, you know?

那是我成长过程中的一部分，也是我和他之间的一种情感纽带。

It was something that I grew up with, and it was sort of a bonding thing between me and him as I was growing up.

我十岁那年看了第一场赛车赛，从那以后每一届印第500我都去了。

And I went to my first race when I was 10 years old, and I've been to every Indy five hundred since then.

哇。

Wow.

除了新冠那年。

Except for the COVID year.

对。

Right.

所以这一直是我身份的一部分，而且由于这一点，再加上我是个数据迷，就像我们很多人，以及很可能听这个播客的许多人一样，我发现自己被吸引去寻找更多关于比赛的数据，因为赛车……

So it was always kind of a part of who I was, and 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, I just found that I was kind of drawn to finding more data about the race, because racing Mhmm.

这是一种运动，你知道的，它不像篮球或足球那样，你能在眼前看到所有正在发生的事情，对吧？

Is a sport where, you know, it's not like basketball or football, really, where everything that's happening you can see in front of you, right?

嗯。

Mhmm.

如果你在电视上看比赛，一次只能看到几辆车，或者可能只能看到几个弯道。

If you're watching a race on TV, you can see a couple cars at a time, or maybe a couple corners at a time.

对。

Right.

即使你在现场，赛道也是非常巨大的，对吧？

Even if you're at the race, the tracks are massive, right?

你根本看不到整个场面。

You just can't see the whole thing.

我记得有一年和我爸爸一起去印地500大赛，我当时有很多关于比赛中发生的事情的问题，但完全无法得到答案，那时候也没有办法获取这些信息。

And so I remember one year being at the Indy five hundred with my dad, and I just had questions about what was happening, what was going on during the race that I just couldn't answer, and there was no way to get that information at the time.

对。

Right.

所以事后，我几乎把互联网上所有能找的数据都翻了一遍。

And so what I did after the fact was I I just kinda scoured the internet for for data.

事实上，印地赛车公司有趣的是，他们发布了一些包含计时数据的公开PDF文件，但这些数据并不十分完善，也不容易使用。

And actually IndyCar, interestingly enough, was, they published public PDFs that contain some timing data, not extremely robust and not extremely easy to use.

是的。

Yeah.

他们发布了一些这样的计时数据。

They published some of that timing data.

于是我抓取了这些数据，开始进行一些分析，并把它们发布到网上。

And so I just scraped that started doing some analysis and I put it on the internet.

是的。

Yeah.

所以我创建了它。

So I created it.

起初我只是有自己的一个Twitter账号，开始在那里发布内容。

Well, I just had a Twitter account for myself originally, and started posting things there.

通过这个，我认识了一位车手，特别是JR Hildebrand，他对我的做法很感兴趣。

Through that, I actually got to know one driver in particular who was JR Hildebrand, and he was just interested in what I was doing.

当时他正好由Salesforce赞助。

He happened to be sponsored by Salesforce at the time.

哦，有意思。

Oh, interesting.

所以当时我正在Salesforce工作，这之间就有了自然的联系。

So there was that kind of natural connection because I was working at Salesforce when I was doing this.

那可以说是我最初的一点小突破，只是因为是这样。

And that was kind of my first, just like, in a little bit, just because Yeah.

你知道，他邀请我作为Salesforce演示的一部分去赛道现场。

You know, he invited me to come to the track as part of the Salesforce demo, basically.

哦。

Oh.

他们在赛道上进行的演示。

That they were doing at the track.

那可以说是我最初的一点小切入。

And that was kind of my first, just a little wedge in.

是的。

Yeah.

然后时间快进，那原本只是一次性的活动。

And then fast forward, you know, that was kind of a one time thing.

几年后，我刚辞去了我的网络安全工作。

Fast forward a few years, and I had just left my cybersecurity job.

我和我妻子当时住在伦敦。

My wife and I were living in London.

我们正准备离开伦敦。

We were moving from London.

我们计划经由瑞士返回美国，那是一个更长的故事，我们这里就不细说了。

We were planning to come back to The States by way of Switzerland, which is a longer story that we don't need to get into here.

但当我身处瑞士时，我想：你知道吗？

But when I was in Switzerland, I was like, you know what?

我真的很喜欢做印地赛车的分析工作。

I really love doing that IndyCar analytics thing.

所以我要重新开始做这个。

So I'm gonna start doing that again.

但这次，我要做得更大、更好。

But this time, I'm gonna do it bigger and better.

对吧？

Right?

我还想把它当作一次学习的经历。

And I wanted to treat it as a learning experience too.

我一直想学习D3，因为我从前对前端网页开发一无所知，而它一直是我感兴趣的东西。

I'd always wanted to learn D3 because I'd never I'd never known really anything about front end web development, and it was just kind of something I had always been interested in.

于是我创建了一个新的Twitter账号。

So I created a new Twitter account.

它叫Rosa three，Yeah。

It was called Rosa three Yeah.

任何熟悉印第安纳波利斯赛车场、印第500赛事的人都知道，赛车会排成11排，每排三辆，这个名字就是这么来的。

Which anybody who's familiar with the Indianapolis Motor Motor Speedway, the Indianapolis Five 100, the cars start in 11 rows of three, that's where that comes from.

所以我的Twitter用户名是Rosa three。

So my my Twitter handle was Rosa three.

我还创建了一个配套网站rosa3.com，然后全身心投入，几乎像全职工作一样，专门制作关于印地赛车的分析内容。

I created an accompanying website called rosa3.com, and I just kind of went all in like it was my full time job basically, creating analytics content basically around IndyCar specifically.

这很容易，因为我有太多问题了。

It was easy I just had so many questions.

所以当你有这么多问题时，你就想追着它们去寻找答案。

So when you had so many questions, you just want to kind of chase after it and answer it, answer those questions.

回望我在那里的工作演变，我觉得挺有趣的，因为我能看到自己在几个月内不断进步——因为你越来越擅长提出正确的问题，也越来越擅长实际的技术开发部分。

And, you know, it's kind of funny for me to look back at the evolution of my work there because I could see myself getting better over a period of months just because you get better at asking the right questions, you get better at the actual technical development part of it.

你看我现在回头看看我做的那个网站，很明显那是我的第一个作品，真的是我的第一个作品。

You know, my website, I look back at the website that I made now and it's, you know, it's clear that it was my first Yeah, was my first yeah.

网站，但我某种程度上依然为它感到骄傲，因为它确实带我走到了今天的这一步。正是通过这个，加文·沃德联系了我，他当时即将成为艾伦·麦拉伦车队的车队领队，也就是麦拉伦赛车的印地赛车队，嗯。

Website, but I'm still proud of it on some level because it really took me where where I ended up so through that, ultimately, Gavin Ward reached out to me who, he was the soon to be team principal at Aaron McLaren in Aaron McLaren's IndyCar team, which is the McLaren racing IndyCar team Mhmm.

在印第安纳波利斯。

In Indianapolis.

他之前曾在红牛F1车队工作，也在印地赛车的潘斯克车队工作过。

Formerly, worked at Red Bull F1, and he worked at Penske Racing in IndyCar as well.

他联系了我，只是问了一个问题：嘿，你有没有想过进入赛车行业工作？

He reached out and just asked the question, you know, hey, have you ever thought about working in motorsport?

我只是有点愣住，心想：

And I was just kinda like,

是的。

yeah.

差不多每次都是。

Kinda every Yeah.

是的。

Yeah.

是的。

Yeah.

这种对话持续了一段时间，直到。

Sort of that conversation went on for a while to to Yeah.

真正确定了它会是什么样子。

Actually determine what it was going to look like.

但几个月后，我加入了团队。

But a few months later, ended up joining the team.

那是2022年。

That was at the 2022.

哇。

Wow.

所以让我问几个问题。

So let let me ask a couple of questions.

在你刚开始尝试那些PDF文件，到后来决定全力投入之间，Indy发布的数据有没有发生根本性的变化？

So between when you first played around with those PDF files and then decided to kinda go all in, did the data that Indy was publishing, did that change fundamentally?

没有。

No.

数据本身没有发生根本性变化，但我的访问权限发生了根本性变化。

It did not change fundamentally, but my access changed fundamentally.

好的。

Okay.

所以发生了一些事情。

So a couple things happened.

你知道，我当时只是尽力用爬虫做我能做的事，对吧？

You know, I was just kind of, I was doing what I could with the scraping, right?

对。

Right.

他们的PDF文件，你知道，都是公开发布的PDF。

Of their PDFs, you know, they're publicly posted PDFs.

PDF解析并不是一门精确的科学。

PDF parsing is not a precise science.

现在可能比当时好一些了，但我确实花了大量时间做数据校正，基本上是在

It's better now probably than it was at the time, but I did I spent a lot of time doing data correction basically in

是的，是的。

the Yeah, yeah.

但有一刻，我确实联系了某个人。

But at one point I actually reached out to somebody.

实际上，我都记不清是我主动联系了他们，还是他们联系了我。

Actually, can't remember if I reached out or if they reached out to me.

无论如何，最后有个人给了我更好的访问权限，让我能使用一些更优质的数据。

In any case, somebody ended up giving me better access some data that I could play with.

明白了。

Gotcha.

我能够获取到CSV格式的数据，这显然让工作变得容易和准确得多。

And I was able to get data in in CSV format, and that made it obviously much, much easier and more accurate.

当然，这些并不是我后来在团队中实际使用的数据，但正是这些数据让我具备了比以往更大规模开展这项工作的能力。

And, obviously, those that was not the data that I ended up working with on the team, but that that is what gave me my kind of the ability to do this at a at a greater scale than I was able to do before.

我很好奇，你可能不知道答案，但我知道很多做类似事情的人，比如冰球、篮球和其他运动项目，他们也做类似的工作，然后他们能接触到更多数据，和球队有关系之类的。

I'm curious, and you may not know the answer to this, but, 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, they have relationships with teams or something like that.

你有没有什么直觉，为什么他们会——或者你为什么会联系他们，或者他们为什么会主动给你更多数据访问权限？

And do you have any instinct as to why they would re or you would reach out to them, or or they would just give you more access to the data.

比如，当时他们是不是根本没有专门做数据可视化和数据分析的人？我的意思是，这里有海量数据，我只是好奇，为什么你这个在外面几乎出于兴趣做这件事的人，他们会说，嘿，给你一堆数据。

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, here's a bunch of data.

是的。

Yeah.

没错。

Exactly.

是的

Yeah.

就是一个普通人。

It's just a guy.

是的

Yeah.

说实话，我也不太知道答案。

You know, I I don't know the answer really.

我只能做一些推测，但我认为，第一，我做的事情很酷。

I can only kind of hypothesize, but I think, number one, what I was doing was cool.

人们喜欢它。

People liked it.

是的

Yeah.

这不仅仅是为了我自己。

It wasn't just for me.

这是为一群真正热爱这项运动的人做的。

It was for a community of people that really loved the sport.

而且是的。

And Yeah.

在我看来，我所做的事情实际上对这项运动非常有帮助，它以一种以前人们无法参与的方式让更多人参与进来。

In my mind, what I was doing was actually quite good for the sport, it was giving people more people engaged in a different way than they were able to be engaged before.

而且也可能吸引到一群他们之前无法触及的新观众，比如那些从未觉得自己在赛车中有归属感的数据爱好者，因为所有数据都一直保密，对吧？

And also, might capture a new audience that they weren't able Maybe to capture there's the data nerd audience that never thought they had a home in racing, because all the data was so under wraps, right?

所以我不知道确切的答案是什么，但我认为它大概就是这类原因，对吧？

So I don't know what the actual answer is, but I think it's something in that vein, right?

是的。

Yeah.

而且，确实有一个人对我的所作所为感到兴奋，并希望帮助我把它做得更好。

And yeah, I just sort of had somebody who who was excited about what I was doing and wanted to help help me make it better.

是的。

Yeah.

我的意思是，这不正是我们所有人都需要的吗？

I mean, that's what that's what we all need, right?

我们需要这些啦啦队队员。

We need those cheerleaders.

是的。

Yeah.

对。

Yeah.

好的。

Okay.

那你去了迈凯伦。

So you get to McLaren.

你现在是回到印第安纳波利斯了吗？

Are you now are you now back in Indianapolis?

是的。

Yeah.

你在伦敦的时候搬回来了吗？

When you were in London, you moved back?

是的。

Yeah.

所以，你知道的，简单说说我的地理变迁，就是我的地理位置变化，是的，是的。

So, you know, quick quick history of geography, like my geographical Yeah, yeah.

演变。

Evolution.

我在湾区上学，之后在湾区工作了一段时间。

Went to school in the Bay Area, worked in the Bay Area for a while.

我和我现在的妻子一起搬到了伦敦，待了大约五年。

My now wife and I moved together to London for about five years.

她家其实是瑞士的，所以当我们决定回美国时，我们想，好吧，在回去之前，不如先在瑞士待一年。

Her family's actually Swiss, and so when we made the decision that we were going to come back to The US, we were like, okay, well before we do that, let's just do a year in Switzerland before we come back.

好的。

Okay.

我们原本计划回加利福尼亚，那是我们离开伦敦前各自称作家的地方。

We were planning to go back to California, which is where both of us called home before we we left London.

但后来印第安纳波利斯出现了机会，你知道，这值得一试。

But then the the opportunity came up in Indianapolis and it it was, you know Yeah.

我得抓住这个机会。

I had take a shot.

对吧？

Right?

当然。

Absolutely.

必须去体验一下那是怎样的。

Had to to see what that's like.

没错。

Right.

当然。

Absolutely.

好的。

Okay.

所以你现在在印第安纳波利斯。

So you're in Indianapolis.

你在迈凯伦工作。

You're working for McLaren.

那日常工作是怎样的？

What is that day to day like?

我的意思是，迈凯伦的办公室在哪里？

I mean, where I mean, is the McLaren offices?

他们是在印第安纳波利斯赛车场吗？我只去过一次赛车场，那是去年夏天去看IMSA比赛的时候。

Are they in I've only been to the Speedway once, and it was actually last summer for an IMSA race.

所以我不太清楚。

So I'm not

好的。

Okay.

我儿子是个狂热的一级方程式粉丝，现在也喜欢GT赛事，但他有点看不起NASCAR，尤其是。

My son's a big f one fan and now a GT fan, but he kinda looks down on on NASCAR, especially.

所以，好吧。

So Okay.

所以所有听这个节目的赛车爱好者都可以去批评他。

So all the racing people who listening to this can can yell at him.

但你每天去办公室，具体是用他们的数据做什么呢？

But you're going to the office every day and, like, what are you doing with with their data?

我的意思是，你是不是一直在接收大量的数据？

I mean, are you just ingesting a ton of data all the time?

是的。

Yeah.

我的意思是，这挺有意思的。

I mean, it was it was interesting.

是的。

Yeah.

现在回头看，我觉得这真的很有意思。

Looking back at it, it's it's really interesting now for me, I guess.

是的。

Yeah.

我刚去的时候，我们几乎不知道该专注于什么，对吧？

When I when I got there, it was almost like we didn't really know was the best thing to focus on, right?

他们以前从未有过像我这样的人。

They had never had somebody like me.

你知道，我觉得在F1方面，他们可能领先了几年，但也没领先太多。

You know, I think on the F1 side maybe they were a few years ahead, but not wildly ahead.

对。

Yeah.

我是第一个被引入印地赛车队的核心数据人员。

And I was really the first core data person that was brought into the IndyCar team.

而这其中很大一部分要归功于加文的远见。

And, you know, a big part of that was was Gavin's vision.

对吧？

Right?

在我加入之前，我们已经讨论了几个月了。

And that's what we had talked about over a few months before I ended up joining.

所以，情况就像是：是的，我们拥有所有这些数据。

So it was it was kind of like, yes, we have all of this data.

第一，数据是怎么存储的？

Number one, how is it stored?

第二，数据能被查询吗？

Number two, like, is it queryable?

第三，我们最终想要从这些数据中得到什么？

Number three, what what is the actual output that we want from this?

工程师们需要什么？

What do the engineers need?

是的。

Yeah.

当时对于那会是什么样子，存在很多不确定性。

There was a lot of kind of uncertainty around what that was going to look like.

所以对我来说，这完全是不断试错的过程。

And so it took for me it was a lot of trial and error, frankly.

我最初试图整合我为在Twitter和我的网站上公开发布而开发的许多内容。

I was trying to initially, was trying to put together a lot of the kinds of things that I was developing for public consumption on Twitter and on my website.

但我想你不会惊讶地发现，这其实并不是工程师们真正想要和需要的，对吧？

But I don't think you'll be surprised to learn that that's not really what the engineers wanted and needed, right?

对。

Right.

我认为他们最初把我做的某些东西看作是‘哦，这些图片挺好看，但实质内容在哪？’，幸运的是，我有传统的数据科学背景，因此能够在这两者之间找到平衡。

And I think they initially regarded some of what I was doing as like, oh, these are pretty pictures, but where's the And substance, so it was, fortunately for me, had sort of the traditional data science background, so I was able to kind of meet in the middle there.

而正是在这个时候，我与团队一起构建的这个应用才真正开始被接受，人们也开始从中获得实际价值。

And that's where I think the application that I built and that I built with the team there really kind of took hold a little bit and people started to really get some value out of it.

所以，当你看赛车或者看布拉德·皮特演的《F1》电影时，你知道，赛道边上有很多屏幕，人们坐在那里。

So were you I mean, when when when you watch racing or you watch the f one movie with Brad Pitt, you know, there's all these screens, people sitting on the on the on the track.

你主要是专注于实时分析吗？

And were you primarily focused on sort of real time analytics?

还是更偏向工程方面，比如他们会测试这个后翼、前翼，测试汽车的其他设置，然后我们把所有这些测试结果收集起来，整合在一起？

Or was it more on the engineering side of, you know, they're gonna test this wing, this back wing, this front wing, they're gonna test this other setup on the car, and let's ingest all of the results of those tests and, you know, put it together?

还是说以上所有都是？

Or, you know, or is it all all of the above?

是的。

Yeah.

可能是以上所有，但我想我们确实。

It was it was probably all of the above, but I think we Yeah.

我们一开始试着把它简化一点，嗯。

Tried to we tried to shrink it a little bit at the beginning Mhmm.

简化成一个更容易接受的版本，毕竟你从零开始，基本上是这样，没错。

To something that's more palatable, I guess, when you're starting from from nothing, basically, which we Right.

所以我们最初从计时和计分数据开始。

And so what we started with was timing and scoring data.

对于那些不了解这个术语的人来说，基本上每个赛车场都有若干个固定的计时点分布在赛道周围，每当赛车经过这些计时点时，系统都会记录下来。

And that's for those that don't understand what that term is, basically every racetrack has a number of a discrete number of timelines around the track, and when every car crosses over that timeline, it's registered.

比如，7号赛车在X时刻通过了起点/终点计时点，对吧？

So car seven crossed timeline startfinish at X time, right?

实际系统比这更复杂一些，但核心原理大致就是这样。

And it's a bit more robust than that, but that's more or less, that's like what at it its core.

仅凭这些数据，你就能做很多事情，但这需要仔细思考如何对这些数据进行转换。

And there's a ton of stuff that you can do with just that alone, but it requires a lot of kind of careful thought about how you're going to how you're going to transform that data.

你可以做各种明显的事情，比如：每圈的速度是多少？

You can do all kinds of things, obvious things from like, okay, how fast are we on every lap?

对吧？

Right?

这非常直观。

That's very obvious.

每个赛段的速度又是多少？

How fast are we in each given sector?

如果我们只追踪一辆车，每秒一次，那么在每个赛段我们有多快？

How fast are we in each given sector if we're following one car at one second?

如果我们处于交通密集的情况，五秒内有五辆车，我们又有多快？

How fast are we if we're in heavy traffic and there's five cars within five seconds?

这类问题，你得从简单开始，逐步构建这种能力。

Those kinds of things, you just start to build, you know, you start small and just kind of build up that capability.

一旦你建立了分析能力，我们接下来要做的就是：如何以一种快速易懂的方式向人们呈现这些数据，对吧？

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 very digestible very quickly, right?

因为这正是关键所在。

Because that's kind of the name of the game.

赛车正在赛道上行驶，对吧？

The car is on track, right?

我们正在开发一个应用程序，既能在赛车在赛道上时使用，也能在回到维修区或卡车后作为数据分析工具使用。

And we are building an application to be used while the car was on track in addition to as a kind of data interrogation tool after we're back in the garage or back in the truck.

在这种情况下，无论哪种场景，人们都希望尽快获得数据。

And, you know, in this scenario, in both scenarios, people want data as fast as possible.

但特别是在赛车在赛道上时，我们得立刻弄清楚当前发生了什么，这个改动是好还是坏？

But especially when the car is on track, it's kind of like, well, we to understand exactly what's going on right now, and was that a good change or was that a bad change?

而很多优化和效率提升正是源于我们为这个工具所加入的这些功能。

And that's kind of where a lot of the sort of optimizations and efficiency gains had to come from that we ended up building into the tool.

但这不仅仅是技术效率，某种程度上也涉及认知效率。

But it's not just technical efficiencies, it's also kind of cognitive efficiencies on some level.

你该如何让人快速理解这些信息？

It's like how do you get people to understand this really fast?

对。

Right.

这对我来说是一个前所未有的挑战。

Which is a challenge that I had not really been faced with before.

对。

Right.

所以，我本可以问一大堆问题，但咱们先从认知这个角度开始吧。

So, I mean, I could ask a ton of questions, but let's start with the folks Let's start with this cognitive question.

所以，你显然是在展示一些内容，如果我们想简化问题，完全可以聚焦在比赛期间，但你展示的视觉信息，有些人可能以前从未以这种方式见过。

So you're you're 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.

也许他们更习惯于视觉化的呈现，而不是表格形式。

Maybe they're way more visual than tabular, maybe is how they were used to them.

所以，你有没有坐下来和这些人交流过？

So, you know, did you sit down with folks?

比如，你有没有进行过正式或非正式的用户测试，来看看什么样的视觉呈现对他们来说最实用、最直观、最即时？

Like, did you do kinda like formal or informal, like, user testing to see what would be the most useful, intuitive, immediate visual that they could get?

你是怎么和团队合作，打造出你们知道他们能用得上的东西的？

Like, how did you work with the team to, like, build something that you know that you knew that they could use?

是的。

Yeah.

这是个很好的问题，因为最初我并没有这么做。

That's a great question because initially, I didn't.

一开始，我只是想：我要直接做出我认为需要的东西。

And I initially, I was just like, I'm gonna go build what I think I need.

这显然不是正确的做法，但你知道，人总是在经历中成长。

Which is not the right way to go about it, obviously, but you know, you live and you learn.

是的，没错。

Yeah, right.

对我来说，我是在寻找那种我犹豫是否该称之为产品倡导者的人——那些我认为会最频繁使用它的人，了解他们真正的痛点以及他们已有的工具。

Yeah, for me, it was kind of finding the, I hesitate to say champions of the product that we were building, 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 and the tooling that they already had.

他们显然是非常有才华的工程师，对吧？

They're, obviously, they're, you know, extremely talented engineers themselves, right?

他们并不陌生于查看数据。

They're not unfamiliar with looking at Right?

对，你们这些技术背景的人，其实是英语文学专业的，对吧？

Right, you're technically English lit majors, Correct.

是的。

Yeah.

所以他们对这个领域了如指掌，远胜于我，对吧？

So they're, you know, they know what, they know the domain 10 times better than I ever will, right?

是的。

Yeah.

但他们都懂得如何分析数据，不过，这可能是我个人的看法，我认为随着我们不断开发这个工具，这一点得到了验证。

But, and they know how to look at data, but they just, you know, this is a personal opinion, I guess, but I think it was sort of proven out as we continued to build the tool.

之前为他们构建的数据工具都是现成的商业产品，人们试图搭建这些工具，但从未真正内部开发过，至少在迈凯伦印地赛车队是如此。

The data tools that had been built for them before, which were all sort of off the shelf products people were trying to build that was never really done in house, at least not at the McLaren IndyCar team.

我们并不是说，我真希望我能给你看看那些工具。

We're not I mean, I almost wish that I could show them to you.

它们在视觉上非常混乱，数据量巨大，但那些工程师非常擅长从中理出头绪。

They're so visually messy, and they're just there's so much data on it that it's, you know, those engineers are extremely adept at making heads and tails of it.

对。

Right.

但对于这种快速查询的需求——比如告诉我我们在哪里快、在哪里慢，和X、Y或Z车队或车手相比，在这种情境下，需要能够快速解析数据，对吧？

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 team or driver or, know, in this kind of scenario, it was to parse, right?

是的。

Yeah.

所以，正是通过这些对话和观察，尤其是当你提到布拉德·皮特的那部F1电影，以及其他任何流行文化中对赛车运动的描绘时。

And so that's where, you know, through those conversations and through just observation, really, because you're talking about the Brad Pitt movie, the F1 movie, and any other sort of popular culture representation of what motorsport is.

看起来非常高科技，然后你看到大屏幕和计时台上的工作人员。

Looks very high-tech, and then you see the big screens and people on the timing stands.

你知道，我非常幸运，我就是其中一员，对吧？

You know, I was very fortunate, I was one of those people, right?

我有机会待在维修区的计时台上，可以看自己的屏幕，也能看到他们的屏幕，清楚地看到他们在看什么。

I got to be I was on the timing stand in the pits, and I could look at my screen, and I could look at their screen, I could see what they were looking at.

我能够与所有工程师和车手交流，无论是在练习赛、正赛还是排位赛中。

I was able to talk to all the engineers, the drivers, as we were kind of in a practice session and in races and in qualifying.

所以，我拥有这样一个反馈循环，而这个循环本身就是工作的一部分，这太棒了。

So I had that feedback loop, and the feedback loop was kind of built into the job, which was great.

对吧？

Right?

如果没有这个机会，我认为我无法打造出我所创造的这一切。

I don't think I could have built what I built without having that opportunity.

如果你只是旁观者，没有真正接触过这个世界的实际情况和需求，那确实是一个非常深奥的领域。

Like, if you were just a you know, if if if you didn't actually have the exposure to what that world is like and what those needs are, it's quite, you know, it's an esoteric domain, actually.

当然。

Sure.

这很复杂，而且真的很难在其他地方找到类似的应用——当然，人们可能会对这一点有争议。

It's complex, and it's, it really doesn't apply anywhere else in So, the real well, people argue with that probably.

但这是一个非常独特的领域。

But it's just a very unique domain.

是的。

Yeah.

毫无疑问。

For sure.

除非你亲眼见过，否则很难理解它是什么样子。

And it's difficult to understand what it's like unless you've seen it.

我很幸运，有机会亲眼目睹并亲身体验，我认为这帮助我更好地构建了这些事物。

And I 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.

是的。

Yeah.

不。

No.

当然。

For sure.

所以我的第一个问题是，在比赛周末你坐在计时站时，你那时在做什么？

So my first question is when you are in the timing stand during a race weekend, what are you doing, during those times?

你在调整视角吗？

Are you adjusting the view?

你到底在做什么？你能快速完成哪些事情，对团队其他成员有帮助？

Are you like, what what is and what can you do quickly enough that will be useful to the rest of the team?

是的。

Yeah.

嗯，你知道的，我在团队中的角色，每个人通常都要承担多种职责。

Well, you know, my role on the team, you know, everybody kinda wears multiple hats.

这些车队并不大。

These teams are not are not huge.

对。

Right.

至少印地赛车的车队并不大。

At least the IndyCar teams are not huge.

F1车队要大得多。

The F1 team is much bigger.

但我的职责是担任比赛策略师，这对我来说是个很难的角色，因为我之前从未担任过比赛策略师，也没怎么接触过这方面的经验。

But, you know, my role on the time extent was as a race strategist, which was a difficult role to be thrown into, having never been a race strategist before, or having Right, been around right, yeah.

但与此同时，这其实很合理，因为很多比赛策略都具有很强的概率性，比如：如果我们现在进站，我们有多大概率能比其他车更快出站？或者从现在到比赛结束之间出现黄旗的概率是多少？这会如何影响我们接下来的策略？

But at the same time, it kind of makes a lot of sense, because a lot of race strategy is very probabilistic, and 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 five Or what's the probability right now of there being a yellow flag between now and the end of the race, and how does that influence our strategy going forward?

我们应该节省燃油，努力撑到终点，指望出现黄旗或安全车情况吗？

Should we conserve fuel and try to make it to the end, hoping we get a yellow flag safety car situation?

还是应该全力冲刺，多加点油，这样才是完成这场比赛最优化的策略？

Or should we just run as fast as we can, splash for fuel, and is that the most optimal to get to the end of this race?

我们有多大可能在赛道上超越任何一辆车？

What's the probability that we're going to be able to pass any given car on track?

因为这也相当困难，对吧？

Because that's quite difficult as well, right?

这些车并不是在真空中行驶，而是多辆车在赛道上一起竞争。

These aren't cars running in a vacuum, they're a bunch of cars running on track together.

是的。

Yeah.

所以，我认为这个策略问题实际上非常适合用数据科学的方法来解决。

So the strategy problem actually does really lend itself to a data science approach, I think.

所以，虽然当时感觉像是被扔进了深水区，有点困难，但我觉得这个角色在某种程度上其实还挺合适的，是的，没错。

And so while it was a difficult, you know, I felt like getting thrown into the deep end a little bit there, It was a role that I think, yeah, was not ill fitting on some level, Yeah, right.

所以，我要回答你最初的问题，我在赛道边到底在做什么？

And so what I'm, you know, to answer your original question, what was I doing on stand?

是多方面的，对吧？

It's multiple things, right?

就是在数据进来时进行观察，查看我们的车相对于赛道上其他车辆的位置，跟踪谁的燃油即将耗尽、谁即将进站、谁看起来能顺利超车、谁不行。

It's looking at the data as it comes in, looking at where our car is relative to other cars on track, keeping track of who is low on fuel, who's going to be pitting soon, who looks like they're able to pass well, who doesn't.

这是其中一个方面。

That's one side.

这是一种非常主动、即时性的工作，但同时也在进行一种被动的工作，那就是：我需要构建什么来让这份工作更轻松？

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?

是的，更轻松，对吧。

Yeah, easier, right.

所以我认为这两件事是同时发生的，尽管后者更多是观察性的，而不是

And so both of those things I think were kind of happening at the same time, although the latter was more observational than

对。

Right.

对。

Right.

对。

Right.

前者。

The former.

所以我有个简单的问题问你。

So one quick question for you.

印地赛车中，每支车队必须共享哪些数据？

What are the rules in IndyCar about what each team has to share?

因为你刚才提到的关于追踪其他赛车的情况， presumably McLaren 并不会收集每辆赛车的数据。

Because to to the point you just made about tracking what other cars are doing, presumably McLaren isn't collecting data about every car.

我的意思是，也许他们确实收集了，而红牛队没有，这是存在数据共享的情况吗？

I mean, maybe they are, and Red Bull's not like there's a data sharing situation that's going on?

嗯。

Mhmm.

是的。

Yeah.

那么规则是？

So rules?