104: Breaking into Sports Analytics w/ Ken Jee

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[00:00:30] I saw they had an internship but it was a normal undergrad internship. And so I reached out and I said, hey, I'm a grad student but can I still apply for this?

[00:00:39] And I ended up getting selected and they gave me more pay and they essentially created a grad student internship for me.

[00:00:48] And to me, that is one of these if you don't ask, you don't know type of things.

[00:00:53] Welcome to the Data Career Podcast. The podcast that helps aspiring data professionals land their next data job.

[00:01:00] Here's your host Avery Smith.

[00:01:03] Hi everyone, welcome back to another episode of the Data Career Podcast. I'm your host Avery Smith and in this episode we have a very special guest.

[00:01:11] It is Mr. Kenji, if you've never checked out Ken before. He has an awesome podcast himself, Ken's nearest neighbors.

[00:01:18] He is a profound YouTuber where we talk about data science stuff and he recently just started a new podcast called The Exponential Athlete

[00:01:26] where he's really diving deep into the stories behind some of the greatest athletes of all time.

[00:01:32] And what makes them as great as they are, how do they think, how are they raised, how do they train and what insights can we glean and apply into our lives.

[00:01:41] So I'll have the link for all those things in the show notes down below but in today's episode we talk about Ken's day job which he is the director of a sports analytics firm.

[00:01:53] So he basically manages data science for a bunch of golf and a bunch of USA basketball, USA golf and USA basketball.

[00:02:01] Which is pretty cool. I think that's kind of a dream job for a lot of people. A lot of people I talk to they want to get into sports analytics.

[00:02:07] And so when you guys ask me that question, I'm going to send you this podcast and you can hear Ken's thoughts and Ken's stories.

[00:02:14] Of course the espion method is going to be really vital and you'll hear that in Ken's story.

[00:02:19] You have to have the skills, you have to have a portfolio and you have to have a good network.

[00:02:24] The combination of those three things is really the only way you're going to break into sports analytics.

[00:02:29] Sports analytics is really interesting because there is high demand.

[00:02:33] Lots of people love sports and lots of people love data and so they want to combine the two. I'm one of those people.

[00:02:38] And there is low demand because if you look at it, there's only like five major sports leagues inside the United States.

[00:02:45] Of course there's more globally but still not that many sports leagues and not that many sports teams.

[00:02:49] So if you just look at like the US five major sports 30 teams what is five times 30 public math right?

[00:02:58] Five carry the theories that's just 150 so there's like a hundred and fifty teams.

[00:03:02] And let's just say for easy math there's three people, three data people for each sports organization.

[00:03:09] Then you're looking at 150 300 450 positions and there's a lot more people who want roles in that.

[00:03:15] Now there might be more jobs than that. It really just depends some like MLS doesn't have a whole lot of analytics going on

[00:03:21] and some teams in the MBA have like 20 people on their staff so it really just depends.

[00:03:27] The point is there is a low supply high demand and when that's the case prices can kind of go whatever wherever they want.

[00:03:35] So these jobs often don't pay the best ever but they're really fun.

[00:03:39] And some of them pay really well, it really just depends but the point when there's not that many opportunities and everyone wants it

[00:03:45] they can kind of offer a lower salary a lot at the time.

[00:03:48] So anyways, this can just my two cents. I was able to land a sports analytics internship with the Utah Jazz

[00:03:55] and I don't remember I told the story in this podcast or not but I will tell the full story in a different podcast episode.

[00:04:01] SPN method is always going to be very true. It's more than just the skills you guys.

[00:04:06] You have to have a portfolio and you have to have a network.

[00:04:08] So this is my weekly reminder SPN method for the win. If you've never heard of the SPN method I have a whole free training about it

[00:04:16] that we'll talk through through about how to land a day of job in 90 days.

[00:04:19] You can find the link to it in the show notes down below.

[00:04:21] I also have lots of really fun free resources like Avery GBT, my YouTube channel all sorts of different things down in the show notes down below.

[00:04:29] We're also cooking up a bunch of stuff. It's like so close and I want to talk about it right now but it's not quite ready.

[00:04:35] But hint hint interview simulator that is coming down the pipeline. We have job hunt shortcut if you guys haven't checked that out.

[00:04:41] You guys can check that out in the show notes down below. We have a lot of fun things going on so if you've never checked out the show notes

[00:04:46] today is the day to check them out. I'm excited to get my conversation with Ken. I think you guys will really enjoy it.

[00:04:53] And if you do, tell a friend. Give this to someone who wants to get into sports analytics because if it was helpful to you

[00:04:59] it will probably be helpful for them. Thank you guys for listening as always and I'll see you in the next week's episode.

[00:05:10] Welcome back to another episode of the Data Queer Podcast. I'm here with Ken G.

[00:05:14] We are in Charleston, South Carolina. Thank you Posit for bringing us here. Ken, welcome to the podcast.

[00:05:21] Thank you so much for having me on. I'm excited.

[00:05:23] I'm excited to have you on because I've actually been on Ken's podcast twice. But you kind of caught me when I was in,

[00:05:30] I took like an eight month break from my podcast. So you kind of caught me for the latest one.

[00:05:34] But if you guys haven't checked out Ken's socials, well, in the link or in the description down below, it's got a great YouTube channel,

[00:05:41] got a great podcast. He has another really great podcast he's been working on that we'll talk about during this episode but

[00:05:47] it's stoked to have you here and also Ken put this together. So thank you for bringing us together and organizing all of this.

[00:05:52] No problem. I'm going to bring my all for the center view but I'll burn down three days organizing 30 people which was amazing and fun,

[00:06:02] but operationally and logistically a little bit more difficult than I would have imagined.

[00:06:07] So again thanks to Posit for making it possible for us.

[00:06:10] Yeah you guys Ken's like our mom and our dad because he cooks for us. He like tells us our schedule.

[00:06:18] I think you're like one of the only people I've seen clean up very much stuff too.

[00:06:22] They've been a couple of people. Shout out to Louis, shout out to Jack, shout out to Keith.

[00:06:27] Yeah. And then some other people.

[00:06:29] Yeah let's not go what you do right now but what's your current job?

[00:06:33] So I'm the head of data science at a company called Scouts Consulta Group.

[00:06:37] So our focus there is helping athletes and teams improve their performance by analyzing the data that's collected on them.

[00:06:44] So most sports organizations teams they either have access to data from a league or they collect data internally

[00:06:52] and our focus is helping the organizations or the players to leverage that data into performance and to win specifically.

[00:07:00] Okay so you're like basically a sports analytics consulting company.

[00:07:04] Correct.

[00:07:05] And what do you like what sport do you guys dabble in the most?

[00:07:09] So we focus on golf and in basketball.

[00:07:12] So we work with pretty much US focused teams.

[00:07:15] So US golf which is Ryder Cup president's cup and USA basketball which is generally World Cup and Olympics.

[00:07:23] Okay so you guys are like yeah that's pretty neat. So it's like the Olympics and then the World competitions that you guys are kind of giving insight into which is pretty super cool.

[00:07:34] Like the Ryder Cup is one of the most enjoyable like entertaining honestly sporting events.

[00:07:40] I was going to say just golfing events but honestly sporting events because it's one of those competitions USA versus the World which is always kind of fun.

[00:07:48] And then international basketball has been pretty entertaining as of late because there's been a lot of parity in the world.

[00:07:55] Like previously the US was typically expected to win and often did win most of these tournaments even with Sunday or we'd send like our CT or something like that.

[00:08:06] But then like I think for the Olympics this year there's I don't even know if the US will be favored to win because there's quite a bit competition.

[00:08:13] Yeah you look at Yannis, you look at Jokeh, you look at... I forgot what his name is. Look at Donjick.

[00:08:22] Yeah. International players are dramatically evolving they're getting better.

[00:08:28] I forgot what the statistic was it's like 50% of the league is international. It's not that high but there's a large portion of international players that are really good playing at the highest level of basketball now and it's no longer just a US centric thing.

[00:08:41] Yeah. And people forget a lot that basketball, a few of basketball is very different than NBA basketballs in terms of the rules and terms of how you use your hands in terms of how you can use your body being close to the rim.

[00:08:54] So there's a lot of small tangible differences that make it difficult for players who grow up in an NBA system to be able to adapt and adjust and then if players are playing Euroball they're used to it.

[00:09:08] And you start to see that at the highest level in international play be a factor.

[00:09:14] Yeah, that's so interesting. So I totally... I'm familiar and maybe the audience says maybe the audience is it in terms of what type of analytics are in basketball.

[00:09:24] You can look at more clear stats. There's your shots, your rebounds, your assists. Most of the stadiums have the second spectrum videos which are basically tracking the player's movements.

[00:09:37] I mean, us the public doesn't have access to that but all the sports teams basically have access to location data.

[00:09:43] They actually moved off of second spectrum. So second spectrum still facilitates. So I forget what the new platform is.

[00:09:50] There are actually a lot of issues with it early on but I think they've been smoothed out but there's a second spectrum layer that the company still exists and does pretty well but they essentially take the new data and transform it into the older second spectrum.

[00:10:06] The older second spectrum output that's structured database style. So teams don't have to go through this massive change of the new technology.

[00:10:15] But it's basically tracking location and the ball location, right? Okay but I don't know anything about golf. Like what stats are there in golf?

[00:10:24] There's a lot of stats in golf. The most famous and prolific one is called Strokescane. Those invented by this guy Mark Brody who is a researcher at I think it's Columbia University.

[00:10:36] And he is generally looked at like the Godfather of golf analytics. And the idea is very similar to what wins above replacement is in baseball.

[00:10:46] The idea is how good is one shot compared to the average shot from that same location. So if you do that you can compare all of the different types of shots in golf to each other.

[00:11:00] So you can compare a drive to a pot, to a chip, to a wedge shot whatever it would be. So generally if you hit a good drive it's in the middle you can pick up maybe like a tenth of a shot on the field in that drive.

[00:11:15] And then it essentially flows down to each shot. And it goes all the way up to each round. So basically if the average player shoots 70 and you shoot 71, you lose one stroke. You minus one stroke scale.

[00:11:30] If you shoot 65 and the average score is 70, you picked up five shots. You've gained five strokes on the field. And you can break that down to the whole level.

[00:11:39] So if on the whole, the average score is 4.2 and you made a four then you picked up 0.2 strokes on the field. And then eventually it leads down to the individual shot levels where it's a little bit more complicated but there's essentially a baseline that's created from each range that changes over time.

[00:12:00] So if you a pot is the easiest example. So if you have an eight foot pot and it's essentially a 50% make it so it's and you very rarely three putt, you almost never three putt in.

[00:12:17] Unless you're me. Yeah exactly. But so if you miss it half the time right then you'd have a 1.5 expected value on the whole.

[00:12:28] Okay, because we're going to make a one or two and if you make it half the time then the expected value is 1.5. So if you make that pot, you pick up half a stroke potting on the field. And if you make the pot, yeah, if you miss the pot then you lose half a stroke to the field.

[00:12:45] And then that you can extrapolate that back further and further to chips and what shots and drives based on a baseline similar to that.

[00:13:23] So you can generate essentially lasers that would track every shot that the player set from every location they mark the where they hit it and how far they are from things.

[00:13:33] And there's a lot of other details around that they also have went some win condition data and they also have data on the individual courses so you can map the shots to the courses as well.

[00:13:44] So the PJ tour manager is that and then I believe that they have handed some of that off to IMG or to some other company to organize how you get access to it though.

[00:13:55] Oh, interesting. That's really fascinating. So they have volunteers looking at it and then they have like kind of just the course data like geophiles stored somewhere.

[00:14:05] Yeah, the geophiles are a little harder to get access to but they have the tabular shot data and like tabular information on the holes themselves or in the course itself.

[00:14:15] So interesting. Okay, so that's like a lot of what your consulting work would be I mean that that's the most basic stat and golf maybe that's not what you guys are doing but you're essentially looking at golf player data and trying to figure out where they could possibly improve on a given course.

[00:14:33] So ours is generally a little bit more team focused so trying to match player performance to the rider cup golf course that they're playing on.

[00:14:45] So one of the challenges with the rider cup a little bit with the president's cup is the players don't often play on that venue.

[00:14:53] And so there's not a history of data for them. So we have to figure out how do you evaluate performance on a course that they haven't necessarily played before.

[00:15:02] Oh, interesting. Yeah, I think the last one was in Italy. Was that right? That's correct. Okay, and so yeah maybe a lot of American players aren't over there playing in Italy so you have to try to figure out.

[00:15:12] And then also pairings are probably important as well because in the rider cup don't you go against each other one at a time or something like that.

[00:15:19] So there's two different formats. So there's four sums where you play alternate shot. So one guy hits the drive the next guy hits the iron shot and you play it one ball.

[00:15:30] There's also four ball which both players hit. Now both players play the whole and you choose the best score and they play against another pair of two people.

[00:15:42] Whoever wins the hole whoever wins the hole with a with a lowest score that team goes one up and you play match play which means that you go.

[00:15:52] You essentially each hole you win you get you go one up if you win one hole you go two up if you win another hole and then if you lose the hole you go back to one on and the goal is to be up.

[00:16:05] Up more than the number of holes that are left. See, that's interesting though because then you have like a resource allocation problem where like it's not like just whoever what team has the lowest like the best score wins it's whoever you know beat the other team the most which is not always necessarily the same thing.

[00:16:21] So that's like you got to figure out you know who do I pair together and who do I like who do we verse on the other team to beat them which sounds hard precisely well it's you look at volatility different in this than you would on a normal event.

[00:16:35] So any one hole is dramatically less disastrous than on a normal PJ Torvent or it's stroke play where if you make a 10 on a hole that counts on your scorecard.

[00:16:45] Yeah, if you make a 10 on a hole in the rider cup the worst is that you lose the only the hole yeah and in some cases if you make a 10 let's say in best ball.

[00:16:56] Yeah, your partner could make a birdie and you could still win the hole yeah so viewing it from a diversification perspective from a portfolio like approach with best ball.

[00:17:06] Yeah, alternate shot you have to be a little bit more conservative because you don't have a balancing force is just you two playing the same ball but yeah there's there's implications for the structure the format of the game as well as the individual players as well as the course conditions.

[00:17:24] Which is probably one of the reasons why like you guys can carve a niche and just like yeah we do you know international golf competitions because like the game is so different because like you said like if you get a quad or let's look like a quintiple bogey on a hole like in the masters

[00:17:44] you're losing you're basically like losing five strokes but you get a quintiple bogey international you're like it's the same as you getting a regular bogey probably because you know it's like not as big of a deal it's just a one point difference.

[00:17:58] So that like allows you guys to like there's like a whole different it's like a whole different math problem that you guys kind of special in.

[00:18:04] Yeah, one of my good friends he does work with a lot of the individual golfers who's named Corey jazz I've had him on my other podcast but he works with the strat on core strategy every week with a bunch of players and he uses analytics obviously is a great platform called Tor IQ.

[00:18:23] But to us the overhead of creating that infrastructure goes a little bit away from the core but we do frankly he does it better than we could so we've created a good business relationship with him so that you know if we refer people those types of things there's there's a little bit you know there's an opportunity to put out but we have a good business relationship with him and he I trust his tools quite a bit.

[00:18:45] That's neat how did you get into this sports analytics so I grew up loving sports.

[00:18:51] I was a good baseball player in high school. I played golf all that time but I eventually ended up according my shoulder and for some reason I could swim to golf club like this but I couldn't throw overhead like that so I decided I want to play golf in college.

[00:19:09] I went through and did that and it's funny golf and data science analytics the interest in them or the acceleration of those took off together so for golf I was trying to figure out how I could improve especially when I was playing in college

[00:19:27] and one of the ways that I saw was to be analyzing my golf game to keep track of my data to figure out largely what type of holes I was struggling on what type of shots I wasn't performing well with and to optimize my practice to figure out what area of my game I would get the largest marginal returns on.

[00:19:46] I don't think people necessarily view practice like that where they're just like oh I'm not great at this but so I'm just going to grind it out.

[00:19:53] It's not necessarily what you're not great at, it's what you could get dramatically better at in comparison.

[00:20:00] So if I can get two shots better at driving really easily even if I'm still good at that and putting I can only get one shot better for the same amount of effort.

[00:20:11] It makes more sense for me to invest in my driving because shots are just shots.

[00:20:16] So to me that was a big eye opener for me in terms of okay I can use numbers to help me with my golf performance and then after that I tried to play golf professionally after college didn't play that well.

[00:20:31] Logically I realized that one maybe it was outside of my capabilities which I've come to terms with.

[00:20:40] The other side of it is that okay maybe I could have done this for five years and been able to make it to the next level but I don't think I could have done it within the first two, three years.

[00:20:50] My game needed a lot of development, my psychology, my mental approach needed a lot of development and what is the opportunity cost of spending all that time in terms of my career and in terms of looking forward?

[00:21:04] Yes it's interesting on a resume that I had played and it's a good talking point but I hadn't built a lot of the skills that would be relevant to keep me going forward.

[00:21:15] And I decided to essentially go back and pursue building a career and learning more about a lot of these tools.

[00:21:25] Coincidentally around the same time I got interested in draft kings in the daily fantasy sports.

[00:21:31] I interned with them and I saw some people doing sequel, I saw some people running code and I said what is this?

[00:21:39] I don't know what this is, I don't know how to use this.

[00:21:42] They're analyzing this stuff with capabilities that I didn't have with the basic stuff I was doing for myself.

[00:21:49] So I went back to grad school, I studied masters in global commerce and my focus was in marketing analytics.

[00:21:58] So I got to work with SPSS, I got to work with SQL, I got to work with some other tools to start to organize and manipulate data.

[00:22:07] And then on my own after I went into management consulting and I started trying to build models to essentially predict the sporting outcomes or to evaluate team portfolios.

[00:22:22] And I realized that I still didn't have the prerequisite toolkit to build a lot of the models that I wanted to.

[00:22:29] I understood how to build, for example, a regression model or something along those lines.

[00:22:33] How do I build this into the specific genre or domain that I'm trying to work in with sports where there's sort of this closed loop ecosystem in the daily fantasy ecosystem?

[00:22:46] And so I decided that I needed more technical computer science skills to be able to build these things.

[00:22:53] I think the math was reasonably solid, I studied economics and undergrad, there was a decent math component there.

[00:23:00] But after I kind of got going, I just said, okay let's go back and figure out where I can learn.

[00:23:08] So I realized that there was more that I had to understand to be able to do exactly what I want to do.

[00:23:15] And at the time, I thought getting another master's degree was the most logical place for me to consume that information.

[00:23:22] Again this was 2014, so almost 10 years ago which is a little different than the domain now where there's a lot of tools, there's a lot of boot camps, there's a lot of online courses, there's YouTube videos,

[00:23:36] there's a lot of places to be able to learn these things but at the time that just wasn't necessarily available.

[00:23:43] So this was kind of long but essentially one of my friends while I was in that transition period getting my master's in computer science, he reached out,

[00:23:53] I grew up playing golf with him and he said, hey I have this great business case study for the Ryder Cup and being able to help this team because over the last 11 years to that point in time were 11 Ryder cups.

[00:24:09] The US had lost 9 of the last 11 I believe is either 8 or 9 of the last 11 when they were dramatically better on paper or in terms of world ranking.

[00:24:16] So it's a great case study of why is this happening and we put some stuff together, I did some light analysis for that and we pitched that to the PJ America.

[00:24:25] We ended up winning that contract and slowly over time we've brought on more clients, brought on more stuff.

[00:24:32] I was doing that part time for a large portion of time I was doing my degree.

[00:24:36] I went full time for a little while, I went back to, I took like an internship at GE and data science.

[00:24:43] I went back to consulting and then I went back and I did some more work outside of the company and switched it to part time working at a startup,

[00:24:54] like a mid growth startup in Chicago and then I eventually again came back and I've been working full time.

[00:25:01] It's got for I believe the last four or a year.

[00:25:04] And now the US has been winning more.

[00:25:06] They have been but not internationally so that is something we have to figure out.

[00:25:11] So interesting.

[00:25:13] With that being said, that was a pretty interesting journey especially like the back and forth stuff but getting into draft games,

[00:25:22] getting into this Scouts Consulting group, I've ended the day at least the Scouts was because you knew someone was someone in your network.

[00:25:31] But like for like draft games, what advice would you give to someone who wants to get into sports analytics?

[00:25:36] So it's really interesting with the draft games going on. I don't think I've told this story in either a long time or ever before but when I was using the product a ton,

[00:25:48] I really liked it. I said okay why don't I try to get an internship there is before my master's in commerce degree and I saw they had an internship but it was a normal undergrad internship.

[00:25:59] And so I reached out and I said hey, I'm a grad student but can I still apply for this?

[00:26:05] And I ended up getting selected and they gave me more pay and they essentially created a grad student internship for me.

[00:26:14] And to me that is one of these if you don't ask you don't know type of things where I was overqualified for the one but they essentially made a role for me.

[00:26:25] I very clearly described that how I use the product, how I appreciate what they do and a lot of those types of things.

[00:26:35] And because I had that story piece, they were able to build something that essentially gave me a job for the summer and gave me more money than the other undergrad intern that they brought on.

[00:26:49] So that to me is a lesson in not necessarily resourcefulness but curiosity in terms of just asking what's available because we all know that there's internal postings.

[00:27:02] We all know that there's asymmetric information where maybe they posted things on one job platform but they forgot to post it on LinkedIn whatever it might be.

[00:27:12] There's this idea that oh I didn't get this job that I really wanted and I applied for it correctly but if you have a contact with the hiring manager you can go,

[00:27:23] are there any other roles you might think I might be a good fit for or anything along those lines.

[00:27:27] So we look at this job search very linearly that oh I have these three options and I have to go through this door but there's probably hidden doors that if you just ask about they almost definitely exist.

[00:27:41] So neat because yeah you're 100% right that what's the Wayne Gretzky quote you missed.

[00:27:47] There's 100 of the shots you don't take.

[00:27:49] Yeah and so you just went for it and I think also you had like a good background for it.

[00:27:53] You played sports at a high level and you were using their products so that probably set you apart where it was like there's a lot of people that have the same skills as Ken but it may not like the same domain experience in sports and then also the passion for the product.

[00:28:08] I think that probably set you apart.

[00:28:09] Well think about any company why wouldn't they want to hire someone that is a power user of their product.

[00:28:18] I use Amazon, I use a bunch of the different features.

[00:28:21] I understand some of the pitfalls or how I would think about the product maybe differently than they do.

[00:28:27] If you're a data scientist and you use Amazon a lot and you want to work there thinking about oh how does their recommendation algorithm work?

[00:28:36] How would you improve that?

[00:28:38] What are some things that you're observing that can go a really long way in a conversation with someone that really cares about the company.

[00:28:46] I think it you know Jeff Bezos is in the camp where you want missionaries not mercenaries and I've heard that from the founders podcast is that you want people that are unbelievably passionate and they're advocates of your product.

[00:29:01] Not just people that you're paying a lot of money to work on it begrudgingly.

[00:29:05] If you view it that way and most companies generally would prefer that, you can show that you're a missionary by talking about the product and how much you like it or how you've used it or some of the things that you've observed that could potentially be improved about it.

[00:29:20] Yeah that's the difference between like just spraying and prying your resume all over the place where you don't really care about it versus like either like having a cover letter

[00:29:29] or some sort of a cold message or maybe it's just in your first interview actually explaining hey this isn't just one of the thousand jobs I applied for.

[00:29:37] Like I'm actually really interested in this because you know A, B and C I've thought about this.

[00:29:41] Here's an example of maybe how we could let how I would approach it if I was in the situation so it's your part.

[00:29:47] Yeah I remember I applied when I was first in the field I applied to a position that I was definitely not qualified for and it was at one of the big ski mountains for essentially leading their way.

[00:29:58] I was essentially leading their data science team in operations and I wrote this the corneus cover letter but it was about how when I was a kid I grew up skiing in the mountain.

[00:30:09] I had these incredible memories of how essentially that that played into my development and some of it was kind of bullshit but it was a fun story to write or regardless.

[00:30:20] And they at least gave me a call back, they gave me an interview which I don't think I would have gotten without writing that cover letter.

[00:30:27] I looked at it as just a fun exercise to be able to get like can I sell myself to this organization and there's different varying views on cover letters is that hey nobody's using them, hey whatever it is but I viewed that as a differentiator if I'm a good writer.

[00:30:45] I can show that but to if I can clearly communicate what the role or the company means to me that's more face time or more time that they're spending thinking about me than other candidates if it's really compelling and interesting.

[00:31:01] So to me there's all these it's a culmination of a lot of little things that you do in the job search that are different than other people that separate you and can push you forward.

[00:31:13] So you know the the drafting thing not a lot of people were sending an emails about the internship and asking about it ask for a fine question asking if it was a good fit that clearly made me stand out because I was different from the other candidates.

[00:31:30] You know they just gave them more consideration you look at you know so they say the nail that gets that sticks out gets hammered down I something along those lines in terms of the name but that can be a good thing in terms of the job market as well is that if you're thinking and choosing between 100 to 100 people.

[00:31:49] You're going to get the big data bowl you have some of these other places where you can use real sports data and create real insight and I believe those are the best places to showcase that you have a skill set and you can already create value for teams to me that is something that is generally.

[00:32:05] So first go to there's other sides of sports you can get into in terms of the front office or in terms of the league ops or the stadium ops but if you want to work in performance you should probably be able to showcase how your analytics or your model building can contribute to team improvement in some way.

[00:32:33] When you first said it's about what you've done in the past I got kind of worried because I was like well then all these people thought want to get into sports analytics it's hopeless because it's like they don't have sports analytics or maybe even sports in their background but what you're saying is like you can go out there and create your own experience by doing like the big data bowl which is basically like an NFL hackathon that they do every year or something like that like you can create your own project and just have at least something to showcase your skills.

[00:33:00] I wouldn't even say it's something to showcase it's something to offer the teams right so sports is very competitive I think it's a little bit different even than a normal organization in the sense that all sports teams they want to proprietize their research they won't don't want other teams to have it because this could be true for business but they're not as explicit about it but because it's their competitive advantage.

[00:33:26] So let's say you make something that can help one of these teams allocate their roster more effectively or organize playing time more effectively a team will hire you but they're also getting the research that you've done and they can probably integrate that relatively shortly into their ecosystem and that also takes it off the market from competition.

[00:33:48] So if you have something that they can start using immediately you build a product or something that can help a team in a way where they see how that solves a problem for them.

[00:33:59] You can very explicitly jump into a role because they're looking for that problem to be solved.

[00:34:06] I think that's harder to understand in larger companies but in sports it's pretty concrete all of them have the same objectives the goal is to win more basketball games.

[00:34:17] And then win the playoffs and win a championship every year or maybe in the next 10 years what a championship.

[00:34:24] What can you do to help them do that?

[00:34:28] Again, operational issues they have strategy issues in terms of essentially how you run plays they have player individual issues how they should be utilized or whatever it would be.

[00:34:41] And if you're building something that can help solve any of the problems I've just mentioned why wouldn't they hire you?

[00:34:49] Yeah, if you do a good enough job kind of convincing them. Like you said you have to have the product or just like the project that you've done then you also take the step almost the social step of you kind of mention it with the cover letter of like actually sending it to them or presenting it to them or getting their attention which is difficult.

[00:35:09] One of the sports one of the heads of analytics and one of the teams that we've worked with he's reading emails from high school kids that they were sending up really.

[00:35:17] The he said, oh what do you think of this analysis or do you think this kid in the next four years might be able to contribute something I like the idea of what he did now right these analytics organizations these teams generally they're under saft

[00:35:32] because the skill set is hard to find but they're always interested in more information they're always interested in trying to understand how to evolve the game and improve their performance right.

[00:35:43] But the problem is figuring out how to hire for these roles find the right people and the channels are just a little bit different than traditionally think.

[00:35:53] Still they're early in the game compared to large business and unless you're at one of the like top analytics organizations like Philadelphia or you're looking at the Astros or some of these places.

[00:36:05] That's how greatness and sports is in my opinion is that if you see something that could be useful it doesn't matter who it came from the goal is to win.

[00:36:14] You just try to optimize for that all information is good. I mean how long does it take to read an email or see an analysis maybe five minutes if five minutes means winning another game over the course of a season I think every single player coach anyone would take that right.

[00:36:34] And you you're bringing up a good point that it's like so let's say someone called messages a or called emails like ahead of analytics.

[00:36:42] They're probably going to read the email even no matter what the email is you usually read first emails and then you decide how to act based off what's in the email.

[00:36:51] If you back that first email with value you're probably going to get more time and more opportunity but if you're like hey like can I show you this analysis or can I ask you a question you might not get a reply but if you're like here's something that I did do find it interesting or not that could be really powerful.

[00:37:08] The best way in my opinion is to create something that's interactive a stream led a shiny or shiny for Python or something along those that those on dashboard and make it so that's easy for them to use you can send that in an email.

[00:37:25] And you don't have I would never ask questions and emails that say hey this is what I mean before you show them what you can do to me if I just get an email that has a question in it or a couple questions in it that don't have very direct concrete answers or that I've answered on the internet before I will not respond to it.

[00:37:46] Oh how do you break into data science I have seven videos on that and this is leasing us on your end because you also didn't give me much of a context if someone sent me an interview that said hey I watched your video on this.

[00:38:01] I was a little bit confused by this point and my unique situation is this and this that makes that makes this little bit more difficult what do you think about that.

[00:38:13] Then I might be inclined to answer that because they're clearly done their homework thought about it they're not just being lazy and asking me to solve the problem for them that I clearly can't because all of this there's nuance in it and everyone has an individual story.

[00:38:26] So if you're doing that in the lens of a email to a director of analytics or just someone who's at an organization you're interested in it goes okay hey I'm really I've been really interested in xyz organization for a long time.

[00:38:41] I thought this analysis might be really helpful to you it is based on xyz research that I've done and I think it solves this problem.

[00:38:51] And then if this is interesting you're like please let me know I'd love to do more I'm also in the market for a job.

[00:38:58] Is there anything available or something does come available please let me know that that's what people don't realize is a lot of the time jobs are related just to timing right you might be a great candidate but we've seen how for example meta and a bunch of these places they laid off a bunch of people even though they're highly profitable.

[00:39:17] Right and that's because at least according to Zoc that they over hired drink cove it are also transitioning what their businesses right there going from.

[00:39:27] No they were really leaning into AI I guess now they're going back into who in augmented reality but those ebbs and flows in a business dictate what the demand for specific skillsets are.

[00:39:39] And so if you think about maybe a coaching change in a NBA team or a management team or leadership change they're probably going to either want more analytics people or one less.

[00:39:50] And so that is something that you can observe or coincide these things with and if you're ready on a list a short list of people to be called back in the if there's roles that open up that really helps you out when those changes come.

[00:40:05] But it's obviously important to have a job now but over a career I think it's more important to have opportunities to come to you and you do that by getting on that short list of someone else by saying if there's something in the future.

[00:40:22] I love that and that's an easy thing if you get rejected be like yeah what other job would I be a good fit for just can you keep me in mind which a lot of times you think that wouldn't be the case but I was rejected from ex on mobile and they said we'll keep you in mind.

[00:40:37] I said sure and then I got my job at ex on mobile six months later when I applied to be a data sports analytics guy at the jazz they said no sorry and then I got an email like I don't maybe a year later hey we have an opening or you're interested like that stuff actually happens which is crazy to think about.

[00:40:56] But let's talk about your new project because you've been living in the sports analytics world you've been making a lot of data contents,

[00:41:03] kind of nearest neighbors YouTube channel all that good stuff and now you're creating a new podcast that's called the exponential athlete.

[00:41:11] That's more focused on like athlete greatness mindset that's going to hide describe it but how would you describe it?

[00:41:19] I view it as a sports or just an overall performance podcast so I take a ton of inspiration from the founders podcast from the acquired podcast how to take over the world podcast.

[00:41:33] I essentially study the greatest athletes all time I read five to ten books I listen all the podcasts I can on individual athlete I create a framework for what I believe made them successful in their sport or their domain

[00:41:47] and these are all things that I think we can learn and apply whether it's to a particular sport or to our life and performance in another domain.

[00:41:55] I think the goal for this is to figure out what what I believe to be the biggest outliers of humans do.

[00:42:03] I think sports even more so than the controversial for these other domains they have to be have a fundamentally different mindset because they're going into like competitive sphere every day.

[00:42:13] And so I wanted to one learn about this because it's interesting to me it goes from my my own perceived deficiency in golf and not making it.

[00:42:23] What was I missing when I was pursuing that?

[00:42:26] What is the you know what are you missing to take it to the next level I in a podcast or in your work I would imagine these things are hidden in the framework that makes up these great athletes

[00:42:42] and these really impressive people. There's also things we can learn on the other side of that one does it go too far when does ego get in the way one does when you know when do our bodies break down and if we're in the frame of professional sports.

[00:42:54] So the way again it works is I do that case study so we do usually three to four episodes on an individual athlete where it's me doing the analysis and talking about it

[00:43:05] and it's not sports analytics there is some analytical components so occasionally. And then I interview people to go into deep dives into the specific tools that I find within the athletes repertoire.

[00:43:19] So for example, the first series is on Kobe Bryant and something really unique about Kobe Bryant is that he created this alter ego the black mamba.

[00:43:27] And the black mamba came at an important part in his life where essentially he was really struggling with the fan perception of the court and he needed to figure out how he could still continue to play well in spite of being booed and having essentially public perception turned on him.

[00:43:45] So he created this alter ego the black mamba so that he is on on the basketball court is essentially turning into a different person and is disassociating from the negative the negative aspect that's that is being brought by the fans.

[00:43:59] And this comes from you know, this is based in science there's something called the batman effect or kids were given a difficult challenge and they were asked to solve it and then they were observed so the control group they don't have any prompt.

[00:44:14] In the action group they're told to either take on the role of batman or do the explorer and you saw when the kids took on the role of batman or do they explore they talk to themselves differently they said you know batman wouldn't give up or do they explore would find curiosity in this order that kids are not saying that specific thing but it's it's the idea there and.

[00:44:36] You know to that point the kids that took on the role of batman or do they explore they stuck with a problem longer and they were more effective at solving the problem so there are real constructs that we can use whether using our imagination or looking and framing things differently they can directly impact our performance and how we perceive these different situations.

[00:45:00] So that is just one example there's plenty of other ones related to for example how these athletes learn what motivates them which I think is really interesting.

[00:45:08] What would motivate someone to grind for 20 years of their life doing the same thing and a lot of these different aspects that I would hope or it's helpful then England.

[00:45:19] I mean generally the podcast is targeted at professional athletes coaches or people that are very specifically trying to reach the pinnacle of their field because it is dense it is heavily researched and to be honest I don't think that people that are just sort of lukewarm about their performance or their success in their domain would really find it as appealing.

[00:45:42] I think that's so interesting because if you think about it like what you're really talking about is like what mindset do people have and at the end of the day as a job seeker as even like as an entrepreneur as a podcast host like your mindset dictates probably way more than your actual skills do.

[00:46:03] And almost any part of your life the way you actually think about yourself and how you envision yourself like do you see yourself as just a normal person or you see yourself as Batman or Dorothy Explorer like that has such a big impact.

[00:46:14] And so I think if we study that you probably would see performance go up that's the goal right.

[00:46:20] Yeah so you know fortunately in the first season I was able to get the person who wrote the alter ego effect book.

[00:46:29] But he also was a person that worked directly with Kobe Bryant to create that black mom alter ego so we go really deep into that it is does go beyond that so in sports there's also a physical training perspective there is an intritional aspect there is a skill acquisition aspect

[00:46:47] of learning new movement patterns and things along those lines. And I dive into those as well maybe not as relevant for like this general audience but to me those are all things that are important to anyone is that okay if I'm learning statistics if I'm learning it took code any of these types of things how do I go deep and actually practice those as effectively as possible there is a right and generally a wrong way to practice or I shouldn't say that there is any fish.

[00:47:16] There is any efficient and effective and a less effective way to practice and as you imagine a lot of the greatest athletes of all time they practice in a slightly better way I mean some some inside baseball and that is the gamification variety these types of things are very important in skill acquisition and retaining them over time.

[00:47:35] Sweet well I'm stoked to be listening to that I've listened to the Kobe Bryant series Tiger just came out I think recently.

[00:47:42] The first episode of the Tiger series yeah. Okay so there's some good stuff out there we'll have the link in the show stand below for you guys to check it out but the exponential athlete you guys got to check it out Ken thanks so much for being on the pod.

[00:47:53] So we're happy to do that. Yeah appreciate it you guys can find Ken on YouTube you can find all the Ken's nearest neighbors podcast as well as the exponential athlete wherever you listen to podcasts and one of the links in the description down below.

[00:48:03] Thank you again for having me this is awesome. Yeah thank you.

[00:48:12] I'm back. I hope you guys enjoyed that episode if you did what are you doing now yeah you might want to listen to another episode you might want to watch a YouTube episode you might want to leave a review for this podcast you might want to tell a friend there's so many different options that you can do it out you might want to check out the show notes and see what's going on the show notes so many different options I can't even contain my joy so I'm excited to know which one you guys pick. See him whatever thing you click next.