197: How to Become a Sports Data Analyst in 2026 (starting from 0)

I applied to like 20 or so jobs and I got calls back from all of them. Wow. And Nick Wan. Nick Wan. Nick Wan. You are the senior director of baseball analytics at the Cincinnati Reds. You kind of have a lot of people's dream job combining sports and data. I'd love to just hear your story from beginning to end, how you went from like a psychology PhD. To ultimately being in charge of data for a baseball team. It was a different era, you know? How hard is it today to land a sports analytics job? I think compared to what people are putting in for time, it seems so much more difficult now than it was. I'd love for you to start at the beginning of like how you ultimately landed that first job. I was writing a blog at the time, and someone from the New York Times said, you're on the front page right now. I'm like, what? Let's say the Cincinnati Reds. Had a job opening right now. Uh, and some of these listeners applied like what would they need to do in order to stand on, like, teams are always looking for person in and people putting out really cool stuff on.

All right, next. Thank you so much for coming on the pod. You are the Senior Director of Baseball analytics at the Cincinnati Reds, which is a pretty sweet title if you ask me. You also, uh, create a decent amount of content around data and sports analytics, and that's how we became friends via like this data creators meetup that we both went to. We'll have all of your links in the show notes down below, but you gotta have. A lot of people's dream job, like combining sports and data. I think a lot of people would love to do that. So first off, I'd love to just hear like your story from beginning to end of how you went from like, no offense, but maybe kind of a boring role as like a psychology PhD student to ultimately like being in charge of data for a baseball team. And I know it's, it's kind of a long story, but I'd, I'd love for you to start at the beginning of like how you got interested in sports analytics and you know, how you ultimately landed that first job.

Yeah. Um, thanks. Starting me on Avery. This is awesome. And yeah, that was, uh, uh, I always, I, I watch these all the time, so I, I'm like, oh, this is cool that I'm on it now. So, um. But yeah, I was at, uh, I was doing, I was at grad school. I went to, uh, Utah State, uh, for my PhD and I was working on, neuroscience and doing, uh. Neuroscience of strategy formation. So, how do, how does a person come up with a strategy? When does it form? Where in the brain does it form? Uh, and about like, I don't know, call it like two or three years into it, I. Was like, am I really the best in my class? Not to say I, you know, was horrible at school or anything, but academia, especially in cognitive neuroscience, it's like extremely competitive. And I, I kind of had like a, uh, an honest realization of like, I don't know if I'm the best at grant writing. I dunno if I'm the best at publishing papers. I really like the research, but I don't know if I could do all of this other academic. important stuff. so I started looking at, uh, other jobs or other, uh, potential paths for, for employment. And, uh, I was looking at some journalism stuff. I was looking at some other, you know, tech related stuff. And finally, like, I, I've always been interested in sports and sports analytics. Uh, and, uh, it was, uh. was writing a blog at the time and I was putting up blog posts and someone. Uh, from the New York Times said, Hey, this is a really cool blog post you have. Can we use it as a part of, uh, a blog post over at the Upshot, which is like the New York Times, they were kinda like competing with 5 38 at the time. It's like their data viz political blog thing they were doing. I think it's still going by the way, but. at least when I last checked, my article was still up, so that was, nice. Um, so I was like, absolutely, you could definitely use it. I'm just a student and I, I, I would love more reach for my blog. Uh, so that was really cool. And then like a couple days after they had put it up, uh, Justin Wolfer is the guy who, who wrote it all up. He was like, Hey, if you're near that sells the New York Times, you're on the front page right now. Like. What, so I ran to like a hotel that You didn't have one. I went to a coffee shop. Uh, they, like, I had the last issue of like a New York Times and it was true. I was like bottom full, talking about free throw shooting, at Arizona State and their student section and the curtain of distraction. So. that kind of made me realize that sports analytics wasn't this inaccessible, you gotta know who's who. Networks kind of career path. I, like, oh, people are really actually interested in different perspectives and uh, uh, methodologies.

be.

uh, I started going to different sports conferences on, uh. budget I had remaining in my student budget and then uh, uh, kind of fork in the road was I could have gone to a postdoctoral fellowship, uh, which is very common for, for cognitive neuroscience people, uh, trying to get into becoming a professor, um, or at least becoming like some sort of like, you know, tenure track to researching person. Um. So I had this postdoc lined up or, uh, or sorry, not, or, uh, I had this postdoc lined up and it was going to start. 18 months from when I got accepted into it. They were, they still had to like, make sure they had the grant money and they had other students and other people in their lab who were leaving, but it wasn't for a year. So I was like, alright, 18 months is a long time and I couldn't just work at the lab. So what job would I do for, you know. An hour for a year before I had to go to those postdoc, started looking at jobs. And then when I realized I was getting a lot of, you know, responses back, uh, from different teams, uh, and being like a sports analytics person, for being a data scientist for a sports team, I decided like, what if I just didn't do it for only a year? What if I just did it for like, maybe the rest of my life? So, um. So that's kind of how I ended up as my first role in, uh, as a data scientist for the Cincinnati Reds. And, uh, it's, I, I've kind of been doing that. There was a small moment in 2020 and 2021 where I wasn't, but, uh, but for the most part it's been, it's been working with red, so.

Very cool. Yeah. During that stint you were analyzing fried chicken at KFC. We, we can talk about that in a bit, but, uh, I want to go back to kind of like where you're, you're doing this blog, so you're writing this blog and was it just about sports analytics or was it about whatever.

Yeah, I, uh, a little, so the whole point, uh, someone, a very smart person once told me like, write down everything, you know, like, whatever you learn, like, to write it down, whether it's like. Taking notes and then just like transposing the notes into something written, like, make sure whatever you hear or whatever you're learning, try to write it down. And so, this blog I was writing, uh, it was, uh, anything I learned in stats or in psychology or neuroscience, I would try to write kind of just like. As notes, but I always would try to convert what I knew into, uh, translate it into like what it would mean if we were talking about a score in sports or stats in sports or, uh, what would the translation be if you did this to like a music, uh, a music file or like some sort of song like how do these manipulations, how do these processing techniques change things that like. We listen to or we hear, we see all the time. And, uh, it helped me be able to explain like different processing techniques, different stats methods, uh, a lot more clearly. Rather than talking about like what this does to a neuron or what this does to an area of the brain. I could say if you did this to a bunch of players on a football field, like this is what their movement would look like now. Or this is like. things that you're making more clear versus the things that you would making, uh, more obscure from, from, from a processing technique,

I guess, I guess we should also clarify that when you're doing the psychology research, um, I'm, I'm inferring here, so correct me if I'm wrong, but you're, you're like, you're looking at brainwaves and when we look at brainwaves like that basically is just a. It's just data, right? You have to interpret the data in a certain way or another. And so there's actually a decent amount of data in psychology and and in neuroscience because the only way you can really study the brain is by checking, I don't know what, what it's called, but these different signals the brain gives off. And basically these signals can be interpreted to mean different things, but in order to extract that signal, you have to be using some sort of data science. Is that correct?

Absolutely. So I use, uh, I use the big, the big thing I was using was electro Ence. Holography or EEG. And like, it's like you said, you put this cap on and there's all these electrodes and they're recording the, uh. The, uh, electrical impulses that are coming off the top of your brain, and then it's trying to read it through your scalp. It's trying to read it through your hair and skin. Um, and so the signal itself is very weak, but it is still electrical impulse. So like, how do you know what's. Um, real, like actually coming from your brain versus what's like muscle movement or what's just like the machine shaking or something. So you have to do all these like cleaning, pre-processing techniques. A lot of this is signal processing from the world of like physics. So you'll, you'll, if, uh, if people are like, interested in time series analysis or, or anything like that, just cleaning, uh, signal over time, uh, then, then, uh, EEG or fni or any of these, uh, neurocognitive imaging techniques, they, they collect all this data. It gets loaded into a CSV. You download the CSV, you clean it, you send it through pre-processing stuff. You ban, pass, filter it. You do all this like, you know, things you would typically do for, for a time series, uh, filter. And uh, it's exactly the same. It's all data at the end of the day,

Side note, this is funny because this is very similar to what my first actual. Analyzing data job was, I, I worked at a biotech startup and they had basically an electric nose, like where basically if different chemicals would hit it, the electrons would either take or donate electrons and that would cause a change in signal. And so my job, well actually my job was to just be the lab tech and just do the tests and hand it off to the data scientists. But when did the data scientists quit And we couldn't find a new one. And I was like, how hard can this be? And I was like, I can figure it out. And uh, it turns out it was a lot harder than I thought. But that's basically how I started. So That's interesting. Very. Very similar analysis to, to what I, I used to do. Um, okay, so you're, you're documenting your learning. You're basically like taking notes via a blog and, and, and maybe relating it not only to neuroscience, but to sports analytics as as well. Um, I guess how good of a programmer were you? Because

I

I read you studied psychology as an undergrad, is that right?

Yeah, I

Okay.

my undergrad in psychology and then my grad school of psychology.

Okay. There was a break in between where you are a music journalist or am I making that up?

In while I was in college, I was, uh, I had a blo uh, yeah, I was a journalist. I was, I, I had, there was this blog that we were, it was a different blog, uh, not the same blog that I was writing the science stuff in. Uh, we all had this, we all were writing for this blog. There was like six of us writing and then, uh, a handful of us doing photography. Yeah.

Okay, so like, were you a good programmer? I guess, did they, did they teach programming and math to undergrads or is that just more of a PhD thing?

Um, really, at least when, maybe now I would hope that it's a little changed. I know some programs of study, they do a little more like, you know, they're working in Python, they're working with r, they're working with some sort of like actual programming language that they're doing stats in. But back when I was doing it, they didn't. Really teach much of it. They, we had like a little exposure to SPSS or SaaS or something, but it was really just like a means to an end. I felt, uh, the, if you really wanted like different statistical techniques or if you wanted, uh, more in depth statistical techniques, you would have to learn how to program. So I ended up learning. Matlab, uh, in grad school. Not because I was told to, but because I felt like I had to. And, uh, and so, so that was really good for me. Uh, but I, before that I had, I was like an Excel guy. I was just living in Excel, living in the spreadsheet world, doing the spreadsheet thing

It. I was a MATLAB guy too, so this, this is super funny. I think, I think we must be extinct now, especially with ai. Like, I can't imagine there's a lot of, a lot of new MATLAB people out there. Um, okay, so, so you taught yourself some programming. You're doing this blog and, um. You, you wrote an article on your blog about the curtain of distraction at at, is it University of Arizona that has like a really good crowd that basically makes you miss free throws.

Arizona State, uh, Arizona State University, current distraction. The whole thing is like, uh, when the. Visiting team is shooting into the, the student section. They have this curtain. Have you ever seen the movie basketball? It's kind of like that, where they're trying to like distract you from making the free throw shot, but they open the curtain and it could be anything. It's like people kayaking and people like, there's this like unicorn thing that's happening. I think Michael Phelps was like a part of the curtain of distraction at some point. So it is very distracting and loud. Uh, but uh. The whole question was, does it have like an effect on the actual game? Uh, and, uh, while I didn't find a significant effect, the, the New York Times article will, will suggest that there's actually more larger effects that I, I didn't find. So do.

Interesting. Okay. And you're just, did you like share the blog article, like on social media? How did this this guy find it?

Yeah, I don't know. Uh, like I don't, in the neuroscience community at the time, uh, social media wise, it's very tiny and small. And so, uh, if one person shared something like all the neuroscientists on social media would see it. But I'm not like a popular guy in social media, so I really have no idea. Like I. Uh, I'll always kind of share the email that I got from him, and it was quite literally, Hey, uh, really like your blog. Um, can we use this? And I was like, who is, how does he know? I still don't know how he found it, so, uh, but.

But he did find. That's, that's the point is like you, this is, so when I try to help people end their first data job, a lot of people are just like, oh yeah, I learned Excel and I learned SQL, and boom, I can get a data job and if I don't, if I don't get a data job, it's 'cause I'm not good, good enough at Excel or not good enough at sql. And it's like.

It's like

Well, you're missing out on two thirds of the equation, which in my opinion is creating projects into kind of your network effects. And so you created a project, essentially, you put it online, someone noticed you, it got read by, you know, millions of of people around the world and. That experience didn't necessarily like lead directly to a job, at least the way you told it, but it sounded like when you started applying for jobs, you could be like, oh, hey, you might've seen me on the front page of the New York Times doing sports analytics. I think I'd be a good fit for your role. Here's some ev like, here's the article if you missed it. Is that kind of how it went?

Yeah, I, yeah, mostly, uh, I, I like how you said it. It, uh, uh, at least in my mind, uh, on top of making sure I was writing stuff down, I also wanted to make sure that people saw me learning. Um. no other reason than like, this is something I'm very interested in and maybe other people are uh, in the same path as me and I wanted to meet other people, so. Uh, so sharing out projects, sharing out research I was looking at, uh, I think was always important and it was extremely helpful. So, uh, it was, this was easily the biggest thing that someone had discovered me for. Uh, and to this day, I've, I can't say I've done anything as crazy as being on the front beach of the New York Times. Maybe some people might attest. Or contest that, uh, I think I've done maybe a few things that would be on the same scale, but to be being a New York Times front pager. So, uh, but yeah, I definitely used that in my portfolio when I was applying to things. It was a conversation starter. It was an easy thing to talk about in interviews. Like, here's a project that I did. ask like, all right, where's the data from? How did you clean the data? Did you consider all these other aspects of it? So, uh, just having a full project that I did from beginning to end was really important. Uh, just, you know, breaking the ice, talking to people who were interviewing me,

With the Red's job originally, did you just apply online or did you like talk to someone at a conference?

apply it online. Like there's this website that most teams, put their thing, put their job ads on. It's, uh. Uh, can I like say the link and stuff? Is that fine?

a hundred percent. Yeah. It's like, yeah, it's like teamwork online or something like that.

it. It's teamwork

Yeah.

So, uh, so it was on teamwork online. Uh, I was literally just going through all the jobs related to, to working in some sort of player operations. So whether it was baseball operations or basketball operations or whatever. And, uh. Yeah, I applied to, I must have applied to like 15 or so. Now, this is gonna sound like rookie numbers to a lot of people, but, uh, I, at the time, the market was the way it was, um, I applied to like 20 or so jobs and, and I got calls back from all of them. So, uh.

Wow.

Yeah, it was a different era, you know, like we're talking like almost a, like we were talking like over a decade ago. So, uh, data.

And, and I will say like you were just having that, that like bone of defeats of like, look at here's my sports analytics project, you know, published in like the biggest newspaper in the us. Like even if they couldn't see that, like that's, that's really powerful I think. Um. So it makes sense. It's like, hey, you, you had someone else thought you were good already. And when someone else thinks you're good, you're way more attractive to everyone else for some reason.

It's very true.

Okay. Wow. Okay. So then you got the job. Um, let's talk about the job a little bit. Um, like what exactly do you do? Like what are you kind of like a manager now? Do you still find yourself like actually doing analysis yourself? Do you, are you just like a communicator, like between parties nowadays?

Yeah. Um, a little of all, definitely LA lot less hands on keyboard these days. Uh, as the director, I try to keep one project just, you know, something that keeps my brain going. So I do have like a project that I'm the lead on still, uh, which, which is great. Uh, it has to do with psychology, so, uh, right up my alley. Um, but. In terms of all the other work that we do, whether it's, you know, helping the team from the day-to-day advanced scouting planning, uh, whether it's, you know, backend engineering, working with the new sources of data that comes in and cleaning it, creating new metrics out of whether it's. You know, the new bat path swing kind of data or anything with a pitch or positioning of players, um, anything related to our biomechanics data, uh, all of that can also be turned into metrics. not really doing a lot of that anymore. Um, there's projection systems, so being able to say what's happening in the future, uh, making sure that you're very accurate, um, and, uh. able to explain, not just the, the estimate, the point estimate, but the range of estimates. there's also that, which I also don't do at, at all anymore. and then overall the communication of it, which I'd say I do a lot more of, but. Um, really the, the analysts, the data scientists, they, they do a lot of that, mostly do that as well. So, out to stakeholders, decision makers, coaches, players, uh, people in player development, people in scouting. Um, I do a lot of that, but I, I'm also supported by my department who does probably way more than I do on that front. So, I, uh, I tend to think of my job more as like the, the person who does all the dirty work that no one wants to do. Uh, someone once said, uh. I get to do all the not baseball stuff so everyone else can get to do the baseball stuff. Um, which it's like, uh, I wear it on, uh, I wear it on my sleeve. It's, there's all the business of baseball that happens and it's not as fun or, or sexy, the talk about, but, uh, it, it's, uh, it what makes, it's what makes the team operate. So whether it's, you know, budgeting or contract stuff, or. You know, just talking to, uh, different groups who might not be related to the players or the coaches being able to, to translate things for them. Uh, it frees up my group from having to do all this administrative stuff and, and I, I, I get to go do all of that so they get to do the fun stuff. So, I do all of that. And then I'd say the biggest thing that's the definitely the most fun is. Uh, talking about player personnel being a part of the group that helps, uh, bring in players being a, a part of the group who helps promote players up to the big league club or promotes them through our system. So doing more roster construction and player personnel decisions. That's, that's probably like. My biggest contributions to, to our organization, me and everyone else who works on that kinda stuff. There's a small group on every team who works on that kinda stuff. And I'm, uh, pretty fortunate to be a part of our small group.

You're basically like real life Moneyball guy for the Reds?

It's funny

Yeah.

uh, our president of baseball operations, Nick Crawl, was on the. A So he was a

Oh, wow.

yeah, he was a part of the Moneyball, uh, the Moneyball team. So,

Very cool. That's awesome. Um, what tools would you say you and your team use the, the most if I if in, in sports analytics? Like what are the top two tools that you're using on like a day-to-day basis?

yeah. Um, I love that you mentioned sql. 'cause like we're absolutely in the database ripping out data, so. It doesn't matter where you like what level you are, you gotta know how to like, rip out data from the database. So, a lot of us, I'd say like 90, call it 95% of us are doing SQL and if we're not in SQL or using Spark, so a lot of us are using PIs. Um, but

Which for those, if you've never heard of that before, just basically think of it's SQL using Python, but really big data.

Exactly like where it's like frame by frame data in baseball, every game's like. Upwards a terabyte of data now, and, you know, you have points from biomechanics and, you know, every player has like 29 points on them. So, it's a lot of data to store. SQL isn't necessarily the best for cutting through all of that data, so there's a handful of us who use Spark, but, um, most of the people, this is a, this is probably a, a secret that the, the Spark people don't. to say, but we use sql. We just write in sql and then that like magically gets converted into Spark. So I think most of us are still writing sql. So SQL's one, definitely one. Uh, and then, uh, after that it's kind of your flavor depending on the team you're working on. Our projections team works in R so uh, they're doing a lot of. Bayesian inference with r and Stan. and then our, uh, research and development team, they're all in Python, so that's just straight up Python.

Cool. Okay. So sql, Python, RI mean, kind of the main stack for data scientists right there. So that, that makes a lot of sense. Um, okay. I wanna go and talk a little bit about like how hard, 'cause like you said, you were getting interviews when you were applying. Now you were a very qualified candidate, but like how hard is it today to land a sports analytics job?

Yeah. I, I go through an exercise every year with my team of like, uh, here is what my, uh, application looked like and I got a job. Right. mainly because it's important to, to kind of understand that. least for me, I don't think my application in retrospect was that, you know, mind blowing, especially what we're expecting of interns, what we're expecting of entry level people today. and so it's important to, like's know that people do have the ability to grow. They're not just like who they are on their application. Um, and uh, I do think it's way harder. The expectations are way higher, and the process of getting a job now seems all like longer, harder. it's more difficult, I would think today than it was, know, a decade plus ago where, uh, you know, my friend, I have a friend, uh, Brad, and he, uh. He was one of the first data scientists, uh, for Uber, and this was back before they had the term data scientist. He was a data evangelist and, uh, uh, he, his job interview was just. Literally drinking coffee with a person. And then they said, okay, well we wanna bring you in as a, you know, someone who helps us with, uh, a quantitative analysis. And he was like, well, I think you need like 20 of me. Uh, ended up getting the job. But yeah. Uh, back then it was that as a nerd review, so mine was like. all this positioning data in, uh, for baseball, where would you put these players if you were hitting against these batters? give, and then you had to estimate, you know, how fast they were, uh, you know, their accuracy in terms of catching their, all their, how, their, their throwing abilities. where would you put them when you were facing, I don't know, like. Schwarber or Bryce Harper. I dunno why I'm naming Phillies, but GT Realdo. Um, but where would you put them? And, uh, mine was super easy. I just did like some clustering, and I said, all right, well the best of these players play in the center and then kind of. Put these players on these other sites. So, it was very not comprehensive. It was one page, and the analysis itself was extremely short. Uh, I, I didn't think I did the. I, I thought I did a fine job, a good job, like people would understand, like I knew what I was doing and how to explain things, but I didn't think I knocked it out of the park or anything. I, uh, and uh, truth be told, still being a PhD student, I was also trying to be a. A PhD student trying to finish all my dissertation work. So I think my, compared to what people are putting in for time now for, uh, technical assessments, uh, doing all sorts of lead code stuff, are all things that I never had to do. Uh, and, uh, it seems so much more difficult now than, than, uh, than it was.

Yeah, I think, I think one of the hard things also, it was so funny that you talked about this earlier, but kind of you were like, yeah, psychology is like really competitive and like academic psychology is really competitive. So I was like, yeah, sports analytics. And I, I'm like, well, I think sports analytics, it's like you look around, there's like, what, five major, major leagues in the us. Each league has about 30 teams, so there's like 150 different teams. Let's just ignore college for right now, although that's becoming more businessy every single day. Let's just, let's just go pro. So you have 150 teams. How many people do analytics at the Reds right now, would you say?

I'll at uh, 12.

Okay, I'm just gonna do 10 for easy math. So that means there's like 1500 open sports analytics jobs in the US and a lot of people like sports and a lot of people like data. And so if you combine the two, that's great. So it's like those are very competitive, you know, 1,500 jobs. So to me it feels near impossible because it's like.

You

You. You have to be really good to even get the job. And then the other issue is when there's a high demand for a role and a low supply for the role, one thing that does oftentimes is drive down salaries. Now, some teams and some organizations do a really good job where they're like, yes, we value analytics, so we're gonna pay really well. But like there's other jobs where it's.

it's like,

They're just not gonna pay you very well. Like, that's, that's the, that's the big give and take. It's like we can go find someone else who will do it for, for this amount. So, to me, from the outside, you know, I, I interned with the jazz like, what, five years ago? But just from the outside, kind of looking over the, the view right now, it seems hard. It seems competitive. It seems stressful.

Yeah, I, I, I agree and I think like, uh, you know, something that, not to say that this is any better, but usually people who are applying to sports analytics jobs on teams, they're all. The, this isn't the only career path they're looking for because of all those reasons, right? Like the, the extremely saturated limited role. when you talk about the 1500 roles, it's 1500 potentially available roles, but really it's like. I don't know, call it like roles at most, uh, available at any year across like all of the leagues. Uh, because some teams aren't hiring. Some teams are all full. Some, some jobs are or not the job that you're interested in. Uh, so. So there's really very small pool of, of open jobs in, in the market versus like tech where some companies are just infinitely hiring for like data analyst, data scientists, it seems. So, um, so I think you're right that I, I went from a competitive industry to a a com, a competi, more, more competitive industry. Uh, I didn't see it that way at the time, but I, I think you're right. Uh, and then the, the ability to just stand out above everyone, it's, you know, having all the feathers in the cap. It's, uh, I think it's a, a lot more difficult these days, but it goes back to the things we were just talking about, like, how do you put yourself out there? Putting your projects out there, making sure that that allows you to start conversations and meet different people, new people, uh, and then hopefully something like that helps you along the way. Uh, maybe directly, maybe more likely indirectly. So.

That's actually what I was gonna ask you is let's say the Cincinnati Reds had a job opening right now, uh, and some of these listeners applied, like what would they need to do in order to stand out? Like what are some things that they could actually, you know, tangibly do to try to land some sort of an interview?

yeah. Uh, I think the, we do our, the way we do it at the Reds, we're all the technical stuff first. So you go through like a very basic, like, do you know how to program that filters out like 90% of the people applying to a team. Uh, so if you know how to do a little database stuff, if you know how to. Make a function in Python, you're more than likely gonna get through to the next round. then, uh, the next part is that take home assessment. So where you send out a problem set and then, uh, you work through it, it's one problem. Uh, and then we ask, uh, a couple different questions within the same dataset. Uh, and then that's pretty open-ended. That's your typical, you know, think of it kind of like a kago competition in a way. Like, here's a data set. We want you to predict this thing, predict this thing. Or like, here's a problem. There is no target. Invent the target and try to train a model to it. So, Uh, that's been the standard for us since early, like 21, 20, 22 ish. And then, um, uh, the way people stand out the most in that process is doing all the technical stuff, uh, and being really diligent about their answers and responses. We're typically trying to highlight certain aspects of the technical assessment, like. You know, some years we want to bring in people who are more data visualization heavy because we have a lot of data visualization or reporting projects on, uh, for the year, or sometimes we want people to have more Bayesian expertise. So, uh, having some sort of Bayesian inference or understanding like Bayes related stats, uh, is important and we want people to showcase that. Or if it's just like straight up. engineering stuff like, all right, take a model. How would you create a set of, uh, functions or packages that would call a model back and then like put that, push that into a database or something. So. really the technical skills are the things that we weigh up most, but once you get past all of that, we do, uh, we, we have interviews. Uh, the best things that stand out for us, uh, at least for me one, is, uh, just the, the ability to communicate clearly is always like a, a huge. Aspect. So because we do all the technical assessments first, we're always talking about your technical assessments as your project. So if, even if you don't have this gigantic portfolio, it doesn't matter because if you're just. You know, a student who never had the time to, to jump into extracurricular stuff, you have this technical assessment, which is a gigantic project and we could talk to you very clearly about it. Everyone we're talking to, we're talking about, their technical assessment. So that kind of levels the playing field for the kind of work that we're talking about. And who could speak most clearly about their methods, like they kind of start standing out more. Just because we're talking about the same project every time. So those who could communicate their results, those who are able to, uh, to understand where the con founds are, those who ask questions about the dataset, like, is this dataset, you know, how complete is it? How accurate is it? How, how relevant is it? Is this similar to the day-to-day that they'd be doing? Uh. the answer to that last question is, yes, it is. Um, that's all really important to us too. So just being thoughtful and, and engaging, uh, when we start engaging you about how you went about your project. Uh, it, it goes a really long way, I would say.

Very cool. I I like that whole process. Thanks for giving us that. A little bit behind the scenes, uh, sneak into it, sneak peek into it. Um, and I, I'm curious also, like what are your best recommendations and resources for people who are interested in getting into sports analytics, like books or blogs or podcasts or those types of things?

yeah. Um, there's a lot of people doing a lot of cool work in the sports analytics space still. Uh, if it's not. Over at my YouTube, you could definitely check out different websites like fan graphs, they're fan graphs, community articles. You have a bunch of people contributing to research there. Uh, those are always really great reads. Baseball prospectus, uh, also always puts out great research. Uh, it's, uh, very interesting to read. From there, analysts and writers, uh, all the things that they're seeing from the public point of view. I, in terms of podcasts, uh, the, I think the number one podcast a lot of people in the baseball world will listen to is rates and barrels, which is put on by Eno C uh, it's, uh, it's baseball. It is very baseball forward, but. There's nuggets of analytics, uh, threaded through the podcast. Uh, of course fan graphs, effectively wild. a, that's a very much more analytic forward podcast for baseball. and then, uh, I, I, I don't have a ton of places in terms of, uh, uh, online resources or analytics, sports analytics. I know you and I do a ton, so, uh. Outside of us. I, I, I, I'm unsure. I don't, I don't really, I don't really have a ton to recommend

I think, I think those recommendations you gave are great. And yeah, I would just encourage you guys to check out some of Nick's links in the show notes. Done like his YouTube, he just released like this like very fun walk and talk podcast episode and like some of the stuff he talks about in that episode are really interesting. I think like there's some really good nuggets kind of buried inside of that episode that I think people should listen to. And then, and then the other thing I suggest. I think is really useful. That helped me, not that I've made it in the sports analytics world. I did one internship with Utah Jazz. Um, but I think anything in life is just finding people that do the things you're interested in and following them on social media. So like, for instance, follow Nick on Blue Sky or YouTube or whatever, right. And, and see what, see what they're talking about. See like what they mention. Um, I'm really into basketball and so I really like, um, oh crap, and I'm gonna forget this guy's name. Uh, well, there's Kirk Goldsberry, uh, who used to work for the San Antonio Spurs and he teaches at the University of Texas. He does like analytics light, like, I wouldn't say like anything crazy in analytics in the MBA, but I like a lot of the stuff that he publishes. And then there's another guy on Instagram who publishes like a ton of MBA content. He does it anymore 'cause he just got hired, I think, by the Nuggets. And then I think they like asked him to stop posting all of his useful, insightful things. They're like, we wanna keep all this stuff to ourselves, but like these people are like literally posting like. As good of analysis that they would be doing for teams. And they like, we'll even like show you the GitHub of how they do everything and it's like, oh, I can just study what this person does and learn a whole heck of a lot from them. So, uh, I think, I think the, like fan graphs and other things that you mentioned are, are really good resources. Um, so yeah, hopefully, hopefully people if they, if they want to and they really want to commit, yes, I'm into sports analytics, I think there's enough resources out there that they can, they can learn quite a bit on their own.

I agree. And that's a great point about people on social media who post a lot, who end up getting hired by teams. Teams are always looking for the next person in. And while I just talked about our interview process, that doesn't dissuade us from seeking out or scouting really, uh, people putting out really cool stuff on, on social media. So if you do have the time to do that, uh uh, it could take you. Uh, in definitely different places.

I, I love that. I love that. That's what you did. That's how you got hired, basically. So.

That's how I did it.

It co it comes full circle. Well, awesome. Dick, thank you so much for all your insights. We'll have all your links and the description down below, and, uh, hopefully some people will, uh, will become sports analytics analysts after watching this, and hopefully the Reds have a good season. Good luck to you guys in your upcoming season.

I appreciate it, Ru. Thanks for having me on.