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If you’ve ever dreamed of working in sports analytics, this episode is for you. Nick walks through how he got hired by doing the work first, plus advice for breaking into the field and hopes for a strong Reds season.
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⌚ TIMESTAMPS
02:00 — Nick's role with the Cincinnati Reds and how his work fits into the organization
05:00 — Pivot into sports analytics and early career decisions
17:00 — How personal projects and initiative directly led to getting hired
21:00 — What the day-to-day really looks like beyond "just baseball data"
35:00 — Advice for aspiring sports analytics professionals on standing out
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Transcripts
I applied to like 20 or so jobs and
I got calls back from all of them.
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Wow.
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And Nick Wan.
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00:00:05,635 --> 00:00:06,165
Nick Wan.
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00:00:06,165 --> 00:00:06,685
Nick Wan.
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You are the senior director of baseball
analytics at the Cincinnati Reds.
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You kind of have a lot of people's
dream job combining sports and data.
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I'd love to just hear your story
from beginning to end, how you
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00:00:17,370 --> 00:00:19,350
went from like a psychology PhD.
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To ultimately being in charge
of data for a baseball team.
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It was a different era, you know?
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How hard is it today to
land a sports analytics job?
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I think compared to what people are
putting in for time, it seems so
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much more difficult now than it was.
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I'd love for you to start at
the beginning of like how you
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ultimately landed that first job.
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I was writing a blog at the time, and
someone from the New York Times said,
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you're on the front page right now.
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I'm like, what?
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Let's say the Cincinnati Reds.
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Had a job opening right now.
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Uh, and some of these listeners applied
like what would they need to do in
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order to stand on, like, teams are
always looking for person in and people
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putting out really cool stuff on.
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Avery: All right, next.
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Thank you so much for coming on the pod.
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You are the Senior Director of Baseball
analytics at the Cincinnati Reds, which
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is a pretty sweet title if you ask me.
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You also, uh, create a decent
amount of content around data and
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sports analytics, and that's how we
became friends via like this data
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creators meetup that we both went to.
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We'll have all of your links in the show
notes down below, but you gotta have.
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A lot of people's dream job,
like combining sports and data.
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I think a lot of people
would love to do that.
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So first off, I'd love to just hear like
your story from beginning to end of how
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you went from like, no offense, but maybe
kind of a boring role as like a psychology
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PhD student to ultimately like being
in charge of data for a baseball team.
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And I know it's, it's kind of a long
story, but I'd, I'd love for you
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to start at the beginning of like
how you got interested in sports
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analytics and you know, how you
ultimately landed that first job.
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nick wan: Yeah.
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Um, thanks.
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Starting me on Avery.
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This is awesome.
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And yeah, that was, uh, uh, I always,
I, I watch these all the time, so I, I'm
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like, oh, this is cool that I'm on it now.
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So, um.
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But yeah, I was at, uh, I was
doing, I was at grad school.
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I went to, uh, Utah State, uh,
for my PhD and I was working
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on, neuroscience and doing, uh.
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Neuroscience of strategy formation.
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So, how do, how does a person
come up with a strategy?
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When does it form?
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Where in the brain does it form?
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Uh, and about like, I don't know, call
it like two or three years into it, I.
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Was like, am I really
the best in my class?
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Not to say I, you know, was horrible
at school or anything, but academia,
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especially in cognitive neuroscience,
it's like extremely competitive.
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And I, I kind of had like a, uh, an
honest realization of like, I don't
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know if I'm the best at grant writing.
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I dunno if I'm the best
at publishing papers.
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I really like the research,
but I don't know if I could
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do all of this other academic.
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important stuff.
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so I started looking at, uh,
other jobs or other, uh, potential
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paths for, for employment.
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And, uh, I was looking
at some journalism stuff.
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I was looking at some other,
you know, tech related stuff.
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And finally, like, I, I've always been
interested in sports and sports analytics.
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Uh, and, uh, it was, uh.
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was writing a blog at the time and I
was putting up blog posts and someone.
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Uh, from the New York Times said, Hey,
this is a really cool blog post you have.
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Can we use it as a part of, uh, a blog
post over at the Upshot, which is like
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the New York Times, they were kinda
like competing with 5 38 at the time.
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It's like their data viz political
blog thing they were doing.
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I think it's still going by the way, but.
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at least when I last checked, my
article was still up, so that was, nice.
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Um, so I was like, absolutely,
you could definitely use it.
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I'm just a student and I, I, I
would love more reach for my blog.
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Uh, so that was really cool.
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And then like a couple days after
they had put it up, uh, Justin Wolfer
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is the guy who, who wrote it all up.
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He was like, Hey, if you're near
that sells the New York Times,
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you're on the front page right now.
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Like.
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What, so I ran to like a hotel
that You didn't have one.
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I went to a coffee shop.
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Uh, they, like, I had the last issue of
like a New York Times and it was true.
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I was like bottom full, talking
about free throw shooting, at Arizona
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State and their student section
and the curtain of distraction.
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So.
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that kind of made me realize
that sports analytics wasn't this
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inaccessible, you gotta know who's who.
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Networks kind of career path.
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I, like, oh, people are really
actually interested in different
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perspectives and uh, uh, methodologies.
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Avery: be.
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nick wan: uh, I started going to
different sports conferences on, uh.
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budget I had remaining in my student
budget and then uh, uh, kind of fork
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in the road was I could have gone to a
postdoctoral fellowship, uh, which is very
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common for, for cognitive neuroscience
people, uh, trying to get into becoming
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a professor, um, or at least becoming
like some sort of like, you know,
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tenure track to researching person.
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Um.
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So I had this postdoc lined up or, uh,
or sorry, not, or, uh, I had this postdoc
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lined up and it was going to start.
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18 months from when I
got accepted into it.
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They were, they still had to like,
make sure they had the grant money
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and they had other students and
other people in their lab who were
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leaving, but it wasn't for a year.
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So I was like, alright, 18
months is a long time and I
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couldn't just work at the lab.
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So what job would I do for, you know.
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An hour for a year before I had to go to
those postdoc, started looking at jobs.
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And then when I realized I was getting
a lot of, you know, responses back,
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uh, from different teams, uh, and
being like a sports analytics person,
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for being a data scientist for a
sports team, I decided like, what if
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I just didn't do it for only a year?
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What if I just did it for like,
maybe the rest of my life?
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So, um.
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So that's kind of how I ended up
as my first role in, uh, as a data
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scientist for the Cincinnati Reds.
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And, uh, it's, I, I've
kind of been doing that.
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There was a small moment in 2020
and 2021 where I wasn't, but, uh,
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but for the most part it's been,
it's been working with red, so.
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Avery: Very cool.
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Yeah.
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During that stint you were
analyzing fried chicken at KFC.
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We, we can talk about that in a bit,
but, uh, I want to go back to kind
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of like where you're, you're doing
this blog, so you're writing this
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blog and was it just about sports
analytics or was it about whatever.
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nick wan: Yeah, I, uh, a little,
so the whole point, uh, someone, a
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very smart person once told me like,
write down everything, you know,
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like, whatever you learn, like, to
write it down, whether it's like.
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Taking notes and then just like
transposing the notes into something
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written, like, make sure whatever
you hear or whatever you're
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learning, try to write it down.
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And so, this blog I was writing, uh,
it was, uh, anything I learned in stats
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or in psychology or neuroscience, I
would try to write kind of just like.
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As notes, but I always would try to
convert what I knew into, uh, translate
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it into like what it would mean if we
were talking about a score in sports
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or stats in sports or, uh, what would
the translation be if you did this to
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like a music, uh, a music file or like
some sort of song like how do these
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manipulations, how do these processing
techniques change things that like.
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We listen to or we hear,
we see all the time.
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And, uh, it helped me be able to
explain like different processing
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techniques, different stats
methods, uh, a lot more clearly.
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Rather than talking about like
what this does to a neuron or what
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this does to an area of the brain.
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I could say if you did this to a bunch of
players on a football field, like this is
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what their movement would look like now.
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Or this is like.
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things that you're making more
clear versus the things that you
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would making, uh, more obscure from,
from, from a processing technique,
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Avery: I guess, I guess we should
also clarify that when you're doing
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the psychology research, um, I'm, I'm
inferring here, so correct me if I'm
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wrong, but you're, you're like, you're
looking at brainwaves and when we look at
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brainwaves like that basically is just a.
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It's just data, right?
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You have to interpret the data
in a certain way or another.
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And so there's actually a decent amount of
data in psychology and and in neuroscience
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because the only way you can really
study the brain is by checking, I don't
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know what, what it's called, but these
different signals the brain gives off.
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And basically these signals can be
interpreted to mean different things, but
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in order to extract that signal, you have
to be using some sort of data science.
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Is that correct?
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nick wan: Absolutely.
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So I use, uh, I use the big, the big
thing I was using was electro Ence.
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Holography or EEG.
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And like, it's like you said, you
put this cap on and there's all these
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electrodes and they're recording the, uh.
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The, uh, electrical impulses that are
coming off the top of your brain, and then
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it's trying to read it through your scalp.
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It's trying to read it
through your hair and skin.
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Um, and so the signal itself is very
weak, but it is still electrical impulse.
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So like, how do you know what's.
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Um, real, like actually coming
from your brain versus what's like
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muscle movement or what's just like
the machine shaking or something.
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So you have to do all these like
cleaning, pre-processing techniques.
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A lot of this is signal processing
from the world of like physics.
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So you'll, you'll, if, uh, if people
are like, interested in time series
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analysis or, or anything like that, just
cleaning, uh, signal over time, uh, then,
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then, uh, EEG or fni or any of these,
uh, neurocognitive imaging techniques,
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they, they collect all this data.
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It gets loaded into a CSV.
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You download the CSV, you clean it, you
send it through pre-processing stuff.
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You ban, pass, filter it.
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You do all this like, you know,
things you would typically do for,
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for a time series, uh, filter.
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And uh, it's exactly the same.
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It's all data at the end of the day,
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Avery: Side note, this is funny
because this is very similar
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to what my first actual.
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Analyzing data job was, I, I worked
at a biotech startup and they had
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basically an electric nose, like
where basically if different chemicals
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would hit it, the electrons would
either take or donate electrons and
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that would cause a change in signal.
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And so my job, well actually my job was to
just be the lab tech and just do the tests
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and hand it off to the data scientists.
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But when did the data scientists
quit And we couldn't find a new one.
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And I was like, how hard can this be?
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And I was like, I can figure it out.
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And uh, it turns out it was
a lot harder than I thought.
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But that's basically how I started.
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So That's interesting.
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Very.
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Very similar analysis to,
to what I, I used to do.
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Um, okay, so you're, you're
documenting your learning.
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You're basically like taking notes
via a blog and, and, and maybe
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relating it not only to neuroscience,
but to sports analytics as as well.
211
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Um, I guess how good of
a programmer were you?
212
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Because
213
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nick wan: I
214
00:12:37,017 --> 00:12:39,897
Avery: I read you studied psychology
as an undergrad, is that right?
215
00:12:40,092 --> 00:12:40,662
nick wan: Yeah, I
216
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Avery: Okay.
217
00:12:40,957 --> 00:12:43,837
nick wan: my undergrad in psychology
and then my grad school of psychology.
218
00:12:44,872 --> 00:12:45,262
Avery: Okay.
219
00:12:45,262 --> 00:12:49,402
There was a break in between where you are
a music journalist or am I making that up?
220
00:12:49,568 --> 00:12:53,767
nick wan: In while I was in
college, I was, uh, I had a blo
221
00:12:53,828 --> 00:12:55,657
uh, yeah, I was a journalist.
222
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I was, I, I had, there was this blog
that we were, it was a different
223
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blog, uh, not the same blog that I
was writing the science stuff in.
224
00:13:03,818 --> 00:13:06,877
Uh, we all had this, we all
were writing for this blog.
225
00:13:06,877 --> 00:13:11,767
There was like six of us writing and then,
uh, a handful of us doing photography.
226
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Yeah.
227
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Avery: Okay, so like, were
you a good programmer?
228
00:13:15,907 --> 00:13:20,077
I guess, did they, did they teach
programming and math to undergrads
229
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or is that just more of a PhD thing?
230
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nick wan: Um, really, at least
when, maybe now I would hope
231
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that it's a little changed.
232
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I know some programs of study, they
do a little more like, you know,
233
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they're working in Python, they're
working with r, they're working with
234
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some sort of like actual programming
language that they're doing stats in.
235
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But back when I was doing it, they didn't.
236
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Really teach much of it.
237
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They, we had like a little exposure to
SPSS or SaaS or something, but it was
238
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really just like a means to an end.
239
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I felt, uh, the, if you really
wanted like different statistical
240
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techniques or if you wanted, uh, more
in depth statistical techniques, you
241
00:14:02,813 --> 00:14:04,043
would have to learn how to program.
242
00:14:04,043 --> 00:14:05,363
So I ended up learning.
243
00:14:05,747 --> 00:14:08,838
Matlab, uh, in grad school.
244
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Not because I was told to, but
because I felt like I had to.
245
00:14:12,497 --> 00:14:16,548
And, uh, and so, so that
was really good for me.
246
00:14:16,698 --> 00:14:20,478
Uh, but I, before that I
had, I was like an Excel guy.
247
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I was just living in Excel,
living in the spreadsheet world,
248
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doing the spreadsheet thing
249
00:14:28,607 --> 00:14:28,787
Avery: It.
250
00:14:28,817 --> 00:14:31,337
I was a MATLAB guy too, so
this, this is super funny.
251
00:14:31,487 --> 00:14:34,217
I think, I think we must be
extinct now, especially with ai.
252
00:14:34,222 --> 00:14:38,537
Like, I can't imagine there's a lot of,
a lot of new MATLAB people out there.
253
00:14:38,717 --> 00:14:41,717
Um, okay, so, so you taught
yourself some programming.
254
00:14:41,867 --> 00:14:43,757
You're doing this blog and, um.
255
00:14:44,227 --> 00:14:49,297
You, you wrote an article on your blog
about the curtain of distraction at
256
00:14:49,387 --> 00:14:52,807
at, is it University of Arizona that
has like a really good crowd that
257
00:14:52,807 --> 00:14:54,277
basically makes you miss free throws.
258
00:14:54,552 --> 00:14:59,152
nick wan: Arizona State, uh, Arizona
State University, current distraction.
259
00:14:59,152 --> 00:15:02,062
The whole thing is like, uh, when the.
260
00:15:03,563 --> 00:15:08,543
Visiting team is shooting
into the, the student section.
261
00:15:08,723 --> 00:15:09,893
They have this curtain.
262
00:15:10,163 --> 00:15:12,113
Have you ever seen the movie basketball?
263
00:15:12,113 --> 00:15:15,923
It's kind of like that, where they're
trying to like distract you from making
264
00:15:15,923 --> 00:15:18,833
the free throw shot, but they open
the curtain and it could be anything.
265
00:15:18,833 --> 00:15:23,093
It's like people kayaking and
people like, there's this like
266
00:15:23,122 --> 00:15:24,863
unicorn thing that's happening.
267
00:15:24,863 --> 00:15:28,613
I think Michael Phelps was like a part of
the curtain of distraction at some point.
268
00:15:28,613 --> 00:15:30,742
So it is very distracting and loud.
269
00:15:30,893 --> 00:15:31,958
Uh, but uh.
270
00:15:32,768 --> 00:15:36,997
The whole question was, does it have
like an effect on the actual game?
271
00:15:37,178 --> 00:15:42,098
Uh, and, uh, while I didn't find
a significant effect, the, the
272
00:15:42,098 --> 00:15:45,308
New York Times article will, will
suggest that there's actually more
273
00:15:45,698 --> 00:15:48,218
larger effects that I, I didn't find.
274
00:15:48,218 --> 00:15:48,398
So do.
275
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Avery: Interesting.
276
00:15:51,037 --> 00:15:51,427
Okay.
277
00:15:51,547 --> 00:15:54,817
And you're just, did you like share
the blog article, like on social media?
278
00:15:54,817 --> 00:15:56,092
How did this this guy find it?
279
00:15:56,468 --> 00:15:58,688
nick wan: Yeah, I don't know.
280
00:15:58,718 --> 00:16:05,318
Uh, like I don't, in the neuroscience
community at the time, uh, social
281
00:16:05,318 --> 00:16:07,898
media wise, it's very tiny and small.
282
00:16:07,898 --> 00:16:12,757
And so, uh, if one person shared
something like all the neuroscientists
283
00:16:12,757 --> 00:16:14,257
on social media would see it.
284
00:16:14,257 --> 00:16:21,668
But I'm not like a popular guy in
social media, so I really have no idea.
285
00:16:21,668 --> 00:16:22,928
Like I.
286
00:16:23,348 --> 00:16:29,708
Uh, I'll always kind of share the email
that I got from him, and it was quite
287
00:16:29,708 --> 00:16:33,398
literally, Hey, uh, really like your blog.
288
00:16:33,878 --> 00:16:35,858
Um, can we use this?
289
00:16:35,858 --> 00:16:38,198
And I was like, who is, how does he know?
290
00:16:38,198 --> 00:16:42,608
I still don't know how
he found it, so, uh, but.
291
00:16:42,687 --> 00:16:43,352
Avery: But he did find.
292
00:16:43,442 --> 00:16:48,422
That's, that's the point is like you,
this is, so when I try to help people
293
00:16:48,422 --> 00:16:52,502
end their first data job, a lot of people
are just like, oh yeah, I learned Excel
294
00:16:52,502 --> 00:16:56,252
and I learned SQL, and boom, I can get a
data job and if I don't, if I don't get a
295
00:16:56,252 --> 00:16:59,582
data job, it's 'cause I'm not good, good
enough at Excel or not good enough at sql.
296
00:16:59,732 --> 00:17:00,022
And it's like.
297
00:17:00,052 --> 00:17:00,413
nick wan: It's like
298
00:17:00,682 --> 00:17:02,932
Avery: Well, you're missing out on
two thirds of the equation, which
299
00:17:02,932 --> 00:17:07,012
in my opinion is creating projects
into kind of your network effects.
300
00:17:07,222 --> 00:17:10,672
And so you created a project, essentially,
you put it online, someone noticed
301
00:17:10,672 --> 00:17:16,102
you, it got read by, you know, millions
of of people around the world and.
302
00:17:17,142 --> 00:17:20,771
That experience didn't necessarily like
lead directly to a job, at least the
303
00:17:20,771 --> 00:17:24,521
way you told it, but it sounded like
when you started applying for jobs, you
304
00:17:24,521 --> 00:17:27,791
could be like, oh, hey, you might've
seen me on the front page of the New
305
00:17:27,791 --> 00:17:29,652
York Times doing sports analytics.
306
00:17:29,832 --> 00:17:31,512
I think I'd be a good fit for your role.
307
00:17:31,512 --> 00:17:34,002
Here's some ev like, here's
the article if you missed it.
308
00:17:34,002 --> 00:17:34,912
Is that kind of how it went?
309
00:17:35,358 --> 00:17:40,943
nick wan: Yeah, I, yeah, mostly,
uh, I, I like how you said it.
310
00:17:40,943 --> 00:17:46,043
It, uh, uh, at least in my mind, uh,
on top of making sure I was writing
311
00:17:46,043 --> 00:17:49,823
stuff down, I also wanted to make
sure that people saw me learning.
312
00:17:50,033 --> 00:17:50,573
Um.
313
00:17:51,413 --> 00:17:55,343
no other reason than like, this is
something I'm very interested in and maybe
314
00:17:55,403 --> 00:18:03,323
other people are uh, in the same path as
me and I wanted to meet other people, so.
315
00:18:04,447 --> 00:18:07,718
Uh, so sharing out projects, sharing
out research I was looking at,
316
00:18:07,838 --> 00:18:11,048
uh, I think was always important
and it was extremely helpful.
317
00:18:11,048 --> 00:18:15,668
So, uh, it was, this was
easily the biggest thing that
318
00:18:15,668 --> 00:18:17,498
someone had discovered me for.
319
00:18:17,558 --> 00:18:23,513
Uh, and to this day, I've, I can't say
I've done anything as crazy as being on
320
00:18:23,888 --> 00:18:25,538
the front beach of the New York Times.
321
00:18:25,538 --> 00:18:26,893
Maybe some people might attest.
322
00:18:27,938 --> 00:18:32,228
Or contest that, uh, I think I've
done maybe a few things that would
323
00:18:32,228 --> 00:18:36,758
be on the same scale, but to be
being a New York Times front pager.
324
00:18:36,758 --> 00:18:41,708
So, uh, but yeah, I definitely
used that in my portfolio
325
00:18:41,708 --> 00:18:43,088
when I was applying to things.
326
00:18:43,088 --> 00:18:44,798
It was a conversation starter.
327
00:18:44,798 --> 00:18:46,928
It was an easy thing to
talk about in interviews.
328
00:18:46,928 --> 00:18:49,418
Like, here's a project that I did.
329
00:18:50,288 --> 00:18:52,658
ask like, all right,
where's the data from?
330
00:18:52,748 --> 00:18:54,938
How did you clean the data?
331
00:18:55,268 --> 00:18:57,458
Did you consider all
these other aspects of it?
332
00:18:57,458 --> 00:19:00,728
So, uh, just having a full
project that I did from beginning
333
00:19:00,728 --> 00:19:02,138
to end was really important.
334
00:19:02,228 --> 00:19:04,868
Uh, just, you know, breaking
the ice, talking to people
335
00:19:04,868 --> 00:19:05,773
who were interviewing me,
336
00:19:07,862 --> 00:19:11,282
Avery: With the Red's job originally,
did you just apply online or did you
337
00:19:11,282 --> 00:19:12,752
like talk to someone at a conference?
338
00:19:13,183 --> 00:19:13,933
nick wan: apply it online.
339
00:19:14,123 --> 00:19:20,973
Like there's this website that most teams,
put their thing, put their job ads on.
340
00:19:20,973 --> 00:19:21,373
It's, uh.
341
00:19:22,358 --> 00:19:25,598
Uh, can I like say the link and stuff?
342
00:19:25,598 --> 00:19:26,318
Is that fine?
343
00:19:26,542 --> 00:19:27,142
Avery: a hundred percent.
344
00:19:27,172 --> 00:19:27,472
Yeah.
345
00:19:27,472 --> 00:19:30,292
It's like, yeah, it's like teamwork
online or something like that.
346
00:19:30,458 --> 00:19:30,698
nick wan: it.
347
00:19:30,698 --> 00:19:31,298
It's teamwork
348
00:19:31,792 --> 00:19:32,092
Avery: Yeah.
349
00:19:32,258 --> 00:19:36,188
nick wan: So, uh, so it
was on teamwork online.
350
00:19:36,278 --> 00:19:40,358
Uh, I was literally just going through
all the jobs related to, to working
351
00:19:40,358 --> 00:19:42,698
in some sort of player operations.
352
00:19:42,698 --> 00:19:48,038
So whether it was baseball operations
or basketball operations or whatever.
353
00:19:48,098 --> 00:19:48,818
And, uh.
354
00:19:49,343 --> 00:19:52,673
Yeah, I applied to, I must
have applied to like 15 or so.
355
00:19:52,793 --> 00:19:58,163
Now, this is gonna sound like rookie
numbers to a lot of people, but, uh, I, at
356
00:19:58,163 --> 00:20:04,973
the time, the market was the way it was,
um, I applied to like 20 or so jobs and,
357
00:20:05,273 --> 00:20:07,073
and I got calls back from all of them.
358
00:20:07,073 --> 00:20:08,603
So, uh.
359
00:20:08,722 --> 00:20:08,942
Avery: Wow.
360
00:20:09,668 --> 00:20:13,808
nick wan: Yeah, it was a different
era, you know, like we're talking
361
00:20:13,808 --> 00:20:16,778
like almost a, like we were
talking like over a decade ago.
362
00:20:16,778 --> 00:20:18,998
So, uh, data.
363
00:20:19,167 --> 00:20:24,112
Avery: And, and I will say like you
were just having that, that like bone of
364
00:20:24,292 --> 00:20:28,222
defeats of like, look at here's my sports
analytics project, you know, published
365
00:20:28,282 --> 00:20:31,612
in like the biggest newspaper in the us.
366
00:20:31,612 --> 00:20:35,992
Like even if they couldn't see that, like
that's, that's really powerful I think.
367
00:20:36,022 --> 00:20:36,442
Um.
368
00:20:37,372 --> 00:20:38,212
So it makes sense.
369
00:20:38,212 --> 00:20:41,722
It's like, hey, you, you had someone
else thought you were good already.
370
00:20:41,722 --> 00:20:44,092
And when someone else thinks you're
good, you're way more attractive
371
00:20:44,152 --> 00:20:45,592
to everyone else for some reason.
372
00:20:46,838 --> 00:20:47,403
nick wan: It's very true.
373
00:20:49,222 --> 00:20:49,552
Avery: Okay.
374
00:20:49,882 --> 00:20:50,182
Wow.
375
00:20:50,212 --> 00:20:50,422
Okay.
376
00:20:50,422 --> 00:20:51,262
So then you got the job.
377
00:20:51,772 --> 00:20:53,722
Um, let's talk about the job a little bit.
378
00:20:53,782 --> 00:20:56,542
Um, like what exactly do you do?
379
00:20:56,572 --> 00:20:59,092
Like what are you kind
of like a manager now?
380
00:20:59,122 --> 00:21:02,602
Do you still find yourself like
actually doing analysis yourself?
381
00:21:02,662 --> 00:21:05,632
Do you, are you just like a communicator,
like between parties nowadays?
382
00:21:06,098 --> 00:21:06,548
nick wan: Yeah.
383
00:21:06,638 --> 00:21:11,708
Um, a little of all, definitely LA
lot less hands on keyboard these days.
384
00:21:11,858 --> 00:21:18,968
Uh, as the director, I try to
keep one project just, you know,
385
00:21:18,968 --> 00:21:20,858
something that keeps my brain going.
386
00:21:21,308 --> 00:21:26,648
So I do have like a project that I'm the
lead on still, uh, which, which is great.
387
00:21:26,858 --> 00:21:30,638
Uh, it has to do with psychology,
so, uh, right up my alley.
388
00:21:31,388 --> 00:21:32,528
Um, but.
389
00:21:32,918 --> 00:21:35,768
In terms of all the other work that
we do, whether it's, you know, helping
390
00:21:35,768 --> 00:21:41,018
the team from the day-to-day advanced
scouting planning, uh, whether it's,
391
00:21:41,048 --> 00:21:44,468
you know, backend engineering, working
with the new sources of data that
392
00:21:44,468 --> 00:21:49,838
comes in and cleaning it, creating
new metrics out of whether it's.
393
00:21:50,243 --> 00:21:54,653
You know, the new bat path swing kind
of data or anything with a pitch or
394
00:21:55,073 --> 00:22:00,833
positioning of players, um, anything
related to our biomechanics data, uh, all
395
00:22:00,833 --> 00:22:03,053
of that can also be turned into metrics.
396
00:22:03,653 --> 00:22:05,333
not really doing a lot of that anymore.
397
00:22:05,693 --> 00:22:10,278
Um, there's projection systems, so
being able to say what's happening
398
00:22:10,278 --> 00:22:16,673
in the future, uh, making sure that
you're very accurate, um, and, uh.
399
00:22:17,633 --> 00:22:22,013
able to explain, not just the,
the estimate, the point estimate,
400
00:22:22,013 --> 00:22:24,053
but the range of estimates.
401
00:22:25,463 --> 00:22:30,173
there's also that, which I also
don't do at, at all anymore.
402
00:22:31,283 --> 00:22:35,453
and then overall the communication of it,
which I'd say I do a lot more of, but.
403
00:22:35,788 --> 00:22:40,528
Um, really the, the analysts, the
data scientists, they, they do a
404
00:22:40,528 --> 00:22:42,748
lot of that, mostly do that as well.
405
00:22:42,748 --> 00:22:49,618
So, out to stakeholders, decision
makers, coaches, players, uh, people in
406
00:22:49,618 --> 00:22:51,958
player development, people in scouting.
407
00:22:52,198 --> 00:22:56,938
Um, I do a lot of that, but I, I'm also
supported by my department who does
408
00:22:57,028 --> 00:22:59,068
probably way more than I do on that front.
409
00:22:59,068 --> 00:23:05,623
So, I, uh, I tend to think of my job
more as like the, the person who does all
410
00:23:05,623 --> 00:23:07,963
the dirty work that no one wants to do.
411
00:23:08,143 --> 00:23:10,783
Uh, someone once said, uh.
412
00:23:11,783 --> 00:23:15,653
I get to do all the not baseball
stuff so everyone else can
413
00:23:15,653 --> 00:23:17,543
get to do the baseball stuff.
414
00:23:18,023 --> 00:23:23,453
Um, which it's like, uh, I wear
it on, uh, I wear it on my sleeve.
415
00:23:23,663 --> 00:23:27,713
It's, there's all the business of
baseball that happens and it's not
416
00:23:27,713 --> 00:23:33,743
as fun or, or sexy, the talk about,
but, uh, it, it's, uh, it what makes,
417
00:23:33,833 --> 00:23:35,663
it's what makes the team operate.
418
00:23:35,663 --> 00:23:40,463
So whether it's, you know,
budgeting or contract stuff, or.
419
00:23:41,453 --> 00:23:46,913
You know, just talking to, uh, different
groups who might not be related to
420
00:23:46,913 --> 00:23:51,203
the players or the coaches being able
to, to translate things for them.
421
00:23:51,623 --> 00:23:55,313
Uh, it frees up my group from having
to do all this administrative stuff
422
00:23:55,313 --> 00:23:59,843
and, and I, I, I get to go do all of
that so they get to do the fun stuff.
423
00:23:59,903 --> 00:24:02,393
So, I do all of that.
424
00:24:02,393 --> 00:24:06,293
And then I'd say the biggest thing
that's the definitely the most fun is.
425
00:24:06,453 --> 00:24:11,463
Uh, talking about player personnel being a
part of the group that helps, uh, bring in
426
00:24:11,463 --> 00:24:15,543
players being a, a part of the group who
helps promote players up to the big league
427
00:24:15,543 --> 00:24:17,703
club or promotes them through our system.
428
00:24:17,703 --> 00:24:22,203
So doing more roster construction
and player personnel decisions.
429
00:24:22,208 --> 00:24:22,863
That's, that's probably like.
430
00:24:24,173 --> 00:24:28,703
My biggest contributions to, to
our organization, me and everyone
431
00:24:28,703 --> 00:24:29,963
else who works on that kinda stuff.
432
00:24:29,963 --> 00:24:33,023
There's a small group on every
team who works on that kinda stuff.
433
00:24:33,023 --> 00:24:37,073
And I'm, uh, pretty fortunate
to be a part of our small group.
434
00:24:38,902 --> 00:24:42,772
Avery: You're basically like real
life Moneyball guy for the Reds?
435
00:24:43,343 --> 00:24:43,913
nick wan: It's funny
436
00:24:44,152 --> 00:24:44,422
Avery: Yeah.
437
00:24:45,323 --> 00:24:48,938
nick wan: uh, our president of baseball
operations, Nick Crawl, was on the.
438
00:24:50,288 --> 00:24:52,418
A So he was a
439
00:24:52,577 --> 00:24:52,957
Avery: Oh, wow.
440
00:24:53,227 --> 00:24:56,828
nick wan: yeah, he was a part of the
Moneyball, uh, the Moneyball team.
441
00:24:56,888 --> 00:24:57,398
So,
442
00:24:59,332 --> 00:24:59,992
Avery: Very cool.
443
00:24:59,992 --> 00:25:00,622
That's awesome.
444
00:25:00,892 --> 00:25:04,012
Um, what tools would you say you
and your team use the, the most
445
00:25:04,012 --> 00:25:05,752
if I if in, in sports analytics?
446
00:25:05,752 --> 00:25:09,237
Like what are the top two tools that
you're using on like a day-to-day basis?
447
00:25:09,602 --> 00:25:09,893
nick wan: yeah.
448
00:25:10,118 --> 00:25:12,213
Um, I love that you mentioned sql.
449
00:25:12,217 --> 00:25:16,748
'cause like we're absolutely in
the database ripping out data, so.
450
00:25:17,513 --> 00:25:21,503
It doesn't matter where you like what
level you are, you gotta know how to
451
00:25:21,503 --> 00:25:23,273
like, rip out data from the database.
452
00:25:23,273 --> 00:25:32,423
So, a lot of us, I'd say like 90,
call it 95% of us are doing SQL
453
00:25:32,453 --> 00:25:36,113
and if we're not in SQL or using
Spark, so a lot of us are using PIs.
454
00:25:37,163 --> 00:25:38,633
Um, but
455
00:25:38,877 --> 00:25:41,047
Avery: Which for those, if you've
never heard of that before, just
456
00:25:41,047 --> 00:25:46,537
basically think of it's SQL using
Python, but really big data.
457
00:25:47,348 --> 00:25:50,858
nick wan: Exactly like where
it's like frame by frame data
458
00:25:50,858 --> 00:25:52,778
in baseball, every game's like.
459
00:25:53,228 --> 00:25:57,188
Upwards a terabyte of data now,
and, you know, you have points from
460
00:25:57,188 --> 00:26:01,478
biomechanics and, you know, every
player has like 29 points on them.
461
00:26:01,478 --> 00:26:05,048
So, it's a lot of data to store.
462
00:26:05,078 --> 00:26:09,728
SQL isn't necessarily the best for
cutting through all of that data, so
463
00:26:09,818 --> 00:26:13,838
there's a handful of us who use Spark,
but, um, most of the people, this
464
00:26:13,838 --> 00:26:18,128
is a, this is probably a, a secret
that the, the Spark people don't.
465
00:26:19,073 --> 00:26:21,683
to say, but we use sql.
466
00:26:21,773 --> 00:26:26,453
We just write in sql and then that like
magically gets converted into Spark.
467
00:26:26,843 --> 00:26:29,903
So I think most of us
are still writing sql.
468
00:26:29,933 --> 00:26:31,973
So SQL's one, definitely one.
469
00:26:32,033 --> 00:26:36,893
Uh, and then, uh, after that it's
kind of your flavor depending
470
00:26:36,893 --> 00:26:38,393
on the team you're working on.
471
00:26:38,393 --> 00:26:42,323
Our projections team works in R
so uh, they're doing a lot of.
472
00:26:42,333 --> 00:26:45,243
Bayesian inference with r and Stan.
473
00:26:46,323 --> 00:26:50,823
and then our, uh, research and
development team, they're all in Python,
474
00:26:50,853 --> 00:26:53,523
so that's just straight up Python.
475
00:26:55,807 --> 00:26:56,137
Avery: Cool.
476
00:26:56,167 --> 00:26:56,407
Okay.
477
00:26:56,407 --> 00:27:00,427
So sql, Python, RI mean, kind of the main
stack for data scientists right there.
478
00:27:00,427 --> 00:27:03,367
So that, that makes a lot of sense.
479
00:27:03,727 --> 00:27:04,597
Um, okay.
480
00:27:04,597 --> 00:27:08,587
I wanna go and talk a little
bit about like how hard, 'cause
481
00:27:08,587 --> 00:27:11,257
like you said, you were getting
interviews when you were applying.
482
00:27:11,257 --> 00:27:14,377
Now you were a very qualified
candidate, but like how hard is it
483
00:27:14,377 --> 00:27:16,537
today to land a sports analytics job?
484
00:27:17,423 --> 00:27:17,903
nick wan: Yeah.
485
00:27:17,903 --> 00:27:23,663
I, I go through an exercise every
year with my team of like, uh,
486
00:27:23,693 --> 00:27:30,473
here is what my, uh, application
looked like and I got a job.
487
00:27:30,562 --> 00:27:31,013
Right.
488
00:27:32,153 --> 00:27:36,593
mainly because it's important
to, to kind of understand that.
489
00:27:37,388 --> 00:27:41,108
least for me, I don't think my
application in retrospect was that,
490
00:27:41,588 --> 00:27:47,168
you know, mind blowing, especially what
we're expecting of interns, what we're
491
00:27:47,168 --> 00:27:49,838
expecting of entry level people today.
492
00:27:51,398 --> 00:27:56,978
and so it's important to, like's know
that people do have the ability to grow.
493
00:27:56,978 --> 00:27:59,288
They're not just like who
they are on their application.
494
00:27:59,738 --> 00:28:03,968
Um, and uh, I do think it's way harder.
495
00:28:04,823 --> 00:28:09,053
The expectations are way higher,
and the process of getting a job
496
00:28:09,053 --> 00:28:13,613
now seems all like longer, harder.
497
00:28:15,053 --> 00:28:19,823
it's more difficult, I would think
today than it was, know, a decade plus
498
00:28:19,823 --> 00:28:26,963
ago where, uh, you know, my friend, I
have a friend, uh, Brad, and he, uh.
499
00:28:27,818 --> 00:28:32,738
He was one of the first data scientists,
uh, for Uber, and this was back before
500
00:28:32,738 --> 00:28:34,088
they had the term data scientist.
501
00:28:34,088 --> 00:28:41,582
He was a data evangelist and, uh,
uh, he, his job interview was just.
502
00:28:42,383 --> 00:28:44,723
Literally drinking coffee with a person.
503
00:28:45,113 --> 00:28:48,263
And then they said, okay, well
we wanna bring you in as a, you
504
00:28:48,263 --> 00:28:52,133
know, someone who helps us with,
uh, a quantitative analysis.
505
00:28:52,133 --> 00:28:56,273
And he was like, well, I
think you need like 20 of me.
506
00:28:56,723 --> 00:28:58,643
Uh, ended up getting the job.
507
00:28:58,643 --> 00:28:59,783
But yeah.
508
00:29:00,023 --> 00:29:04,343
Uh, back then it was that as a
nerd review, so mine was like.
509
00:29:05,333 --> 00:29:09,443
all this positioning data in,
uh, for baseball, where would
510
00:29:09,443 --> 00:29:13,733
you put these players if you were
hitting against these batters?
511
00:29:15,053 --> 00:29:19,193
give, and then you had to estimate,
you know, how fast they were, uh,
512
00:29:19,223 --> 00:29:23,183
you know, their accuracy in terms
of catching their, all their, how,
513
00:29:23,183 --> 00:29:24,293
their, their throwing abilities.
514
00:29:25,133 --> 00:29:27,713
where would you put them when you
were facing, I don't know, like.
515
00:29:28,553 --> 00:29:31,493
Schwarber or Bryce Harper.
516
00:29:31,583 --> 00:29:34,583
I dunno why I'm naming
Phillies, but GT Realdo.
517
00:29:35,603 --> 00:29:37,913
Um, but where would you put them?
518
00:29:37,913 --> 00:29:40,913
And, uh, mine was super easy.
519
00:29:40,913 --> 00:29:46,703
I just did like some clustering,
and I said, all right, well the
520
00:29:46,703 --> 00:29:49,493
best of these players play in
the center and then kind of.
521
00:29:50,048 --> 00:29:52,478
Put these players on these other sites.
522
00:29:52,478 --> 00:29:56,168
So, it was very not comprehensive.
523
00:29:56,288 --> 00:30:00,998
It was one page, and the analysis
itself was extremely short.
524
00:30:01,538 --> 00:30:04,813
Uh, I, I didn't think I did the.
525
00:30:06,188 --> 00:30:09,923
I, I thought I did a fine job, a good
job, like people would understand,
526
00:30:09,923 --> 00:30:14,513
like I knew what I was doing and how
to explain things, but I didn't think I
527
00:30:14,513 --> 00:30:16,433
knocked it out of the park or anything.
528
00:30:16,493 --> 00:30:23,163
I, uh, and uh, truth be told, still being
a PhD student, I was also trying to be a.
529
00:30:23,648 --> 00:30:27,187
A PhD student trying to finish
all my dissertation work.
530
00:30:27,578 --> 00:30:35,588
So I think my, compared to what people
are putting in for time now for,
531
00:30:36,488 --> 00:30:42,818
uh, technical assessments, uh, doing
all sorts of lead code stuff, are
532
00:30:42,818 --> 00:30:44,437
all things that I never had to do.
533
00:30:44,678 --> 00:30:50,318
Uh, and, uh, it seems so much more
difficult now than, than, uh, than it was.
534
00:30:52,842 --> 00:30:55,722
Avery: Yeah, I think, I think one of
the hard things also, it was so funny
535
00:30:55,722 --> 00:30:59,082
that you talked about this earlier, but
kind of you were like, yeah, psychology
536
00:30:59,082 --> 00:31:02,502
is like really competitive and like
academic psychology is really competitive.
537
00:31:02,712 --> 00:31:04,152
So I was like, yeah, sports analytics.
538
00:31:04,467 --> 00:31:07,167
And I, I'm like, well, I think
sports analytics, it's like you
539
00:31:07,167 --> 00:31:10,437
look around, there's like, what,
five major, major leagues in the us.
540
00:31:10,677 --> 00:31:13,707
Each league has about 30 teams, so
there's like 150 different teams.
541
00:31:13,887 --> 00:31:16,137
Let's just ignore college for
right now, although that's becoming
542
00:31:16,137 --> 00:31:18,087
more businessy every single day.
543
00:31:18,327 --> 00:31:19,527
Let's just, let's just go pro.
544
00:31:19,707 --> 00:31:20,997
So you have 150 teams.
545
00:31:21,177 --> 00:31:23,517
How many people do analytics at
the Reds right now, would you say?
546
00:31:24,638 --> 00:31:26,798
nick wan: I'll at uh, 12.
547
00:31:28,387 --> 00:31:30,247
Avery: Okay, I'm just
gonna do 10 for easy math.
548
00:31:30,487 --> 00:31:34,927
So that means there's like 1500
open sports analytics jobs in the
549
00:31:34,927 --> 00:31:38,917
US and a lot of people like sports
and a lot of people like data.
550
00:31:38,917 --> 00:31:40,927
And so if you combine
the two, that's great.
551
00:31:40,927 --> 00:31:44,917
So it's like those are very
competitive, you know, 1,500 jobs.
552
00:31:45,277 --> 00:31:48,037
So to me it feels near
impossible because it's like.
553
00:31:48,173 --> 00:31:48,233
nick wan: You
554
00:31:48,592 --> 00:31:49,132
Avery: You.
555
00:31:49,162 --> 00:31:51,052
You have to be really
good to even get the job.
556
00:31:51,172 --> 00:31:54,982
And then the other issue is when there's
a high demand for a role and a low
557
00:31:54,982 --> 00:31:59,092
supply for the role, one thing that
does oftentimes is drive down salaries.
558
00:31:59,092 --> 00:32:01,882
Now, some teams and some organizations
do a really good job where they're
559
00:32:01,882 --> 00:32:04,192
like, yes, we value analytics,
so we're gonna pay really well.
560
00:32:04,582 --> 00:32:06,547
But like there's other jobs where it's.
561
00:32:06,698 --> 00:32:06,978
nick wan: it's like,
562
00:32:08,197 --> 00:32:09,577
Avery: They're just not
gonna pay you very well.
563
00:32:09,637 --> 00:32:12,097
Like, that's, that's the,
that's the big give and take.
564
00:32:12,097 --> 00:32:15,007
It's like we can go find someone else
who will do it for, for this amount.
565
00:32:15,007 --> 00:32:18,547
So, to me, from the outside,
you know, I, I interned with the
566
00:32:18,547 --> 00:32:20,347
jazz like, what, five years ago?
567
00:32:20,617 --> 00:32:22,777
But just from the outside,
kind of looking over the, the
568
00:32:22,777 --> 00:32:24,577
view right now, it seems hard.
569
00:32:24,577 --> 00:32:25,657
It seems competitive.
570
00:32:25,837 --> 00:32:27,067
It seems stressful.
571
00:32:27,263 --> 00:32:33,798
nick wan: Yeah, I, I, I agree and I
think like, uh, you know, something that,
572
00:32:34,218 --> 00:32:37,913
not to say that this is any better, but
usually people who are applying to sports
573
00:32:37,913 --> 00:32:40,133
analytics jobs on teams, they're all.
574
00:32:41,168 --> 00:32:45,398
The, this isn't the only career
path they're looking for because
575
00:32:45,398 --> 00:32:46,628
of all those reasons, right?
576
00:32:46,628 --> 00:32:51,458
Like the, the extremely
saturated limited role.
577
00:32:52,208 --> 00:32:57,878
when you talk about the 1500 roles,
it's 1500 potentially available
578
00:32:57,878 --> 00:32:59,288
roles, but really it's like.
579
00:32:59,933 --> 00:33:06,953
I don't know, call it like roles
at most, uh, available at any year
580
00:33:06,983 --> 00:33:08,723
across like all of the leagues.
581
00:33:09,203 --> 00:33:11,633
Uh, because some teams aren't hiring.
582
00:33:11,663 --> 00:33:13,163
Some teams are all full.
583
00:33:13,193 --> 00:33:16,703
Some, some jobs are or not the
job that you're interested in.
584
00:33:17,123 --> 00:33:18,443
Uh, so.
585
00:33:18,818 --> 00:33:23,888
So there's really very small pool of,
of open jobs in, in the market versus
586
00:33:23,888 --> 00:33:29,378
like tech where some companies are
just infinitely hiring for like data
587
00:33:29,378 --> 00:33:31,298
analyst, data scientists, it seems.
588
00:33:31,298 --> 00:33:37,058
So, um, so I think you're right
that I, I went from a competitive
589
00:33:37,058 --> 00:33:42,098
industry to a a com, a competi,
more, more competitive industry.
590
00:33:43,878 --> 00:33:48,288
Uh, I didn't see it that way at the
time, but I, I think you're right.
591
00:33:48,348 --> 00:33:54,317
Uh, and then the, the ability to just
stand out above everyone, it's, you
592
00:33:54,317 --> 00:33:56,297
know, having all the feathers in the cap.
593
00:33:56,297 --> 00:34:01,788
It's, uh, I think it's a, a lot more
difficult these days, but it goes back
594
00:34:01,788 --> 00:34:04,788
to the things we were just talking about,
like, how do you put yourself out there?
595
00:34:05,152 --> 00:34:08,873
Putting your projects out there,
making sure that that allows you
596
00:34:08,873 --> 00:34:13,433
to start conversations and meet
different people, new people, uh,
597
00:34:13,433 --> 00:34:16,853
and then hopefully something like
that helps you along the way.
598
00:34:17,002 --> 00:34:20,212
Uh, maybe directly, maybe
more likely indirectly.
599
00:34:20,333 --> 00:34:20,752
So.
600
00:34:22,956 --> 00:34:25,507
Avery: That's actually what I was
gonna ask you is let's say the
601
00:34:25,507 --> 00:34:29,887
Cincinnati Reds had a job opening
right now, uh, and some of these
602
00:34:29,887 --> 00:34:32,947
listeners applied, like what would
they need to do in order to stand out?
603
00:34:32,947 --> 00:34:36,667
Like what are some things that they
could actually, you know, tangibly do to
604
00:34:36,667 --> 00:34:38,197
try to land some sort of an interview?
605
00:34:38,737 --> 00:34:39,098
nick wan: yeah.
606
00:34:39,188 --> 00:34:46,568
Uh, I think the, we do our, the
way we do it at the Reds, we're
607
00:34:46,598 --> 00:34:48,158
all the technical stuff first.
608
00:34:48,158 --> 00:34:52,268
So you go through like a very
basic, like, do you know how to
609
00:34:52,268 --> 00:34:56,678
program that filters out like 90%
of the people applying to a team.
610
00:34:57,248 --> 00:35:00,128
Uh, so if you know how to do a little
database stuff, if you know how to.
611
00:35:00,788 --> 00:35:04,718
Make a function in Python,
you're more than likely gonna
612
00:35:04,718 --> 00:35:05,798
get through to the next round.
613
00:35:07,388 --> 00:35:11,648
then, uh, the next part is
that take home assessment.
614
00:35:11,678 --> 00:35:15,518
So where you send out a problem
set and then, uh, you work
615
00:35:15,518 --> 00:35:17,168
through it, it's one problem.
616
00:35:17,648 --> 00:35:20,978
Uh, and then we ask, uh, a
couple different questions
617
00:35:20,978 --> 00:35:22,538
within the same dataset.
618
00:35:22,913 --> 00:35:25,013
Uh, and then that's pretty open-ended.
619
00:35:25,373 --> 00:35:30,053
That's your typical, you know, think of it
kind of like a kago competition in a way.
620
00:35:30,053 --> 00:35:32,272
Like, here's a data set.
621
00:35:32,272 --> 00:35:34,883
We want you to predict this
thing, predict this thing.
622
00:35:34,913 --> 00:35:37,013
Or like, here's a problem.
623
00:35:37,133 --> 00:35:38,513
There is no target.
624
00:35:38,843 --> 00:35:41,573
Invent the target and try
to train a model to it.
625
00:35:41,633 --> 00:35:42,053
So,
626
00:35:44,288 --> 00:35:50,858
Uh, that's been the standard for us
since early, like 21, 20, 22 ish.
627
00:35:50,978 --> 00:35:55,898
And then, um, uh, the way people
stand out the most in that process
628
00:35:55,898 --> 00:36:00,488
is doing all the technical stuff,
uh, and being really diligent
629
00:36:00,488 --> 00:36:02,138
about their answers and responses.
630
00:36:02,438 --> 00:36:05,258
We're typically trying to
highlight certain aspects of
631
00:36:05,258 --> 00:36:06,698
the technical assessment, like.
632
00:36:07,448 --> 00:36:11,558
You know, some years we want to bring in
people who are more data visualization
633
00:36:11,558 --> 00:36:15,098
heavy because we have a lot of data
visualization or reporting projects on,
634
00:36:15,248 --> 00:36:19,358
uh, for the year, or sometimes we want
people to have more Bayesian expertise.
635
00:36:19,358 --> 00:36:25,328
So, uh, having some sort of Bayesian
inference or understanding like Bayes
636
00:36:25,328 --> 00:36:30,368
related stats, uh, is important and
we want people to showcase that.
637
00:36:30,368 --> 00:36:32,378
Or if it's just like straight up.
638
00:36:33,248 --> 00:36:35,918
engineering stuff like,
all right, take a model.
639
00:36:36,128 --> 00:36:44,648
How would you create a set of, uh,
functions or packages that would call
640
00:36:44,738 --> 00:36:48,638
a model back and then like put that,
push that into a database or something.
641
00:36:48,968 --> 00:36:49,478
So.
642
00:36:50,813 --> 00:36:53,873
really the technical skills are
the things that we weigh up most,
643
00:36:53,873 --> 00:36:58,883
but once you get past all of that,
we do, uh, we, we have interviews.
644
00:36:59,303 --> 00:37:06,053
Uh, the best things that stand out for
us, uh, at least for me one, is, uh,
645
00:37:06,083 --> 00:37:11,333
just the, the ability to communicate
clearly is always like a, a huge.
646
00:37:11,803 --> 00:37:12,433
Aspect.
647
00:37:12,433 --> 00:37:16,363
So because we do all the technical
assessments first, we're always
648
00:37:16,363 --> 00:37:18,673
talking about your technical
assessments as your project.
649
00:37:18,673 --> 00:37:23,022
So if, even if you don't have this
gigantic portfolio, it doesn't
650
00:37:23,022 --> 00:37:24,793
matter because if you're just.
651
00:37:25,223 --> 00:37:29,033
You know, a student who never had the
time to, to jump into extracurricular
652
00:37:29,033 --> 00:37:33,293
stuff, you have this technical assessment,
which is a gigantic project and we
653
00:37:33,293 --> 00:37:34,973
could talk to you very clearly about it.
654
00:37:35,033 --> 00:37:39,533
Everyone we're talking to, we're talking
about, their technical assessment.
655
00:37:39,533 --> 00:37:42,173
So that kind of levels the
playing field for the kind of
656
00:37:42,173 --> 00:37:43,673
work that we're talking about.
657
00:37:43,793 --> 00:37:49,073
And who could speak most clearly
about their methods, like they
658
00:37:49,073 --> 00:37:51,023
kind of start standing out more.
659
00:37:51,428 --> 00:37:53,978
Just because we're talking about
the same project every time.
660
00:37:54,008 --> 00:37:59,198
So those who could communicate their
results, those who are able to, uh,
661
00:37:59,348 --> 00:38:03,848
to understand where the con founds
are, those who ask questions about
662
00:38:03,848 --> 00:38:08,108
the dataset, like, is this dataset,
you know, how complete is it?
663
00:38:08,138 --> 00:38:09,548
How accurate is it?
664
00:38:09,578 --> 00:38:11,288
How, how relevant is it?
665
00:38:11,288 --> 00:38:14,468
Is this similar to the
day-to-day that they'd be doing?
666
00:38:14,858 --> 00:38:14,948
Uh.
667
00:38:16,598 --> 00:38:18,398
the answer to that last
question is, yes, it is.
668
00:38:19,238 --> 00:38:22,087
Um, that's all really important to us too.
669
00:38:22,087 --> 00:38:27,248
So just being thoughtful and, and
engaging, uh, when we start engaging you
670
00:38:27,248 --> 00:38:29,918
about how you went about your project.
671
00:38:30,008 --> 00:38:33,877
Uh, it, it goes a really
long way, I would say.
672
00:38:35,997 --> 00:38:36,477
Avery: Very cool.
673
00:38:36,537 --> 00:38:37,887
I I like that whole process.
674
00:38:37,887 --> 00:38:38,907
Thanks for giving us that.
675
00:38:39,397 --> 00:38:43,627
A little bit behind the scenes, uh,
sneak into it, sneak peek into it.
676
00:38:44,137 --> 00:38:49,027
Um, and I, I'm curious also, like what are
your best recommendations and resources
677
00:38:49,027 --> 00:38:53,317
for people who are interested in getting
into sports analytics, like books or blogs
678
00:38:53,317 --> 00:38:54,957
or podcasts or those types of things?
679
00:38:55,628 --> 00:38:55,958
nick wan: yeah.
680
00:38:56,018 --> 00:38:58,688
Um, there's a lot of people
doing a lot of cool work in the
681
00:38:58,688 --> 00:39:00,488
sports analytics space still.
682
00:39:00,668 --> 00:39:02,058
Uh, if it's not.
683
00:39:02,828 --> 00:39:05,348
Over at my YouTube, you could
definitely check out different
684
00:39:05,408 --> 00:39:09,698
websites like fan graphs, they're
fan graphs, community articles.
685
00:39:09,968 --> 00:39:12,728
You have a bunch of people
contributing to research there.
686
00:39:12,878 --> 00:39:15,128
Uh, those are always really great reads.
687
00:39:15,428 --> 00:39:19,628
Baseball prospectus, uh, also
always puts out great research.
688
00:39:19,718 --> 00:39:22,148
Uh, it's, uh, very interesting to read.
689
00:39:24,143 --> 00:39:27,502
From there, analysts and writers,
uh, all the things that they're
690
00:39:27,502 --> 00:39:28,973
seeing from the public point of view.
691
00:39:30,502 --> 00:39:36,832
I, in terms of podcasts, uh, the, I think
the number one podcast a lot of people
692
00:39:36,832 --> 00:39:40,852
in the baseball world will listen to
is rates and barrels, which is put on
693
00:39:40,852 --> 00:39:45,383
by Eno C uh, it's, uh, it's baseball.
694
00:39:45,383 --> 00:39:46,567
It is very baseball forward, but.
695
00:39:47,288 --> 00:39:52,898
There's nuggets of analytics,
uh, threaded through the podcast.
696
00:39:52,928 --> 00:39:55,778
Uh, of course fan
graphs, effectively wild.
697
00:39:56,648 --> 00:40:00,548
a, that's a very much more analytic
forward podcast for baseball.
698
00:40:02,048 --> 00:40:09,098
and then, uh, I, I, I don't have a ton
of places in terms of, uh, uh, online
699
00:40:09,098 --> 00:40:11,648
resources or analytics, sports analytics.
700
00:40:11,648 --> 00:40:15,698
I know you and I do a ton, so, uh.
701
00:40:16,823 --> 00:40:17,543
Outside of us.
702
00:40:17,543 --> 00:40:19,343
I, I, I, I'm unsure.
703
00:40:19,343 --> 00:40:22,043
I don't, I don't really, I don't
really have a ton to recommend
704
00:40:23,367 --> 00:40:25,797
Avery: I think, I think those
recommendations you gave are great.
705
00:40:25,797 --> 00:40:28,227
And yeah, I would just encourage
you guys to check out some of
706
00:40:28,227 --> 00:40:29,247
Nick's links in the show notes.
707
00:40:29,247 --> 00:40:33,177
Done like his YouTube, he just
released like this like very fun walk
708
00:40:33,177 --> 00:40:36,567
and talk podcast episode and like
some of the stuff he talks about in
709
00:40:36,567 --> 00:40:38,097
that episode are really interesting.
710
00:40:38,097 --> 00:40:41,247
I think like there's some really
good nuggets kind of buried
711
00:40:41,247 --> 00:40:43,527
inside of that episode that I
think people should listen to.
712
00:40:43,587 --> 00:40:45,057
And then, and then the
other thing I suggest.
713
00:40:45,892 --> 00:40:47,392
I think is really useful.
714
00:40:47,392 --> 00:40:50,722
That helped me, not that I've made
it in the sports analytics world.
715
00:40:50,722 --> 00:40:52,432
I did one internship with Utah Jazz.
716
00:40:52,732 --> 00:40:56,302
Um, but I think anything in life
is just finding people that do the
717
00:40:56,302 --> 00:40:58,492
things you're interested in and
following them on social media.
718
00:40:58,492 --> 00:41:02,392
So like, for instance, follow Nick on
Blue Sky or YouTube or whatever, right.
719
00:41:02,722 --> 00:41:04,972
And, and see what, see
what they're talking about.
720
00:41:04,972 --> 00:41:06,232
See like what they mention.
721
00:41:06,502 --> 00:41:11,542
Um, I'm really into basketball and
so I really like, um, oh crap, and
722
00:41:11,542 --> 00:41:12,862
I'm gonna forget this guy's name.
723
00:41:13,507 --> 00:41:17,437
Uh, well, there's Kirk Goldsberry, uh,
who used to work for the San Antonio Spurs
724
00:41:17,437 --> 00:41:18,937
and he teaches at the University of Texas.
725
00:41:19,087 --> 00:41:22,117
He does like analytics light, like,
I wouldn't say like anything crazy
726
00:41:22,117 --> 00:41:25,237
in analytics in the MBA, but I like
a lot of the stuff that he publishes.
727
00:41:25,357 --> 00:41:30,067
And then there's another guy on Instagram
who publishes like a ton of MBA content.
728
00:41:30,097 --> 00:41:32,797
He does it anymore 'cause he just
got hired, I think, by the Nuggets.
729
00:41:32,797 --> 00:41:35,137
And then I think they like
asked him to stop posting all of
730
00:41:35,137 --> 00:41:37,357
his useful, insightful things.
731
00:41:37,357 --> 00:41:40,177
They're like, we wanna keep all this
stuff to ourselves, but like these
732
00:41:40,177 --> 00:41:41,737
people are like literally posting like.
733
00:41:42,292 --> 00:41:44,812
As good of analysis that they
would be doing for teams.
734
00:41:44,812 --> 00:41:47,482
And they like, we'll even like
show you the GitHub of how they do
735
00:41:47,482 --> 00:41:50,482
everything and it's like, oh, I can
just study what this person does and
736
00:41:50,482 --> 00:41:51,802
learn a whole heck of a lot from them.
737
00:41:51,802 --> 00:41:55,402
So, uh, I think, I think the, like
fan graphs and other things that you
738
00:41:55,402 --> 00:41:57,352
mentioned are, are really good resources.
739
00:41:57,412 --> 00:42:02,722
Um, so yeah, hopefully, hopefully
people if they, if they want to and they
740
00:42:02,722 --> 00:42:06,442
really want to commit, yes, I'm into
sports analytics, I think there's enough
741
00:42:06,442 --> 00:42:09,172
resources out there that they can, they
can learn quite a bit on their own.
742
00:42:10,013 --> 00:42:10,343
nick wan: I agree.
743
00:42:10,343 --> 00:42:14,183
And that's a great point about people
on social media who post a lot,
744
00:42:14,183 --> 00:42:15,803
who end up getting hired by teams.
745
00:42:15,893 --> 00:42:19,733
Teams are always looking
for the next person in.
746
00:42:19,733 --> 00:42:23,483
And while I just talked about our
interview process, that doesn't
747
00:42:23,873 --> 00:42:29,273
dissuade us from seeking out or
scouting really, uh, people putting out
748
00:42:29,273 --> 00:42:31,493
really cool stuff on, on social media.
749
00:42:31,523 --> 00:42:36,143
So if you do have the time to do
that, uh uh, it could take you.
750
00:42:37,073 --> 00:42:39,442
Uh, in definitely different places.
751
00:42:41,552 --> 00:42:42,227
Avery: I, I love that.
752
00:42:42,287 --> 00:42:42,947
I love that.
753
00:42:42,947 --> 00:42:43,487
That's what you did.
754
00:42:43,517 --> 00:42:44,987
That's how you got hired, basically.
755
00:42:44,987 --> 00:42:45,197
So.
756
00:42:45,278 --> 00:42:46,258
nick wan: That's how I did it.
757
00:42:46,327 --> 00:42:48,307
Avery: It co it comes full circle.
758
00:42:48,307 --> 00:42:48,817
Well, awesome.
759
00:42:48,817 --> 00:42:50,677
Dick, thank you so much
for all your insights.
760
00:42:50,677 --> 00:42:54,217
We'll have all your links and the
description down below, and, uh, hopefully
761
00:42:54,217 --> 00:42:59,557
some people will, uh, will become sports
analytics analysts after watching this,
762
00:42:59,557 --> 00:43:01,357
and hopefully the Reds have a good season.
763
00:43:01,357 --> 00:43:03,157
Good luck to you guys
in your upcoming season.
764
00:43:03,323 --> 00:43:04,223
nick wan: I appreciate it, Ru.
765
00:43:04,223 --> 00:43:05,273
Thanks for having me on.
