Frontiers: Exploring the World of Data | Revolutionizing Sports Betting Through Analytics
In this episode our guest host, Jazmin Furtado, speaks with Brian Beachkofski about his work with Rithmm, a company that provides predictive analytics for sports betting.
Brian shares about…
- How he got into this industry.
- How his team applies data and analytics to change the sports betting playing field.
- How they’ve tackled common industry problems, from handling proprietary data to maintaining a competitive advantage.
- The future of the field, including advice for people who are interested in entering the space.
- And much more!
About today’s host: Jazmin Furtado has been a part of the software innovation realm for the Department of Defense where she has overseen large-scale Data and Artificial Intelligence programs in the Air Force and Space Force. She has also held various leadership and advisory roles with organizations such as Google, SpaceX, and Massachusetts General Hospital, where she designed and scaled AI, data, and education and training programs.
About today’s guest: …
About the series: Our new series, “Frontiers: Exploring the World of Data”, dives into how people are using their data science minds to shape organizations and change the landscape outside of “Big Tech”. In each episode, we explore the far-reaching corners of the world of data. If you’re curious about how data-minded individuals are making a difference in interesting, impactful and creative ways, then tune in!
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Transcript
Hey listeners, Tim Winkler here, your host of the pair program.
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:We've got exciting news introducing our
latest partner series beyond the program.
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:In these special episodes, we're passing
the mic to some of our savvy, former
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:guests who are returning as guest hosts.
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:Get ready for unfiltered
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:and unexpected twist as our alumni
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expert in a unique technical field.
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:Think data, product management,
and engineering, all with a keen
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:focus on startups and career growth.
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:Look out for these bonus episodes
dropping every other week,
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:bridging the gaps between our
traditional pair program episodes.
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:So buckle up and get ready to
venture beyond the program.
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:Enjoy.
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:Brian Beachkofski: Hello
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:Jazmin Furtado: everyone, and welcome to
Frontiers, exploring the world of data.
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:Frontiers dives into how people
are using their data science minds
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:to shape organizations and change
the landscape outside of big tech.
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:In each episode, we explore the far
reaching corners of the world of data.
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:My name is Jasmine and I'm
your host for this series.
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:I myself am passionate about
empowering people to make data
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:driven decisions, and I'm always
amazed at how others do it every day.
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:Today, we're exploring the
sea of sports analytics.
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:How advanced analytics can
reshape the sports landscape.
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:With that, I want to introduce our
guest for today, Brian Beachkovsky.
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:Brian is a leader in innovation
who has worked in a variety of
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:industries in both the private and
public sectors, but a common thread
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:has been his focus on creating value
through software, leveraging data
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:and analytics to empower people.
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:I had the pleasure of working for him
while at Kessel Run, an organization
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:building software for the Air Force.
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:And while Kessel Run was only a small part
of his career, he made a lasting impact
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:on not just the organization, but also the
team he led and the users he supported,
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:which I strongly suspect is a theme for
him with all the teams he has worked with.
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:He is currently co founder and CTO
of Rhythm, a tool that provides
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:predictive analytics for sports betting,
which is our main topic for today.
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:And I'm so curious about how he
got into this space, into this...
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:Arena, um, but more of
that to come in a bit.
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:So thank you so much
for being here, Brian.
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:It is a pleasure to say the least.
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:Brian Beachkofski: No,
thanks for having me.
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:I look forward to the
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:Jazmin Furtado: conversation.
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:It should be fun.
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:So start, uh, speaking of fun, our
icebreaker question, I wanted to start
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:off to, uh, get the ball rolling.
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:So the question is, if you were to
compete in one sport on the world
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:stage, does it matter whether you're
good at it or not you to choose one?
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:Which sport would it be?
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:And why?
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:So I'll start with, I would choose rock
climbing because I got into it while I
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:was in college and I really enjoyed it.
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:Uh, and I really liked how it's kind
of like a puzzle and you feel kind of
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:like a superhero and a ballerina at
the same time while you're doing it.
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:So it's like a really cool mix
of like brain strength and grace.
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:So I just think it'd be really
cool to just do that on the world
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:stage and not have to worry about
whether I'm good at it or not.
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:Just, I think it'd just be
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:Brian Beachkofski: really fun.
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:Yeah, that's awesome.
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:I love, I love climbing, but
I think I would pick hockey.
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:I, uh, I grew up playing hockey.
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:I'm I love, I love ice hockey.
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:I was a goalie growing up and,
uh, uh, there's just no better
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:sport in the world in my eyes.
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:It has everything from.
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:Speed and contact to, uh, uh, strategy
and, and all of it mixed together
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:and watching the elite athletes play
is, is just an amazing thing to do.
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:And to be able to be out there on
the ice with them, even if I was
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:the worst person there, amazing.
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:Are
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:Jazmin Furtado: you still
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:Brian Beachkofski: playing now?
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:Oh yeah.
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:I was still in men's league.
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:That's one of the things that has one
of the highest rates of people playing.
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:And we have a guy who's
65 on our, on our team.
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:So, uh, there's really no
upper, upper end of that.
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:You can, you can keep going with the,
with everyone far into your later years.
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:Jazmin Furtado: Wow.
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:Yeah.
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:I imagine on the East coast,
there's like a lot of that, a lot
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:of snow, especially Northeast.
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:So it's a big, big field out there.
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:So, Brian, our main topic for today is
to discuss how you've applied, like, your
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:data and analytics mindset to change the
playing field in sports, uh, specifically
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:when it comes to sports betting, but
you haven't always been in this space.
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:So, uh, I'm curious if we could start
off with you speaking 1st about rhythm
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:and speak about, um, what you do and the,
the tool and then walk us through and
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:take us back a little bit how you got.
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:Brian Beachkofski: Yeah, absolutely.
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:Uh, so, so like you said in the
intro, rhythm helps, uh, helps the
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:average person make smarter bets.
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:What we realized is that people have been,
uh, betting the same way for an eternity.
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:Uh, they, they go with their gut.
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:They ask their buddy who they like
on tonight's game, or they just
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:think someone's due or, uh, have some
other hunch based way of doing it.
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:And that just seems out of place
in a world where that person
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:probably won't even pick a
restaurant based on a hunch, right?
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:They check, they check for reviews
and they look at the data and they,
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:they make a data informed decision
on, on where to get dinner, but they.
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:They go with their gut when it comes
to putting money down on a bet.
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:So that shouldn't be.
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:And like, why, why is there
not something out there?
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:And it looked like, uh, it, it hadn't
existed because there are a couple
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:of things that made it hard to, to
create models for people, um, uh,
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:and to let people have that insight
into their own modeling approach.
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:So that's basically our goal is to
make, uh, to make modeling easier for
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:people to understand sports betting.
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:Uh, Ultimately trying to get to a no
code analytic service for folks to
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:make their own models to play around
with things and have access to tools
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:that will enable them to make data
informed decisions when they're,
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:when they're doing sports betting.
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:Uh, so the other part of the question
is how do, how do I end up there from,
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:uh, all the other things I've done?
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:Um, well.
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:I started at, in the air force research
labs doing analysis of jet engine
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:reliability and like, uh, how do we
make maintenance decisions better?
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:When can you predict, uh, maintenance
will be needed or when there
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:could be a higher probability.
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:Of an incident with a, with an engine,
uh, but believe it or not, uh, even
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:within the air force, it's hard to
get engine data, uh, the, the, the
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:companies, uh, who manufactured the
data on that data, there's usage, right?
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:So I found it was much easier for
me to go out and scrape sports data.
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:Like I said, I grew up playing sports.
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:I like sports.
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:Uh, so being able to learn some of the
analytical, uh, approaches and apply it on
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:sports data was just a way for me to teach
myself and have that internal professional
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:development, uh, while, uh, while trying
to apply that within the Air Force.
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:So that was my, my first approach into
sports analytics was, was just using
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:that to teach myself the techniques
that I was using for my day job.
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:Um, from there, I worked in the Pentagon
for a while, doing some budget analysis,
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:doing, uh, uh, uh, more analytics
around, um, patent and scientific
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:journal publication data to identify
emerging tech, uh, research areas.
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:So that got more into, uh, the
language modeling there and in
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:predictive analytics from that side.
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:Uh, and then after that job, I went
to private industry and worked at a
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:startup, uh, in the nonprofit sector.
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:Thank you.
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:That was doing data analytics to
understand the impact of human
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:service delivery in a local community.
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:So how can we use that community's
data to understand if work
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:programs are actually developing
people into a career as intended?
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:Are programs for homeless services
getting people self sufficient and
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:in stable housing down the road?
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:But still using that to understand,
using the data of outcomes to understand.
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:How to make those
decisions up front better.
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:So that was like the path I got here.
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:And then I met my co founder, Megan,
who said, what if we did something
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:similar, uh, for sports betting?
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:And I said, yeah, I think, you know,
at that time I was then doing college
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:basketball analytics as my side
project, trying to teach myself stuff.
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:And, uh, she was a division one
basketball coach in a former life.
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:So being able to, uh, connect there.
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:And, uh, put what was, you know, a
hobby at the time into practice was
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:Jazmin Furtado: awesome.
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:And I think when people talk about their
careers today, in retrospect, you're like,
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:Oh, these were like the common things
are like, you know, it just seems to all
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:piece together when you look back at it.
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:Is that how it was kind of unfolding
to you as you were experiencing
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:it, or were there a lot of.
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:Whether it's a be dead ends or a lot of
exploration and other fields to see what's
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:stuck, you know, what was the journey
like, as you were going through it?
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:Brian Beachkofski: Yeah, that's
a great question because no,
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:that story did not play out.
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:Like, it does have a common
theme in retrospect, but it was
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:not part of a grand strategy.
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:Uh, uh, I.
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:I left the research lab
because I was at that point.
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:A lot of us get to, uh, when, when
you get around 30 of, am I going
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:to be a deep technical expert
or go into a managerial track?
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:And, uh, I realized that I was
more interested in making an
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:impact in a way that was possible
from the management side of.
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:The, uh, the business.
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:So that's where I went into the
Pentagon to get a breadth of
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:experience within the, my air force
career to be able to set that up.
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:Uh, and then when I, when I went
to third sector capital partners,
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:uh, that, that nonprofit, uh,
social impact team, uh, that was.
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:Uh, relationship I had developed
at business school that they were
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:looking for someone to expand
their quantitative analysis.
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:And I had that experience.
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:So part of that career track was
based on my quantitative experience
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:bringing it in, but it was, it was there
because it was an interesting problem.
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:Uh, and, and I wanted to solve hard and
interesting problems and it was there.
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:And that's, that's a continuing theme too,
is just take something that's interesting.
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:And I think your career.
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:Story will write itself in retrospect.
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:If you keep challenging yourself with,
uh, interesting and challenging problem,
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:meaningful problems that actually address,
uh, some, something someone needs fixed.
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:Jazmin Furtado: And it, but with sports
betting or with sports generally,
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:you've always had an interest.
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:It's something that has been something
that's kind of been on the outskirts
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:and then you've been able to like
tap into, um, as you follow your more
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:professional or like formal career, I
guess, path, um, for lack of better terms.
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:Uh, The sports interest, though,
became more of a reality later
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:on in your career in terms of
an actual career move or pivot.
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:What was it about the timing of that
or was there like, what other factors
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:at play that made you realize, okay,
this is a good time to actually get
198
:into this space and actually start to
capitalize on these interests of mine.
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:Outside of just something that's
more on the side of more of a hobby.
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:Yeah,
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:Brian Beachkofski: it's a,
it's an interesting story
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:because the timing was sort of.
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:Uh, fortuitous, I guess.
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:So it started out with me and Megan,
uh, talking about, uh, is this possible?
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:And it was a hobby.
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:So on nights and weekends wrote some
Python code and, uh, uh, was making
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:predictions, uh, for games and, and
tracking out how they were actually.
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:Working in a spreadsheet, and we did
that enough and said, actually, it
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:looks like this is working pretty
well and outsourced a very rough first
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:iteration of the web app to go out there.
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:And we actually spent about 2 years.
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:Just collecting data with about 50 friends
and family users that were tracking things
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:on there, uh, and got about:of predictions and showed that there
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:was, that it was working and Megan was in
business school at the time and wrote it
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:up as a, as a project plan for one of, uh,
one of her classes to, to build out a, uh,
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:a pitch deck and business plan and through
a friend that got into, uh, uh, Um, A VC
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:seed company, uh, who wanted to take a
look and got interested and made an offer
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:and the timing worked out pretty well that
it was right at the time my command tour
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:at Kessel Run was coming to an end too.
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:So, uh, it was actually a little earlier
than we were planning it to be, uh,
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:but that's how, you know, if, if you
have a hobby and you're developing
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:your skills and the data is there and
someone else is making a data driven.
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:Decision, it can, uh, it
can, it can drive your time.
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:Jazmin Furtado: Were there other, uh,
like more macro influences at play in
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:terms of like the landscape of sports
betting or the landscape of technology
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:that really enabled or jumpstarted the
development or the fruition of this tool?
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:Brian Beachkofski: Yeah, there was a
Supreme Court decision with PASPA a few
228
:years ago, which was the one that took
away the federal prohibition against
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:states, uh, making sports betting legal.
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:Uh, so that, that
happened a few years ago.
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:And so you saw more states coming on and
in passing legalization of sports betting.
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:Um, and I think that drove a lot more
investment into this area of tools
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:and, and how to foster that what people
knew was going to be a growing sector.
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:Uh, so they're making investments there.
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:So that was definitely
part of the timing as well.
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:Yeah, I can
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:Jazmin Furtado: imagine that with all of
the, the increased focus or the ability
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:to be able to, um, put more out into the
market and the space that there's a lot
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:of competition is probably like a lot
of people that are trying to, you know,
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:it's like a little bit of a new frontier.
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:Um, I was wondering what.
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:You found and how you came upon
your value proposition and your
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:niche, like, how to differentiate
yourself in such a growing field.
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:Brian Beachkofski: Yeah, no.
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:So, so this is where focusing on the user
problem is something that you hinted.
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:We probably get to, uh,
was really important.
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:Um, because, uh, the, the vast majority
of companies out there that are providing,
248
:uh, pick support are just telling you
what to pick and are not transparent
249
:at all about how they got to that side.
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:Um, so people follow them until
they don't win and then they move
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:on and you really don't know.
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:And there's, there's quite
a bit of fraud, right?
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:Right.
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:Famously, a lot of pick services in the
past would call and tell, uh, let's say
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:there was a game on tonight, they would,
the first person to call, they'd say, uh,
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:take team a, the second person to call,
they'd say, take team B and they know that
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:half their customers would win, right?
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:So, like, that is, uh, uh, fraud.
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:I think is a good way to say it, but when
somebody wins their first bet, they're
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:more likely to pay for your service.
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:And, uh, but, but you could do that
when there's nothing behind it, right?
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:Like when there's no transparency
in what your record is.
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:So like part of this is what is,
what is the core user problem?
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:One, the user doesn't have any
idea of whether or not it's
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:working or it's not working.
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:Um, and, and they are, are still not
solving the core problem, which is.
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:I know when people who bet sports are
usually interested in sports and have
268
:an opinion, but they're calling a pick
service because they don't have a way to
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:put that opinion into their own model.
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:Right?
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:So if they think, I think offense
is very important, or I think
272
:high tempo is very important.
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:They.
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:The average person doesn't have
the time or the skills to make an
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:analytical model that would turn that,
that, uh, theory into, into practice.
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:That's what we're trying to solve
is how do you take someone who's
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:interested in sports, has a theory
about sports, but doesn't have the
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:modeling skills or the time to maintain
their, their data feeds and to, to
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:have their server up and running and
constantly doing the analysis on it.
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:If we offload that and just make
it very easy for someone to say,
281
:here's what I think is important.
282
:And then get that.
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:What does that mean in terms of my
expected value on all these plays?
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:That's what we're trying to solve.
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:And that was actually, uh, there's not,
there's not anyone out there in the
286
:market that is truly focused on that
problem of how do you provide no code
287
:analytical model development for users?
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:Jazmin Furtado: How do you find that
sweet spot when it comes to transparency?
289
:Versus accessibility, or, like,
understanding, you know, when it comes to
290
:these models, they get very complicated.
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:And.
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:I imagine when you're, you know, trying
to tell people what your model is doing,
293
:you have to figure out how much to be
able to, you know, what's, what are
294
:the important things to get across?
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:So how did you figure that out?
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:And then what, where did
you find that sweet spot?
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:Brian Beachkofski: Yeah, so this
is, this is a real importance
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:of having a great team.
299
:So I am a quantitative
background kind of person.
300
:So my default position is actually to over
explain and to get into too much detail.
301
:Megan comes from a different
background that hasn't had that
302
:quantitative, uh, uh, experience.
303
:So she's always the tension between us
is, is, is how do you get it so that
304
:you are explaining what's happening
without getting so technical that you
305
:lose people and actually having, uh, two
folks with very different perspectives on
306
:the same problem, trying to communicate
it to people is incredibly helpful.
307
:So that's been really good and
how we've balanced that what we
308
:don't have that tension around us.
309
:We always want the UI to
be as simple as possible.
310
:A lot of a lot of stats pages out
there give the error of give the error
311
:of being, um, Technical by making
the UI, like actually terrible, they
312
:overwhelm you with numbers and graphs
and plots and things to make it feel
313
:like something's going on there.
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:But actually pulling information
out is hard when they just
315
:dump raw numbers at you.
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:So that's that's the key for
us is how do you make the UI?
317
:Simple, sleek, easy to understand,
while also having the technical level of
318
:support be really high while not confusing
people by overwhelming them with detail.
319
:So that, the tension is where do
you, how do you explain it in a way
320
:that doesn't overwhelm people, uh,
while keeping that UI very clean,
321
:very straightforward and simplified.
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:Jazmin Furtado: So there's, I can
see there's, there's really a level
323
:of creativity when it comes to like.
324
:What visually does this need to look
like, but I imagine there's also a
325
:level of creativity on the back end in
terms of how everything's put together.
326
:Um, how do you like translate
user inputs into something that's
327
:actually valuable to the model?
328
:And then, you know, what do you wait
most important with, you know, that
329
:stuff going on in the background.
330
:So how did you come up, come to what
were the important things to consider?
331
:What, or the, what, what do
you need to get from your users
332
:versus where do you need to get
from else more automated sources?
333
:So can you talk a little bit more
about how you come to the backside, the
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:Brian Beachkofski: backend?
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:This was a really
complicated problem as well.
336
:And I think this is one where if, if
there's core proprietary information that
337
:we have, it is in that, how do we simplify
it while still having a complex model?
338
:So, because, um, so in the basic
sense, what we we've done is for every
339
:sport, we do the feature engineering
as a whole, uh, and do a lot of the
340
:testing to make sure that the baseline.
341
:Models are predictive, uh, and then
to make, which would be great, but
342
:now you only have one model, right?
343
:So now how do you get personalization when
you've done all of the feature engineering
344
:to, to meet the baseline problem?
345
:That's that actually reduces
the flexibility in the model.
346
:So how do you reintroduce the flexibility?
347
:We got our inspiration from another
data scientist I was talking to about
348
:these issues, and he said, well, for
computer vision, they can change this
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:sensitivity for cancer screenings.
350
:If you take a biopsy and you have
a slide of the biopsy, the way they
351
:train it to be sensitive to the
cancer cells is in the training
352
:set, they overrepresent Thank you.
353
:Thank you.
354
:The cancerous cells so that it becomes
more sensitive to those pieces.
355
:Now we took that as inspiration.
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:And so we take our baseline model
and we have the sensitivity for the
357
:classification of every, of every
game in history, uh, that in our
358
:dataset to each of those factors.
359
:So now if the user says, I
think offense is more important.
360
:We take the games where offense
was more determinative of how
361
:that game was classified by the
model, and over represent games
362
:like that in the training set.
363
:So we make a bespoke training set for
every individual user, where the model
364
:dynamics underneath it are the same,
but the differences in training make it
365
:more sensitive to different features.
366
:Per the per the users, um, intuition
367
:Jazmin Furtado: that we're, um, you're
talking about features and I get very
368
:excited whenever we're talking about
features because I think it's just.
369
:The hard part and a lot of data science
applications is, like, finding the
370
:data and, like, getting good features.
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:Do you have that issue when it comes
to sports and for sports betting?
372
:Do you have a problem with, like,
finding good data or finding enough data?
373
:Uh, where are your challenges
if any, when it comes to.
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:Obtaining the database.
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:Brian Beachkofski: So, so the good
part in sports, there are a lot of,
376
:uh, good data providers out there.
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:So we actually work with a
data provider company, uh,
378
:who gives us historical stats.
379
:Uh, and that's all been, um.
380
:Curated and so we don't have to
do a lot of the time consuming of
381
:data cleansing and keeping your
scraping feeds and things open.
382
:So that that has been very helpful for us.
383
:Um, we do we do do a little
bit of scraping here and there.
384
:And that takes a little bit of time.
385
:For example, like we know what we know.
386
:It's very important to get how far a
a road team travel to get to a game,
387
:uh, both in terms of miles traveled,
but elevation change is really A
388
:factor, uh, having gone to the Air
Force Academy, which is 7, 258 feet
389
:above sea level, I can tell you,
uh, altitude or elevation matters.
390
:So, uh, we have that.
391
:It's like
392
:Jazmin Furtado: you
memorized that or something.
393
:Brian Beachkofski: Yeah, yeah.
394
:Uh, so they, uh, so we have to
scrape some of that and use,
395
:uh, some other API interfaces to
turn that long into an elevation.
396
:Um, and, and those can be a bit tricky.
397
:But basically, we were able to
procure the information we need
398
:from data providers, uh, and then we
also, um, are able to make our own
399
:proprietary, um, uh, stats internally.
400
:So, um, we use a Bayesian ranking process
to be able to take, uh, the results of
401
:games plus, uh, uh, Initial estimates of
team rankings and then take the initial
402
:estimates as a prior, you, you put the
actual game results in, uh, as observed,
403
:and you can get a Bayesian ranking
formula out of that and how we, what,
404
:how we actually use those parameters
is how we make our own ranking system.
405
:But so we do that as well, where
we take those source inputs that we
406
:get from our data provider and then
build our own derived statistics
407
:off of, off of those as well.
408
:Jazmin Furtado: Yeah, when you can
offload that, like, data wrangling and
409
:data cleaning, it's amazing how much
free time that frees up so you're able
410
:to actually, like, really focus on,
like, the meat and potatoes of the work.
411
:Brian Beachkofski: Well, and it's still
like, as they famously say, 90% of your
412
:time is in the data cleansing, right?
413
:And the treating of it.
414
:And then what we found is the other,
still, even when that's taken care of, 90%
415
:of our time is still in those edge cases.
416
:So like in football, wacky things happen
all the time where like, uh, this, this
417
:play didn't end right, or this happened.
418
:So there's always weird things that happen
in sports that you still are like, Oh,
419
:I got to take, I got to figure that out.
420
:I got to solve for this edge case.
421
:And you go, why, why
does that not look right?
422
:And then you still have to go back.
423
:So we still spend about 90% of our time
looking at the edge cases, even when
424
:we, when we get clean data that come in.
425
:Jazmin Furtado: Interesting, I'm
wondering what your observations
426
:have been when it comes to sports and
sports data and the sports analytics.
427
:So, what have you seen as unique
to this field that you haven't seen
428
:in the other work that you've done?
429
:Brian Beachkofski: Yeah, so I'll say.
430
:The thing that has been most different is
the rate of uptake of insights from data.
431
:Um, and what I mean by that,
I can give a couple examples.
432
:One is, uh, when you find someone
who's doing very interesting analytics
433
:work, and oftentimes it's like on
Twitter, you just find people who put
434
:interesting results out there on Twitter.
435
:Um, Some of them get picked up by the
pro teams to be on their analytics team.
436
:So, uh, uh, Namita was a great
follow and she's now on the
437
:Kraken staff, uh, in the NHL.
438
:So like you, you see, um, you see a
quick adoption of, if somebody has
439
:something that is actually providing
real value to an organization, or it
440
:could be useful to an organization,
they go after it very quickly.
441
:And you see that.
442
:Um, from the beginning of, uh, uh,
Moneyball all through today, I was
443
:listening to a podcast before where
they said, you know, um, An advantage in
444
:draft order now persists a year or two.
445
:So if you're an organization and you
find some little metric that gives you
446
:an advantage to be able to get someone of
higher quality than you should be getting.
447
:You can maintain that advantage
for a year, maybe two.
448
:And then other people start asking,
why would they have picked that person
449
:and can actually back into what you
were doing and they copy each other.
450
:So that speed of adoption is
incredibly different than it was.
451
:Like when I was working with state
and local governments and they said,
452
:Hey, can you, can you get us an AI
algorithm to predict what's the best
453
:practice to be able to, uh, to help
children, uh, children in our community?
454
:And I say, how do you use data today?
455
:And they go, well.
456
:We have one person with a
spreadsheet in the back office.
457
:I'll go again.
458
:So we can't, we can't take you there
overnight, but that's like the difference
459
:is, um, the desires there, but the actual
adoption rate is so much faster in sports
460
:than it was in other areas I've worked.
461
:Jazmin Furtado: Yeah, it really
requires you to stay on your toes.
462
:Yeah, does that mean that the competition
is just really cutthroat, like, it's
463
:hard to, like, maintain a competitive
advantage, um, because the, your
464
:algorithm is only as good as its last run.
465
:Brian Beachkofski: Yeah, that's right.
466
:It's it's a classic Red Queen
problem where you have to run
467
:faster to stay in place, right?
468
:So that's what we do all the time.
469
:We're always looking at our
model predictions and making sure
470
:that there's still predictive
that things are still working.
471
:So this is we've had over 60, 000
track bets within the rhythm platform,
472
:and we have a positive almost 1%
ROI across all track bets, which is
473
:great when you compare it to you.
474
:The minus 5% that a naive better usually
gets so that, uh, we, we track that and
475
:we want to make sure that we're still
offering massive value to our, uh, to our
476
:users, but that's, you can't just say I
had a good model and then next season,
477
:assume it's going to work every, before
every season, we have to go back in and
478
:retune and tweak and see if there's any
learnings that we can incorporate in
479
:because you have to make those advantages
just to keep the same position you've had.
480
:Jazmin Furtado: Yeah, you
have to really stay on top of.
481
:On the latest and greatest, like, outside
of just like, the, the, you can't just
482
:look at your model to see how it performs.
483
:You really have to keep a not
awareness of the whole industry
484
:in terms of how that affects your,
your team makeup and the people that
485
:need to come together to build this.
486
:Do you need to have a
diversity in, you know.
487
:Backgrounds, or do you find that
people really are just like clumps?
488
:You know, you have a lot of you are
able to get a lot of diversity and
489
:like, you know, few core individuals.
490
:What does your team makeup have to do
to reflect the challenges of the field?
491
:Brian Beachkofski: Yeah, it's, it's
interesting because we, uh, we talked a
492
:lot about this up front and, and I think,
I mean, I, I've even brought it up here.
493
:The diversity of viewpoints,
even between the CEO and me
494
:has made our product better.
495
:And we definitely are, are very interested
in getting great people who have a great
496
:Uh, skill set on the team and having that
domain knowledge is kind of secondary
497
:because people can learn a new domain.
498
:It's having the things that are
most important is, is being curious.
499
:It is having a right breadth of
experience to be able to handle
500
:new problems because when.
501
:When you do have to reinvent every season
to be able to maintain that advantage,
502
:you need someone on your team who is okay
with having to scrap the old and start
503
:over and, and not know exactly what it's
going to look like when you're done,
504
:because there is no rinse repeat here.
505
:It is always pushing the frontier.
506
:So that's what has been the thing
that's been most important for us.
507
:A group of people who are a curious
problem solvers that are okay
508
:working in ambiguity, uh, rather
than people who know the ins and
509
:outs of sports, uh, or betting.
510
:Jazmin Furtado: Yeah, I can, I could, I
could see how, how many new things you
511
:can pull and learn while you're actually
just like building the model in of itself.
512
:Things that you insights that
maybe even didn't know before that
513
:you found out the model showed.
514
:I think it's like, both ways.
515
:You need to, you learn and you
provide input and it's just like,
516
:there's like, positive feedback loop.
517
:Talking about feedback loops,
actually, I'm curious how in your.
518
:Your experiences to date.
519
:Being at rhythm or being in sports,
what are the skills that you've
520
:had to pull on the most in this
field at this point in your career?
521
:.
Brian Beachkofski: Yeah, great question.
522
:So I would say the, the, the ones that
matter the most right now have been.
523
:Kind of that team building and problem
solving because like any startup,
524
:uh, you're, you're solving problems
more than you are implementing the
525
:pristine plan you had ahead of time.
526
:So having, uh, having an
ability to, um, understand.
527
:What's most important and stay
focused on the thing that matters
528
:most is critically important.
529
:Um, and bringing the team together
so that you can have, uh, diverse.
530
:Ways of solving problems,
but still be a cohesive team.
531
:That's one that like, uh, cause there
are plenty of stories out there.
532
:People who have people with
fundamentally different views on
533
:how to solve a problem that can't
work together because they can't.
534
:See themselves on the same team.
535
:So that teamwork and
problem solving number one.
536
:The second is having that,
um, technical curiosity.
537
:So like you said, the exploring and
finding insights that you didn't
538
:know, uh, when you're building the
models, there's, you know, two phases.
539
:The first is exploratory where you're just
in a Python notebook and you're looking at
540
:the data and trying to figure out, okay,
is this predictive is that near exploring
541
:and making combinations and being able
to find those insights and just being.
542
:Continuously curious about what's there
and then saying, why would this happen?
543
:Is it spurious?
544
:Is it just a random correlation or
is there truly an insight there?
545
:And then being able to talk through
that without it becoming, I'm always in
546
:exploration and never actually moving
it to prod because that you can run
547
:into that instance as well, where, and
there's a lot of technical people who.
548
:Always say, but I could make this better
and this better and this better, and
549
:then it never actually gets the prod.
550
:Right?
551
:So having that balance is another one.
552
:That's really important of saying
yes, but right now we're good enough
553
:and there can be another iteration.
554
:Jazmin Furtado: Yeah,
it's it's I think that.
555
:Is always a tough bridge to cross
when you get people that are.
556
:Just they're used to maybe
building something in a lab.
557
:Or does it just continuing
on the research?
558
:Oh, I just want to make it a
little bit more better a little bit
559
:better when it comes to actually
points thing out into production.
560
:You need to.
561
:Find something that's going to, you
have all these other things to consider
562
:in terms of like the timeliness and
the how much, how much overhead that
563
:provides that gives you and you're
creating and you're deploying the app.
564
:There's there's not just
the algorithm itself.
565
:The algorithm performance
itself is not the only factor.
566
:So,
567
:Brian Beachkofski: uh, it's
not providing user value if
568
:it's not in the user's hands.
569
:Jazmin Furtado: Right, right.
570
:Uh, the last question I wanted
to post to you was, um, talking
571
:about the future of your field.
572
:Um, where do you see sports betting going?
573
:Obviously, you're talking about,
you know, the just such a fast
574
:pace of iteration and advancement.
575
:Where do you see it heading
in the next, you know, in the
576
:near future or more long term?
577
:And then what advice do you have for
folks that are interested in this field?
578
:And what advice do you
have to get them into it?
579
:Brian Beachkofski: Yeah, so I
think we're at a really fun and
580
:interesting part in this space, right?
581
:We, we.
582
:We're betting the same way we
have for 150 years at least.
583
:I think I said earlier in eternity, but
in Casey at the bat, the poem, they say,
584
:I put up even money with Casey at the bat.
585
:So somebody is, is, uh, wants to
live bet that that guy's going
586
:to get a hit at a baseball game.
587
:Like this could be what my buddies
are doing this weekend is, is
588
:betting on a hunch that this guy's
going to get a hit this weekend.
589
:Right?
590
:So things are going to change.
591
:Uh, and how do you give people
the tools to make that happen?
592
:And, and.
593
:The space, the, the, the, the sports
betting space has been relatively
594
:resistant to real fundamental change,
like it changes with now it's on an
595
:app, but, or we use more parlays or
we, it changes on the margins, but
596
:at its core, it's largely the same.
597
:And I think we're going to
see that continue to evolve.
598
:So there, there's going to be more,
uh, more diversity in what people can
599
:play on, but I think more importantly.
600
:Like I said before, no one chooses
where to have dinner now without
601
:consulting reviews and, and no
one picks out what, uh, what thing
602
:they're going to buy online without
seeing the reviews and the ratings.
603
:We're going to see more of that in sports
betting, where you use tools to make
604
:picks because it just seems backwards
to go with your gut or to just use what
605
:somebody flashed up in front of you.
606
:And in what we've seen are there two big.
607
:Different groups, so 1 is the
person who is is just want some
608
:support the equivalent of of of a
rating and say, I trust this thing.
609
:I'm going to go do it.
610
:So that's 1 group.
611
:And they're going to they're
they're want to model.
612
:They want some sort of reason for doing
it, but they don't want to go deep.
613
:But there are a lot of people who
really want to go that extra level.
614
:And that's what's going to be most
exciting for where we're going is having.
615
:This premium experience.
616
:That's a no code modeling.
617
:So rather than us do all the feature
engineering, let people go in and
618
:see here are the base level stats.
619
:How can I group them together in a way
that makes sense to me, make my own
620
:features, get get feedback on how, how,
uh, effective they are in and build
621
:their own models, change different,
uh Uh, modeling approaches switch from
622
:a linear model to a tree based model.
623
:And maybe some people, like I said,
don't want to go into that depth, but
624
:there's a lot of people who are very
interested in doing that, but just don't
625
:have the time or training to do it.
626
:And if we can reduce that barrier,
I think it's going to be amazing.
627
:The number of people that will have
the tools and be able to make their
628
:own insights and be more engaged, not
only with sports, but within analytics
629
:itself and get excited about it.
630
:The potential like Saber metrics is
huge with baseball analytics and people
631
:doing that and imagine if that same
kind of engagement can happen, but with
632
:with without having to have your own
coding solution there, you can just.
633
:Go in and pick what you
want and get those results.
634
:So I think the future is amazing.
635
:I think we're at a point where
everyone will be using some sort of,
636
:uh, uh, analytical tool to help them
get engaged in sports betting and in
637
:the amount of engagements people are
going to have with sports betting in
638
:general is going to grow because it's
such a great fan engagement tool for
639
:the leagues and the teams themselves.
640
:So how would someone get into this?
641
:I, I would do it just kind of like I did
is the great thing about sports data is
642
:there's so much out there that's free.
643
:Um, you can go to baseball reference
dot com and scrape their data.
644
:And I think most sports now have a
football reference, soccer reference,
645
:like the basketball reference.
646
:They're all out there and you can scrape
the data and, and just like how that's
647
:where I started because it was easier to
get sports data than it was engine data.
648
:Um, I was, I was just talking to
a recent grad today who was asking
649
:for, for mentorship advice and.
650
:And that's what I said to
him is just go find problems.
651
:If you listen to sports talk radio,
there's 50 great research topics said
652
:every hour about like, Oh, what's
the value of this, uh, draft pick?
653
:Oh, this guy's going to be injured.
654
:What's that going to cost us
in terms of playoff position?
655
:People throw out these
questions and then move on.
656
:But if you're an analyst, you're
like, man, that's awesome.
657
:I should go figure that out.
658
:And in all the data you
need is publicly available.
659
:So that's what I would do.
660
:I'd spend a half hour listening to
sports talk, hear the, hear the guys
661
:spout off questions they never really
want to answer, but then go in and
662
:get the actual analytical solution to
those questions and, and, uh, put it to
663
:Jazmin Furtado: practice.
664
:Yeah, it's such a low barrier to entry,
uh, to just start getting interested
665
:and start tinkering in the space.
666
:And I think that, uh, is a breath of
fresh air for people that have trouble
667
:just trying to get in, get information.
668
:What you were talking about earlier
with, uh, people being interested
669
:in the space and the future of
this field kind of just exploding.
670
:It's just so interesting that
you're looking at the user.
671
:In the future, you know, people
you're expecting for people to
672
:be more digitally competent.
673
:You're expecting for people to expect.
674
:Certain features, be able to dive a
little bit deeper under the hood of
675
:these algorithms, because people are
just have more of that knowledge in
676
:this digital age that they're in.
677
:So it's.
678
:Great have to look forward, looking
not just at the industry, but also
679
:the users who's going to be like,
what do they expect from your tools?
680
:And that really brings
back at the end of the day.
681
:It's the user.
682
:It's what.
683
:You know, it's you don't have a product
if it's not valuable to them, you can
684
:have the coolest thing under the hood.
685
:But at the end of the day,
they want to be able to.
686
:People interact with it and use
it as useful for them where their
687
:skill levels at and everything
needs to be centered around there.
688
:So I think it.
689
:That is just very exciting.
690
:It's so fast paced.
691
:That is also very different.
692
:It's like a great arena where you have
technology that you're able to rapidly
693
:iterate on, but then also rapidly show to
users just rapidly iterate the whole loop.
694
:So that's just such a cool,
cool space that you're in.
695
:I love it.
696
:Thank you for doing that.
697
:No, thank you for having me.
698
:It was great.
699
:So, for the last part of this
episode, I wanted to close with this
700
:game I'm calling Fact or Fiction.
701
:So, I have a few statements, uh,
here about sports, and I'm not
702
:expecting you to know the answers.
703
:I was hoping to make it so hard so
that you wouldn't know the answers,
704
:uh, and I want you to tell me if you
think that they are fact or fiction.
705
:So, are you ready?
706
:I am ready.
707
:All right, so the first one.
708
:There are over 8, 000 sports
played around the world.
709
:That's not the, that's not the,
that's not the one in question.
710
:But about 4 of them, 4% of them
are represented in the Olympics.
711
:Is that fact or fiction?
712
:Brian Beachkofski: Uh, well, 4% of 8, 000?
713
:Is, uh, what?
714
:320.
715
:So I'm going to say that seems fact.
716
:Jazmin Furtado: That is fiction.
717
:There, there are 33 sports
played at the Olympics.
718
:In:
719
:Brian Beachkofski: Okay.
720
:Yeah.
721
:Was leading events, not sports.
722
:Jazmin Furtado: So less than 0.4% of
sports are represented in the Olympics.
723
:Yes.
724
:Brian Beachkofski: That makes sense.
725
:Yeah.
726
:I was thinking like, uh, like track,
uh, like running would be one.
727
:Not like the 100, 200, 400, 800.
728
:Yeah.
729
:Yeah.
730
:Jazmin Furtado: Okay.
731
:I need to find some machine to
recalculate this for advance.
732
:. . Alright.
733
:A second.
734
:About 20% of people in the U.
735
:S.
736
:participate in sports on a daily
basis, or like, on any given day.
737
:Brian Beachkofski: Oh,
that's, that's too high.
738
:I'm saying false.
739
:Jazmin Furtado: That is fact!
740
:Sports, as in sports,
exercise, and recreation.
741
:So, based off the Bureau
of Labor Statistics.
742
:So, about 20%.
743
:Doesn't mean that 20% of
people exercise daily.
744
:It's on any given day,
about 20% of folks do.
745
:All right, you're 0 for 2.
746
:Or 2 for 2, it depends.
747
:Or 2 for 2.
748
:Number 3.
749
:Basketball has the highest estimated
rate of sports related injuries among
750
:individuals above the age of 25.
751
:Brian Beachkofski: I would say true.
752
:I've seen so many angles roll.
753
:I'm going to say true.
754
:That
755
:Jazmin Furtado: is...
756
:False.
757
:I'm just, I'm like, so proud of myself.
758
:Um, cycling, bicycling has the highest
rate of sports related injuries.
759
:Then basketball is next.
760
:Brian Beachkofski:
Basketball's number two.
761
:It's true.
762
:I, uh, I had a cycling injury myself.
763
:I was going downhill, hit a pothole,
flew over my handlebars, broke
764
:my helmet on, uh, right, right
next to, uh, a telephone pole.
765
:That was, uh, it was a bad day.
766
:That
767
:Jazmin Furtado: was, was that in Boston?
768
:It was in DC.
769
:Oh, in DC.
770
:Oh, that's interesting.
771
:Oh, I'm glad you're okay.
772
:No, those cycling accidents
are, they're, they're real.
773
:And, um, apparently the biggest
sports related injury is in the knees.
774
:So like knees are like
the most popular, I guess.
775
:Most most injuries are in the knees.
776
:All right.
777
:4th 1 in the U.
778
:S.
779
:crime rates can drop during
sporting events, but up to 25%.
780
:But that's not the fact in question.
781
:However, in the capital of the
Philippines, when boxer, many fights,
782
:the crime rate can drop up to 100%.
783
:Or reach 0%.
784
:I say true.
785
:Yes, that is true.
786
:Benny Pacquiao is a hero
to many in the Philippines.
787
:And so, I found that very interesting.
788
:I was like, really?
789
:Crime rate was zero.
790
:At least reported crime rates were zero.
791
:For the whole 7 hours that he was playing.
792
:Which is interesting because that's...
793
:In the capital, which is so dense, that's
so many people, but yeah, yeah, the
794
:correlation to sporting games and crime,
everyone should just go see more sporting
795
:events for the betterment of humanity.
796
:All right, so you got 1.
797
:All right, so last 1, let's
see if we make that 2.
798
:MMA is the fastest growing sport in
America, as it has consistently grown
799
:about 20% annually for the past 3 years.
800
:Brian Beachkofski: Is this in viewership?
801
:Jazmin Furtado: This is in
just like the Or participation.
802
:The dollar value of the industry.
803
:It's not participation,
it's just like of the whole
804
:Brian Beachkofski: industry.
805
:Okay.
806
:Then I'll say yes.
807
:Jazmin Furtado: That is fiction.
808
:Pickleball.
809
:Oh, that's right.
810
:Brian Beachkofski: I forgot pickleball.
811
:Of course it is.
812
:Of course it's pickleball.
813
:Jazmin Furtado: Pickleball is the
fastest growing sport in America.
814
:I have done, my parents introduced
me to pickleball, and it's fun!
815
:It's addicting.
816
:I don't know if you've tried it yourself.
817
:I haven't, I haven't tried it.
818
:You need to get you may get on a court.
819
:There's like, I think
10, 000 courts in the U.
820
:S.
821
:I know all these things now because
I've been on the depths of the
822
:Internet for the past a couple of days.
823
:Brian Beachkofski: Well, maybe we'll get
coverage and rhythm here pretty soon.
824
:Jazmin Furtado: Yeah,
you should look into it.
825
:I don't know what the stats are
behind it or like how much data
826
:is being captured on it yet.
827
:But, you know, I'm sure
it's only going to grow.
828
:Yeah.
829
:Thank you for playing and
for entertaining that game.
830
:I know it's like really tough,
but hopefully you're able to
831
:learn a new a couple new facts.
832
:Oh, of course.
833
:Thank you as well for being my
guest and a fellow data explorer
834
:in this world of data we're in.
835
:I know firsthand how
you've inspired people.
836
:All around you and all the teams that
you've been a part of, and your focus
837
:on people has been really reflected
in every step of your journey.
838
:And I know that your story really
resonates with folks who feel like
839
:there's such a breadth of opportunities
out there, but haven't seen someone or
840
:seen too many people that have really
gone out and explored and ventured
841
:all those opportunities in real life.
842
:So.
843
:Thank you for being that example.
844
:Well,
845
:Brian Beachkofski: thank you.
846
:I know you've been a great
example to many people, too, and
847
:congratulations on your post service
career and where that takes you.
848
:It's going to be amazing.
849
:Jazmin Furtado: Thank you so much.
850
:That's the one thing I love about tech
is that you're just constantly learning.
851
:So, uh, I'm excited just to
explore more of the world and
852
:just be enveloped in it even more.
853
:Uh, I also wanted to thank Hatch
IT for sponsoring this episode and
854
:allowing me to host this series.
855
:And as always, I'd like to thank you,
the listener for tuning into this episode
856
:and exploring the world of data with us.
857
:Thanks everyone.
858
:Take care.
859
:Tim Winkler: Calling all
startup technologists.
860
:podcast, but don't know where to start?
861
:Well, here's your chance to shine.
862
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the program, our exclusive mini
863
:series, and we want you to be a part
of it as tech leaders and mentors.
864
:You'll get the exclusive opportunity to
become a guest host right here on the pair
865
:program podcast, share your expertise,
insights, and stories with our audience
866
:of startup focused technologist excited.
867
:We knew you would be.
868
:To be considered, head over to myhatchpad.
869
:com backslash contribute.
870
:Fill out a brief form
and submit it our way.
871
:Let's co create something
amazing together.
872
:Don't miss this chance to elevate your
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873
:Visit myhatchpad.
874
:com backslash contribute.