STEPHANIE WONG: I'm Stephanie Wong from the Google
campus in Sunnyvale.
And we're talking baseball.
[MUSIC PLAYING]
Today, we're trying to figure out
what makes players popular with fans in Major League Baseball
and how you can use machine learning to predict it.
And joining me, MJ Jacob, Cloud Engineer.
MJ JACOB: From New York City.
STEPHANIE WONG: And Dale Markowitz, applied
AI engineer here at Google.
Hey guys, good stuff.
OK, so first of all, are you both baseball fans at all?
DALE MARKOWITZ: I'm a baseball fan,
but I'm really a data nerd.
So I think being a data nerd makes you
kind of be a baseball fan because there's just
so much to analyze.
It's like being in a candy store.
What about you, MJ?
MJ JACOB: Yeah, I would say I have
a pretty healthy relationship with baseball for two reasons.
Number one, when I was a kid growing up,
I used to play a home run derby all the time.
And nothing's like just knocking some pitches out of the park.
And number two, I love going to in-person baseball games,
like, being able to eat a hot dog
with your drink with your friends, your sunglasses on,
it's a beautiful show.
STEPHANIE WONG: So let's get to our lineup.
We have some amazing guests today.
We have Josh Frost, vice president of Product
at Major League Baseball.
MJ JACOB: And Kevin Youkilis, a three-time MLB All-Star.
A player who was incredibly popular for reasons
that weren't obvious unless you were a dating nerd.
[DRUMS]
STEPHANIE WONG: All right, so you guys, what do you think
are the types of data sets that would help determine
a really popular player online?
MJ JACOB: Number one, I'd love to collect data from TikTok.
Lots of trends coming out a TikTok, of course.
Number two, the most popular songs
that are currently trending.
And then number three, the most popular dances.
And the reason for this is think about back in the day
when John Wall was trending, right?
Everyone was like, do the John Wall-- do the John Wall.
[MUSIC PLAYING]
It was crazy because while he was an awesome basketball
player, I think a lot more people knew him.
And he was a lot more popular because of that dance
and that song that came out.
Dale, what about you?
Can you fill the rest of the--
DALE MARKOWITZ: I went on social media with TikTok stuff
that you want too.
But I also want some more boring things,
like maybe how is popularity measured?
And like, what sort of users are more likely to submit answers,
like what is their ages?
What device types?
So all of the unsexy stuff that actually can make
a big difference in the end.
What do you think, Stephanie?
STEPHANIE WONG: Yeah, I think there are
a couple in between things--
more fun and boring, I guess.
It's TikTok dances because, obviously, those
are a big driver for what's trending these days.
Or does a player have a orange or red beard
like Justin Turner?
So there are ton of things that you can look at and even
social justice issues, like if they've been really active
about those things in the past.
MJ JACOB: Yeah, and I think that's super important,
that last point you just touched on,
Steph, because at the end of the day,
I love whe players can use their platform
to make a difference in the world.
And when they become popular because of that,
I think that's an incredible-- two birds, one stone.
STEPHANIE WONG: Hard to put that into a column,
but it would be neat.
Joining us now is Josh Frost with a little bit more
about how to define an all-star baseball player
and to talk about an exciting challenge for data scientists
who love the game.
JOSH FROST: Hey, how's it going?
STEPHANIE WONG: OK, so Josh, we know
that there is an exciting new Kaggle competition and MLB.
Can you tell us a little bit more about the details of it?
JOSH FROST: Yeah, it's a really exciting one
because it's pretty different, we
feel, from what's been done before in the Kaggle space
and certainly in our world.
Effectively, we're trying to ask AI and ML
enthusiasts to try and predict what makes a popular player.
So digital engagement is really how we measure that.
And we're giving pretty rich data sets
to challenge people to say, can you divine some signal?
Can you create an algorithm to help predict player popularity?
And we have really rich data sets
there, lots of documentation on what the information is.
But ultimately, we think it's a really unique opportunity
for you to try your skill sets at making
a prediction at a business problem
that we think about every day and that lots
of sports and media companies alike are thinking
about every day as well.
[MUSIC PLAYING]
- Wow.
MJ JACOB: All right, so Josh, I got to know.
What makes an all-star player in your opinion?
So I think the question that's really interesting
is what makes someone beloved by the fans,
not just for what you do on the field that's very creative.
But staying after, sign autograph,
creating a cool video, collabing with local artists.
All those things are really strong signals
that help define the modern all-stars.
[MUSIC PLAYING]
DALE MARKOWITZ: So Josh, talking about players
that they'd love it, I got to tell you.
So I'm an applied AI engineer here at Google.
And I'm like a big fan girl actually for what you do.
And what the MLB does with data, I
mean, we're going to enter into this Kaggle competition,
like, what sort of data are we going to have to work with?
JOSH FROST: Yeah, we think it's really unique because it's not
just an entertainment product.
It's a performance product as well.
It's really a business, of course.
You're getting these really unique data sets
combining the differences between on-field performance,
the business metrics of how fans interact in the digital space,
both on our core platforms and even on social as well.
So you'll get some pretty distinct data sets there.
And then what we call our stats API
says this huge range of information
that allows you to marry it up with something that you really
want to think about.
So something that we've found a lot of interest
in looking at already is when you
see spikes for someone on either the player page views
or under social [INAUDIBLE] like,
did that correlate with anything that happened on the field?
DALE MARKOWITZ: Is there anything
that you've learned using all this now combined
data that was very surprising that you didn't expect?
JOSH FROST: Yeah, I think I'm always
surprised and impressed how nationality
and, like country of origin can be a really strong influence.
So when Shohei Ohtani plays, there's
going to be strong correlations really throughout Asia.
But there are also other, like less well-known correlations
on when this player comes and plays at the stadium,
he overindexes.
Why?
Maybe he's from that town.
Maybe he's from that area or played college ball there.
STEPHANIE WONG: So being from San Francisco myself,
I totally agree that an origin is a huge part
of the whole equation for me.
But who would you vote for in this case?
JOSH FROST: Well, I'm super biased.
I'm from Chicago or outside Chicago.
So I'm always going to vote for the Cubs.
But this year, I think I have a pretty good case.
Recently, Javy Báez had this incredible play.
He's called "El Mago" which means a magician in Spanish.
And he can literally create something out of nothing.
And he is a top performer.
So I'll definitely be voting for him.
But he's also a top performer because he's
so dynamic and having swag--
impossible to define but also definitely noticeable as well.
MJ JACOB: Or could popularity be based off
the shoes or the bling that they're wearing.
JOSH FROST: We were joking that I
would love to create a metric for the swag chain
that Fernando Tatis Jr. wears that like, every time he
makes this mind-blowing grand slam,
he's putting this very cool, hilarious, big gold chain on,
like, no data can measure that.
But like, plenty of data could measure how people
react and feel about it, right?
DALE MARKOWITZ: As we learn about the more subtle things
that make players really valuable,
are fans understanding that?
Are they understanding when a player is valuable
even if it's not in a Swagway?
JOSH FROST: Yeah, that's what I love about this era of baseball
is that there's never been a deeper appreciation from a fan
or a front office perspective for on-field performance,
right?
So it's really fun and exciting to talk about swag
and all those other topics.
Yeah, I've always believed that all players have a data story
mostly because that helps track the way that they've evolved
from someone who was playing little league all
the way to being a major leaguer, right?
And the data story can be in how a scout discovered them.
It could be an anomaly or a pattern.
Kevin Youkilis is a famous example, commonly referred
to as the Greek God of Walks where
he was noticed because he was excellent at getting
on base through walks.
- Ball four.
JOSH FROST: Something else that you've
seen in the last couple of seasons
is that we're using not just data, but video as well.
We have consumer data, on field data.
We have business data and industry data.
Now, machine learning at AI is giving us these opportunities
to start gleaning insights across all
of these different data sets.
[MUSIC PLAYING]
STEPHANIE WONG: Thank you so much for joining us today.
And we hope that we get to see you on the field
out there someday.
JOSH FROST: Yeah, can't wait.
STEPHANIE WONG: Do you think you can have
an impact on America's pastime?
Maybe you can.
Join the MLB Player Digital Engagement Forecasting
Competition.
The competition will dive deeper into the player
fandom of America's pastime.
MJ JACOB: The result--
the ability to predict the most popular players for MLB.
And yo, you can win $50,000 in prize money.
[MUSIC PLAYING]
STEPHANIE WONG: So Josh mentioned three-time MLB
All-Star player Kevin Youkilis.
And we just happen to have Kevin Youkilis
here on the show with us today.
Kevin, Youk.
MJ JACOB: Youk.
Let's get it.
[LAUGHS]
KEVIN YOUKILIS: Good morning.
DALE MARKOWITZ: [LAUGHS]
STEPHANIE WONG: Morning.
Thank you so much for being here.
I have to ask first off, what is it
like being known as the Greek God of Walks?
MJ JACOB: And do you think that's why you were so popular?
KEVIN YOUKILIS: Oh, the Greek God of Walks.
Well, this is something that's been a part of my life
since I was in, wow, AA baseball around 2003.
I got dubbed this by the book, "Moneyball."
And it's funny because I'm not Greek.
But of Romanian descent of my father's
side with the Youkilis name.
So it was a name change.
But the Greek community still loves me to this day.
So and I love the Greek culture and love the food.
So I still consider myself Greek in some ways.
Yeah, it was one of those times where it's wild.
They got a lot of recognition in the minor leagues.
I never had recognition ever in my life--
a little bit here and there.
But I was an underdog.
DALE MARKOWITZ: So do you think that life
would have been different if you were there
before big data became a real big thing?
KEVIN YOUKILIS: It's funny, like,
so when analytics started coming about in the early years,
a lot of it was back behind the scenes.
As baseball players, we just went out there every day.
We practiced.
We worked hard.
So analytics became a big deal probably, yeah,
right around the time when I got drafted.
It started to become more of the norm of learning
about on-base percentage understanding
that the key to baseball is getting on base.
On-base percentage is also a part of getting hits.
So average was a big deal for a long time.
But on-base percentage included walks, hit by pitches.
And it became a focal point.
The data and analytics is really good for the coaching.
There's a lot of good information
that is quick and easy, whereas before, you'd
have to have different video angles of the hitters,
trying to assess and see what kind of body movements
they have.
Now, we have the ability to assess those body movements.
So when a player is in a funk or doing something, maybe
there's something little that we can take out of it.
[MUSIC PLAYING]
MJ JACOB: So Kevin, on that point, I'm curious.
When you watch the video and you assess the data,
how frequent is it that the data actually
is accurate with what you're seeing in the video?
KEVIN YOUKILIS: We talk about this all the time is
players make the analytics.
And then the analytics are evaluated
by other people that are very smart and intelligent
and understand how to break it down.
So that's one of the funny parts about it.
And the video doesn't lie a lot of times.
DALE MARKOWITZ: I'm sure some players must
be more connected with the data than others.
They have like a data head.
KEVIN YOUKILIS: Yes, there are some players that
are very, very into the data.
There's other players that don't want any data.
There's a lot of analytics, like driven into the pitching side
because you're really on offense.
You're in control of throwing the ball.
It's like golf.
The ball's sitting right there.
And that's why it's become super big, analytic-driven,
because you control what you're doing.
As a hitter, you're actually on defense.
You're reacting to what they're doing.
[MUSIC PLAYING]
- Strike one.
MJ JACOB: Now, you're a rookie on the Boston Red Sox when
y'all vanquished the Curse of the Bambino.
Do you really think data had anything to do with that?
KEVIN YOUKILIS: Front office used data
to get the team, right?
So you plug and play players.
I mean, Theo Epstein has done a great job of that of years
is building a lineup and trying to figure out
what kind of players you want.
And I think he did that in a lot of ways.
He saw value in certain players.
And yeah, so like, we got a David Ortiz
for the Minnesota Twins that he was a Major League player.
But they just didn't see him because they
had other players that they thought were more valuable.
MJ JACOB: Wow, so the data helped get the players
on the team.
But once the players were on the team,
that's where the full human kicked in.
And let's feel.
Let's practice.
Let's get better day by day.
No data apart of that.
KEVIN YOUKILIS: Once you get to a certain level,
pretty much everyone's on a playing field.
Some are greater athletes than others physically.
But it's the mind and how you slow down.
And there's not a lot of analytics for that.
A good coach understands the player and how they
can handle certain analytics.
And if you understand the player first,
then you can introduce the things
slowly into the player's mind in order
to make them better every day.
STEPHANIE WONG: Thank you so much
for your time today, Kevin.
KEVIN YOUKILIS: Thank you.
Yeah, we're excited.
Go Sox.
STEPHANIE WONG: Go Sox.
Youk.
MJ JACOB: Youk.
STEPHANIE WONG: Kevin Youk.
[MUSIC PLAYING]
So now let's talk about the Kaggle challenge with Major
League Baseball.
It's the MLB Player Digital Engagement Forecasting
Competition.
The competition will dive deeper into the player
fandom of America's pastime.
MJ JACOB: Don't forget.
There are $50,000 in prizes and, of course,
serious bragging rights up for grabs.
[MUSIC PLAYING]
STEPHANIE WONG: Hope you're feeling inspired at home.
Remember, you can try Vertex AI for free in the Google Cloud
console and visit the companion assets to build
what you've said here today.
That's all for today.
Thanks, everyone.
Bye, Dale.
Bye, MJ.
DALE MARKOWITZ: See ya, Steph.
MJ JACOB: Peace from NYC.
STEPHANIE WONG: Youk.
DALE MARKOWITZ: Youk.