ADITI MISHRA: Hi, everyone, and welcome to an amazing session
with Major League Baseball.
Today, we will uncover how you can better
harness the power of your data for smarter outcomes.
Today, MLB has reimagined the fan experience
by consolidating their data platform with Google Cloud.
Using a combination of Anthos, Google Kubernetes Engine, Cloud
SQL, BigQuery, Looker, and many other products,
MLB has been able to drive deeper engagement
with fans for today as well as the future.
Before going any further, I would like to introduce myself.
I'm Aditi Mishra within Google Cloud's Data Analytics team,
and we have here Rob, who is the Senior
Director of Software Engineering for Statcast Data at Major
League Baseball.
Thank you so much, Rob, for joining us today.
Let me set the stage for our audience.
More and more, the world is becoming digital in nature,
and recent events have only accelerated the transformation.
Companies today recognize that competing in a digital world
ultimately means transforming both your technology stack
and the operating model in your business.
Based on a Bain consulting study in 2021,
experts identified four patterns of digital transformation
that are triggering companies to rebuild their entire IT stack
to compete most effectively.
Which of these ones do you identify with?
The first one is called upheaval.
This is the one where, as a company,
you feel so much pressure to implement
a new digital infrastructure, and it
requires an entirely new set of foundational capabilities,
hence the upheaval.
The second one is called fragmentation.
And this one is a company that has so many digital projects
bubbling up across the company, but they
lack the ability to actually prioritize them and scale them.
That's because you need a unified data platform.
The third one is called speed with modular updates.
In this situation, your company needs
to isolate the legacy systems that do work
and rebuild or replace the part that does not work, and needs
to match your business needs.
The fourth option is called new venture,
and this is the type of company that
realizes that instead of retooling their core operation,
they may as well just create a brand-new side business
and start driving digital transformation from there.
All four of these patterns require companies
to invest in a data platform that helps them modernize
and create business value, and Google is designed
to help with just that.
We've been a data company since the beginning.
We've developed tools, frameworks,
and open-source systems that have powered Google
and its core search engine.
We run on the same proven and reliable technology principles
that power Google's multi-billion user services.
Here are four fundamental reasons why
organizations choose Google.
Number one, Google's platform is a complete unified data
and AI solution.
This means we have the ability to manage
each and every stage of the data lifecycle,
from running operations to managing
transactional applications, all the way across to your data
warehouse, data lakes, databases,
and creating really rich data-driven experiences.
Along with AI and ML, we find organizations
are able to not only build improved insights,
but also automate entire core business processes.
The second reason why customers pick Google Cloud
is because we were designed for speed, scale, security
and reliability for any size organization.
Many of our customers actually have
adopted our leading products such as BigQuery, Spanner
Looker, and our AI solutions to achieve that lower TCO
and improve productivity for their data-driven applications.
Google, as number three, is also very focused on openness.
Our integration with open source standards and APIs
ensures portability and extensibility to prevent
lock-in.
For example, a customer can leverage their Open Database
implementation of choice when working with Google.
They can also leverage open-source technologies
for data lake processing.
Also, Google's data platform can run anywhere.
For example, you can natively query data living on AWS,
and Azure, and Google Cloud.
And finally, Google has strategic partnerships
with leading data-driven companies
that help customers easily access insights
from their data.
Number four, Google has built-in ML and AI.
At Google, AI is in our DNA.
AI powers Google products that are loved and used
by so many people, such as our search, our ads and YouTube.
We have leveraged this expertise to deliver a unified AI
experience for our customers.
Add to that our automated machine learning.
We're able to enable entire data teams
to drive business outcomes.
All in all, we are a unified ecosystem.
We connect the different types of data processing and data
sources to our customers' analytics, database,
and data lake environments.
Let me give you a few examples.
BigQuery is our enterprise data warehouse,
which enables customers to run analytics
at scale with an industry-leading four-nines SLA
and up to 34% lower TCO than Cloud data warehouse
alternatives.
Spanner is Google's battle tested
mission critical database that provides awesome scale
and consistency across regions, and high availability
of five-nines uptime.
It can process billions of requests in seconds.
Looker is Google Cloud's premier business intelligence
and embedded analytics offering.
Looker also has a unique semantic modeling
layer that allows organizations to easily define
business logic.
And then we have Vertex AI, in which organizations can now
build custom models with 80% less code than some
of our competitive platforms.
With that, I want to hand it over to Rob
to discuss MLB and its data transformation journey
with Google.
ROB ENGEL: Thank you, Aditi.
My name is Rob Engel, and I lead the software engineering group
at Major League Baseball that's responsible for Statcast.
Statcast is a tracking system that tracks everything
about the game of baseball in the field,
including all the players moving around,
the speed at which the ball's thrown,
the distance at which home run is hit,
as well as all the outcome-based results
such as the hit versus an error or a ball versus a strike.
I'll get into now MLB and our history a little bit.
So Major League Baseball is the oldest professional sports
league in the world.
We currently have 30 teams that play for us, including
29 in the United States and one in Canada.
Major League Baseball has a wide array of data use cases
and a rich history in technology.
We have a digital ticketing platform,
which allows fans to buy tickets online and check in on using
the Ballpark app at the game.
We have the video streaming platform
in mlb.tv, which is a world-class streaming
platform that lets fans all around the world
watch the game at home, on their connected devices,
or on the road when they're using our app on their phone.
Also, we have content publishing to produce many articles
about everything going on around the game.
Lastly, we have award-winning mobile and web
apps that leverage our live and historical statistics.
That's what I'm here to talk to you about today.
So dating back to 2001, we first launched MLB's Game Day
product.
This is an online version of a box score
and a play by play that tells you
everything that's going on about the game,
with the current score, the line score, the current batter
and the current pitcher.
Fast forward to 2006.
We were able to add 3D ball tracking to this
that was able to draw the trail of the ball,
show the speed of the pitch, and even classify
each pitch as a fastball or a curveball,
or whatever pitch type it is.
Then in 2015, we introduced live player and ball tracking.
This is able to capture every ball movement, including
the hit, the home run distance, every bounce, every throw
and even every pickoff attempt.
Additionally, the player tracking
allowed us to gain new insights about who the fastest
players were, who the best arm strengths are for outfielders,
what the probability is a ball will be caught as it's
being hit into the air, and who the best blocking catchers are.
Today, we're proud to introduce our 3D pose tracking.
This is 18 points per body that we're recording in real time
at 30 frames per second.
This allows you to render an entirely 3D immersive
view of the game that's never been seen before.
Think of it almost like a video game.
And there's so many things that we
could do with this technology, which I'll
get into a little bit later.
So let's talk about the data ingestion pipeline.
Everything starts at the ballpark.
We have sensors at the ballpark recording every movement
of the ball and the players.
This amounts to 30-plus terabytes
of data now per season, now that we've introduced player
tracking with pose and 3D.
We record all this in real time and deliver it
into Google's Cloud.
Additionally, we also have an operator
at every ballpark reporting the outcome of everything.
We have human and subjective outcomes that still happen
in the game of baseball, from the umpire calling
a ball or a strike, or the runner
being safe or out at first, or even the official score
determining if a hit or an error happened.
So all this data is recorded in real time
and delivered to Google's Cloud to our OLTP data
analysis pipeline.
This leverages Cloud SQL Postgres
for transactional and operational type workloads.
This allows us, in subsequent latencies,
to deliver the current score of the game
from the park to the Cloud and out to all of our fans.
Secondarily we're also able to record
the pitch of every speed in real time,
deliver that to scoreboards, to broadcasters, as well
as to all of our applications.
Similarly, we're able to get the distance of every single home
run, the launching and the exit velocity,
and get that information distributed out there
before the batter's even done rounding the bases.
And of course, we update our standings
as soon as every single game is over.
This makes for a very exciting experience
during the postseason run.
And lastly, we have stat leaderboards
that update throughout the game as different stats are being
recorded and allows for searchable stats
to be updated to compare your favorite players
and track different MVP races.
Beyond the transactional data we just mentioned,
we have OLAP data analysis.
I like to call this more contextual information.
So for instance, as soon as a ball is hit,
we want to learn if this was the longest ball that's ever
been hit, or the longest for that player,
provide context about that.
How many ballparks would this have been a home run?
Additionally, we were able to identify
cool things about rookie, debuts, for instance.
So for instance Steven Matz was the first pitcher ever
to record three hits and four RBIs
in his first Major League game.
Similarly, we're able to find when new streaks are achieved.
This year, rookie phenom Wander Franco
had a 39 game on base streak.
That was good for the second longest streak
in Rays franchise history.
And of course, just in 2021, MLB set a new record
for the number of no-hitters thrown in a single season.
Now, all this data is excellent and us data nerds love it,
but we also want to present it visually to all of our fans.
So we have a big suite of great products
that are able to take all of this data
that we're generating, processing in Google's Cloud,
and delivering it in real time through a vast array of APIs
to our fans.
Also, we're able to leverage the same technology
for the scoreboards in the stadium, and our broadcasters,
so we have also different views for the fans to see the game.
Finally, we deliver all the data to the 30 MLB clubs
so they're able to leverage all of this data
for different insights.
They're able to use this to determine who the best
players are and help them with the roster building process.
Now we'll get into the technologies
used to make this all happen in Google's Cloud.
So first, we want to use a world-class, big data
company like Google and leverage what they have.
Of course, with Google Search, they're
able to return all sorts of content in subsecond time
to billions of users around the world, always.
If they're able to do that, I think
that we could use that technology
to also help baseball data and what we're doing here.
So that is very true.
As we're able to use things like BigQuery
for constant time query results for analytical queries,
this lets us run ad hoc queries to find
all sorts of different things, whether it's
trying to determine what the next new stat might be,
or helping us look at the trend of baseball play
over the last few years and see why things are changing.
It also helps us use all this big data for different business
decisions, and that leads us to the second item
here, business intelligence and embedded analytics.
We're able to leverage Looker, which
is the user interface that can sit on top of something
like BigQuery, to draw reports in a very visually
compelling sense.
We're able to use these to help quickly identify trends
throughout the season, whether it's measuring our system
performance or measuring the trends of actual data,
or anything else that we might be
able to help make different important business decisions.
Third, we need something that's modern, scalable and performant
for our applications.
As I said earlier, everything starts in the ballpark.
So we're able to use Google's Anthos, which
is an on-premise Kubernetes solution, in our ballpark.
We record and process a lot of data in the ballpark,
and then we mirror it back up to Google's Cloud,
where we use GKE, which is Google's Cloud-based Kubernetes
offering.
In the Cloud, we're able to scale up
our APIs in just a matter of seconds
to meet demand with the traffic numbers that come.
Likewise, we're able to scale back down when traffic numbers
come back down after games end.
I'll get into this later, but this is a huge cost
savings for us as a company.
We don't have to worry about keeping servers up and running
at 24/7 if we could dynamically scale up and down
as the traffic demands.
And finally but not least, Google's
Cloud's big data capabilities.
Leveraging Bigtable for all this big 30-hertz data--
I was just mentioning it, with the pose tracking and the 3D
different kinds of ways to track data--
traditionally, we would use relational databases for this,
but now that we're recording so much data,
we're truly entering a big data space.
With this, we're able to scale and store
hundreds of terabytes of data through many seasons
of baseball, and with Bigtable, we can report and say this
all in real time with very low latencies,
and it has five nines of availability.
Likewise, we're using Cloud SQL Postgres for all
of our transactional workloads.
Cloud SQL Postgres has great availability and uptime,
and it has subsecond replication across all of the regions
that we utilize.
This ensures that we have high availability and uptime
to all of our end users, and we're
able to fail over very fast.
Now we'll get into some reasons we chose Google.
So number one, Google has foundations within big data.
As Aditi and I mentioned, Google Search
is the premier product when it comes to big data
and returning it to lots of users in real time.
If Google can solve that, why not
baseball using Google's products?
We also have a great trust in our partnership with Google.
We're able to work closely with the product managers for each
of the Google Cloud products we use,
and mainly get advice on how we can use them best.
Likewise, we're able to use Google product managers
to learn what's on the roadmap, or even suggest
new features, potentially.
This has been an invaluable exercise
so we can make our own plans and know when
to expect things to be ready.
Lastly, we gain massive value from the cost savings,
especially using Kubernetes.
We're able to scale up and down based on traffic demands,
rather than have dedicated servers
at all times running just in case there's a traffic spike.
Now we'll get into some of the results
we've achieved with Google.
MLB has forever had first-in-class digital
products, and we want to keep up the pace
and continue to innovate in this space.
We want to be able to compete with other sports.
Baseball has a huge, rich history in statistics,
and we have great products that visualize this.
Now that we're recording 3D player tracking in real time,
just think of all the things that we'll
be able to do with this data.
Attached here is a screenshot of our field vision product.
Some of you may have seen this on MLB Network.
Our analysts are able to use this immersive 3D experience
to dive into a play in ways never
been able to do it before.
There are plays where the traditional broadcast
views just aren't able to capture a certain part
of the play on camera.
We're able to use this technology
to place yourself anywhere on the field at any point in time
on a play and really analyze something
such as a cut-off throw or a pick-off attempt.
Or where a player is backing up another player, or even
positioning of players before, during,
and after the player that you wouldn't otherwise
be able to see.
Also, just think about how this could
be leveraged in the future in products.
Just imagine a virtual reality product in the future
where you'd be able to put on a headset
and place yourself anywhere in the stands,
on the field, or just as a player.
The possibilities are truly limitless,
and this technology is going to be extremely
exciting in the years to come.
And of course, this technology can be leveraged by the clubs.
They're pulling this all in in real time
to make insights about how they build their rosters, about what
trades they're going to make, about what
free agents are going to sign.
All this data is imperative to that decision making.
And these are just a few of many results
that we've been able to achieve by our partnership
with Google Cloud.
Well, now I'll go back to you, Aditi.
ADITI MISHRA: Thank you so much, Rob.
Really, really appreciate it, and thank you to everyone.
We hope you enjoyed today's session.
For more information about Google's data platform
and any of our products, please visit our website
and check out other sessions that go deeper
into these topic areas.
Thank you.