- With the bases loaded and one out.
- Baseball and statistics go way back.
Almanacs are filled with stories
from games over the last 100 plus years.
But answering deep questions intuitively
has been a giant challenge.
Like, grand slam big.
- Going back. That is gone!
The MLB has teamed with Google Cloud
to push what's possible
with data on and off the field.
And I'm on a mission to find out
just how they've done it.
- Play Ball!
- Is first to a left hander
and this one to a wide open space.
McCann with a great diving stop
and he hits Peterson for the out!
His first play as a first baseman!
- Thanks to the data analytics tools in the cloud,
answers to burning questions
that fans have are within reach now.
So in a single game,
how many data points
are you actually tracking?
- In an individual game, we'll capture
25 million unique data points.
There are 2,430 regular season
games every year,
so that creates unique challenges
for us as we think about how to deliver
that to our customers.
- What kind of data is
needed to decode the game?
- One of the more powerful things
that has been done with Major League
Baseball is by marrying
the data to the video with MLB Film Room.
Now we're getting to the point
where you're tracking the ball,
the players multiple times every second.
You really need to have
the right instrumentation
and then have the right technologies
to help you process that data
and answer the types of questions
you can get with that data.
- There it is! It's a triple play!
- What are you using to process
that data on the field?
-So we actually are using Anthos,
which is a deployed
Kubernetes inside the ballpark.
So we send the data from the ballpark
Anthos Kubernetes to our Kubernetes
cluster running and Google Cloud.
From there, we're able to scale it out
and stored in Cloud SQL
for our database and
read it back into our Stats API
We're able to serve it to millions of fans
around the world in real time.
- Data and baseball is
clearly a beautiful marriage,
but I would like to dig deeper
and find out what's under the hood.
As the ball is moving,
can you walk me through the process
of collecting the data
and processing it?
Now, as soon as the pitch is released,
we have twelve Hawkeye
cameras around the stadium
tracking everything about that pitch
at 30 frames per second.
So that includes about 60 more data points
about every single pitch.
So we have processors
collecting all this data from Hawkeye,
putting it into our data center
within the ballpark,
and we're able to make
these determinations using the raw data.
The end of the season, it amounts to
about over 25 terabytes of data.
So we can't easily query
this data inside the ballpark,
so we need a highly scalable technology
such as Bigtable to be able
to store this fast
and read it back out fast
in order to get it to our consumer
products in real time.
Bigtable is
awesome to use for things
like post tracking data.
So one new, exciting project
that we're working on
is what we're calling FieldVision.
This will be a constant
stream of post tracking data
throughout the game,
visible for a fan on their desktop
or their mobile phone.
So using our API,
we're able to front Bigtable
with a scalable technology
that we can serve to millions of fans .
- How do you process it
further to make it available
for the broadcasters and the APIs?
- Any time you watch a game,
you'll see this live strike zone
over the center of home plate.
It's imperative
that we're able to deliver
that information to the broadcast trucks
at very low latency.
So every night we have a batch job
that runs and we use data flow as a key
mechanism to make this work.
We're able to move data
out of Bigtable,
out of Cloud SQL, into GCS buckets,
and then from GCS buckets
into BigQuery.
Once the data lands in BigQuery,
we're able to give that to analysts
and share that with all 30 of the clubs.
- How could teams take
advantage of all this data?
What does it mean to everyday operations?
So how do MLB teams actually use data?
- Basically, you're pulling in data
from the API for everything
from reviewing major league on field
performance, to player acquisitions,
to running our models.
- What do you do with the data?
- We're ingesting the data
from the various stats
API resources that we have available.
Regular gumbo feeds, the pose data,
and then we're aggregating it
and basically filtering it down
into actionable information
for our personnel.
- What are some of the tools
that you use to gain the insights?
I'm not sure I want to answer that one.
- So I tried to get John to tell me
how his team is using
specific Statcast
data to get better players
and improve performance,
but he wasn't willing to spill the tea.
Competitive advantage and all.
- Slider in, fastball in,
changeup, curveball,
partridge in a pear tree.
It makes me wonder:
how can analysts use the cloud to help
tell the whole story of baseball?
And what about announcers?
How do they consume this data
and translate it to a fan
who might not care much
about that underlying data?
- I say this many times on MLB Now:
this is not math class,
this is baseball,
and it's always about asking
the question that you want
answered in the ballgame
and how you find answers now,
frequently, is in the data.
- And so when you're looking at this data,
what is the type of information
you're looking for?
- It depends whether I'm
looking for an extreme,
a specific instance,
like a longest career homerun
or a fastest pitch,
or whether I'm looking at something
that spans more of a career.
That's where I would use
any combination of a website,
like a baseball Savant,
FanGraphs, a baseball reference.
Statcast allows us to measure
everything on the baseball field.
We can tell you how fast a pitch was,
how hard a ball was hit.
We can tell you how far a homerun went,
and those are the things that I think
can really speak to fans.
- Now I get it.
A massive amount of data from
the field is captured, processed,
and published as APIs
to power tools like Savant, Film Room,
and internal and external research.
Data and analytics in the
cloud are powerful tools,
and it's been amazing learning
how it's applied
in the sports world today.
I am so excited for the future
of sports data analytics.
Thanks for joining me on this journey.