February 28, 2025 • 39 mins
In this episode of the Frictionless Marketing podcast, host Lori Rubinson, Managing Partner at /prompt and WFAN Sports Talk Radio host, dives into the realm of sports analytics with MLB.com's Mike Petriello. Lori and Mike discuss the often misunderstood role of analytics in sports, especially baseball, addressing common criticisms and explaining how analytics can improve decision-making.
Mike shares his journey into baseball analytics, starting from his history degree to his current work with MLB and ESPN. They explore the definition of analytics, its application in baseball through pitch design labs, and the broader implications for players, fans, and the sport as a whole. The conversation touches on rule changes, the impact of sports betting, and the future of AI in baseball. This episode provides valuable insights for both sports enthusiasts and those interested in data-driven decision-making.
00:00 Introduction and Guest Welcome
00:48 Mike Petriello's Journey into Baseball Analytics
02:19 Defining Analytics in Baseball
04:32 The Evolution and Impact of Analytics in Baseball
09:04 Pitching Labs and Technological Advancements
11:08 Incorporating Data into Storytelling and Broadcasting
14:24 The Role of Analytics in Player Contracts and Performance
23:49 Rule Changes and Their Impact on the Game
29:38 The Future of Analytics and AI in Baseball
34:33 Closing Thoughts and Fun Questions
Frictionless Marketing is a production from /prompt, the leading earned first creative marketing and communications agency. Grounded in the present, yet attuned to the future.
To learn more about how to make marketing frictionless, purchase Friction Fatigue by /prompt CEO Paul Dyer online and at booksellers worldwide.
Produced and distributed by Simpler Media Productions.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
>> Lori Rubinson (00:12):
Welcome to Frictionless Marketing, the podcast that dives deep into
the stories of the most innovative brands and the people moving them
forward. Today,
our host, Lori Rubinson, managing director of PROMPT
and WFAN sports talk radio host, sits down
with Mike Petriello. Mike is
the director of Stats and Research at Major League Baseball, where
(00:32):
he's been at the forefront of advancing how analytics are used to
understand, play and enjoy the game.
A pioneer in applying cutting edge technology,
Mike's work has transformed how teams make decisions,
how fans engage with the sport, and how
broadcasters tell compelling stories.
Together, they will dive into the realm of sports analytics as
Laurie and Mike discuss the often misunderstood role of analytics in
(00:55):
sports, especially baseball, addressing common
criticisms and explaining how analytics can improve decision
making.
>> Speaker B (01:03):
Foreign
welcome to another episode of the
Frictionless Marketing podcast. This is Lori
Rubinson. Really excited today to wear both
hats. Usually I am just managing partner here,
(01:25):
uh, at uh, Prompt, but today I'm also the sports
talk radio host from wfan. I've had a
topic on my mind for a really long time that
bothers me when I talk to listeners,
callers and interact with people on social media and on the
radio. And it is that sometimes analytics in
sports and in baseball in particular, get
vilified as if it's the enemy to all decisions or
(01:48):
things that happen with our teams that we don't like. To get to the
bottom of that and to talk about sports and analytics, I can
think of no better person to talk to than Mike
Petriello. Mike, welcome and look
forward to talking to you.
>> Mike Petriello (02:00):
Laurie, thanks so much for having me. Looking forward to it.
>> Speaker B (02:02):
First thing I wanted to get into before we dive into the
integration of analytics into baseball, I wanted
to understand a little bit back up. I think people have seen you
on espn, Statcast, broadcast, the Nerdcast
and all. It's like that. How did you actually
get started in baseball?
Analytics? Sports analytics? How did this come about?
>> Mike Petriello (02:22):
I, uh, wasn't with my history degree, I can tell you that
much. There's a long version, but I will give you the short
version. It's funny because college kids come up to me all the time and they're like,
hey, how do I get a job in baseball? And nobody wants to hear my
answer of I didn't get my first full time job Till I was 35
years old. Basically what happened was I went to Boston University,
I got a history degree, spent most of my twenties at
(02:42):
startups like a video on demand startup. I actually spent five
years working at a place you probably know well, which is Ketchum the
PR firm as like project manager, building websites and
that kind of stuff. Baseball was always a passion and kind of
a side hustle. Uh, when I was 26, I guess I
started a blog which was the style of 2007
and I just kept at it. That got me opportunities, that got me
(03:03):
opportunities with fan graphs and baseball perspectives
and ESPN and eventually here at mlb where
I've been coming up on nine years full time. Now
that's branched out into tv. It gets more in depth than that, but
that's the short version. Turning, uh, a passion into a career.
>> Speaker B (03:17):
We'll compare notes at some point because people
say the same to me with the WFAN part of my
business. How do I get into sports media?
And I always say, I'm a history major from Brown
University, don't do what I did. It's too
circuitous. There has to be a different path. So the
second thing, before we understand
sports analytics within the context of baseball,
(03:39):
I wanted to understand how you actually
define analytics, Fans, media
and others. It's a catch all term for a lot
of things. How do you define it?
>> Mike Petriello (03:51):
I mean, analytics is information. It's the study of
data, it's the study of patterns. Uh, nothing that I just
said is specific to sports or baseball. You could say that across any
industry. Certainly anybody else is using that. So that's what it is.
It's data and patterns and information. If you go
back through all of baseball history, people in the game
have been using information to inform future decisions.
(04:11):
Maybe that information is what they saw, what an intern wrote down on
a pad of paper, their gut, uh, feeling. The only thing that's changed
now is that the information is, it's a lot better and
we know a lot more about the context of it, how to use it, how to
change the game. That's all analytics is, is trying to take all
this information that's out there and use it to make informed decisions
to win games.
>> Speaker B (04:30):
Yeah, I, uh, always say that to fans when they
do call in and blame
analytics if they don't like the pitching decision
and manager comes, takes a pitcher out of the game, goes
to the bullpen, it goes wrong. And then they say, well, that's
analytics. And my response
is, data doesn't make decisions, people
do. If you're angry at the decision,
(04:52):
you know, analytics is information. That is how I think
about it. And you're right. Look at prompt. That is a big part of our
business is data and information to inform
decision making. So you do work better with better
results.
>> Mike Petriello (05:05):
I think that's right. What people think fail to understand is
sometimes there's not a right answer. The Yankee manager brings in the
lefty and he gives up a home run. Well, you made the wrong decision. As
though if you bring in the righty, he wouldn't also have given up a hit. Like
the saying goes, the other guys live in big houses too. You can
improve your decision making, you can put it based on better
processes, but it doesn't guarantee anything. The
smartest team in baseball, whoever you define that to be,
(05:28):
does not win 162 games every year. And they make better
decisions, they have better outcomes, but nothing is guaranteed. Because
at least in sports, there's still people, there's still human beings
on the field and you can try to put them in better situations,
but at the end of the day, the, I don't know, 28 year old on the
mound still has to get the job done. And that's never going to be
perfect.
>> Speaker B (05:46):
A lot of people, I think when they think of baseball and
analytics, they think of the movie, Moneyball, Billy Bean,
and it's about on base percentage and
things like that. And the way I think of that story
is it's about finding ways to
uncover value. An exploit value
that might be an undervalued asset that someone else
isn't seeing. And I think of it and use that term often. We
(06:09):
do influencer marketing here at the agency, want to
uncover influencers who are on their way up.
There's value to tap into
as opposed to someone who's already plateaued. Or you might know them
better, but they're on their way down. Now you're paying for maximum value.
You want to catch an asset on the way up.
With baseball, clearly that was something and that's a
(06:30):
part of Moneyball. But what about the game of baseball? Made
it, in my mind a, uh, pioneer
in using analytics in sports in a way that yes,
basketball does now, yes, football of every other sport does
now. But it feels like baseball was really
a pioneer early on with the use of data and
information.
>> Mike Petriello (06:48):
That's a great question. There's actually two answers to that. The short
answer is data availability. I think just the way
the game is played. A pitch is a pitch. There's an outcome
on the pitch if it was a curveball, if it was swung at or not. It's a
lot harder in other sports where you have an offensive lineman
surrounded by 10 other guys and 2ft to isolate what he did.
It's difficult in baseball. That data set has been there
(07:08):
and not even just the Current statcast stuff going back
decades. You've got retro sheet data from these hobbyists really, who
have put that information together for people to study. So that's
the big thing. The data is there and the game is played. Even though it's a
team game, it's very much a, uh, one on one game too. Pitcher versus
batter, fielders, et cetera. The other thing, and I didn't know,
or that you were a history major as I am, so I'm not going to
(07:29):
take up too much of your time with this. People think that analytics
started when the movie Moneyball or maybe the book Moneyball
came out. I'll send this to you later. If you want. You can go back to the
early 1900s. There's a famous article in what was called
baseball magazine in 1917, before the end
of World War I, where the author, F.C. lane, was
complaining about batting average and wondering why we used batting
(07:49):
average as though it implies every hit was the same. The analogy he
used was if you ask so much, uh, someone how much money they had
in their pocket, they wouldn't just say, I have six coins. You'd
say, well I want to do it. Dimes, do I have nickels? That's
108 years ago at this point.
And that has gone on for a long time. The only thing that's
accelerated in the last 20, 25 years
(08:09):
would be the Internet bringing these people closer together
and that the availability of data has improved. You can
get a lot more granular information now
obviously than you could have 20 years ago, 40 years
ago. But people have always been thinking about this way
beyond you think they might have.
>> Speaker B (08:25):
How do you think the use of data
analytics information has changed the
way fans, teams, players engage with sports
or specifically baseball? What ways has
it actually enhanced our
experience with the game?
>> Mike Petriello (08:41):
So I think you hit on something very important there when you said players, uh,
teams, fans, in whatever order you said it, because, uh, it's a
different answer for different audiences. For sure.
Teams are very much interested not only in choosing
the best players, but having a very good idea of how those players
might perform in uh, the future. To give a Mets example,
Juan Soto just got an enormous contract from the
(09:01):
Mets, at least at the time we're speaking. Pete Alonso has had a very
difficult time finding a contract. It's not that they're not both good
players, it's just that the ages they are, the types of player
profiles they are. You have a lot more confidence in Juan
Soto being very good for 10 years than you do in Pete Alonso
for the next couple of years. Um, for players, and this
has really been interesting over the last couple of years. I think
(09:21):
at first players looked at it from an old school point of view
with a bit of a side eye, saying, I got to the major leagues. Get out of
here, nerd. What can you teach me? Fair enough.
But over the last decade or so, you've had a lot
of players using this information to improve their games,
improve their careers, improve their salaries. Uh, you go back 10
years, and it was J.D. martinez and Justin Turner getting
(09:41):
the ball off the ground. And now you've got pitch design,
which is a whole other conversation. But basically you can
use biomechanical data and say, hey, here's the reason why
your curveball is not very good. We can either help you make it
better, or the way your body moves, it's just never going to be
good. Try a different kind of pitch. And, uh, there are so
many guys across the majors right now who have become better
(10:02):
players just because of that. And it's so funny to me, when I
started, I'd go talk to players and I'm like, I know so much more
about this than you do now. I talk to the pitchers, the young
guys. I barely know what they're talking about. And it's my job to
know this. It is wild to hear these guys talk about it. Every player
is a nerd now, which is kind of fun to think about.
>> Speaker B (10:18):
Yeah. For people who are, let's
say, not as into baseball or, uh, as you and I might be,
one of the interesting phenomenons are pitching labs.
When you talk about all of the things like pitch,
shape, you're talking about the shape of a
curveball and the spin rate and the things that we're
capturing. But for people who are less familiar, teams
like the Yankees, the Mets just instituted theirs,
(10:41):
and others around the league. Explain what a
pitching lab is and how
that data is captured and how you can
quantify so much data and
synthesize it into something that becomes
actionable and that improves decision making.
>> Mike Petriello (10:58):
A pretty famous story in baseball. Mariano Rivera, probably
the best relief pitcher who ever lived, came up as a starter in the mid-90s.
He was okay, and he was just screwing around in the
outfield one day and he threw a pitch, uh, that he'd never thrown before, and
it became his cutter. One of the probably five most dominant
pitches anyone's ever thrown. He turned that into a Hall of Fame career. And that was
an accident. What you can do now with the pitching labs is you
(11:18):
go in there in Front of all the high speed cameras and force plates and
all sorts of crazy stuff they got. And you go through
your pitch and they'll say, okay, we see how your body moves. You're
maybe you're a pronator, maybe you're a supinator, which means
what direction does your arm move as you throw it? Uh,
you're in a good position to throw. Let's say
your body type says a splitter, a split finger fastball.
(11:38):
That'll work for you or it won't. And we
can reduce the accidents of. Oh, hey. My
sister's uncle's groundskeeper from high school told me how to
throw this grip and it worked out for me and actually get there
pretty quick and say, okay, we're going to have feedback on whether
this pitch works. Not in months, not after the batters kill
it, but in about 20 minutes because you're going to throw 10 of
(11:59):
them and we're going to see what the numbers say on it and say, oh, the movement
on that, that's really good. Let's work on this. That's what it is. It
helps them not only learn what they can and can't do,
but get there a lot faster. If you go back through all of
history, there's probably a lot of pitchers who
had the talent to be great and you never heard of them because they
never, dumb luck, stumbled upon that right pitch. And
(12:19):
maybe today, in front of the technology, they could have learned it a lot
faster.
>> Speaker B (12:22):
You know, with all the use of data, your job
and the job of broadcasters today is to
figure out how to
synthesize data and
analytics into storytelling and to make it
interesting to people. I joked before about the nerdcast
I always love. This is ESPN for a number of years would
do a specific statcast alternate broadcast.
(12:45):
People are familiar with the Manning cast for NFL Monday Night
Football. This was a statistically
oriented alternate broadcast, which I
always loved. And a lot of people would never always trend on
Twitter and everybody be hashtagging, statcast, nerdcast, all this.
Mike, you were one of the broadcasters who
were analyzing, commentating, bringing that to
people. Now I think
(13:07):
data and analytics have become
more a part of mainstream broadcasts. So how
do you think about in your job with Major League
Baseball and when you talk to all the different networks where the games
are broadcast and the different outlets, how
do you incorporate data and storytelling?
>> Mike Petriello (13:24):
I joke a lot about having the history degree in this job, but what is
a history degree? It's explaining why did this country invade that
country, explaining why these important certain events in history happened.
And that's how I approach this too. You need to be able to
explain these things because teams and players are
making decisions based upon them. Um, like before the shift was banned,
you needed to explain why the third baseman was standing in right field.
(13:45):
Because it's a real weird thing. Why would a team trade a
guy with a.280 batting average for a guy with a.240 batting average?
There's reasons, but you need to be able to explain it.
So, uh, that's what we do. And I would say it's gotten simultaneously
easier and harder easier because you don't have to
sell anybody on the utility of it anymore. Years ago
it was, I don't need this stuff. This isn't interesting. And now it's,
(14:05):
yeah, we know that teams, players are using this. We need to be able to
explain this. But the harder part is now the details have gotten
so complicated. You try not to have everything turn into an
algebra class because the number one takeaway, and if we
proved anything on that show, which we still did a few of them last year,
hopefully might do some this year as well. You can still have
fun talking about nerd stuff. You don't
(14:26):
have to go in and explain the launch angle on every single
batted ball or the spin rate on every single pitch, because I
can tell you, even I don't want to know that. But if you can
put stuff into context, this was like, hey, the hardest hit ball of
the year. That's cool. Because so much of it's just baseball.
You couldn't before 2015 or 2020, depending
on which metric. Say, who had the strongest outfield
(14:46):
throwing arm, who was the fastest runner. That's baseball
stuff. That's the stuff people have been arguing about in bars forever.
To some extent, that's just putting numbers behind what you've already
seen.
>> Speaker B (14:56):
How has the rise in
sports betting and gambling changed how
analytics are incorporated into the fan experience,
media coverage, and how much it is accepted as
a part of the game?
>> Mike Petriello (15:08):
That's an interesting question. I try my best to avoid
sports betting as much as I possibly can. I work
for mlb, um, so I'm not allowed to bet on baseball,
obviously. So I try not to pay attention to it too
much because I just can't have anything to do with it. But I think
fantasy baseball has been a thing for many, many years.
Certainly those people who want to win their leagues are looking at numbers
and data to inform their own decisions. I would imagine
(15:31):
that the people who are putting money in the games are doing much the Same
thing. But we're not, at least I'm not directly involved in
that world at all.
>> Speaker B (15:38):
You mentioned Pete Alonso as an example of a player
that I would agree, uh, has been hurt by analytics.
The way we perceive things, somebody who has
been known, he may have hit 33 home runs
last year, but generally is good for 35
plus 40 home runs season. He would have
been someone who would have gotten a big contract in seasons
(15:58):
past and now here he is sweating it out to try and get somebody
to sign him based on position
and on base percentage
declining and base running and
defensive metrics. And
just there are a number of things where age, where
people are saying, okay, yes, you hit a lot of home runs
10 years ago, he would have been 15 years ago, snapped up
(16:21):
and not today. The question though is, so if
he's one who struggled, who's an example of somebody that
you would say was an early analytics darling?
>> Mike Petriello (16:30):
I think going back a number of years, Joey Votto, I
think is the first name that comes to mind. And it's not that he didn't hit 30
home runs and 100 RBIs, he did, but he got on base
a lot and that is such a valuable thing. He ended up
with a huge contract, uh, even though he was like Alonso in the
sense that he is a sort of slow footed first baseman, a
better defender, sure. But he ended up getting a pretty
(16:50):
massive contract in the hundreds of millions of dollars.
And I don't think he would have gotten that 20 years earlier
because he wasn't the prototypical hairy chested
slugger that first baseman were back in the day.
So I would agree with you that Pete Alonso
probably doesn't get the contract he wants because of what we've learned
about aging curves and all this. To give you another example, Luke
(17:11):
Weaver, who is a pitcher, he's not a great example because he didn't
sign a big deal. But there are guys like that, terribly
unsuccessful for like seven years. Comes to the
Yankees, they change his grips. All of a sudden he's awesome. He's like one
of the 10 best relievers in baseball right now. If he was a free agent this year,
he'd have gotten a huge contract based not on his career
to date, but based on what they think he'll do going forward. So
it's not that the analytics is taking money away, the players are
(17:33):
just distributing it in a different way based less on
what you have done so far and more on educated guesses
about what you might do going forward.
>> Speaker B (17:41):
Yeah, and I think it's Interesting with fans,
they want their teams to sign big names
to some extent based on. It's like the stock market based on
past performance. But when you
sign a guy for a long term contract that
doesn't profile well with predictive analytics, that this is
going to go well over time and then it does not go well
(18:02):
and now that team is stuck with that contract for
years, then fans are super, you know, uh, I think of
Chris Davis with the Baltimore Orioles as an example at first
base. Then fans are super frustrated that
we're still paying this guy's salary and he fell off a cliff.
And it was like, well, the data analyst did tell you that might happen.
People didn't want to listen. We're talking about some of the ways in which
analytics are a, uh, positive. They are just a part of today's
(18:25):
game. They've been a part, as you point out, since
1917, more and more prevalent over
time. Why is it you think I get
callers who want to blame the data and
analytics for
decisions in baseball that don't go well?
Why does analytics get vilified?
>> Mike Petriello (18:43):
We could talk about the difference in what the data
says and what someone's gut says, and then I'm not sure we're
talking about sports anymore because that's how happened in a lot of different
places around the world. But if something doesn't go
right, you want to blame something, right? Well, I wouldn't have put
that guy in and the nerd number said to put them in and it didn't work.
So I blame the nerd numbers. That's basically what it comes down to. If your
(19:03):
team lost, you want to put it on somebody and it's easy to put it
on the player. Sure. But if there's a decision that was made
based on numbers that you don't feel comfortable with or
familiar with or don't agree with, I think that's the number one place to look.
Even though, like I said before, it doesn't mean the other thing would have worked.
You just didn't see it fail. And we're really bad about thinking
about that as humans.
>> Speaker B (19:22):
What would you say
to people who would argue,
even if we're looking at data and analytics to make a decision
on should a pitcher come in and out of a game or what should we do
here? But we're looking at something that is a
relatively small sample size and for fans
who say, okay, so this guy's going by the book, this
one. Lefty, lefty. Or here's how this guy, he's a reverse
(19:45):
Split, he does better against righties or lefties or whatever that is.
And they're looking at the data. But we might be talking about
something that's this matchup has happened four times
or ten times. At what point is the sample
size statistically significant enough
that you should be staying strictly with
the data versus what your eyes are telling you?
>> Mike Petriello (20:04):
Yeah, that's funny. That actually also touches on,
uh, something I should have brought up before. We don't, as fans in
the public, have the same information that the team does, that the
players do, that the managers do. So when you say lefty on
lefty, lefty batter and lefty pitcher, that it for
years was probably the decision that was made. Now it's, we know the swing
path of this and we know the angle the pitch comes in. And now we're making
(20:24):
decisions based on that. As far as sample size goes,
it's a really important question. And it's very different
based on what metric you're looking at. For example,
let's talk about fastball velocity. I don't need to see but two
pitches to know that a guy throws harder doesn't. I don't need to see hundreds
of pitches to figure that out. But for something
like, uh, batting average, you need like hundreds of
(20:44):
plate appearances to feel confident that a guy really
is a.300 hitter. And the problem with that is by the time you get
to hundreds of plate appearances, now we're talking like two
seasons maybe. Well, the beginning of those plate appearances were from
a, uh, younger guy who they may not be as valuable
anymore. So for the skills
stuff, you can get to that really fast. I know a guy's fast
real fast. I know you throw hard really fast. Some
(21:07):
of the stuff like your exit velocity, maybe it's 50
or so batted balls. So that could take a couple of weeks.
It's a really, really important question because you wouldn't
want to say that a guy's a.500 hitter because he got one hit in his
first two plate appearances. That's totally meaningless. But I
would believe a 99 mile an hour fastball in his first two pitches.
It's very case by case, depending on what metric you're talking
(21:28):
about.
>> Speaker B (21:29):
What do you think about today
as a, the same day today as
a data point? Now we have the analytics that say
that a particular pitcher. So something common for people
who don't follow as much would be
conventionalism says that oftentimes
with pitchers, if you're leaving them in to go the third
time through the order, they're not going to do as well.
(21:52):
And hitters are going to be more effective at a particular pitcher
third time through the order. So teams are quite cautious
about leaving most starting pitchers
in beyond two times through the order.
Is today a, uh, data point where
you're looking at a guy and his stuff
just looks electric today? And here we
(22:12):
are getting through the second time in the order, and he
looks amazing. Should a manager be
trusting? Okay, that's what my eyes are telling me
versus going to my bullpen, you know, and even factors like my
bullpen's a little bit spent, or does the
data only work and the information only work
if I follow it religiously time after
time after time? Because over the long haul, it will
(22:35):
be right more often than it's wrong.
>> Mike Petriello (22:37):
Yeah, I think the. The word in baseball there is dealing. Oh, the
pitcher was dealing. How did you take him out? And of
course, every pitcher is dealing right up until the moment he's not
dealing. A pretty famous example of that over the
last couple years was in the 2020 World Series.
>> Speaker B (22:50):
Blake Snell.
>> Mike Petriello (22:51):
Blake Snell, exactly.
>> Speaker B (22:53):
I was about to bring it up if you hadn't. It's a classic example.
>> Mike Petriello (22:56):
It's a classic example.
>> Speaker B (22:57):
And for those people who don't know. So, yes, explain what happened.
>> Mike Petriello (22:59):
Blake Snow, uh, I don't remember the exact score or whatever, but
Dodgers raise. He was on the raise at the time, pitching great.
Just like mowing the Dodgers down left and right.
>> Speaker B (23:08):
Under underdog Tampa Bay. Underdog Rays
versus the mighty Dodgers. We should say that. And this
is their kind of an ace, like, pitcher mowing guys
down. Keep going.
>> Mike Petriello (23:18):
Yeah. And right. He's pitching great. He's pitching against the Dodgers. They're
winning the game. And there wasn't anything super obvious
in terms of his pitch metrics. That's the first thing you look for, is the
velocity starting to drop, is the movement starting to fade. Those
are signs of fatigue. I don't think there was anything serious like that.
And because the Rays had a very serious
adherence to their model and their method, their manager
(23:39):
came and took him out despite the fact that he was dealing.
And the reliever came in and blew the game. And they lost the World Series. It's like
one of the most famous moments of the last couple years. I remember watching this and
thinking, I wouldn't have taken him out then. But the
biggest problem is I thought they brought in the wrong reliever. That guy,
to your point, Nick Anderson was spent at that point.
But the point here is I remember someone and I
(23:59):
can't remember his name. It was Connor. Somebody did a bit of a
study on this. He went back and he found all of
the starts that were similar in innings
pitched, uh, out Scott and 0 earned runs. Whatever
Snell had done that day, he found very similar starts. These are obviously
extremely good starts by extremely good pitchers.
And he looked okay. What did those guys do after
(24:20):
that, the ones who were left in the game and the outcomes
were bad. It was like an average ERA of, I don't know, four
and a half or whatever. It's not going to work every time.
Nothing's going to work every time. I wouldn't have taken him out right
then and there, but I probably wouldn't have waited very much longer
either dealing or not, because the numbers
were pretty clear. If you leave him in, it's not going to end
(24:40):
well. You're sort of pushing your luck until that
happens. But that's not going to make any Tampa Bay fan feel
better. All they're going to remember is they lost the World Series.
>> Speaker B (24:48):
Mike, one of the things you, me,
we may enjoy,
statistics and
how analytics is making the game
and teams smarter. That's something that I find fun, that
I enjoy. I think there are, there's a
conventionalism and even you working for Major League Baseball,
(25:09):
Major League Baseball has implemented some rule
changes to try and do things to
speed up the game, add action to the game. Are,
uh, there ways in which making all the teams
smarter, leveling that playing field when
everybody has data has taken
away action or had a negative impact
on the game?
>> Mike Petriello (25:30):
I think the first thing I would say is that's not a baseball specific
issue. I'm not the world's biggest basketball fan, but I do hear the
complaining about three pointers like all the time. So
this is happening across a lot of sports. Uh, in baseball, I think the
biggest issue is that, uh, the pitchers have gotten so good
because of the pitch design, the pitch labs, the emphasis on velocity,
that there's just not as much contact as there used to be. Too many
(25:50):
strikeouts. Right. This has been an issue for 20 years and
nobody's really cracked that code yet. I, uh, do think some of
the rule changes that have been put in place have worked out well because
baseball has long been seen as maybe the old school,
sometimes dinosaur of sports, maybe slow to adapt.
That's probably a deserved label for a long time and that's changed
over the last couple years. The pitch clock, which came in two
(26:11):
years ago, which everybody lost their minds about, they can't have a clock
in baseball. Well, you can and it worked great.
It's been fantastic. The ratings have been up, the fan
attendance has been up. So I think that the
sport has done a better job now where it didn't
previously of going out and doing fan surveys, listening
to fans trying to get a handle on what kind of action
(26:31):
they like to see. And you can get into some real wild
rule changes. Someone wrote the other day we should have smaller gloves
for outfielders. Which I thought was pretty funny. The other thing is people
hate change. Time you pro propose a rule change, you'll see
everybody on social media saying, ah, uh, the game is perfect, don't
change it as though the game hasn't changed a
hundred times over the last 150 years. So
(26:51):
it's that you got to make changes, but you've also got to not make too many
changes or people get upset.
>> Speaker B (26:56):
Yeah. And so with the
increase of information, it added
into the game that
hitting home runs, launch angle, getting the ball
into the air was going to be more valuable for
players then hitting a single, hitting it
on the ground, more likely to hit it to a fielder. With
that an outcome ended up being you
(27:19):
and I would know that what they call the three true outcomes where
hitters would tend to focus on I want to hit a home run or
I want to walk high, um, on base percentage and if
I strike out, I'm not as worried about it because I'm going to get paid for
hitting those home runs or getting on base, having a high on
base percentage. As teams and
players get smarter with the use of data,
(27:39):
what I think has been interesting with baseball is then that's
where and to your point, it's not just baseball, it's across the world
is when you want a change in behavior, you
can legislate that. You can change the rules. As
the example being larger bases and
you can't throw over to first base as much. You limit the
number of times a pitcher can throw to first base. Now suddenly stolen
(27:59):
bases are up, stolen base success is
up. And as it's easier to steal bases, players steal more
bases and that becomes a part of the game. And now you've got more action in the
game. So you can do things or the shift you
can limit. If defenses get so smart with
the use of data and where you're placing people
that it gets really hard to get a
ball, uh, through an infield, then you can
(28:22):
legislate and change the rules on those things.
You mentioned the NBA, what you're referencing is that ah, the data
tells us that the least efficient
shot in the NBA is A long two point shot.
You basically should never take a long two point shot. You
either want to take a three or take a in the
paint short shot. Something that has a much higher
(28:43):
percentage of success. But that long two is
like a stupid shot. If the NBA
wants to see changes in the game and more playmaking and
not as many guys sitting there popping away from three all
the time, they're gonna have to change the rules. You can't ask people
to once they understand something and it is smart and
efficient to go back and be stupid.
>> Mike Petriello (29:02):
Yeah, I think that's right. At the end of the day, all of these sports are
entertainment products. Listen, I am a big hockey fan and I
vividly remember when I used to live in Boston, I went to this Bruins wild
game in like 2006 and I'm like, this is awful. This is
no fun to watch. There's no offense. It's all clutching and
grabbing. They changed a lot of the rules and then the offense came back
and it's been a lot more fun to watch. Basically everybody wants
(29:22):
the game to be like it was when they were 14 years old and just want to like
freeze it in amber. You go back to like 19, 20
and I'll tell you, the game looks a little bit different. Aside from the fact that it
was segregated, the players did not look the same, they did not
act the same. Pitchers would pitch nine innings every third day.
The sport has always, always, always changed. Night
games cross continental flights. So I do think that the sport is going
(29:43):
to continue to evolve. And like I said, when
people say that baseball didn't evolve, that was a totally fair
criticism. I think the last couple years they've really
gotten their heads around the fact that the world is changing. The sport needs to change
too.
>> Speaker B (29:55):
Yeah, I always think, uh, give it a chance.
For instance, the new roles of baseball, I ended up really liking one.
Although since you work with Major League baseball in the league office, one
change I would make with limiting how many times a
pitcher can throw over to first base instead
of once they've thrown over twice. The rule is then if you
throw a third time, then that runner is entitled to a free
base. I was thinking that should be more like a balk.
(30:18):
If you've thrown twice instead of rewarding with a
free base, seems like such a big penalty
versus giving away. Okay, now it's giving the
batter a ball.
>> Mike Petriello (30:28):
One thing. You can throw over a third time, but you have to get them.
So it's only if you, if you don't get them, if you don't get.
>> Speaker B (30:33):
Them, then they get a free base. So you better be right on
that third time or you're giving away a three base. I'm
saying the penalty should be if you don't get them that third time.
Make it a ball anyway. Make a change.
>> Mike Petriello (30:44):
Make it a ball. Yeah, fair enough. I think that would lessen the
penalty and, uh, change behaviors. So I think that'd be an
interesting experiment.
>> Speaker B (30:50):
Yeah, that's the only one I would tweak. But otherwise, I love the new
rules. So one thing that we're seeing,
certainly with prompt in our line of work, but the whole
world is, of course, is embracing AI
and augmented intelligence. So in terms
of baseball, how is Major League
Baseball using AI? Whether
(31:10):
that is in terms of fan experience
or in terms of teams and
predictive intelligence. Leveraging
data. How is AI a part of the game?
>> Mike Petriello (31:20):
Yeah, I like to think that there's multiple kinds of AI. There's
smart AI, which is using technology to
consume large data sets and help you get to patterns and
answers you wouldn't have, and, uh, obnoxious AI, which
is like my mom having to see AI attached to every brand
that she's ever seen in commercials, which I find wildly
unnecessary. As far as how any
(31:41):
baseball or really any company uses AI, it is to try
to get to those informed decisions maybe a little bit
faster, uh, especially as the size of these data
sets, uh, increases, I'm pretty sure. And I can't
speak to this in first person because I don't know, but I would be shocked if
MLB isn't using ad optimize ticket sales and
marketing in some way because that would just make sense as far as on
(32:01):
the field stuff goes. I know that some of the pitching
labs are using AI to, you know,
you think about all of the biomechanical data that comes in
when you've got all of these pitchers throwing all these pitches. That's
huge data. And that helps you get to what combination of
these things leads to more optimal outcomes. And whether
you want to think about it as AI or just the
Googling that we've been doing for 25 years, I'm not sure it matters that much
(32:24):
to most people. You don't see it under the hood. But if that
kind of tool can help you get to better answers faster, that's
the entire point of any of this, really.
>> Speaker B (32:31):
In terms of looking ahead, what sort of
innovations in analytics, in
information are you most
excited about for the future?
>> Mike Petriello (32:41):
Well, I think the Holy grail, if anybody can figure
this out, they will be the Richest person in baseball is how do you
keep pitchers healthy? This has been an
ongoing issue as pitchers got bigger and
stronger and worked on maximizing velocity.
It turns out it's really hard to strengthen that little ligament in
your elbow. Guys keep getting hurt. It's bad for the game. You
(33:02):
want the stars in the field, it's bad for the players. Nobody wants to get hurt.
So that is something the entire industry is thinking about
how to do in terms of metrics and stuff. We're continuing
to push forward because the technology on the field keeps getting better.
Up until last year, you could never really tell anything
about the way the bat moves. You knew a lot about the pitch, a lot about the
ball, but nobody could track the bat because it moves at
(33:22):
like 100ft per second. Now the technology got
upgraded. All of a sudden that's a thing we can measure. More
and more metrics on that are coming out. I, uh, bring that up because it's really
interesting. When you start to measure something
that you couldn't measure before, it's not just a curiosity,
then it becomes something you can quantify and value.
And when you can value it, then players start working towards
(33:42):
it because teams start paying money for it. For example, the bat
speed. I don't think it's revolutionary to say if you swing the
bat faster, you'll hit the ball harder. That's something you can see
with your eyes back to Babe Ruth's time. But now that you can
measure it and say, hey, every extra miles an hour in your
bat speed gets you this much distance and this many points of
slug. And we value that. Now you got these guys who are coming up from
(34:02):
the Miners saying, yeah, I spent my winter not working on my defense,
but trying to improve my bat speed. And I think that's what's going to
keep happening.
>> Speaker B (34:10):
So bat speed is a really interesting one.
And spin rate on pitches, things like that, those are
interesting ones. Then there's results outcome based. And
there are fans who've been around the game forever and they look at and
wanted batting average, home runs,
RBIs, counting stats, things like that.
When you look at stats, is there
one metric that you
(34:33):
personally would find the most valuable? If I said to you,
I want to compare players or know how good a player
is, are you looking at WAR wins above
replacement? Are you looking at, if we're talking about position
players, not pitchers, are you looking at OPS
plus, are you looking at weighted runs created
plus? There's so many different good statistics that, uh, are out
there. Is there one that you have, that's a favorite.
>> Mike Petriello (34:55):
Those are all different answers to different questions. So if
I just want a quick at a glance, who are the most valuable
players all in like hitting, defense, running. Yes, wins above
replacement. Uh, that's the, the best we have. It's not perfect, but
it's really good. A lot of the other stuff you mentioned is very
specific just to hitting. So if I want to see who the best hitters are,
Parker jocks said yes, I'll go to weighted roads created. Plus
if I want to see who, uh, hits the ball the
(35:17):
hardest, go look up statcast, go look at hard hit rate. But
there's a lot of different ways to answer those questions. It just depends on
what you're looking at. But I look at all of them as a starting point
and not necessarily an ending point. If I look at the leaders in
hard hit rate, I'm probably going to find Aaron Judge and I'm going to find
Giancarlo Stanton. I'm going to find guys who hit the ball really,
really hard. Doesn't necessarily guarantee I'm
(35:37):
finding the best hitters in baseball because Luisa Rice does not
hit the ball hard and he always has a very good batting average. So
it all comes with a certain amount of contextual knowledge
to make any of these numbers useful.
>> Speaker B (35:48):
As we finish a couple last questions here, what
advice would you give to marketers looking to
adapt a more data driven approach and what
can they learn from baseball that would be
applicable?
>> Mike Petriello (36:00):
Number one, listen to the fans or your audience or whoever. I
don't think baseball has always done that, as I said, and now that's really
helped a lot to understand what the audience wants. If
you have a sort of complicated and dense data set, make sure
you explain it in a way that people can enjoy or
understand, even be entertained by. Uh, because if
not, everybody's going to tune it out, no matter how valuable it might
(36:20):
be.
>> Speaker B (36:20):
And then to close, we like to ask a fun question
here. And so if you could pick any
player from any sport, past
or present, to join you for a Mike
Petriello dream dinner
party, who would you want to sit there and talk to
and why?
>> Mike Petriello (36:38):
I would like to say that I have an extremely deep
cut and a really thought out answer, but I'm going to give you one of the
most famous people of all time. Uh, but for a good reason. The answer
would be Ted Williams, who had a fascinating
life, obviously served in two wars, you know, all around
amazing life and career. He was
maybe the first real baseball nerd. He literally
(36:59):
wrote a book on this called the Science of hitting in
1971. And he didn't actually say exophilosity and launch
angle, but you go read it and he basically did. He
drew charts and diagrams to the inch of
saying, here's where I'm good when I hit the ball here and there. And it's
funny because we'll bring out a lot of the new nerd stuff and people be
like, oh, Lou Gehrig, Ted Williams, they'd have hated this stuff. And I'm like,
(37:19):
no, no, Ted Williams would have loved this stuff.
And I would just love to take them all through it and see what he'd have to say about
it.
>> Speaker B (37:25):
We think about old school, new school Ted
Williams using all of that information and science
of hitting to hit.400, a.400
batting average, an old school stat. But he's leveraging data to be
able to accomplish something that is a feat that we haven't
seen in the sport in a while.
>> Mike Petriello (37:41):
I'd ask him about that. Uh, that'd be great.
>> Speaker B (37:43):
All right, well, invite me. I'd like to sit and listen to you and Ted
Williams and lob in a question or two on that. Mike
Petriello, really, really appreciate the
time. It's been fun talking to you and thinking
about some of the ways in which fans and
media and others. There's a certain resistance at
times to data and analytics. And yet when you wake up
(38:03):
and realize over time, whether it's with
the hall of Fame inductees and others, how it has
become so embraced and so much a part of the game, uh,
that information and increased information will only
just increase over time. So anyway,
really enjoy talking to you. Thanks so much.
>> Mike Petriello (38:20):
Thanks a lot, Laurie.
>> Lori Rubinson (38:27):
Thank you for listening to this episode of the Frictionless Marketing
Podcast. For a complete transcript of this
conversation or more information on Prompt,
please visit us at meetprompt. Co.
If you found this episode insightful, share it with your connections
on LinkedIn to learn
more about how to make marketing Frictionless Purchase Friction
(38:49):
Fatigue by Prompt CEO Paul Dyer online
and at Booksellers Worldwide
Frictionless Marketing is a production from Prompt, the leading
earned first creative marketing and communications agency
grounded in the present, yet attuned to the future.
Produced and distributed by Simpler Media Productions.
Welcome to Frictionless Marketing, the podcast that dives deep into
the stories of the most innovative brands and the people moving them
forward. Today,
our host, Lori Rubinson, managing director of PROMPT
and WFAN sports talk radio host, sits down
with Mike Petriello. Mike is
the director of Stats and Research at Major League Baseball, where
(00:32):
he's been at the forefront of advancing how analytics are used to
understand, play and enjoy the game.
A pioneer in applying cutting edge technology,
Mike's work has transformed how teams make decisions,
how fans engage with the sport, and how
broadcasters tell compelling stories.
Together, they will dive into the realm of sports analytics as
Laurie and Mike discuss the often misunderstood role of analytics in
(00:55):
sports, especially baseball, addressing common
criticisms and explaining how analytics can improve decision
making.
>> Speaker B (01:03):
Foreign
welcome to another episode of the
Frictionless Marketing podcast. This is Lori
Rubinson. Really excited today to wear both
hats. Usually I am just managing partner here,
(01:25):
uh, at uh, Prompt, but today I'm also the sports
talk radio host from wfan. I've had a
topic on my mind for a really long time that
bothers me when I talk to listeners,
callers and interact with people on social media and on the
radio. And it is that sometimes analytics in
sports and in baseball in particular, get
vilified as if it's the enemy to all decisions or
(01:48):
things that happen with our teams that we don't like. To get to the
bottom of that and to talk about sports and analytics, I can
think of no better person to talk to than Mike
Petriello. Mike, welcome and look
forward to talking to you.
>> Mike Petriello (02:00):
Laurie, thanks so much for having me. Looking forward to it.
>> Speaker B (02:02):
First thing I wanted to get into before we dive into the
integration of analytics into baseball, I wanted
to understand a little bit back up. I think people have seen you
on espn, Statcast, broadcast, the Nerdcast
and all. It's like that. How did you actually
get started in baseball?
Analytics? Sports analytics? How did this come about?
>> Mike Petriello (02:22):
I, uh, wasn't with my history degree, I can tell you that
much. There's a long version, but I will give you the short
version. It's funny because college kids come up to me all the time and they're like,
hey, how do I get a job in baseball? And nobody wants to hear my
answer of I didn't get my first full time job Till I was 35
years old. Basically what happened was I went to Boston University,
I got a history degree, spent most of my twenties at
(02:42):
startups like a video on demand startup. I actually spent five
years working at a place you probably know well, which is Ketchum the
PR firm as like project manager, building websites and
that kind of stuff. Baseball was always a passion and kind of
a side hustle. Uh, when I was 26, I guess I
started a blog which was the style of 2007
and I just kept at it. That got me opportunities, that got me
(03:03):
opportunities with fan graphs and baseball perspectives
and ESPN and eventually here at mlb where
I've been coming up on nine years full time. Now
that's branched out into tv. It gets more in depth than that, but
that's the short version. Turning, uh, a passion into a career.
>> Speaker B (03:17):
We'll compare notes at some point because people
say the same to me with the WFAN part of my
business. How do I get into sports media?
And I always say, I'm a history major from Brown
University, don't do what I did. It's too
circuitous. There has to be a different path. So the
second thing, before we understand
sports analytics within the context of baseball,
(03:39):
I wanted to understand how you actually
define analytics, Fans, media
and others. It's a catch all term for a lot
of things. How do you define it?
>> Mike Petriello (03:51):
I mean, analytics is information. It's the study of
data, it's the study of patterns. Uh, nothing that I just
said is specific to sports or baseball. You could say that across any
industry. Certainly anybody else is using that. So that's what it is.
It's data and patterns and information. If you go
back through all of baseball history, people in the game
have been using information to inform future decisions.
(04:11):
Maybe that information is what they saw, what an intern wrote down on
a pad of paper, their gut, uh, feeling. The only thing that's changed
now is that the information is, it's a lot better and
we know a lot more about the context of it, how to use it, how to
change the game. That's all analytics is, is trying to take all
this information that's out there and use it to make informed decisions
to win games.
>> Speaker B (04:30):
Yeah, I, uh, always say that to fans when they
do call in and blame
analytics if they don't like the pitching decision
and manager comes, takes a pitcher out of the game, goes
to the bullpen, it goes wrong. And then they say, well, that's
analytics. And my response
is, data doesn't make decisions, people
do. If you're angry at the decision,
(04:52):
you know, analytics is information. That is how I think
about it. And you're right. Look at prompt. That is a big part of our
business is data and information to inform
decision making. So you do work better with better
results.
>> Mike Petriello (05:05):
I think that's right. What people think fail to understand is
sometimes there's not a right answer. The Yankee manager brings in the
lefty and he gives up a home run. Well, you made the wrong decision. As
though if you bring in the righty, he wouldn't also have given up a hit. Like
the saying goes, the other guys live in big houses too. You can
improve your decision making, you can put it based on better
processes, but it doesn't guarantee anything. The
smartest team in baseball, whoever you define that to be,
(05:28):
does not win 162 games every year. And they make better
decisions, they have better outcomes, but nothing is guaranteed. Because
at least in sports, there's still people, there's still human beings
on the field and you can try to put them in better situations,
but at the end of the day, the, I don't know, 28 year old on the
mound still has to get the job done. And that's never going to be
perfect.
>> Speaker B (05:46):
A lot of people, I think when they think of baseball and
analytics, they think of the movie, Moneyball, Billy Bean,
and it's about on base percentage and
things like that. And the way I think of that story
is it's about finding ways to
uncover value. An exploit value
that might be an undervalued asset that someone else
isn't seeing. And I think of it and use that term often. We
(06:09):
do influencer marketing here at the agency, want to
uncover influencers who are on their way up.
There's value to tap into
as opposed to someone who's already plateaued. Or you might know them
better, but they're on their way down. Now you're paying for maximum value.
You want to catch an asset on the way up.
With baseball, clearly that was something and that's a
(06:30):
part of Moneyball. But what about the game of baseball? Made
it, in my mind a, uh, pioneer
in using analytics in sports in a way that yes,
basketball does now, yes, football of every other sport does
now. But it feels like baseball was really
a pioneer early on with the use of data and
information.
>> Mike Petriello (06:48):
That's a great question. There's actually two answers to that. The short
answer is data availability. I think just the way
the game is played. A pitch is a pitch. There's an outcome
on the pitch if it was a curveball, if it was swung at or not. It's a
lot harder in other sports where you have an offensive lineman
surrounded by 10 other guys and 2ft to isolate what he did.
It's difficult in baseball. That data set has been there
(07:08):
and not even just the Current statcast stuff going back
decades. You've got retro sheet data from these hobbyists really, who
have put that information together for people to study. So that's
the big thing. The data is there and the game is played. Even though it's a
team game, it's very much a, uh, one on one game too. Pitcher versus
batter, fielders, et cetera. The other thing, and I didn't know,
or that you were a history major as I am, so I'm not going to
(07:29):
take up too much of your time with this. People think that analytics
started when the movie Moneyball or maybe the book Moneyball
came out. I'll send this to you later. If you want. You can go back to the
early 1900s. There's a famous article in what was called
baseball magazine in 1917, before the end
of World War I, where the author, F.C. lane, was
complaining about batting average and wondering why we used batting
(07:49):
average as though it implies every hit was the same. The analogy he
used was if you ask so much, uh, someone how much money they had
in their pocket, they wouldn't just say, I have six coins. You'd
say, well I want to do it. Dimes, do I have nickels? That's
108 years ago at this point.
And that has gone on for a long time. The only thing that's
accelerated in the last 20, 25 years
(08:09):
would be the Internet bringing these people closer together
and that the availability of data has improved. You can
get a lot more granular information now
obviously than you could have 20 years ago, 40 years
ago. But people have always been thinking about this way
beyond you think they might have.
>> Speaker B (08:25):
How do you think the use of data
analytics information has changed the
way fans, teams, players engage with sports
or specifically baseball? What ways has
it actually enhanced our
experience with the game?
>> Mike Petriello (08:41):
So I think you hit on something very important there when you said players, uh,
teams, fans, in whatever order you said it, because, uh, it's a
different answer for different audiences. For sure.
Teams are very much interested not only in choosing
the best players, but having a very good idea of how those players
might perform in uh, the future. To give a Mets example,
Juan Soto just got an enormous contract from the
(09:01):
Mets, at least at the time we're speaking. Pete Alonso has had a very
difficult time finding a contract. It's not that they're not both good
players, it's just that the ages they are, the types of player
profiles they are. You have a lot more confidence in Juan
Soto being very good for 10 years than you do in Pete Alonso
for the next couple of years. Um, for players, and this
has really been interesting over the last couple of years. I think
(09:21):
at first players looked at it from an old school point of view
with a bit of a side eye, saying, I got to the major leagues. Get out of
here, nerd. What can you teach me? Fair enough.
But over the last decade or so, you've had a lot
of players using this information to improve their games,
improve their careers, improve their salaries. Uh, you go back 10
years, and it was J.D. martinez and Justin Turner getting
(09:41):
the ball off the ground. And now you've got pitch design,
which is a whole other conversation. But basically you can
use biomechanical data and say, hey, here's the reason why
your curveball is not very good. We can either help you make it
better, or the way your body moves, it's just never going to be
good. Try a different kind of pitch. And, uh, there are so
many guys across the majors right now who have become better
(10:02):
players just because of that. And it's so funny to me, when I
started, I'd go talk to players and I'm like, I know so much more
about this than you do now. I talk to the pitchers, the young
guys. I barely know what they're talking about. And it's my job to
know this. It is wild to hear these guys talk about it. Every player
is a nerd now, which is kind of fun to think about.
>> Speaker B (10:18):
Yeah. For people who are, let's
say, not as into baseball or, uh, as you and I might be,
one of the interesting phenomenons are pitching labs.
When you talk about all of the things like pitch,
shape, you're talking about the shape of a
curveball and the spin rate and the things that we're
capturing. But for people who are less familiar, teams
like the Yankees, the Mets just instituted theirs,
(10:41):
and others around the league. Explain what a
pitching lab is and how
that data is captured and how you can
quantify so much data and
synthesize it into something that becomes
actionable and that improves decision making.
>> Mike Petriello (10:58):
A pretty famous story in baseball. Mariano Rivera, probably
the best relief pitcher who ever lived, came up as a starter in the mid-90s.
He was okay, and he was just screwing around in the
outfield one day and he threw a pitch, uh, that he'd never thrown before, and
it became his cutter. One of the probably five most dominant
pitches anyone's ever thrown. He turned that into a Hall of Fame career. And that was
an accident. What you can do now with the pitching labs is you
(11:18):
go in there in Front of all the high speed cameras and force plates and
all sorts of crazy stuff they got. And you go through
your pitch and they'll say, okay, we see how your body moves. You're
maybe you're a pronator, maybe you're a supinator, which means
what direction does your arm move as you throw it? Uh,
you're in a good position to throw. Let's say
your body type says a splitter, a split finger fastball.
(11:38):
That'll work for you or it won't. And we
can reduce the accidents of. Oh, hey. My
sister's uncle's groundskeeper from high school told me how to
throw this grip and it worked out for me and actually get there
pretty quick and say, okay, we're going to have feedback on whether
this pitch works. Not in months, not after the batters kill
it, but in about 20 minutes because you're going to throw 10 of
(11:59):
them and we're going to see what the numbers say on it and say, oh, the movement
on that, that's really good. Let's work on this. That's what it is. It
helps them not only learn what they can and can't do,
but get there a lot faster. If you go back through all of
history, there's probably a lot of pitchers who
had the talent to be great and you never heard of them because they
never, dumb luck, stumbled upon that right pitch. And
(12:19):
maybe today, in front of the technology, they could have learned it a lot
faster.
>> Speaker B (12:22):
You know, with all the use of data, your job
and the job of broadcasters today is to
figure out how to
synthesize data and
analytics into storytelling and to make it
interesting to people. I joked before about the nerdcast
I always love. This is ESPN for a number of years would
do a specific statcast alternate broadcast.
(12:45):
People are familiar with the Manning cast for NFL Monday Night
Football. This was a statistically
oriented alternate broadcast, which I
always loved. And a lot of people would never always trend on
Twitter and everybody be hashtagging, statcast, nerdcast, all this.
Mike, you were one of the broadcasters who
were analyzing, commentating, bringing that to
people. Now I think
(13:07):
data and analytics have become
more a part of mainstream broadcasts. So how
do you think about in your job with Major League
Baseball and when you talk to all the different networks where the games
are broadcast and the different outlets, how
do you incorporate data and storytelling?
>> Mike Petriello (13:24):
I joke a lot about having the history degree in this job, but what is
a history degree? It's explaining why did this country invade that
country, explaining why these important certain events in history happened.
And that's how I approach this too. You need to be able to
explain these things because teams and players are
making decisions based upon them. Um, like before the shift was banned,
you needed to explain why the third baseman was standing in right field.
(13:45):
Because it's a real weird thing. Why would a team trade a
guy with a.280 batting average for a guy with a.240 batting average?
There's reasons, but you need to be able to explain it.
So, uh, that's what we do. And I would say it's gotten simultaneously
easier and harder easier because you don't have to
sell anybody on the utility of it anymore. Years ago
it was, I don't need this stuff. This isn't interesting. And now it's,
(14:05):
yeah, we know that teams, players are using this. We need to be able to
explain this. But the harder part is now the details have gotten
so complicated. You try not to have everything turn into an
algebra class because the number one takeaway, and if we
proved anything on that show, which we still did a few of them last year,
hopefully might do some this year as well. You can still have
fun talking about nerd stuff. You don't
(14:26):
have to go in and explain the launch angle on every single
batted ball or the spin rate on every single pitch, because I
can tell you, even I don't want to know that. But if you can
put stuff into context, this was like, hey, the hardest hit ball of
the year. That's cool. Because so much of it's just baseball.
You couldn't before 2015 or 2020, depending
on which metric. Say, who had the strongest outfield
(14:46):
throwing arm, who was the fastest runner. That's baseball
stuff. That's the stuff people have been arguing about in bars forever.
To some extent, that's just putting numbers behind what you've already
seen.
>> Speaker B (14:56):
How has the rise in
sports betting and gambling changed how
analytics are incorporated into the fan experience,
media coverage, and how much it is accepted as
a part of the game?
>> Mike Petriello (15:08):
That's an interesting question. I try my best to avoid
sports betting as much as I possibly can. I work
for mlb, um, so I'm not allowed to bet on baseball,
obviously. So I try not to pay attention to it too
much because I just can't have anything to do with it. But I think
fantasy baseball has been a thing for many, many years.
Certainly those people who want to win their leagues are looking at numbers
and data to inform their own decisions. I would imagine
(15:31):
that the people who are putting money in the games are doing much the Same
thing. But we're not, at least I'm not directly involved in
that world at all.
>> Speaker B (15:38):
You mentioned Pete Alonso as an example of a player
that I would agree, uh, has been hurt by analytics.
The way we perceive things, somebody who has
been known, he may have hit 33 home runs
last year, but generally is good for 35
plus 40 home runs season. He would have
been someone who would have gotten a big contract in seasons
(15:58):
past and now here he is sweating it out to try and get somebody
to sign him based on position
and on base percentage
declining and base running and
defensive metrics. And
just there are a number of things where age, where
people are saying, okay, yes, you hit a lot of home runs
10 years ago, he would have been 15 years ago, snapped up
(16:21):
and not today. The question though is, so if
he's one who struggled, who's an example of somebody that
you would say was an early analytics darling?
>> Mike Petriello (16:30):
I think going back a number of years, Joey Votto, I
think is the first name that comes to mind. And it's not that he didn't hit 30
home runs and 100 RBIs, he did, but he got on base
a lot and that is such a valuable thing. He ended up
with a huge contract, uh, even though he was like Alonso in the
sense that he is a sort of slow footed first baseman, a
better defender, sure. But he ended up getting a pretty
(16:50):
massive contract in the hundreds of millions of dollars.
And I don't think he would have gotten that 20 years earlier
because he wasn't the prototypical hairy chested
slugger that first baseman were back in the day.
So I would agree with you that Pete Alonso
probably doesn't get the contract he wants because of what we've learned
about aging curves and all this. To give you another example, Luke
(17:11):
Weaver, who is a pitcher, he's not a great example because he didn't
sign a big deal. But there are guys like that, terribly
unsuccessful for like seven years. Comes to the
Yankees, they change his grips. All of a sudden he's awesome. He's like one
of the 10 best relievers in baseball right now. If he was a free agent this year,
he'd have gotten a huge contract based not on his career
to date, but based on what they think he'll do going forward. So
it's not that the analytics is taking money away, the players are
(17:33):
just distributing it in a different way based less on
what you have done so far and more on educated guesses
about what you might do going forward.
>> Speaker B (17:41):
Yeah, and I think it's Interesting with fans,
they want their teams to sign big names
to some extent based on. It's like the stock market based on
past performance. But when you
sign a guy for a long term contract that
doesn't profile well with predictive analytics, that this is
going to go well over time and then it does not go well
(18:02):
and now that team is stuck with that contract for
years, then fans are super, you know, uh, I think of
Chris Davis with the Baltimore Orioles as an example at first
base. Then fans are super frustrated that
we're still paying this guy's salary and he fell off a cliff.
And it was like, well, the data analyst did tell you that might happen.
People didn't want to listen. We're talking about some of the ways in which
analytics are a, uh, positive. They are just a part of today's
(18:25):
game. They've been a part, as you point out, since
1917, more and more prevalent over
time. Why is it you think I get
callers who want to blame the data and
analytics for
decisions in baseball that don't go well?
Why does analytics get vilified?
>> Mike Petriello (18:43):
We could talk about the difference in what the data
says and what someone's gut says, and then I'm not sure we're
talking about sports anymore because that's how happened in a lot of different
places around the world. But if something doesn't go
right, you want to blame something, right? Well, I wouldn't have put
that guy in and the nerd number said to put them in and it didn't work.
So I blame the nerd numbers. That's basically what it comes down to. If your
(19:03):
team lost, you want to put it on somebody and it's easy to put it
on the player. Sure. But if there's a decision that was made
based on numbers that you don't feel comfortable with or
familiar with or don't agree with, I think that's the number one place to look.
Even though, like I said before, it doesn't mean the other thing would have worked.
You just didn't see it fail. And we're really bad about thinking
about that as humans.
>> Speaker B (19:22):
What would you say
to people who would argue,
even if we're looking at data and analytics to make a decision
on should a pitcher come in and out of a game or what should we do
here? But we're looking at something that is a
relatively small sample size and for fans
who say, okay, so this guy's going by the book, this
one. Lefty, lefty. Or here's how this guy, he's a reverse
(19:45):
Split, he does better against righties or lefties or whatever that is.
And they're looking at the data. But we might be talking about
something that's this matchup has happened four times
or ten times. At what point is the sample
size statistically significant enough
that you should be staying strictly with
the data versus what your eyes are telling you?
>> Mike Petriello (20:04):
Yeah, that's funny. That actually also touches on,
uh, something I should have brought up before. We don't, as fans in
the public, have the same information that the team does, that the
players do, that the managers do. So when you say lefty on
lefty, lefty batter and lefty pitcher, that it for
years was probably the decision that was made. Now it's, we know the swing
path of this and we know the angle the pitch comes in. And now we're making
(20:24):
decisions based on that. As far as sample size goes,
it's a really important question. And it's very different
based on what metric you're looking at. For example,
let's talk about fastball velocity. I don't need to see but two
pitches to know that a guy throws harder doesn't. I don't need to see hundreds
of pitches to figure that out. But for something
like, uh, batting average, you need like hundreds of
(20:44):
plate appearances to feel confident that a guy really
is a.300 hitter. And the problem with that is by the time you get
to hundreds of plate appearances, now we're talking like two
seasons maybe. Well, the beginning of those plate appearances were from
a, uh, younger guy who they may not be as valuable
anymore. So for the skills
stuff, you can get to that really fast. I know a guy's fast
real fast. I know you throw hard really fast. Some
(21:07):
of the stuff like your exit velocity, maybe it's 50
or so batted balls. So that could take a couple of weeks.
It's a really, really important question because you wouldn't
want to say that a guy's a.500 hitter because he got one hit in his
first two plate appearances. That's totally meaningless. But I
would believe a 99 mile an hour fastball in his first two pitches.
It's very case by case, depending on what metric you're talking
(21:28):
about.
>> Speaker B (21:29):
What do you think about today
as a, the same day today as
a data point? Now we have the analytics that say
that a particular pitcher. So something common for people
who don't follow as much would be
conventionalism says that oftentimes
with pitchers, if you're leaving them in to go the third
time through the order, they're not going to do as well.
(21:52):
And hitters are going to be more effective at a particular pitcher
third time through the order. So teams are quite cautious
about leaving most starting pitchers
in beyond two times through the order.
Is today a, uh, data point where
you're looking at a guy and his stuff
just looks electric today? And here we
(22:12):
are getting through the second time in the order, and he
looks amazing. Should a manager be
trusting? Okay, that's what my eyes are telling me
versus going to my bullpen, you know, and even factors like my
bullpen's a little bit spent, or does the
data only work and the information only work
if I follow it religiously time after
time after time? Because over the long haul, it will
(22:35):
be right more often than it's wrong.
>> Mike Petriello (22:37):
Yeah, I think the. The word in baseball there is dealing. Oh, the
pitcher was dealing. How did you take him out? And of
course, every pitcher is dealing right up until the moment he's not
dealing. A pretty famous example of that over the
last couple years was in the 2020 World Series.
>> Speaker B (22:50):
Blake Snell.
>> Mike Petriello (22:51):
Blake Snell, exactly.
>> Speaker B (22:53):
I was about to bring it up if you hadn't. It's a classic example.
>> Mike Petriello (22:56):
It's a classic example.
>> Speaker B (22:57):
And for those people who don't know. So, yes, explain what happened.
>> Mike Petriello (22:59):
Blake Snow, uh, I don't remember the exact score or whatever, but
Dodgers raise. He was on the raise at the time, pitching great.
Just like mowing the Dodgers down left and right.
>> Speaker B (23:08):
Under underdog Tampa Bay. Underdog Rays
versus the mighty Dodgers. We should say that. And this
is their kind of an ace, like, pitcher mowing guys
down. Keep going.
>> Mike Petriello (23:18):
Yeah. And right. He's pitching great. He's pitching against the Dodgers. They're
winning the game. And there wasn't anything super obvious
in terms of his pitch metrics. That's the first thing you look for, is the
velocity starting to drop, is the movement starting to fade. Those
are signs of fatigue. I don't think there was anything serious like that.
And because the Rays had a very serious
adherence to their model and their method, their manager
(23:39):
came and took him out despite the fact that he was dealing.
And the reliever came in and blew the game. And they lost the World Series. It's like
one of the most famous moments of the last couple years. I remember watching this and
thinking, I wouldn't have taken him out then. But the
biggest problem is I thought they brought in the wrong reliever. That guy,
to your point, Nick Anderson was spent at that point.
But the point here is I remember someone and I
(23:59):
can't remember his name. It was Connor. Somebody did a bit of a
study on this. He went back and he found all of
the starts that were similar in innings
pitched, uh, out Scott and 0 earned runs. Whatever
Snell had done that day, he found very similar starts. These are obviously
extremely good starts by extremely good pitchers.
And he looked okay. What did those guys do after
(24:20):
that, the ones who were left in the game and the outcomes
were bad. It was like an average ERA of, I don't know, four
and a half or whatever. It's not going to work every time.
Nothing's going to work every time. I wouldn't have taken him out right
then and there, but I probably wouldn't have waited very much longer
either dealing or not, because the numbers
were pretty clear. If you leave him in, it's not going to end
(24:40):
well. You're sort of pushing your luck until that
happens. But that's not going to make any Tampa Bay fan feel
better. All they're going to remember is they lost the World Series.
>> Speaker B (24:48):
Mike, one of the things you, me,
we may enjoy,
statistics and
how analytics is making the game
and teams smarter. That's something that I find fun, that
I enjoy. I think there are, there's a
conventionalism and even you working for Major League Baseball,
(25:09):
Major League Baseball has implemented some rule
changes to try and do things to
speed up the game, add action to the game. Are,
uh, there ways in which making all the teams
smarter, leveling that playing field when
everybody has data has taken
away action or had a negative impact
on the game?
>> Mike Petriello (25:30):
I think the first thing I would say is that's not a baseball specific
issue. I'm not the world's biggest basketball fan, but I do hear the
complaining about three pointers like all the time. So
this is happening across a lot of sports. Uh, in baseball, I think the
biggest issue is that, uh, the pitchers have gotten so good
because of the pitch design, the pitch labs, the emphasis on velocity,
that there's just not as much contact as there used to be. Too many
(25:50):
strikeouts. Right. This has been an issue for 20 years and
nobody's really cracked that code yet. I, uh, do think some of
the rule changes that have been put in place have worked out well because
baseball has long been seen as maybe the old school,
sometimes dinosaur of sports, maybe slow to adapt.
That's probably a deserved label for a long time and that's changed
over the last couple years. The pitch clock, which came in two
(26:11):
years ago, which everybody lost their minds about, they can't have a clock
in baseball. Well, you can and it worked great.
It's been fantastic. The ratings have been up, the fan
attendance has been up. So I think that the
sport has done a better job now where it didn't
previously of going out and doing fan surveys, listening
to fans trying to get a handle on what kind of action
(26:31):
they like to see. And you can get into some real wild
rule changes. Someone wrote the other day we should have smaller gloves
for outfielders. Which I thought was pretty funny. The other thing is people
hate change. Time you pro propose a rule change, you'll see
everybody on social media saying, ah, uh, the game is perfect, don't
change it as though the game hasn't changed a
hundred times over the last 150 years. So
(26:51):
it's that you got to make changes, but you've also got to not make too many
changes or people get upset.
>> Speaker B (26:56):
Yeah. And so with the
increase of information, it added
into the game that
hitting home runs, launch angle, getting the ball
into the air was going to be more valuable for
players then hitting a single, hitting it
on the ground, more likely to hit it to a fielder. With
that an outcome ended up being you
(27:19):
and I would know that what they call the three true outcomes where
hitters would tend to focus on I want to hit a home run or
I want to walk high, um, on base percentage and if
I strike out, I'm not as worried about it because I'm going to get paid for
hitting those home runs or getting on base, having a high on
base percentage. As teams and
players get smarter with the use of data,
(27:39):
what I think has been interesting with baseball is then that's
where and to your point, it's not just baseball, it's across the world
is when you want a change in behavior, you
can legislate that. You can change the rules. As
the example being larger bases and
you can't throw over to first base as much. You limit the
number of times a pitcher can throw to first base. Now suddenly stolen
(27:59):
bases are up, stolen base success is
up. And as it's easier to steal bases, players steal more
bases and that becomes a part of the game. And now you've got more action in the
game. So you can do things or the shift you
can limit. If defenses get so smart with
the use of data and where you're placing people
that it gets really hard to get a
ball, uh, through an infield, then you can
(28:22):
legislate and change the rules on those things.
You mentioned the NBA, what you're referencing is that ah, the data
tells us that the least efficient
shot in the NBA is A long two point shot.
You basically should never take a long two point shot. You
either want to take a three or take a in the
paint short shot. Something that has a much higher
(28:43):
percentage of success. But that long two is
like a stupid shot. If the NBA
wants to see changes in the game and more playmaking and
not as many guys sitting there popping away from three all
the time, they're gonna have to change the rules. You can't ask people
to once they understand something and it is smart and
efficient to go back and be stupid.
>> Mike Petriello (29:02):
Yeah, I think that's right. At the end of the day, all of these sports are
entertainment products. Listen, I am a big hockey fan and I
vividly remember when I used to live in Boston, I went to this Bruins wild
game in like 2006 and I'm like, this is awful. This is
no fun to watch. There's no offense. It's all clutching and
grabbing. They changed a lot of the rules and then the offense came back
and it's been a lot more fun to watch. Basically everybody wants
(29:22):
the game to be like it was when they were 14 years old and just want to like
freeze it in amber. You go back to like 19, 20
and I'll tell you, the game looks a little bit different. Aside from the fact that it
was segregated, the players did not look the same, they did not
act the same. Pitchers would pitch nine innings every third day.
The sport has always, always, always changed. Night
games cross continental flights. So I do think that the sport is going
(29:43):
to continue to evolve. And like I said, when
people say that baseball didn't evolve, that was a totally fair
criticism. I think the last couple years they've really
gotten their heads around the fact that the world is changing. The sport needs to change
too.
>> Speaker B (29:55):
Yeah, I always think, uh, give it a chance.
For instance, the new roles of baseball, I ended up really liking one.
Although since you work with Major League baseball in the league office, one
change I would make with limiting how many times a
pitcher can throw over to first base instead
of once they've thrown over twice. The rule is then if you
throw a third time, then that runner is entitled to a free
base. I was thinking that should be more like a balk.
(30:18):
If you've thrown twice instead of rewarding with a
free base, seems like such a big penalty
versus giving away. Okay, now it's giving the
batter a ball.
>> Mike Petriello (30:28):
One thing. You can throw over a third time, but you have to get them.
So it's only if you, if you don't get them, if you don't get.
>> Speaker B (30:33):
Them, then they get a free base. So you better be right on
that third time or you're giving away a three base. I'm
saying the penalty should be if you don't get them that third time.
Make it a ball anyway. Make a change.
>> Mike Petriello (30:44):
Make it a ball. Yeah, fair enough. I think that would lessen the
penalty and, uh, change behaviors. So I think that'd be an
interesting experiment.
>> Speaker B (30:50):
Yeah, that's the only one I would tweak. But otherwise, I love the new
rules. So one thing that we're seeing,
certainly with prompt in our line of work, but the whole
world is, of course, is embracing AI
and augmented intelligence. So in terms
of baseball, how is Major League
Baseball using AI? Whether
(31:10):
that is in terms of fan experience
or in terms of teams and
predictive intelligence. Leveraging
data. How is AI a part of the game?
>> Mike Petriello (31:20):
Yeah, I like to think that there's multiple kinds of AI. There's
smart AI, which is using technology to
consume large data sets and help you get to patterns and
answers you wouldn't have, and, uh, obnoxious AI, which
is like my mom having to see AI attached to every brand
that she's ever seen in commercials, which I find wildly
unnecessary. As far as how any
(31:41):
baseball or really any company uses AI, it is to try
to get to those informed decisions maybe a little bit
faster, uh, especially as the size of these data
sets, uh, increases, I'm pretty sure. And I can't
speak to this in first person because I don't know, but I would be shocked if
MLB isn't using ad optimize ticket sales and
marketing in some way because that would just make sense as far as on
(32:01):
the field stuff goes. I know that some of the pitching
labs are using AI to, you know,
you think about all of the biomechanical data that comes in
when you've got all of these pitchers throwing all these pitches. That's
huge data. And that helps you get to what combination of
these things leads to more optimal outcomes. And whether
you want to think about it as AI or just the
Googling that we've been doing for 25 years, I'm not sure it matters that much
(32:24):
to most people. You don't see it under the hood. But if that
kind of tool can help you get to better answers faster, that's
the entire point of any of this, really.
>> Speaker B (32:31):
In terms of looking ahead, what sort of
innovations in analytics, in
information are you most
excited about for the future?
>> Mike Petriello (32:41):
Well, I think the Holy grail, if anybody can figure
this out, they will be the Richest person in baseball is how do you
keep pitchers healthy? This has been an
ongoing issue as pitchers got bigger and
stronger and worked on maximizing velocity.
It turns out it's really hard to strengthen that little ligament in
your elbow. Guys keep getting hurt. It's bad for the game. You
(33:02):
want the stars in the field, it's bad for the players. Nobody wants to get hurt.
So that is something the entire industry is thinking about
how to do in terms of metrics and stuff. We're continuing
to push forward because the technology on the field keeps getting better.
Up until last year, you could never really tell anything
about the way the bat moves. You knew a lot about the pitch, a lot about the
ball, but nobody could track the bat because it moves at
(33:22):
like 100ft per second. Now the technology got
upgraded. All of a sudden that's a thing we can measure. More
and more metrics on that are coming out. I, uh, bring that up because it's really
interesting. When you start to measure something
that you couldn't measure before, it's not just a curiosity,
then it becomes something you can quantify and value.
And when you can value it, then players start working towards
(33:42):
it because teams start paying money for it. For example, the bat
speed. I don't think it's revolutionary to say if you swing the
bat faster, you'll hit the ball harder. That's something you can see
with your eyes back to Babe Ruth's time. But now that you can
measure it and say, hey, every extra miles an hour in your
bat speed gets you this much distance and this many points of
slug. And we value that. Now you got these guys who are coming up from
(34:02):
the Miners saying, yeah, I spent my winter not working on my defense,
but trying to improve my bat speed. And I think that's what's going to
keep happening.
>> Speaker B (34:10):
So bat speed is a really interesting one.
And spin rate on pitches, things like that, those are
interesting ones. Then there's results outcome based. And
there are fans who've been around the game forever and they look at and
wanted batting average, home runs,
RBIs, counting stats, things like that.
When you look at stats, is there
one metric that you
(34:33):
personally would find the most valuable? If I said to you,
I want to compare players or know how good a player
is, are you looking at WAR wins above
replacement? Are you looking at, if we're talking about position
players, not pitchers, are you looking at OPS
plus, are you looking at weighted runs created
plus? There's so many different good statistics that, uh, are out
there. Is there one that you have, that's a favorite.
>> Mike Petriello (34:55):
Those are all different answers to different questions. So if
I just want a quick at a glance, who are the most valuable
players all in like hitting, defense, running. Yes, wins above
replacement. Uh, that's the, the best we have. It's not perfect, but
it's really good. A lot of the other stuff you mentioned is very
specific just to hitting. So if I want to see who the best hitters are,
Parker jocks said yes, I'll go to weighted roads created. Plus
if I want to see who, uh, hits the ball the
(35:17):
hardest, go look up statcast, go look at hard hit rate. But
there's a lot of different ways to answer those questions. It just depends on
what you're looking at. But I look at all of them as a starting point
and not necessarily an ending point. If I look at the leaders in
hard hit rate, I'm probably going to find Aaron Judge and I'm going to find
Giancarlo Stanton. I'm going to find guys who hit the ball really,
really hard. Doesn't necessarily guarantee I'm
(35:37):
finding the best hitters in baseball because Luisa Rice does not
hit the ball hard and he always has a very good batting average. So
it all comes with a certain amount of contextual knowledge
to make any of these numbers useful.
>> Speaker B (35:48):
As we finish a couple last questions here, what
advice would you give to marketers looking to
adapt a more data driven approach and what
can they learn from baseball that would be
applicable?
>> Mike Petriello (36:00):
Number one, listen to the fans or your audience or whoever. I
don't think baseball has always done that, as I said, and now that's really
helped a lot to understand what the audience wants. If
you have a sort of complicated and dense data set, make sure
you explain it in a way that people can enjoy or
understand, even be entertained by. Uh, because if
not, everybody's going to tune it out, no matter how valuable it might
(36:20):
be.
>> Speaker B (36:20):
And then to close, we like to ask a fun question
here. And so if you could pick any
player from any sport, past
or present, to join you for a Mike
Petriello dream dinner
party, who would you want to sit there and talk to
and why?
>> Mike Petriello (36:38):
I would like to say that I have an extremely deep
cut and a really thought out answer, but I'm going to give you one of the
most famous people of all time. Uh, but for a good reason. The answer
would be Ted Williams, who had a fascinating
life, obviously served in two wars, you know, all around
amazing life and career. He was
maybe the first real baseball nerd. He literally
(36:59):
wrote a book on this called the Science of hitting in
1971. And he didn't actually say exophilosity and launch
angle, but you go read it and he basically did. He
drew charts and diagrams to the inch of
saying, here's where I'm good when I hit the ball here and there. And it's
funny because we'll bring out a lot of the new nerd stuff and people be
like, oh, Lou Gehrig, Ted Williams, they'd have hated this stuff. And I'm like,
(37:19):
no, no, Ted Williams would have loved this stuff.
And I would just love to take them all through it and see what he'd have to say about
it.
>> Speaker B (37:25):
We think about old school, new school Ted
Williams using all of that information and science
of hitting to hit.400, a.400
batting average, an old school stat. But he's leveraging data to be
able to accomplish something that is a feat that we haven't
seen in the sport in a while.
>> Mike Petriello (37:41):
I'd ask him about that. Uh, that'd be great.
>> Speaker B (37:43):
All right, well, invite me. I'd like to sit and listen to you and Ted
Williams and lob in a question or two on that. Mike
Petriello, really, really appreciate the
time. It's been fun talking to you and thinking
about some of the ways in which fans and
media and others. There's a certain resistance at
times to data and analytics. And yet when you wake up
(38:03):
and realize over time, whether it's with
the hall of Fame inductees and others, how it has
become so embraced and so much a part of the game, uh,
that information and increased information will only
just increase over time. So anyway,
really enjoy talking to you. Thanks so much.
>> Mike Petriello (38:20):
Thanks a lot, Laurie.
>> Lori Rubinson (38:27):
Thank you for listening to this episode of the Frictionless Marketing
Podcast. For a complete transcript of this
conversation or more information on Prompt,
please visit us at meetprompt. Co.
If you found this episode insightful, share it with your connections
on LinkedIn to learn
more about how to make marketing Frictionless Purchase Friction
(38:49):
Fatigue by Prompt CEO Paul Dyer online
and at Booksellers Worldwide
Frictionless Marketing is a production from Prompt, the leading
earned first creative marketing and communications agency
grounded in the present, yet attuned to the future.
Produced and distributed by Simpler Media Productions.
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