Episode 6
The Intersection of Sports Analytics and Baseball: A Conversation with Mike Petriello
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.
Transcript
>> Lori Rubinson: Welcome to Frictionless Marketing, the podcast that dives deep into
Speaker:the stories of the most innovative brands and the people moving them
Speaker:forward. Today,
Speaker:our host, Lori Rubinson, managing director of PROMPT
Speaker:and WFAN sports talk radio host, sits down
Speaker:with Mike Petriello. Mike is
Speaker:the director of Stats and Research at Major League Baseball, where
Speaker:he's been at the forefront of advancing how analytics are used to
Speaker:understand, play and enjoy the game.
Speaker:A pioneer in applying cutting edge technology,
Speaker:Mike's work has transformed how teams make decisions,
Speaker:how fans engage with the sport, and how
Speaker:broadcasters tell compelling stories.
Speaker:Together, they will dive into the realm of sports analytics as
Speaker:Laurie and Mike discuss the often misunderstood role of analytics in
Speaker:sports, especially baseball, addressing common
Speaker:criticisms and explaining how analytics can improve decision
Speaker:making.
Speaker:>> Speaker B: Foreign
Speaker:welcome to another episode of the
Speaker:Frictionless Marketing podcast. This is Lori
Speaker:Rubinson. Really excited today to wear both
Speaker:hats. Usually I am just managing partner here,
Speaker:uh, at uh, Prompt, but today I'm also the sports
Speaker:talk radio host from wfan. I've had a
Speaker:topic on my mind for a really long time that
Speaker:bothers me when I talk to listeners,
Speaker:callers and interact with people on social media and on the
Speaker:radio. And it is that sometimes analytics in
Speaker:sports and in baseball in particular, get
Speaker:vilified as if it's the enemy to all decisions or
Speaker:things that happen with our teams that we don't like. To get to the
Speaker:bottom of that and to talk about sports and analytics, I can
Speaker:think of no better person to talk to than Mike
Speaker:Petriello. Mike, welcome and look
Speaker:forward to talking to you.
Speaker:>> Mike Petriello: Laurie, thanks so much for having me. Looking forward to it.
Speaker:>> Speaker B: First thing I wanted to get into before we dive into the
Speaker:integration of analytics into baseball, I wanted
Speaker:to understand a little bit back up. I think people have seen you
Speaker:on espn, Statcast, broadcast, the Nerdcast
Speaker:and all. It's like that. How did you actually
Speaker:get started in baseball?
Speaker:Analytics? Sports analytics? How did this come about?
Speaker:>> Mike Petriello: I, uh, wasn't with my history degree, I can tell you that
Speaker:much. There's a long version, but I will give you the short
Speaker:version. It's funny because college kids come up to me all the time and they're like,
Speaker:hey, how do I get a job in baseball? And nobody wants to hear my
Speaker:answer of I didn't get my first full time job Till I was 35
Speaker:years old. Basically what happened was I went to Boston University,
Speaker:I got a history degree, spent most of my twenties at
Speaker:startups like a video on demand startup. I actually spent five
Speaker:years working at a place you probably know well, which is Ketchum the
Speaker:PR firm as like project manager, building websites and
Speaker:that kind of stuff. Baseball was always a passion and kind of
Speaker:a side hustle. Uh, when I was 26, I guess I
Speaker:started a blog which was the style of 2007
Speaker:and I just kept at it. That got me opportunities, that got me
Speaker:opportunities with fan graphs and baseball perspectives
Speaker:and ESPN and eventually here at mlb where
Speaker:I've been coming up on nine years full time. Now
Speaker:that's branched out into tv. It gets more in depth than that, but
Speaker:that's the short version. Turning, uh, a passion into a career.
Speaker:>> Speaker B: We'll compare notes at some point because people
Speaker:say the same to me with the WFAN part of my
Speaker:business. How do I get into sports media?
Speaker:And I always say, I'm a history major from Brown
Speaker:University, don't do what I did. It's too
Speaker:circuitous. There has to be a different path. So the
Speaker:second thing, before we understand
Speaker:sports analytics within the context of baseball,
Speaker:I wanted to understand how you actually
Speaker:define analytics, Fans, media
Speaker:and others. It's a catch all term for a lot
Speaker:of things. How do you define it?
Speaker:>> Mike Petriello: I mean, analytics is information. It's the study of
Speaker:data, it's the study of patterns. Uh, nothing that I just
Speaker:said is specific to sports or baseball. You could say that across any
Speaker:industry. Certainly anybody else is using that. So that's what it is.
Speaker:It's data and patterns and information. If you go
Speaker:back through all of baseball history, people in the game
Speaker:have been using information to inform future decisions.
Speaker:Maybe that information is what they saw, what an intern wrote down on
Speaker:a pad of paper, their gut, uh, feeling. The only thing that's changed
Speaker:now is that the information is, it's a lot better and
Speaker:we know a lot more about the context of it, how to use it, how to
Speaker:change the game. That's all analytics is, is trying to take all
Speaker:this information that's out there and use it to make informed decisions
Speaker:to win games.
Speaker:>> Speaker B: Yeah, I, uh, always say that to fans when they
Speaker:do call in and blame
Speaker:analytics if they don't like the pitching decision
Speaker:and manager comes, takes a pitcher out of the game, goes
Speaker:to the bullpen, it goes wrong. And then they say, well, that's
Speaker:analytics. And my response
Speaker:is, data doesn't make decisions, people
Speaker:do. If you're angry at the decision,
Speaker:you know, analytics is information. That is how I think
Speaker:about it. And you're right. Look at prompt. That is a big part of our
Speaker:business is data and information to inform
Speaker:decision making. So you do work better with better
Speaker:results.
Speaker:>> Mike Petriello: I think that's right. What people think fail to understand is
Speaker:sometimes there's not a right answer. The Yankee manager brings in the
Speaker:lefty and he gives up a home run. Well, you made the wrong decision. As
Speaker:though if you bring in the righty, he wouldn't also have given up a hit. Like
Speaker:the saying goes, the other guys live in big houses too. You can
Speaker:improve your decision making, you can put it based on better
Speaker:processes, but it doesn't guarantee anything. The
Speaker:smartest team in baseball, whoever you define that to be,
Speaker:does not win 162 games every year. And they make better
Speaker:decisions, they have better outcomes, but nothing is guaranteed. Because
Speaker:at least in sports, there's still people, there's still human beings
Speaker:on the field and you can try to put them in better situations,
Speaker:but at the end of the day, the, I don't know, 28 year old on the
Speaker:mound still has to get the job done. And that's never going to be
Speaker:perfect.
Speaker:>> Speaker B: A lot of people, I think when they think of baseball and
Speaker:analytics, they think of the movie, Moneyball, Billy Bean,
Speaker:and it's about on base percentage and
Speaker:things like that. And the way I think of that story
Speaker:is it's about finding ways to
Speaker:uncover value. An exploit value
Speaker:that might be an undervalued asset that someone else
Speaker:isn't seeing. And I think of it and use that term often. We
Speaker:do influencer marketing here at the agency, want to
Speaker:uncover influencers who are on their way up.
Speaker:There's value to tap into
Speaker:as opposed to someone who's already plateaued. Or you might know them
Speaker:better, but they're on their way down. Now you're paying for maximum value.
Speaker:You want to catch an asset on the way up.
Speaker:With baseball, clearly that was something and that's a
Speaker:part of Moneyball. But what about the game of baseball? Made
Speaker:it, in my mind a, uh, pioneer
Speaker:in using analytics in sports in a way that yes,
Speaker:basketball does now, yes, football of every other sport does
Speaker:now. But it feels like baseball was really
Speaker:a pioneer early on with the use of data and
Speaker:information.
Speaker:>> Mike Petriello: That's a great question. There's actually two answers to that. The short
Speaker:answer is data availability. I think just the way
Speaker:the game is played. A pitch is a pitch. There's an outcome
Speaker:on the pitch if it was a curveball, if it was swung at or not. It's a
Speaker:lot harder in other sports where you have an offensive lineman
Speaker:surrounded by 10 other guys and 2ft to isolate what he did.
Speaker:It's difficult in baseball. That data set has been there
Speaker:and not even just the Current statcast stuff going back
Speaker:decades. You've got retro sheet data from these hobbyists really, who
Speaker:have put that information together for people to study. So that's
Speaker:the big thing. The data is there and the game is played. Even though it's a
Speaker:team game, it's very much a, uh, one on one game too. Pitcher versus
Speaker:batter, fielders, et cetera. The other thing, and I didn't know,
Speaker:or that you were a history major as I am, so I'm not going to
Speaker:take up too much of your time with this. People think that analytics
Speaker:started when the movie Moneyball or maybe the book Moneyball
Speaker:came out. I'll send this to you later. If you want. You can go back to the
Speaker:early 1900s. There's a famous article in what was called
Speaker:baseball magazine in 1917, before the end
Speaker:of World War I, where the author, F.C. lane, was
Speaker:complaining about batting average and wondering why we used batting
Speaker:average as though it implies every hit was the same. The analogy he
Speaker:used was if you ask so much, uh, someone how much money they had
Speaker:in their pocket, they wouldn't just say, I have six coins. You'd
Speaker:say, well I want to do it. Dimes, do I have nickels? That's
Speaker:108 years ago at this point.
Speaker:And that has gone on for a long time. The only thing that's
Speaker:accelerated in the last 20, 25 years
Speaker:would be the Internet bringing these people closer together
Speaker:and that the availability of data has improved. You can
Speaker:get a lot more granular information now
Speaker:obviously than you could have 20 years ago, 40 years
Speaker:ago. But people have always been thinking about this way
Speaker:beyond you think they might have.
Speaker:>> Speaker B: How do you think the use of data
Speaker:analytics information has changed the
Speaker:way fans, teams, players engage with sports
Speaker:or specifically baseball? What ways has
Speaker:it actually enhanced our
Speaker:experience with the game?
Speaker:>> Mike Petriello: So I think you hit on something very important there when you said players, uh,
Speaker:teams, fans, in whatever order you said it, because, uh, it's a
Speaker:different answer for different audiences. For sure.
Speaker:Teams are very much interested not only in choosing
Speaker:the best players, but having a very good idea of how those players
Speaker:might perform in uh, the future. To give a Mets example,
Speaker:Juan Soto just got an enormous contract from the
Speaker:Mets, at least at the time we're speaking. Pete Alonso has had a very
Speaker:difficult time finding a contract. It's not that they're not both good
Speaker:players, it's just that the ages they are, the types of player
Speaker:profiles they are. You have a lot more confidence in Juan
Speaker:Soto being very good for 10 years than you do in Pete Alonso
Speaker:for the next couple of years. Um, for players, and this
Speaker:has really been interesting over the last couple of years. I think
Speaker:at first players looked at it from an old school point of view
Speaker:with a bit of a side eye, saying, I got to the major leagues. Get out of
Speaker:here, nerd. What can you teach me? Fair enough.
Speaker:But over the last decade or so, you've had a lot
Speaker:of players using this information to improve their games,
Speaker:improve their careers, improve their salaries. Uh, you go back 10
Speaker:years, and it was J.D. martinez and Justin Turner getting
Speaker:the ball off the ground. And now you've got pitch design,
Speaker:which is a whole other conversation. But basically you can
Speaker:use biomechanical data and say, hey, here's the reason why
Speaker:your curveball is not very good. We can either help you make it
Speaker:better, or the way your body moves, it's just never going to be
Speaker:good. Try a different kind of pitch. And, uh, there are so
Speaker:many guys across the majors right now who have become better
Speaker:players just because of that. And it's so funny to me, when I
Speaker:started, I'd go talk to players and I'm like, I know so much more
Speaker:about this than you do now. I talk to the pitchers, the young
Speaker:guys. I barely know what they're talking about. And it's my job to
Speaker:know this. It is wild to hear these guys talk about it. Every player
Speaker:is a nerd now, which is kind of fun to think about.
Speaker:>> Speaker B: Yeah. For people who are, let's
Speaker:say, not as into baseball or, uh, as you and I might be,
Speaker:one of the interesting phenomenons are pitching labs.
Speaker:When you talk about all of the things like pitch,
Speaker:shape, you're talking about the shape of a
Speaker:curveball and the spin rate and the things that we're
Speaker:capturing. But for people who are less familiar, teams
Speaker:like the Yankees, the Mets just instituted theirs,
Speaker:and others around the league. Explain what a
Speaker:pitching lab is and how
Speaker:that data is captured and how you can
Speaker:quantify so much data and
Speaker:synthesize it into something that becomes
Speaker:actionable and that improves decision making.
Speaker:>> Mike Petriello: A pretty famous story in baseball. Mariano Rivera, probably
Speaker:the best relief pitcher who ever lived, came up as a starter in the mid-90s.
Speaker:He was okay, and he was just screwing around in the
Speaker:outfield one day and he threw a pitch, uh, that he'd never thrown before, and
Speaker:it became his cutter. One of the probably five most dominant
Speaker:pitches anyone's ever thrown. He turned that into a Hall of Fame career. And that was
Speaker:an accident. What you can do now with the pitching labs is you
Speaker:go in there in Front of all the high speed cameras and force plates and
Speaker:all sorts of crazy stuff they got. And you go through
Speaker:your pitch and they'll say, okay, we see how your body moves. You're
Speaker:maybe you're a pronator, maybe you're a supinator, which means
Speaker:what direction does your arm move as you throw it? Uh,
Speaker:you're in a good position to throw. Let's say
Speaker:your body type says a splitter, a split finger fastball.
Speaker:That'll work for you or it won't. And we
Speaker:can reduce the accidents of. Oh, hey. My
Speaker:sister's uncle's groundskeeper from high school told me how to
Speaker:throw this grip and it worked out for me and actually get there
Speaker:pretty quick and say, okay, we're going to have feedback on whether
Speaker:this pitch works. Not in months, not after the batters kill
Speaker:it, but in about 20 minutes because you're going to throw 10 of
Speaker:them and we're going to see what the numbers say on it and say, oh, the movement
Speaker:on that, that's really good. Let's work on this. That's what it is. It
Speaker:helps them not only learn what they can and can't do,
Speaker:but get there a lot faster. If you go back through all of
Speaker:history, there's probably a lot of pitchers who
Speaker:had the talent to be great and you never heard of them because they
Speaker:never, dumb luck, stumbled upon that right pitch. And
Speaker:maybe today, in front of the technology, they could have learned it a lot
Speaker:faster.
Speaker:>> Speaker B: You know, with all the use of data, your job
Speaker:and the job of broadcasters today is to
Speaker:figure out how to
Speaker:synthesize data and
Speaker:analytics into storytelling and to make it
Speaker:interesting to people. I joked before about the nerdcast
Speaker:I always love. This is ESPN for a number of years would
Speaker:do a specific statcast alternate broadcast.
Speaker:People are familiar with the Manning cast for NFL Monday Night
Speaker:Football. This was a statistically
Speaker:oriented alternate broadcast, which I
Speaker:always loved. And a lot of people would never always trend on
Speaker:Twitter and everybody be hashtagging, statcast, nerdcast, all this.
Speaker:Mike, you were one of the broadcasters who
Speaker:were analyzing, commentating, bringing that to
Speaker:people. Now I think
Speaker:data and analytics have become
Speaker:more a part of mainstream broadcasts. So how
Speaker:do you think about in your job with Major League
Speaker:Baseball and when you talk to all the different networks where the games
Speaker:are broadcast and the different outlets, how
Speaker:do you incorporate data and storytelling?
Speaker:>> Mike Petriello: I joke a lot about having the history degree in this job, but what is
Speaker:a history degree? It's explaining why did this country invade that
Speaker:country, explaining why these important certain events in history happened.
Speaker:And that's how I approach this too. You need to be able to
Speaker:explain these things because teams and players are
Speaker:making decisions based upon them. Um, like before the shift was banned,
Speaker:you needed to explain why the third baseman was standing in right field.
Speaker:Because it's a real weird thing. Why would a team trade a
Speaker:guy with a.280 batting average for a guy with a.240 batting average?
Speaker:There's reasons, but you need to be able to explain it.
Speaker:So, uh, that's what we do. And I would say it's gotten simultaneously
Speaker:easier and harder easier because you don't have to
Speaker:sell anybody on the utility of it anymore. Years ago
Speaker:it was, I don't need this stuff. This isn't interesting. And now it's,
Speaker:yeah, we know that teams, players are using this. We need to be able to
Speaker:explain this. But the harder part is now the details have gotten
Speaker:so complicated. You try not to have everything turn into an
Speaker:algebra class because the number one takeaway, and if we
Speaker:proved anything on that show, which we still did a few of them last year,
Speaker:hopefully might do some this year as well. You can still have
Speaker:fun talking about nerd stuff. You don't
Speaker:have to go in and explain the launch angle on every single
Speaker:batted ball or the spin rate on every single pitch, because I
Speaker:can tell you, even I don't want to know that. But if you can
Speaker:put stuff into context, this was like, hey, the hardest hit ball of
Speaker:the year. That's cool. Because so much of it's just baseball.
Speaker:You couldn't before 2015 or 2020, depending
Speaker:on which metric. Say, who had the strongest outfield
Speaker:throwing arm, who was the fastest runner. That's baseball
Speaker:stuff. That's the stuff people have been arguing about in bars forever.
Speaker:To some extent, that's just putting numbers behind what you've already
Speaker:seen.
Speaker:>> Speaker B: How has the rise in
Speaker:sports betting and gambling changed how
Speaker:analytics are incorporated into the fan experience,
Speaker:media coverage, and how much it is accepted as
Speaker:a part of the game?
Speaker:>> Mike Petriello: That's an interesting question. I try my best to avoid
Speaker:sports betting as much as I possibly can. I work
Speaker:for mlb, um, so I'm not allowed to bet on baseball,
Speaker:obviously. So I try not to pay attention to it too
Speaker:much because I just can't have anything to do with it. But I think
Speaker:fantasy baseball has been a thing for many, many years.
Speaker:Certainly those people who want to win their leagues are looking at numbers
Speaker:and data to inform their own decisions. I would imagine
Speaker:that the people who are putting money in the games are doing much the Same
Speaker:thing. But we're not, at least I'm not directly involved in
Speaker:that world at all.
Speaker:>> Speaker B: You mentioned Pete Alonso as an example of a player
Speaker:that I would agree, uh, has been hurt by analytics.
Speaker:The way we perceive things, somebody who has
Speaker:been known, he may have hit 33 home runs
Speaker:last year, but generally is good for 35
Speaker:plus 40 home runs season. He would have
Speaker:been someone who would have gotten a big contract in seasons
Speaker:past and now here he is sweating it out to try and get somebody
Speaker:to sign him based on position
Speaker:and on base percentage
Speaker:declining and base running and
Speaker:defensive metrics. And
Speaker:just there are a number of things where age, where
Speaker:people are saying, okay, yes, you hit a lot of home runs
Speaker:10 years ago, he would have been 15 years ago, snapped up
Speaker:and not today. The question though is, so if
Speaker:he's one who struggled, who's an example of somebody that
Speaker:you would say was an early analytics darling?
Speaker:>> Mike Petriello: I think going back a number of years, Joey Votto, I
Speaker:think is the first name that comes to mind. And it's not that he didn't hit 30
Speaker:home runs and 100 RBIs, he did, but he got on base
Speaker:a lot and that is such a valuable thing. He ended up
Speaker:with a huge contract, uh, even though he was like Alonso in the
Speaker:sense that he is a sort of slow footed first baseman, a
Speaker:better defender, sure. But he ended up getting a pretty
Speaker:massive contract in the hundreds of millions of dollars.
Speaker:And I don't think he would have gotten that 20 years earlier
Speaker:because he wasn't the prototypical hairy chested
Speaker:slugger that first baseman were back in the day.
Speaker:So I would agree with you that Pete Alonso
Speaker:probably doesn't get the contract he wants because of what we've learned
Speaker:about aging curves and all this. To give you another example, Luke
Speaker:Weaver, who is a pitcher, he's not a great example because he didn't
Speaker:sign a big deal. But there are guys like that, terribly
Speaker:unsuccessful for like seven years. Comes to the
Speaker:Yankees, they change his grips. All of a sudden he's awesome. He's like one
Speaker:of the 10 best relievers in baseball right now. If he was a free agent this year,
Speaker:he'd have gotten a huge contract based not on his career
Speaker:to date, but based on what they think he'll do going forward. So
Speaker:it's not that the analytics is taking money away, the players are
Speaker:just distributing it in a different way based less on
Speaker:what you have done so far and more on educated guesses
Speaker:about what you might do going forward.
Speaker:>> Speaker B: Yeah, and I think it's Interesting with fans,
Speaker:they want their teams to sign big names
Speaker:to some extent based on. It's like the stock market based on
Speaker:past performance. But when you
Speaker:sign a guy for a long term contract that
Speaker:doesn't profile well with predictive analytics, that this is
Speaker:going to go well over time and then it does not go well
Speaker:and now that team is stuck with that contract for
Speaker:years, then fans are super, you know, uh, I think of
Speaker:Chris Davis with the Baltimore Orioles as an example at first
Speaker:base. Then fans are super frustrated that
Speaker:we're still paying this guy's salary and he fell off a cliff.
Speaker:And it was like, well, the data analyst did tell you that might happen.
Speaker:People didn't want to listen. We're talking about some of the ways in which
Speaker:analytics are a, uh, positive. They are just a part of today's
Speaker:game. They've been a part, as you point out, since
Speaker:1917, more and more prevalent over
Speaker:time. Why is it you think I get
Speaker:callers who want to blame the data and
Speaker:analytics for
Speaker:decisions in baseball that don't go well?
Speaker:Why does analytics get vilified?
Speaker:>> Mike Petriello: We could talk about the difference in what the data
Speaker:says and what someone's gut says, and then I'm not sure we're
Speaker:talking about sports anymore because that's how happened in a lot of different
Speaker:places around the world. But if something doesn't go
Speaker:right, you want to blame something, right? Well, I wouldn't have put
Speaker:that guy in and the nerd number said to put them in and it didn't work.
Speaker:So I blame the nerd numbers. That's basically what it comes down to. If your
Speaker:team lost, you want to put it on somebody and it's easy to put it
Speaker:on the player. Sure. But if there's a decision that was made
Speaker:based on numbers that you don't feel comfortable with or
Speaker:familiar with or don't agree with, I think that's the number one place to look.
Speaker:Even though, like I said before, it doesn't mean the other thing would have worked.
Speaker:You just didn't see it fail. And we're really bad about thinking
Speaker:about that as humans.
Speaker:>> Speaker B: What would you say
Speaker:to people who would argue,
Speaker:even if we're looking at data and analytics to make a decision
Speaker:on should a pitcher come in and out of a game or what should we do
Speaker:here? But we're looking at something that is a
Speaker:relatively small sample size and for fans
Speaker:who say, okay, so this guy's going by the book, this
Speaker:one. Lefty, lefty. Or here's how this guy, he's a reverse
Speaker:Split, he does better against righties or lefties or whatever that is.
Speaker:And they're looking at the data. But we might be talking about
Speaker:something that's this matchup has happened four times
Speaker:or ten times. At what point is the sample
Speaker:size statistically significant enough
Speaker:that you should be staying strictly with
Speaker:the data versus what your eyes are telling you?
Speaker:>> Mike Petriello: Yeah, that's funny. That actually also touches on,
Speaker:uh, something I should have brought up before. We don't, as fans in
Speaker:the public, have the same information that the team does, that the
Speaker:players do, that the managers do. So when you say lefty on
Speaker:lefty, lefty batter and lefty pitcher, that it for
Speaker:years was probably the decision that was made. Now it's, we know the swing
Speaker:path of this and we know the angle the pitch comes in. And now we're making
Speaker:decisions based on that. As far as sample size goes,
Speaker:it's a really important question. And it's very different
Speaker:based on what metric you're looking at. For example,
Speaker:let's talk about fastball velocity. I don't need to see but two
Speaker:pitches to know that a guy throws harder doesn't. I don't need to see hundreds
Speaker:of pitches to figure that out. But for something
Speaker:like, uh, batting average, you need like hundreds of
Speaker:plate appearances to feel confident that a guy really
Speaker:is a.300 hitter. And the problem with that is by the time you get
Speaker:to hundreds of plate appearances, now we're talking like two
Speaker:seasons maybe. Well, the beginning of those plate appearances were from
Speaker:a, uh, younger guy who they may not be as valuable
Speaker:anymore. So for the skills
Speaker:stuff, you can get to that really fast. I know a guy's fast
Speaker:real fast. I know you throw hard really fast. Some
Speaker:of the stuff like your exit velocity, maybe it's 50
Speaker:or so batted balls. So that could take a couple of weeks.
Speaker:It's a really, really important question because you wouldn't
Speaker:want to say that a guy's a.500 hitter because he got one hit in his
Speaker:first two plate appearances. That's totally meaningless. But I
Speaker:would believe a 99 mile an hour fastball in his first two pitches.
Speaker:It's very case by case, depending on what metric you're talking
Speaker:about.
Speaker:>> Speaker B: What do you think about today
Speaker:as a, the same day today as
Speaker:a data point? Now we have the analytics that say
Speaker:that a particular pitcher. So something common for people
Speaker:who don't follow as much would be
Speaker:conventionalism says that oftentimes
Speaker:with pitchers, if you're leaving them in to go the third
Speaker:time through the order, they're not going to do as well.
Speaker:And hitters are going to be more effective at a particular pitcher
Speaker:third time through the order. So teams are quite cautious
Speaker:about leaving most starting pitchers
Speaker:in beyond two times through the order.
Speaker:Is today a, uh, data point where
Speaker:you're looking at a guy and his stuff
Speaker:just looks electric today? And here we
Speaker:are getting through the second time in the order, and he
Speaker:looks amazing. Should a manager be
Speaker:trusting? Okay, that's what my eyes are telling me
Speaker:versus going to my bullpen, you know, and even factors like my
Speaker:bullpen's a little bit spent, or does the
Speaker:data only work and the information only work
Speaker:if I follow it religiously time after
Speaker:time after time? Because over the long haul, it will
Speaker:be right more often than it's wrong.
Speaker:>> Mike Petriello: Yeah, I think the. The word in baseball there is dealing. Oh, the
Speaker:pitcher was dealing. How did you take him out? And of
Speaker:course, every pitcher is dealing right up until the moment he's not
Speaker:dealing. A pretty famous example of that over the
Speaker:last couple years was in the 2020 World Series.
Speaker:>> Speaker B: Blake Snell.
Speaker:>> Mike Petriello: Blake Snell, exactly.
Speaker:>> Speaker B: I was about to bring it up if you hadn't. It's a classic example.
Speaker:>> Mike Petriello: It's a classic example.
Speaker:>> Speaker B: And for those people who don't know. So, yes, explain what happened.
Speaker:>> Mike Petriello: Blake Snow, uh, I don't remember the exact score or whatever, but
Speaker:Dodgers raise. He was on the raise at the time, pitching great.
Speaker:Just like mowing the Dodgers down left and right.
Speaker:>> Speaker B: Under underdog Tampa Bay. Underdog Rays
Speaker:versus the mighty Dodgers. We should say that. And this
Speaker:is their kind of an ace, like, pitcher mowing guys
Speaker:down. Keep going.
Speaker:>> Mike Petriello: Yeah. And right. He's pitching great. He's pitching against the Dodgers. They're
Speaker:winning the game. And there wasn't anything super obvious
Speaker:in terms of his pitch metrics. That's the first thing you look for, is the
Speaker:velocity starting to drop, is the movement starting to fade. Those
Speaker:are signs of fatigue. I don't think there was anything serious like that.
Speaker:And because the Rays had a very serious
Speaker:adherence to their model and their method, their manager
Speaker:came and took him out despite the fact that he was dealing.
Speaker:And the reliever came in and blew the game. And they lost the World Series. It's like
Speaker:one of the most famous moments of the last couple years. I remember watching this and
Speaker:thinking, I wouldn't have taken him out then. But the
Speaker:biggest problem is I thought they brought in the wrong reliever. That guy,
Speaker:to your point, Nick Anderson was spent at that point.
Speaker:But the point here is I remember someone and I
Speaker:can't remember his name. It was Connor. Somebody did a bit of a
Speaker:study on this. He went back and he found all of
Speaker:the starts that were similar in innings
Speaker:pitched, uh, out Scott and 0 earned runs. Whatever
Speaker:Snell had done that day, he found very similar starts. These are obviously
Speaker:extremely good starts by extremely good pitchers.
Speaker:And he looked okay. What did those guys do after
Speaker:that, the ones who were left in the game and the outcomes
Speaker:were bad. It was like an average ERA of, I don't know, four
Speaker:and a half or whatever. It's not going to work every time.
Speaker:Nothing's going to work every time. I wouldn't have taken him out right
Speaker:then and there, but I probably wouldn't have waited very much longer
Speaker:either dealing or not, because the numbers
Speaker:were pretty clear. If you leave him in, it's not going to end
Speaker:well. You're sort of pushing your luck until that
Speaker:happens. But that's not going to make any Tampa Bay fan feel
Speaker:better. All they're going to remember is they lost the World Series.
Speaker:>> Speaker B: Mike, one of the things you, me,
Speaker:we may enjoy,
Speaker:statistics and
Speaker:how analytics is making the game
Speaker:and teams smarter. That's something that I find fun, that
Speaker:I enjoy. I think there are, there's a
Speaker:conventionalism and even you working for Major League Baseball,
Speaker:Major League Baseball has implemented some rule
Speaker:changes to try and do things to
Speaker:speed up the game, add action to the game. Are,
Speaker:uh, there ways in which making all the teams
Speaker:smarter, leveling that playing field when
Speaker:everybody has data has taken
Speaker:away action or had a negative impact
Speaker:on the game?
Speaker:>> Mike Petriello: I think the first thing I would say is that's not a baseball specific
Speaker:issue. I'm not the world's biggest basketball fan, but I do hear the
Speaker:complaining about three pointers like all the time. So
Speaker:this is happening across a lot of sports. Uh, in baseball, I think the
Speaker:biggest issue is that, uh, the pitchers have gotten so good
Speaker:because of the pitch design, the pitch labs, the emphasis on velocity,
Speaker:that there's just not as much contact as there used to be. Too many
Speaker:strikeouts. Right. This has been an issue for 20 years and
Speaker:nobody's really cracked that code yet. I, uh, do think some of
Speaker:the rule changes that have been put in place have worked out well because
Speaker:baseball has long been seen as maybe the old school,
Speaker:sometimes dinosaur of sports, maybe slow to adapt.
Speaker:That's probably a deserved label for a long time and that's changed
Speaker:over the last couple years. The pitch clock, which came in two
Speaker:years ago, which everybody lost their minds about, they can't have a clock
Speaker:in baseball. Well, you can and it worked great.
Speaker:It's been fantastic. The ratings have been up, the fan
Speaker:attendance has been up. So I think that the
Speaker:sport has done a better job now where it didn't
Speaker:previously of going out and doing fan surveys, listening
Speaker:to fans trying to get a handle on what kind of action
Speaker:they like to see. And you can get into some real wild
Speaker:rule changes. Someone wrote the other day we should have smaller gloves
Speaker:for outfielders. Which I thought was pretty funny. The other thing is people
Speaker:hate change. Time you pro propose a rule change, you'll see
Speaker:everybody on social media saying, ah, uh, the game is perfect, don't
Speaker:change it as though the game hasn't changed a
Speaker:hundred times over the last 150 years. So
Speaker:it's that you got to make changes, but you've also got to not make too many
Speaker:changes or people get upset.
Speaker:>> Speaker B: Yeah. And so with the
Speaker:increase of information, it added
Speaker:into the game that
Speaker:hitting home runs, launch angle, getting the ball
Speaker:into the air was going to be more valuable for
Speaker:players then hitting a single, hitting it
Speaker:on the ground, more likely to hit it to a fielder. With
Speaker:that an outcome ended up being you
Speaker:and I would know that what they call the three true outcomes where
Speaker:hitters would tend to focus on I want to hit a home run or
Speaker:I want to walk high, um, on base percentage and if
Speaker:I strike out, I'm not as worried about it because I'm going to get paid for
Speaker:hitting those home runs or getting on base, having a high on
Speaker:base percentage. As teams and
Speaker:players get smarter with the use of data,
Speaker:what I think has been interesting with baseball is then that's
Speaker:where and to your point, it's not just baseball, it's across the world
Speaker:is when you want a change in behavior, you
Speaker:can legislate that. You can change the rules. As
Speaker:the example being larger bases and
Speaker:you can't throw over to first base as much. You limit the
Speaker:number of times a pitcher can throw to first base. Now suddenly stolen
Speaker:bases are up, stolen base success is
Speaker:up. And as it's easier to steal bases, players steal more
Speaker:bases and that becomes a part of the game. And now you've got more action in the
Speaker:game. So you can do things or the shift you
Speaker:can limit. If defenses get so smart with
Speaker:the use of data and where you're placing people
Speaker:that it gets really hard to get a
Speaker:ball, uh, through an infield, then you can
Speaker:legislate and change the rules on those things.
Speaker:You mentioned the NBA, what you're referencing is that ah, the data
Speaker:tells us that the least efficient
Speaker:shot in the NBA is A long two point shot.
Speaker:You basically should never take a long two point shot. You
Speaker:either want to take a three or take a in the
Speaker:paint short shot. Something that has a much higher
Speaker:percentage of success. But that long two is
Speaker:like a stupid shot. If the NBA
Speaker:wants to see changes in the game and more playmaking and
Speaker:not as many guys sitting there popping away from three all
Speaker:the time, they're gonna have to change the rules. You can't ask people
Speaker:to once they understand something and it is smart and
Speaker:efficient to go back and be stupid.
Speaker:>> Mike Petriello: Yeah, I think that's right. At the end of the day, all of these sports are
Speaker:entertainment products. Listen, I am a big hockey fan and I
Speaker:vividly remember when I used to live in Boston, I went to this Bruins wild
Speaker:game in like 2006 and I'm like, this is awful. This is
Speaker:no fun to watch. There's no offense. It's all clutching and
Speaker:grabbing. They changed a lot of the rules and then the offense came back
Speaker:and it's been a lot more fun to watch. Basically everybody wants
Speaker:the game to be like it was when they were 14 years old and just want to like
Speaker:freeze it in amber. You go back to like 19, 20
Speaker:and I'll tell you, the game looks a little bit different. Aside from the fact that it
Speaker:was segregated, the players did not look the same, they did not
Speaker:act the same. Pitchers would pitch nine innings every third day.
Speaker:The sport has always, always, always changed. Night
Speaker:games cross continental flights. So I do think that the sport is going
Speaker:to continue to evolve. And like I said, when
Speaker:people say that baseball didn't evolve, that was a totally fair
Speaker:criticism. I think the last couple years they've really
Speaker:gotten their heads around the fact that the world is changing. The sport needs to change
Speaker:too.
Speaker:>> Speaker B: Yeah, I always think, uh, give it a chance.
Speaker:For instance, the new roles of baseball, I ended up really liking one.
Speaker:Although since you work with Major League baseball in the league office, one
Speaker:change I would make with limiting how many times a
Speaker:pitcher can throw over to first base instead
Speaker:of once they've thrown over twice. The rule is then if you
Speaker:throw a third time, then that runner is entitled to a free
Speaker:base. I was thinking that should be more like a balk.
Speaker:If you've thrown twice instead of rewarding with a
Speaker:free base, seems like such a big penalty
Speaker:versus giving away. Okay, now it's giving the
Speaker:batter a ball.
Speaker:>> Mike Petriello: One thing. You can throw over a third time, but you have to get them.
Speaker:So it's only if you, if you don't get them, if you don't get.
Speaker:>> Speaker B: Them, then they get a free base. So you better be right on
Speaker:that third time or you're giving away a three base. I'm
Speaker:saying the penalty should be if you don't get them that third time.
Speaker:Make it a ball anyway. Make a change.
Speaker:>> Mike Petriello: Make it a ball. Yeah, fair enough. I think that would lessen the
Speaker:penalty and, uh, change behaviors. So I think that'd be an
Speaker:interesting experiment.
Speaker:>> Speaker B: Yeah, that's the only one I would tweak. But otherwise, I love the new
Speaker:rules. So one thing that we're seeing,
Speaker:certainly with prompt in our line of work, but the whole
Speaker:world is, of course, is embracing AI
Speaker:and augmented intelligence. So in terms
Speaker:of baseball, how is Major League
Speaker:Baseball using AI? Whether
Speaker:that is in terms of fan experience
Speaker:or in terms of teams and
Speaker:predictive intelligence. Leveraging
Speaker:data. How is AI a part of the game?
Speaker:>> Mike Petriello: Yeah, I like to think that there's multiple kinds of AI. There's
Speaker:smart AI, which is using technology to
Speaker:consume large data sets and help you get to patterns and
Speaker:answers you wouldn't have, and, uh, obnoxious AI, which
Speaker:is like my mom having to see AI attached to every brand
Speaker:that she's ever seen in commercials, which I find wildly
Speaker:unnecessary. As far as how any
Speaker:baseball or really any company uses AI, it is to try
Speaker:to get to those informed decisions maybe a little bit
Speaker:faster, uh, especially as the size of these data
Speaker:sets, uh, increases, I'm pretty sure. And I can't
Speaker:speak to this in first person because I don't know, but I would be shocked if
Speaker:MLB isn't using ad optimize ticket sales and
Speaker:marketing in some way because that would just make sense as far as on
Speaker:the field stuff goes. I know that some of the pitching
Speaker:labs are using AI to, you know,
Speaker:you think about all of the biomechanical data that comes in
Speaker:when you've got all of these pitchers throwing all these pitches. That's
Speaker:huge data. And that helps you get to what combination of
Speaker:these things leads to more optimal outcomes. And whether
Speaker:you want to think about it as AI or just the
Speaker:Googling that we've been doing for 25 years, I'm not sure it matters that much
Speaker:to most people. You don't see it under the hood. But if that
Speaker:kind of tool can help you get to better answers faster, that's
Speaker:the entire point of any of this, really.
Speaker:>> Speaker B: In terms of looking ahead, what sort of
Speaker:innovations in analytics, in
Speaker:information are you most
Speaker:excited about for the future?
Speaker:>> Mike Petriello: Well, I think the Holy grail, if anybody can figure
Speaker:this out, they will be the Richest person in baseball is how do you
Speaker:keep pitchers healthy? This has been an
Speaker:ongoing issue as pitchers got bigger and
Speaker:stronger and worked on maximizing velocity.
Speaker:It turns out it's really hard to strengthen that little ligament in
Speaker:your elbow. Guys keep getting hurt. It's bad for the game. You
Speaker:want the stars in the field, it's bad for the players. Nobody wants to get hurt.
Speaker:So that is something the entire industry is thinking about
Speaker:how to do in terms of metrics and stuff. We're continuing
Speaker:to push forward because the technology on the field keeps getting better.
Speaker:Up until last year, you could never really tell anything
Speaker:about the way the bat moves. You knew a lot about the pitch, a lot about the
Speaker:ball, but nobody could track the bat because it moves at
Speaker:like 100ft per second. Now the technology got
Speaker:upgraded. All of a sudden that's a thing we can measure. More
Speaker:and more metrics on that are coming out. I, uh, bring that up because it's really
Speaker:interesting. When you start to measure something
Speaker:that you couldn't measure before, it's not just a curiosity,
Speaker:then it becomes something you can quantify and value.
Speaker:And when you can value it, then players start working towards
Speaker:it because teams start paying money for it. For example, the bat
Speaker:speed. I don't think it's revolutionary to say if you swing the
Speaker:bat faster, you'll hit the ball harder. That's something you can see
Speaker:with your eyes back to Babe Ruth's time. But now that you can
Speaker:measure it and say, hey, every extra miles an hour in your
Speaker:bat speed gets you this much distance and this many points of
Speaker:slug. And we value that. Now you got these guys who are coming up from
Speaker:the Miners saying, yeah, I spent my winter not working on my defense,
Speaker:but trying to improve my bat speed. And I think that's what's going to
Speaker:keep happening.
Speaker:>> Speaker B: So bat speed is a really interesting one.
Speaker:And spin rate on pitches, things like that, those are
Speaker:interesting ones. Then there's results outcome based. And
Speaker:there are fans who've been around the game forever and they look at and
Speaker:wanted batting average, home runs,
Speaker:RBIs, counting stats, things like that.
Speaker:When you look at stats, is there
Speaker:one metric that you
Speaker:personally would find the most valuable? If I said to you,
Speaker:I want to compare players or know how good a player
Speaker:is, are you looking at WAR wins above
Speaker:replacement? Are you looking at, if we're talking about position
Speaker:players, not pitchers, are you looking at OPS
Speaker:plus, are you looking at weighted runs created
Speaker:plus? There's so many different good statistics that, uh, are out
Speaker:there. Is there one that you have, that's a favorite.
Speaker:>> Mike Petriello: Those are all different answers to different questions. So if
Speaker:I just want a quick at a glance, who are the most valuable
Speaker:players all in like hitting, defense, running. Yes, wins above
Speaker:replacement. Uh, that's the, the best we have. It's not perfect, but
Speaker:it's really good. A lot of the other stuff you mentioned is very
Speaker:specific just to hitting. So if I want to see who the best hitters are,
Speaker:Parker jocks said yes, I'll go to weighted roads created. Plus
Speaker:if I want to see who, uh, hits the ball the
Speaker:hardest, go look up statcast, go look at hard hit rate. But
Speaker:there's a lot of different ways to answer those questions. It just depends on
Speaker:what you're looking at. But I look at all of them as a starting point
Speaker:and not necessarily an ending point. If I look at the leaders in
Speaker:hard hit rate, I'm probably going to find Aaron Judge and I'm going to find
Speaker:Giancarlo Stanton. I'm going to find guys who hit the ball really,
Speaker:really hard. Doesn't necessarily guarantee I'm
Speaker:finding the best hitters in baseball because Luisa Rice does not
Speaker:hit the ball hard and he always has a very good batting average. So
Speaker:it all comes with a certain amount of contextual knowledge
Speaker:to make any of these numbers useful.
Speaker:>> Speaker B: As we finish a couple last questions here, what
Speaker:advice would you give to marketers looking to
Speaker:adapt a more data driven approach and what
Speaker:can they learn from baseball that would be
Speaker:applicable?
Speaker:>> Mike Petriello: Number one, listen to the fans or your audience or whoever. I
Speaker:don't think baseball has always done that, as I said, and now that's really
Speaker:helped a lot to understand what the audience wants. If
Speaker:you have a sort of complicated and dense data set, make sure
Speaker:you explain it in a way that people can enjoy or
Speaker:understand, even be entertained by. Uh, because if
Speaker:not, everybody's going to tune it out, no matter how valuable it might
Speaker:be.
Speaker:>> Speaker B: And then to close, we like to ask a fun question
Speaker:here. And so if you could pick any
Speaker:player from any sport, past
Speaker:or present, to join you for a Mike
Speaker:Petriello dream dinner
Speaker:party, who would you want to sit there and talk to
Speaker:and why?
Speaker:>> Mike Petriello: I would like to say that I have an extremely deep
Speaker:cut and a really thought out answer, but I'm going to give you one of the
Speaker:most famous people of all time. Uh, but for a good reason. The answer
Speaker:would be Ted Williams, who had a fascinating
Speaker:life, obviously served in two wars, you know, all around
Speaker:amazing life and career. He was
Speaker:maybe the first real baseball nerd. He literally
Speaker:wrote a book on this called the Science of hitting in
Speaker:1971. And he didn't actually say exophilosity and launch
Speaker:angle, but you go read it and he basically did. He
Speaker:drew charts and diagrams to the inch of
Speaker:saying, here's where I'm good when I hit the ball here and there. And it's
Speaker:funny because we'll bring out a lot of the new nerd stuff and people be
Speaker:like, oh, Lou Gehrig, Ted Williams, they'd have hated this stuff. And I'm like,
Speaker:no, no, Ted Williams would have loved this stuff.
Speaker:And I would just love to take them all through it and see what he'd have to say about
Speaker:it.
Speaker:>> Speaker B: We think about old school, new school Ted
Speaker:Williams using all of that information and science
Speaker:of hitting to hit.400, a.400
Speaker:batting average, an old school stat. But he's leveraging data to be
Speaker:able to accomplish something that is a feat that we haven't
Speaker:seen in the sport in a while.
Speaker:>> Mike Petriello: I'd ask him about that. Uh, that'd be great.
Speaker:>> Speaker B: All right, well, invite me. I'd like to sit and listen to you and Ted
Speaker:Williams and lob in a question or two on that. Mike
Speaker:Petriello, really, really appreciate the
Speaker:time. It's been fun talking to you and thinking
Speaker:about some of the ways in which fans and
Speaker:media and others. There's a certain resistance at
Speaker:times to data and analytics. And yet when you wake up
Speaker:and realize over time, whether it's with
Speaker:the hall of Fame inductees and others, how it has
Speaker:become so embraced and so much a part of the game, uh,
Speaker:that information and increased information will only
Speaker:just increase over time. So anyway,
Speaker:really enjoy talking to you. Thanks so much.
Speaker:>> Mike Petriello: Thanks a lot, Laurie.
Speaker:>> Lori Rubinson: Thank you for listening to this episode of the Frictionless Marketing
Speaker:Podcast. For a complete transcript of this
Speaker:conversation or more information on Prompt,
Speaker:please visit us at meetprompt. Co.
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