The Intersection of Sports Analytics and Baseball: A Conversation with Mike Petriello

>> Lori Rubinson: 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 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 sports, especially baseball, addressing common criticisms and explaining how analytics can improve decision making. >> Speaker B: 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, 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 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: Laurie, thanks so much for having me. Looking forward to it. >> Speaker B: 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: 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 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 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: 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, 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: 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. 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: 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, 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: 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, 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: 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 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

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: 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 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 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 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 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: 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: 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 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 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 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 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: 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, 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: 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 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. 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 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 maybe today, in front of the technology, they could have learned it a lot faster. >> Speaker B: 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. 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 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: 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.

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, 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

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 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: 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: 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 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: 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 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

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: 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 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 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 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: 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 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 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: 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 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: 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 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: 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 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 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 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 about. >> Speaker B: 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. 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 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 be right more often than it's wrong. >> Mike Petriello: 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: Blake Snell. >> Mike Petriello: Blake Snell, exactly.

>> Speaker B: I was about to bring it up if you hadn't. It's a classic example. >> Mike Petriello: It's a classic example. >> Speaker B: And for those people who don't know. So, yes, explain what happened. >> Mike Petriello: 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: 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: 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 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 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 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 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: 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, 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: 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 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 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 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 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: 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 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, 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 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 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 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: 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 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 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: 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. 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: 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: 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: 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: 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 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: 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 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

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

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: In terms of looking ahead, what sort of innovations in analytics, in information are you most excited about for the future? >> Mike Petriello: 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 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 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 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 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: 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

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: 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 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 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: 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: 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 be. >> Speaker B: 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: 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 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, 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: 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: I'd ask him about that. Uh, that'd be great. >> Speaker B: 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 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: Thanks a lot, Laurie. >> Lori Rubinson: 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 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.