1 00:00:12,240 --> 00:00:15,160 >> Lori Rubinson: Welcome to Frictionless Marketing, the podcast that dives deep into 2 00:00:15,200 --> 00:00:17,880 the stories of the most innovative brands and the people moving them 3 00:00:17,920 --> 00:00:20,664 forward. Today, 4 00:00:20,752 --> 00:00:23,544 our host, Lori Rubinson, managing director of PROMPT 5 00:00:23,592 --> 00:00:26,552 and WFAN sports talk radio host, sits down 6 00:00:26,576 --> 00:00:29,512 with Mike Petriello. Mike is 7 00:00:29,536 --> 00:00:32,532 the director of Stats and Research at Major League Baseball, where 8 00:00:32,556 --> 00:00:35,412 he's been at the forefront of advancing how analytics are used to 9 00:00:35,436 --> 00:00:37,680 understand, play and enjoy the game. 10 00:00:38,300 --> 00:00:40,692 A pioneer in applying cutting edge technology, 11 00:00:40,876 --> 00:00:43,604 Mike's work has transformed how teams make decisions, 12 00:00:43,732 --> 00:00:46,100 how fans engage with the sport, and how 13 00:00:46,140 --> 00:00:48,040 broadcasters tell compelling stories. 14 00:00:49,660 --> 00:00:52,532 Together, they will dive into the realm of sports analytics as 15 00:00:52,556 --> 00:00:55,492 Laurie and Mike discuss the often misunderstood role of analytics in 16 00:00:55,516 --> 00:00:58,404 sports, especially baseball, addressing common 17 00:00:58,452 --> 00:01:01,396 criticisms and explaining how analytics can improve decision 18 00:01:01,468 --> 00:01:02,080 making. 19 00:01:03,720 --> 00:01:04,580 >> Speaker B: Foreign 20 00:01:13,880 --> 00:01:16,720 welcome to another episode of the 21 00:01:16,760 --> 00:01:19,264 Frictionless Marketing podcast. This is Lori 22 00:01:19,312 --> 00:01:22,048 Rubinson. Really excited today to wear both 23 00:01:22,104 --> 00:01:25,088 hats. Usually I am just managing partner here, 24 00:01:25,100 --> 00:01:28,032 uh, at uh, Prompt, but today I'm also the sports 25 00:01:28,096 --> 00:01:30,902 talk radio host from wfan. I've had a 26 00:01:30,926 --> 00:01:33,910 topic on my mind for a really long time that 27 00:01:33,950 --> 00:01:36,662 bothers me when I talk to listeners, 28 00:01:36,726 --> 00:01:39,574 callers and interact with people on social media and on the 29 00:01:39,582 --> 00:01:42,470 radio. And it is that sometimes analytics in 30 00:01:42,510 --> 00:01:44,998 sports and in baseball in particular, get 31 00:01:45,054 --> 00:01:48,022 vilified as if it's the enemy to all decisions or 32 00:01:48,046 --> 00:01:50,854 things that happen with our teams that we don't like. To get to the 33 00:01:50,862 --> 00:01:53,782 bottom of that and to talk about sports and analytics, I can 34 00:01:53,806 --> 00:01:56,166 think of no better person to talk to than Mike 35 00:01:56,198 --> 00:01:59,152 Petriello. Mike, welcome and look 36 00:01:59,176 --> 00:02:00,400 forward to talking to you. 37 00:02:00,520 --> 00:02:02,800 >> Mike Petriello: Laurie, thanks so much for having me. Looking forward to it. 38 00:02:02,920 --> 00:02:05,632 >> Speaker B: First thing I wanted to get into before we dive into the 39 00:02:05,656 --> 00:02:08,576 integration of analytics into baseball, I wanted 40 00:02:08,608 --> 00:02:11,520 to understand a little bit back up. I think people have seen you 41 00:02:11,560 --> 00:02:14,464 on espn, Statcast, broadcast, the Nerdcast 42 00:02:14,512 --> 00:02:17,344 and all. It's like that. How did you actually 43 00:02:17,432 --> 00:02:19,872 get started in baseball? 44 00:02:19,936 --> 00:02:22,406 Analytics? Sports analytics? How did this come about? 45 00:02:22,528 --> 00:02:25,050 >> Mike Petriello: I, uh, wasn't with my history degree, I can tell you that 46 00:02:25,090 --> 00:02:28,010 much. There's a long version, but I will give you the short 47 00:02:28,050 --> 00:02:31,002 version. It's funny because college kids come up to me all the time and they're like, 48 00:02:31,026 --> 00:02:34,010 hey, how do I get a job in baseball? And nobody wants to hear my 49 00:02:34,050 --> 00:02:36,922 answer of I didn't get my first full time job Till I was 35 50 00:02:36,946 --> 00:02:39,834 years old. Basically what happened was I went to Boston University, 51 00:02:39,922 --> 00:02:42,602 I got a history degree, spent most of my twenties at 52 00:02:42,626 --> 00:02:45,530 startups like a video on demand startup. I actually spent five 53 00:02:45,570 --> 00:02:48,362 years working at a place you probably know well, which is Ketchum the 54 00:02:48,386 --> 00:02:51,232 PR firm as like project manager, building websites and 55 00:02:51,256 --> 00:02:54,224 that kind of stuff. Baseball was always a passion and kind of 56 00:02:54,232 --> 00:02:57,200 a side hustle. Uh, when I was 26, I guess I 57 00:02:57,240 --> 00:03:00,176 started a blog which was the style of 2007 58 00:03:00,328 --> 00:03:03,312 and I just kept at it. That got me opportunities, that got me 59 00:03:03,336 --> 00:03:06,144 opportunities with fan graphs and baseball perspectives 60 00:03:06,192 --> 00:03:09,072 and ESPN and eventually here at mlb where 61 00:03:09,096 --> 00:03:12,000 I've been coming up on nine years full time. Now 62 00:03:12,040 --> 00:03:14,912 that's branched out into tv. It gets more in depth than that, but 63 00:03:14,936 --> 00:03:17,638 that's the short version. Turning, uh, a passion into a career. 64 00:03:17,784 --> 00:03:20,618 >> Speaker B: We'll compare notes at some point because people 65 00:03:20,674 --> 00:03:23,642 say the same to me with the WFAN part of my 66 00:03:23,666 --> 00:03:25,990 business. How do I get into sports media? 67 00:03:26,290 --> 00:03:28,794 And I always say, I'm a history major from Brown 68 00:03:28,842 --> 00:03:31,130 University, don't do what I did. It's too 69 00:03:31,170 --> 00:03:34,042 circuitous. There has to be a different path. So the 70 00:03:34,066 --> 00:03:36,746 second thing, before we understand 71 00:03:36,898 --> 00:03:39,754 sports analytics within the context of baseball, 72 00:03:39,882 --> 00:03:42,410 I wanted to understand how you actually 73 00:03:42,530 --> 00:03:45,418 define analytics, Fans, media 74 00:03:45,474 --> 00:03:48,322 and others. It's a catch all term for a lot 75 00:03:48,346 --> 00:03:50,750 of things. How do you define it? 76 00:03:51,290 --> 00:03:54,002 >> Mike Petriello: I mean, analytics is information. It's the study of 77 00:03:54,026 --> 00:03:56,962 data, it's the study of patterns. Uh, nothing that I just 78 00:03:56,986 --> 00:03:59,922 said is specific to sports or baseball. You could say that across any 79 00:03:59,946 --> 00:04:02,882 industry. Certainly anybody else is using that. So that's what it is. 80 00:04:02,906 --> 00:04:05,762 It's data and patterns and information. If you go 81 00:04:05,786 --> 00:04:08,610 back through all of baseball history, people in the game 82 00:04:08,650 --> 00:04:11,410 have been using information to inform future decisions. 83 00:04:11,490 --> 00:04:14,432 Maybe that information is what they saw, what an intern wrote down on 84 00:04:14,456 --> 00:04:17,296 a pad of paper, their gut, uh, feeling. The only thing that's changed 85 00:04:17,328 --> 00:04:20,208 now is that the information is, it's a lot better and 86 00:04:20,264 --> 00:04:23,152 we know a lot more about the context of it, how to use it, how to 87 00:04:23,176 --> 00:04:26,112 change the game. That's all analytics is, is trying to take all 88 00:04:26,136 --> 00:04:29,136 this information that's out there and use it to make informed decisions 89 00:04:29,168 --> 00:04:29,984 to win games. 90 00:04:30,072 --> 00:04:32,752 >> Speaker B: Yeah, I, uh, always say that to fans when they 91 00:04:32,856 --> 00:04:35,648 do call in and blame 92 00:04:35,744 --> 00:04:38,432 analytics if they don't like the pitching decision 93 00:04:38,536 --> 00:04:41,466 and manager comes, takes a pitcher out of the game, goes 94 00:04:41,498 --> 00:04:44,426 to the bullpen, it goes wrong. And then they say, well, that's 95 00:04:44,458 --> 00:04:47,034 analytics. And my response 96 00:04:47,082 --> 00:04:49,978 is, data doesn't make decisions, people 97 00:04:50,034 --> 00:04:52,714 do. If you're angry at the decision, 98 00:04:52,882 --> 00:04:55,882 you know, analytics is information. That is how I think 99 00:04:55,906 --> 00:04:58,842 about it. And you're right. Look at prompt. That is a big part of our 100 00:04:58,866 --> 00:05:01,802 business is data and information to inform 101 00:05:01,906 --> 00:05:04,682 decision making. So you do work better with better 102 00:05:04,706 --> 00:05:05,270 results. 103 00:05:05,730 --> 00:05:08,610 >> Mike Petriello: I think that's right. What people think fail to understand is 104 00:05:08,650 --> 00:05:11,554 sometimes there's not a right answer. The Yankee manager brings in the 105 00:05:11,562 --> 00:05:14,482 lefty and he gives up a home run. Well, you made the wrong decision. As 106 00:05:14,506 --> 00:05:17,378 though if you bring in the righty, he wouldn't also have given up a hit. Like 107 00:05:17,434 --> 00:05:20,178 the saying goes, the other guys live in big houses too. You can 108 00:05:20,234 --> 00:05:22,930 improve your decision making, you can put it based on better 109 00:05:22,970 --> 00:05:25,570 processes, but it doesn't guarantee anything. The 110 00:05:25,610 --> 00:05:28,514 smartest team in baseball, whoever you define that to be, 111 00:05:28,602 --> 00:05:31,170 does not win 162 games every year. And they make better 112 00:05:31,210 --> 00:05:34,192 decisions, they have better outcomes, but nothing is guaranteed. Because 113 00:05:34,306 --> 00:05:37,188 at least in sports, there's still people, there's still human beings 114 00:05:37,284 --> 00:05:40,084 on the field and you can try to put them in better situations, 115 00:05:40,132 --> 00:05:42,964 but at the end of the day, the, I don't know, 28 year old on the 116 00:05:42,972 --> 00:05:45,652 mound still has to get the job done. And that's never going to be 117 00:05:45,676 --> 00:05:46,280 perfect. 118 00:05:46,860 --> 00:05:49,412 >> Speaker B: A lot of people, I think when they think of baseball and 119 00:05:49,436 --> 00:05:52,340 analytics, they think of the movie, Moneyball, Billy Bean, 120 00:05:52,420 --> 00:05:55,156 and it's about on base percentage and 121 00:05:55,308 --> 00:05:58,052 things like that. And the way I think of that story 122 00:05:58,156 --> 00:06:00,788 is it's about finding ways to 123 00:06:00,844 --> 00:06:03,662 uncover value. An exploit value 124 00:06:03,766 --> 00:06:06,734 that might be an undervalued asset that someone else 125 00:06:06,782 --> 00:06:09,742 isn't seeing. And I think of it and use that term often. We 126 00:06:09,766 --> 00:06:12,702 do influencer marketing here at the agency, want to 127 00:06:12,726 --> 00:06:15,582 uncover influencers who are on their way up. 128 00:06:15,686 --> 00:06:18,686 There's value to tap into 129 00:06:18,758 --> 00:06:21,662 as opposed to someone who's already plateaued. Or you might know them 130 00:06:21,686 --> 00:06:24,558 better, but they're on their way down. Now you're paying for maximum value. 131 00:06:24,614 --> 00:06:27,150 You want to catch an asset on the way up. 132 00:06:27,270 --> 00:06:30,224 With baseball, clearly that was something and that's a 133 00:06:30,232 --> 00:06:33,040 part of Moneyball. But what about the game of baseball? Made 134 00:06:33,080 --> 00:06:35,872 it, in my mind a, uh, pioneer 135 00:06:35,936 --> 00:06:38,784 in using analytics in sports in a way that yes, 136 00:06:38,832 --> 00:06:41,760 basketball does now, yes, football of every other sport does 137 00:06:41,800 --> 00:06:44,720 now. But it feels like baseball was really 138 00:06:44,760 --> 00:06:47,232 a pioneer early on with the use of data and 139 00:06:47,256 --> 00:06:47,872 information. 140 00:06:48,056 --> 00:06:50,800 >> Mike Petriello: That's a great question. There's actually two answers to that. The short 141 00:06:50,840 --> 00:06:53,824 answer is data availability. I think just the way 142 00:06:53,832 --> 00:06:56,736 the game is played. A pitch is a pitch. There's an outcome 143 00:06:56,768 --> 00:06:59,664 on the pitch if it was a curveball, if it was swung at or not. It's a 144 00:06:59,672 --> 00:07:02,384 lot harder in other sports where you have an offensive lineman 145 00:07:02,432 --> 00:07:05,424 surrounded by 10 other guys and 2ft to isolate what he did. 146 00:07:05,512 --> 00:07:08,432 It's difficult in baseball. That data set has been there 147 00:07:08,536 --> 00:07:11,360 and not even just the Current statcast stuff going back 148 00:07:11,400 --> 00:07:14,304 decades. You've got retro sheet data from these hobbyists really, who 149 00:07:14,312 --> 00:07:17,248 have put that information together for people to study. So that's 150 00:07:17,264 --> 00:07:20,224 the big thing. The data is there and the game is played. Even though it's a 151 00:07:20,232 --> 00:07:23,184 team game, it's very much a, uh, one on one game too. Pitcher versus 152 00:07:23,232 --> 00:07:26,000 batter, fielders, et cetera. The other thing, and I didn't know, 153 00:07:26,040 --> 00:07:29,004 or that you were a history major as I am, so I'm not going to 154 00:07:29,012 --> 00:07:31,900 take up too much of your time with this. People think that analytics 155 00:07:31,980 --> 00:07:34,860 started when the movie Moneyball or maybe the book Moneyball 156 00:07:34,940 --> 00:07:37,852 came out. I'll send this to you later. If you want. You can go back to the 157 00:07:37,876 --> 00:07:40,780 early 1900s. There's a famous article in what was called 158 00:07:40,820 --> 00:07:43,724 baseball magazine in 1917, before the end 159 00:07:43,732 --> 00:07:46,492 of World War I, where the author, F.C. lane, was 160 00:07:46,516 --> 00:07:49,516 complaining about batting average and wondering why we used batting 161 00:07:49,548 --> 00:07:52,540 average as though it implies every hit was the same. The analogy he 162 00:07:52,580 --> 00:07:55,564 used was if you ask so much, uh, someone how much money they had 163 00:07:55,572 --> 00:07:58,526 in their pocket, they wouldn't just say, I have six coins. You'd 164 00:07:58,558 --> 00:08:01,022 say, well I want to do it. Dimes, do I have nickels? That's 165 00:08:01,086 --> 00:08:03,710 108 years ago at this point. 166 00:08:03,830 --> 00:08:06,686 And that has gone on for a long time. The only thing that's 167 00:08:06,718 --> 00:08:09,534 accelerated in the last 20, 25 years 168 00:08:09,622 --> 00:08:12,302 would be the Internet bringing these people closer together 169 00:08:12,406 --> 00:08:15,022 and that the availability of data has improved. You can 170 00:08:15,046 --> 00:08:17,774 get a lot more granular information now 171 00:08:17,862 --> 00:08:20,702 obviously than you could have 20 years ago, 40 years 172 00:08:20,726 --> 00:08:23,502 ago. But people have always been thinking about this way 173 00:08:23,526 --> 00:08:25,374 beyond you think they might have. 174 00:08:25,542 --> 00:08:27,934 >> Speaker B: How do you think the use of data 175 00:08:28,022 --> 00:08:31,022 analytics information has changed the 176 00:08:31,046 --> 00:08:33,982 way fans, teams, players engage with sports 177 00:08:34,046 --> 00:08:36,686 or specifically baseball? What ways has 178 00:08:36,758 --> 00:08:39,566 it actually enhanced our 179 00:08:39,638 --> 00:08:41,038 experience with the game? 180 00:08:41,174 --> 00:08:44,090 >> Mike Petriello: So I think you hit on something very important there when you said players, uh, 181 00:08:44,438 --> 00:08:47,374 teams, fans, in whatever order you said it, because, uh, it's a 182 00:08:47,382 --> 00:08:50,116 different answer for different audiences. For sure. 183 00:08:50,238 --> 00:08:53,144 Teams are very much interested not only in choosing 184 00:08:53,192 --> 00:08:56,008 the best players, but having a very good idea of how those players 185 00:08:56,064 --> 00:08:59,000 might perform in uh, the future. To give a Mets example, 186 00:08:59,080 --> 00:09:01,592 Juan Soto just got an enormous contract from the 187 00:09:01,616 --> 00:09:04,552 Mets, at least at the time we're speaking. Pete Alonso has had a very 188 00:09:04,576 --> 00:09:07,320 difficult time finding a contract. It's not that they're not both good 189 00:09:07,360 --> 00:09:10,248 players, it's just that the ages they are, the types of player 190 00:09:10,304 --> 00:09:12,936 profiles they are. You have a lot more confidence in Juan 191 00:09:12,968 --> 00:09:15,928 Soto being very good for 10 years than you do in Pete Alonso 192 00:09:15,944 --> 00:09:18,922 for the next couple of years. Um, for players, and this 193 00:09:18,946 --> 00:09:21,770 has really been interesting over the last couple of years. I think 194 00:09:21,810 --> 00:09:24,746 at first players looked at it from an old school point of view 195 00:09:24,818 --> 00:09:27,754 with a bit of a side eye, saying, I got to the major leagues. Get out of 196 00:09:27,762 --> 00:09:30,314 here, nerd. What can you teach me? Fair enough. 197 00:09:30,482 --> 00:09:33,354 But over the last decade or so, you've had a lot 198 00:09:33,362 --> 00:09:35,994 of players using this information to improve their games, 199 00:09:36,082 --> 00:09:39,050 improve their careers, improve their salaries. Uh, you go back 10 200 00:09:39,090 --> 00:09:41,962 years, and it was J.D. martinez and Justin Turner getting 201 00:09:41,986 --> 00:09:44,560 the ball off the ground. And now you've got pitch design, 202 00:09:44,680 --> 00:09:47,632 which is a whole other conversation. But basically you can 203 00:09:47,656 --> 00:09:50,592 use biomechanical data and say, hey, here's the reason why 204 00:09:50,616 --> 00:09:53,312 your curveball is not very good. We can either help you make it 205 00:09:53,336 --> 00:09:56,224 better, or the way your body moves, it's just never going to be 206 00:09:56,232 --> 00:09:59,088 good. Try a different kind of pitch. And, uh, there are so 207 00:09:59,144 --> 00:10:01,968 many guys across the majors right now who have become better 208 00:10:02,024 --> 00:10:04,592 players just because of that. And it's so funny to me, when I 209 00:10:04,616 --> 00:10:07,552 started, I'd go talk to players and I'm like, I know so much more 210 00:10:07,576 --> 00:10:10,250 about this than you do now. I talk to the pitchers, the young 211 00:10:10,290 --> 00:10:13,194 guys. I barely know what they're talking about. And it's my job to 212 00:10:13,202 --> 00:10:16,170 know this. It is wild to hear these guys talk about it. Every player 213 00:10:16,210 --> 00:10:18,630 is a nerd now, which is kind of fun to think about. 214 00:10:18,930 --> 00:10:21,866 >> Speaker B: Yeah. For people who are, let's 215 00:10:21,898 --> 00:10:24,874 say, not as into baseball or, uh, as you and I might be, 216 00:10:24,962 --> 00:10:27,962 one of the interesting phenomenons are pitching labs. 217 00:10:28,026 --> 00:10:30,618 When you talk about all of the things like pitch, 218 00:10:30,714 --> 00:10:32,874 shape, you're talking about the shape of a 219 00:10:32,882 --> 00:10:35,866 curveball and the spin rate and the things that we're 220 00:10:35,898 --> 00:10:38,646 capturing. But for people who are less familiar, teams 221 00:10:38,678 --> 00:10:41,542 like the Yankees, the Mets just instituted theirs, 222 00:10:41,606 --> 00:10:44,230 and others around the league. Explain what a 223 00:10:44,270 --> 00:10:46,854 pitching lab is and how 224 00:10:46,942 --> 00:10:49,510 that data is captured and how you can 225 00:10:49,550 --> 00:10:52,358 quantify so much data and 226 00:10:52,414 --> 00:10:54,838 synthesize it into something that becomes 227 00:10:55,014 --> 00:10:57,878 actionable and that improves decision making. 228 00:10:58,014 --> 00:11:00,966 >> Mike Petriello: A pretty famous story in baseball. Mariano Rivera, probably 229 00:11:00,998 --> 00:11:03,894 the best relief pitcher who ever lived, came up as a starter in the mid-90s. 230 00:11:03,942 --> 00:11:06,932 He was okay, and he was just screwing around in the 231 00:11:06,956 --> 00:11:09,924 outfield one day and he threw a pitch, uh, that he'd never thrown before, and 232 00:11:09,932 --> 00:11:12,836 it became his cutter. One of the probably five most dominant 233 00:11:12,868 --> 00:11:15,812 pitches anyone's ever thrown. He turned that into a Hall of Fame career. And that was 234 00:11:15,836 --> 00:11:18,772 an accident. What you can do now with the pitching labs is you 235 00:11:18,796 --> 00:11:21,764 go in there in Front of all the high speed cameras and force plates and 236 00:11:21,772 --> 00:11:24,660 all sorts of crazy stuff they got. And you go through 237 00:11:24,700 --> 00:11:27,476 your pitch and they'll say, okay, we see how your body moves. You're 238 00:11:27,508 --> 00:11:30,308 maybe you're a pronator, maybe you're a supinator, which means 239 00:11:30,364 --> 00:11:32,900 what direction does your arm move as you throw it? Uh, 240 00:11:32,900 --> 00:11:35,892 you're in a good position to throw. Let's say 241 00:11:35,916 --> 00:11:38,740 your body type says a splitter, a split finger fastball. 242 00:11:38,820 --> 00:11:41,780 That'll work for you or it won't. And we 243 00:11:41,820 --> 00:11:44,276 can reduce the accidents of. Oh, hey. My 244 00:11:44,348 --> 00:11:47,252 sister's uncle's groundskeeper from high school told me how to 245 00:11:47,276 --> 00:11:50,180 throw this grip and it worked out for me and actually get there 246 00:11:50,220 --> 00:11:53,220 pretty quick and say, okay, we're going to have feedback on whether 247 00:11:53,260 --> 00:11:56,132 this pitch works. Not in months, not after the batters kill 248 00:11:56,156 --> 00:11:59,044 it, but in about 20 minutes because you're going to throw 10 of 249 00:11:59,052 --> 00:12:01,966 them and we're going to see what the numbers say on it and say, oh, the movement 250 00:12:01,998 --> 00:12:04,782 on that, that's really good. Let's work on this. That's what it is. It 251 00:12:04,806 --> 00:12:07,582 helps them not only learn what they can and can't do, 252 00:12:07,686 --> 00:12:10,302 but get there a lot faster. If you go back through all of 253 00:12:10,326 --> 00:12:13,262 history, there's probably a lot of pitchers who 254 00:12:13,286 --> 00:12:16,142 had the talent to be great and you never heard of them because they 255 00:12:16,166 --> 00:12:19,102 never, dumb luck, stumbled upon that right pitch. And 256 00:12:19,126 --> 00:12:21,710 maybe today, in front of the technology, they could have learned it a lot 257 00:12:21,750 --> 00:12:22,370 faster. 258 00:12:22,710 --> 00:12:25,582 >> Speaker B: You know, with all the use of data, your job 259 00:12:25,606 --> 00:12:28,542 and the job of broadcasters today is to 260 00:12:28,566 --> 00:12:30,170 figure out how to 261 00:12:31,020 --> 00:12:33,652 synthesize data and 262 00:12:33,676 --> 00:12:36,452 analytics into storytelling and to make it 263 00:12:36,476 --> 00:12:39,364 interesting to people. I joked before about the nerdcast 264 00:12:39,412 --> 00:12:42,292 I always love. This is ESPN for a number of years would 265 00:12:42,316 --> 00:12:45,124 do a specific statcast alternate broadcast. 266 00:12:45,172 --> 00:12:47,892 People are familiar with the Manning cast for NFL Monday Night 267 00:12:47,916 --> 00:12:50,228 Football. This was a statistically 268 00:12:50,324 --> 00:12:53,140 oriented alternate broadcast, which I 269 00:12:53,260 --> 00:12:56,100 always loved. And a lot of people would never always trend on 270 00:12:56,140 --> 00:12:59,140 Twitter and everybody be hashtagging, statcast, nerdcast, all this. 271 00:12:59,180 --> 00:13:02,062 Mike, you were one of the broadcasters who 272 00:13:02,086 --> 00:13:04,670 were analyzing, commentating, bringing that to 273 00:13:04,710 --> 00:13:06,570 people. Now I think 274 00:13:07,430 --> 00:13:10,222 data and analytics have become 275 00:13:10,326 --> 00:13:13,278 more a part of mainstream broadcasts. So how 276 00:13:13,334 --> 00:13:15,870 do you think about in your job with Major League 277 00:13:15,910 --> 00:13:18,910 Baseball and when you talk to all the different networks where the games 278 00:13:18,950 --> 00:13:21,838 are broadcast and the different outlets, how 279 00:13:21,894 --> 00:13:24,526 do you incorporate data and storytelling? 280 00:13:24,718 --> 00:13:27,662 >> Mike Petriello: I joke a lot about having the history degree in this job, but what is 281 00:13:27,686 --> 00:13:30,622 a history degree? It's explaining why did this country invade that 282 00:13:30,646 --> 00:13:33,450 country, explaining why these important certain events in history happened. 283 00:13:33,530 --> 00:13:36,250 And that's how I approach this too. You need to be able to 284 00:13:36,290 --> 00:13:39,290 explain these things because teams and players are 285 00:13:39,330 --> 00:13:42,330 making decisions based upon them. Um, like before the shift was banned, 286 00:13:42,410 --> 00:13:45,290 you needed to explain why the third baseman was standing in right field. 287 00:13:45,330 --> 00:13:48,314 Because it's a real weird thing. Why would a team trade a 288 00:13:48,322 --> 00:13:51,322 guy with a.280 batting average for a guy with a.240 batting average? 289 00:13:51,386 --> 00:13:54,090 There's reasons, but you need to be able to explain it. 290 00:13:54,210 --> 00:13:57,146 So, uh, that's what we do. And I would say it's gotten simultaneously 291 00:13:57,178 --> 00:14:00,042 easier and harder easier because you don't have to 292 00:14:00,066 --> 00:14:02,858 sell anybody on the utility of it anymore. Years ago 293 00:14:02,914 --> 00:14:05,834 it was, I don't need this stuff. This isn't interesting. And now it's, 294 00:14:05,882 --> 00:14:08,842 yeah, we know that teams, players are using this. We need to be able to 295 00:14:08,866 --> 00:14:11,866 explain this. But the harder part is now the details have gotten 296 00:14:11,898 --> 00:14:14,602 so complicated. You try not to have everything turn into an 297 00:14:14,626 --> 00:14:17,402 algebra class because the number one takeaway, and if we 298 00:14:17,426 --> 00:14:20,426 proved anything on that show, which we still did a few of them last year, 299 00:14:20,498 --> 00:14:23,290 hopefully might do some this year as well. You can still have 300 00:14:23,330 --> 00:14:26,004 fun talking about nerd stuff. You don't 301 00:14:26,132 --> 00:14:29,044 have to go in and explain the launch angle on every single 302 00:14:29,092 --> 00:14:32,052 batted ball or the spin rate on every single pitch, because I 303 00:14:32,076 --> 00:14:35,012 can tell you, even I don't want to know that. But if you can 304 00:14:35,036 --> 00:14:38,004 put stuff into context, this was like, hey, the hardest hit ball of 305 00:14:38,012 --> 00:14:40,756 the year. That's cool. Because so much of it's just baseball. 306 00:14:40,868 --> 00:14:43,684 You couldn't before 2015 or 2020, depending 307 00:14:43,732 --> 00:14:46,628 on which metric. Say, who had the strongest outfield 308 00:14:46,644 --> 00:14:49,604 throwing arm, who was the fastest runner. That's baseball 309 00:14:49,652 --> 00:14:52,356 stuff. That's the stuff people have been arguing about in bars forever. 310 00:14:52,468 --> 00:14:54,910 To some extent, that's just putting numbers behind what you've already 311 00:14:54,950 --> 00:14:55,530 seen. 312 00:14:56,150 --> 00:14:58,814 >> Speaker B: How has the rise in 313 00:14:58,902 --> 00:15:01,646 sports betting and gambling changed how 314 00:15:01,718 --> 00:15:04,558 analytics are incorporated into the fan experience, 315 00:15:04,694 --> 00:15:07,662 media coverage, and how much it is accepted as 316 00:15:07,686 --> 00:15:08,638 a part of the game? 317 00:15:08,774 --> 00:15:11,566 >> Mike Petriello: That's an interesting question. I try my best to avoid 318 00:15:11,598 --> 00:15:14,190 sports betting as much as I possibly can. I work 319 00:15:14,230 --> 00:15:17,054 for mlb, um, so I'm not allowed to bet on baseball, 320 00:15:17,102 --> 00:15:20,062 obviously. So I try not to pay attention to it too 321 00:15:20,086 --> 00:15:22,780 much because I just can't have anything to do with it. But I think 322 00:15:22,820 --> 00:15:25,340 fantasy baseball has been a thing for many, many years. 323 00:15:25,460 --> 00:15:28,348 Certainly those people who want to win their leagues are looking at numbers 324 00:15:28,364 --> 00:15:31,196 and data to inform their own decisions. I would imagine 325 00:15:31,228 --> 00:15:33,820 that the people who are putting money in the games are doing much the Same 326 00:15:33,860 --> 00:15:36,764 thing. But we're not, at least I'm not directly involved in 327 00:15:36,772 --> 00:15:38,040 that world at all. 328 00:15:38,580 --> 00:15:41,548 >> Speaker B: You mentioned Pete Alonso as an example of a player 329 00:15:41,604 --> 00:15:44,412 that I would agree, uh, has been hurt by analytics. 330 00:15:44,476 --> 00:15:47,436 The way we perceive things, somebody who has 331 00:15:47,508 --> 00:15:50,268 been known, he may have hit 33 home runs 332 00:15:50,364 --> 00:15:53,068 last year, but generally is good for 35 333 00:15:53,124 --> 00:15:55,880 plus 40 home runs season. He would have 334 00:15:55,920 --> 00:15:58,856 been someone who would have gotten a big contract in seasons 335 00:15:58,888 --> 00:16:01,816 past and now here he is sweating it out to try and get somebody 336 00:16:01,848 --> 00:16:04,420 to sign him based on position 337 00:16:04,960 --> 00:16:07,032 and on base percentage 338 00:16:07,096 --> 00:16:09,976 declining and base running and 339 00:16:10,048 --> 00:16:12,420 defensive metrics. And 340 00:16:12,800 --> 00:16:15,800 just there are a number of things where age, where 341 00:16:15,840 --> 00:16:18,680 people are saying, okay, yes, you hit a lot of home runs 342 00:16:18,760 --> 00:16:21,752 10 years ago, he would have been 15 years ago, snapped up 343 00:16:21,776 --> 00:16:24,744 and not today. The question though is, so if 344 00:16:24,752 --> 00:16:27,664 he's one who struggled, who's an example of somebody that 345 00:16:27,672 --> 00:16:30,048 you would say was an early analytics darling? 346 00:16:30,224 --> 00:16:33,152 >> Mike Petriello: I think going back a number of years, Joey Votto, I 347 00:16:33,176 --> 00:16:36,160 think is the first name that comes to mind. And it's not that he didn't hit 30 348 00:16:36,200 --> 00:16:39,200 home runs and 100 RBIs, he did, but he got on base 349 00:16:39,280 --> 00:16:42,272 a lot and that is such a valuable thing. He ended up 350 00:16:42,296 --> 00:16:45,232 with a huge contract, uh, even though he was like Alonso in the 351 00:16:45,256 --> 00:16:48,144 sense that he is a sort of slow footed first baseman, a 352 00:16:48,152 --> 00:16:50,816 better defender, sure. But he ended up getting a pretty 353 00:16:50,848 --> 00:16:53,704 massive contract in the hundreds of millions of dollars. 354 00:16:53,872 --> 00:16:56,776 And I don't think he would have gotten that 20 years earlier 355 00:16:56,848 --> 00:16:59,656 because he wasn't the prototypical hairy chested 356 00:16:59,688 --> 00:17:02,552 slugger that first baseman were back in the day. 357 00:17:02,656 --> 00:17:05,288 So I would agree with you that Pete Alonso 358 00:17:05,384 --> 00:17:08,328 probably doesn't get the contract he wants because of what we've learned 359 00:17:08,344 --> 00:17:11,256 about aging curves and all this. To give you another example, Luke 360 00:17:11,288 --> 00:17:14,248 Weaver, who is a pitcher, he's not a great example because he didn't 361 00:17:14,264 --> 00:17:16,696 sign a big deal. But there are guys like that, terribly 362 00:17:16,728 --> 00:17:19,272 unsuccessful for like seven years. Comes to the 363 00:17:19,296 --> 00:17:22,264 Yankees, they change his grips. All of a sudden he's awesome. He's like one 364 00:17:22,272 --> 00:17:25,072 of the 10 best relievers in baseball right now. If he was a free agent this year, 365 00:17:25,136 --> 00:17:28,016 he'd have gotten a huge contract based not on his career 366 00:17:28,048 --> 00:17:30,944 to date, but based on what they think he'll do going forward. So 367 00:17:30,952 --> 00:17:33,952 it's not that the analytics is taking money away, the players are 368 00:17:33,976 --> 00:17:36,976 just distributing it in a different way based less on 369 00:17:37,048 --> 00:17:39,888 what you have done so far and more on educated guesses 370 00:17:39,904 --> 00:17:41,456 about what you might do going forward. 371 00:17:41,608 --> 00:17:43,940 >> Speaker B: Yeah, and I think it's Interesting with fans, 372 00:17:44,600 --> 00:17:47,504 they want their teams to sign big names 373 00:17:47,552 --> 00:17:50,400 to some extent based on. It's like the stock market based on 374 00:17:50,440 --> 00:17:53,410 past performance. But when you 375 00:17:53,450 --> 00:17:56,338 sign a guy for a long term contract that 376 00:17:56,394 --> 00:17:59,314 doesn't profile well with predictive analytics, that this is 377 00:17:59,322 --> 00:18:02,082 going to go well over time and then it does not go well 378 00:18:02,186 --> 00:18:04,850 and now that team is stuck with that contract for 379 00:18:04,890 --> 00:18:07,794 years, then fans are super, you know, uh, I think of 380 00:18:07,802 --> 00:18:10,418 Chris Davis with the Baltimore Orioles as an example at first 381 00:18:10,474 --> 00:18:13,186 base. Then fans are super frustrated that 382 00:18:13,258 --> 00:18:16,162 we're still paying this guy's salary and he fell off a cliff. 383 00:18:16,226 --> 00:18:18,936 And it was like, well, the data analyst did tell you that might happen. 384 00:18:19,058 --> 00:18:21,948 People didn't want to listen. We're talking about some of the ways in which 385 00:18:22,004 --> 00:18:24,988 analytics are a, uh, positive. They are just a part of today's 386 00:18:25,004 --> 00:18:26,908 game. They've been a part, as you point out, since 387 00:18:26,964 --> 00:18:29,708 1917, more and more prevalent over 388 00:18:29,764 --> 00:18:32,508 time. Why is it you think I get 389 00:18:32,564 --> 00:18:35,468 callers who want to blame the data and 390 00:18:35,524 --> 00:18:37,772 analytics for 391 00:18:37,956 --> 00:18:40,828 decisions in baseball that don't go well? 392 00:18:40,884 --> 00:18:42,876 Why does analytics get vilified? 393 00:18:43,068 --> 00:18:45,996 >> Mike Petriello: We could talk about the difference in what the data 394 00:18:46,068 --> 00:18:48,908 says and what someone's gut says, and then I'm not sure we're 395 00:18:48,924 --> 00:18:51,720 talking about sports anymore because that's how happened in a lot of different 396 00:18:51,760 --> 00:18:54,472 places around the world. But if something doesn't go 397 00:18:54,496 --> 00:18:57,384 right, you want to blame something, right? Well, I wouldn't have put 398 00:18:57,392 --> 00:19:00,392 that guy in and the nerd number said to put them in and it didn't work. 399 00:19:00,496 --> 00:19:03,272 So I blame the nerd numbers. That's basically what it comes down to. If your 400 00:19:03,296 --> 00:19:06,232 team lost, you want to put it on somebody and it's easy to put it 401 00:19:06,256 --> 00:19:09,144 on the player. Sure. But if there's a decision that was made 402 00:19:09,232 --> 00:19:12,040 based on numbers that you don't feel comfortable with or 403 00:19:12,080 --> 00:19:15,080 familiar with or don't agree with, I think that's the number one place to look. 404 00:19:15,120 --> 00:19:18,114 Even though, like I said before, it doesn't mean the other thing would have worked. 405 00:19:18,162 --> 00:19:21,106 You just didn't see it fail. And we're really bad about thinking 406 00:19:21,138 --> 00:19:22,546 about that as humans. 407 00:19:22,738 --> 00:19:24,190 >> Speaker B: What would you say 408 00:19:24,970 --> 00:19:27,570 to people who would argue, 409 00:19:27,730 --> 00:19:30,690 even if we're looking at data and analytics to make a decision 410 00:19:30,810 --> 00:19:33,762 on should a pitcher come in and out of a game or what should we do 411 00:19:33,786 --> 00:19:36,722 here? But we're looking at something that is a 412 00:19:36,746 --> 00:19:39,554 relatively small sample size and for fans 413 00:19:39,602 --> 00:19:42,450 who say, okay, so this guy's going by the book, this 414 00:19:42,490 --> 00:19:45,346 one. Lefty, lefty. Or here's how this guy, he's a reverse 415 00:19:45,378 --> 00:19:48,310 Split, he does better against righties or lefties or whatever that is. 416 00:19:48,410 --> 00:19:51,198 And they're looking at the data. But we might be talking about 417 00:19:51,254 --> 00:19:54,158 something that's this matchup has happened four times 418 00:19:54,214 --> 00:19:56,734 or ten times. At what point is the sample 419 00:19:56,782 --> 00:19:59,406 size statistically significant enough 420 00:19:59,558 --> 00:20:02,430 that you should be staying strictly with 421 00:20:02,470 --> 00:20:04,350 the data versus what your eyes are telling you? 422 00:20:04,470 --> 00:20:07,190 >> Mike Petriello: Yeah, that's funny. That actually also touches on, 423 00:20:07,190 --> 00:20:10,062 uh, something I should have brought up before. We don't, as fans in 424 00:20:10,086 --> 00:20:12,862 the public, have the same information that the team does, that the 425 00:20:12,886 --> 00:20:15,662 players do, that the managers do. So when you say lefty on 426 00:20:15,686 --> 00:20:18,590 lefty, lefty batter and lefty pitcher, that it for 427 00:20:18,630 --> 00:20:21,534 years was probably the decision that was made. Now it's, we know the swing 428 00:20:21,582 --> 00:20:24,462 path of this and we know the angle the pitch comes in. And now we're making 429 00:20:24,486 --> 00:20:27,246 decisions based on that. As far as sample size goes, 430 00:20:27,358 --> 00:20:29,998 it's a really important question. And it's very different 431 00:20:30,134 --> 00:20:32,974 based on what metric you're looking at. For example, 432 00:20:33,022 --> 00:20:35,982 let's talk about fastball velocity. I don't need to see but two 433 00:20:36,006 --> 00:20:38,958 pitches to know that a guy throws harder doesn't. I don't need to see hundreds 434 00:20:38,974 --> 00:20:41,948 of pitches to figure that out. But for something 435 00:20:42,004 --> 00:20:44,972 like, uh, batting average, you need like hundreds of 436 00:20:44,996 --> 00:20:47,932 plate appearances to feel confident that a guy really 437 00:20:47,956 --> 00:20:50,860 is a.300 hitter. And the problem with that is by the time you get 438 00:20:50,900 --> 00:20:53,580 to hundreds of plate appearances, now we're talking like two 439 00:20:53,620 --> 00:20:56,620 seasons maybe. Well, the beginning of those plate appearances were from 440 00:20:56,660 --> 00:20:59,196 a, uh, younger guy who they may not be as valuable 441 00:20:59,228 --> 00:21:01,884 anymore. So for the skills 442 00:21:01,932 --> 00:21:04,700 stuff, you can get to that really fast. I know a guy's fast 443 00:21:04,780 --> 00:21:07,764 real fast. I know you throw hard really fast. Some 444 00:21:07,772 --> 00:21:10,708 of the stuff like your exit velocity, maybe it's 50 445 00:21:10,764 --> 00:21:13,700 or so batted balls. So that could take a couple of weeks. 446 00:21:13,820 --> 00:21:16,676 It's a really, really important question because you wouldn't 447 00:21:16,708 --> 00:21:19,604 want to say that a guy's a.500 hitter because he got one hit in his 448 00:21:19,612 --> 00:21:22,564 first two plate appearances. That's totally meaningless. But I 449 00:21:22,572 --> 00:21:25,556 would believe a 99 mile an hour fastball in his first two pitches. 450 00:21:25,588 --> 00:21:28,244 It's very case by case, depending on what metric you're talking 451 00:21:28,292 --> 00:21:28,880 about. 452 00:21:29,420 --> 00:21:32,244 >> Speaker B: What do you think about today 453 00:21:32,412 --> 00:21:35,312 as a, the same day today as 454 00:21:35,336 --> 00:21:38,128 a data point? Now we have the analytics that say 455 00:21:38,184 --> 00:21:41,040 that a particular pitcher. So something common for people 456 00:21:41,080 --> 00:21:43,232 who don't follow as much would be 457 00:21:43,416 --> 00:21:46,416 conventionalism says that oftentimes 458 00:21:46,448 --> 00:21:49,088 with pitchers, if you're leaving them in to go the third 459 00:21:49,144 --> 00:21:52,016 time through the order, they're not going to do as well. 460 00:21:52,088 --> 00:21:54,912 And hitters are going to be more effective at a particular pitcher 461 00:21:54,976 --> 00:21:57,712 third time through the order. So teams are quite cautious 462 00:21:57,856 --> 00:22:00,616 about leaving most starting pitchers 463 00:22:00,648 --> 00:22:03,220 in beyond two times through the order. 464 00:22:03,680 --> 00:22:06,180 Is today a, uh, data point where 465 00:22:06,880 --> 00:22:09,688 you're looking at a guy and his stuff 466 00:22:09,784 --> 00:22:12,760 just looks electric today? And here we 467 00:22:12,800 --> 00:22:15,640 are getting through the second time in the order, and he 468 00:22:15,680 --> 00:22:18,040 looks amazing. Should a manager be 469 00:22:18,080 --> 00:22:20,920 trusting? Okay, that's what my eyes are telling me 470 00:22:20,960 --> 00:22:23,672 versus going to my bullpen, you know, and even factors like my 471 00:22:23,696 --> 00:22:26,372 bullpen's a little bit spent, or does the 472 00:22:26,396 --> 00:22:29,108 data only work and the information only work 473 00:22:29,244 --> 00:22:32,228 if I follow it religiously time after 474 00:22:32,284 --> 00:22:35,172 time after time? Because over the long haul, it will 475 00:22:35,196 --> 00:22:37,092 be right more often than it's wrong. 476 00:22:37,236 --> 00:22:40,052 >> Mike Petriello: Yeah, I think the. The word in baseball there is dealing. Oh, the 477 00:22:40,076 --> 00:22:42,692 pitcher was dealing. How did you take him out? And of 478 00:22:42,716 --> 00:22:45,700 course, every pitcher is dealing right up until the moment he's not 479 00:22:45,740 --> 00:22:48,644 dealing. A pretty famous example of that over the 480 00:22:48,652 --> 00:22:50,756 last couple years was in the 2020 World Series. 481 00:22:50,828 --> 00:22:51,758 >> Speaker B: Blake Snell. 482 00:22:51,884 --> 00:22:53,098 >> Mike Petriello: Blake Snell, exactly. 483 00:22:53,194 --> 00:22:56,058 >> Speaker B: I was about to bring it up if you hadn't. It's a classic example. 484 00:22:56,154 --> 00:22:57,274 >> Mike Petriello: It's a classic example. 485 00:22:57,402 --> 00:22:59,914 >> Speaker B: And for those people who don't know. So, yes, explain what happened. 486 00:22:59,962 --> 00:23:02,810 >> Mike Petriello: Blake Snow, uh, I don't remember the exact score or whatever, but 487 00:23:02,850 --> 00:23:05,834 Dodgers raise. He was on the raise at the time, pitching great. 488 00:23:05,922 --> 00:23:08,122 Just like mowing the Dodgers down left and right. 489 00:23:08,226 --> 00:23:11,130 >> Speaker B: Under underdog Tampa Bay. Underdog Rays 490 00:23:11,210 --> 00:23:14,202 versus the mighty Dodgers. We should say that. And this 491 00:23:14,226 --> 00:23:17,146 is their kind of an ace, like, pitcher mowing guys 492 00:23:17,178 --> 00:23:18,294 down. Keep going. 493 00:23:18,482 --> 00:23:21,438 >> Mike Petriello: Yeah. And right. He's pitching great. He's pitching against the Dodgers. They're 494 00:23:21,454 --> 00:23:24,430 winning the game. And there wasn't anything super obvious 495 00:23:24,510 --> 00:23:27,454 in terms of his pitch metrics. That's the first thing you look for, is the 496 00:23:27,462 --> 00:23:30,350 velocity starting to drop, is the movement starting to fade. Those 497 00:23:30,390 --> 00:23:33,342 are signs of fatigue. I don't think there was anything serious like that. 498 00:23:33,446 --> 00:23:36,062 And because the Rays had a very serious 499 00:23:36,126 --> 00:23:39,086 adherence to their model and their method, their manager 500 00:23:39,118 --> 00:23:41,854 came and took him out despite the fact that he was dealing. 501 00:23:41,982 --> 00:23:44,974 And the reliever came in and blew the game. And they lost the World Series. It's like 502 00:23:44,982 --> 00:23:47,982 one of the most famous moments of the last couple years. I remember watching this and 503 00:23:48,006 --> 00:23:50,952 thinking, I wouldn't have taken him out then. But the 504 00:23:50,976 --> 00:23:53,928 biggest problem is I thought they brought in the wrong reliever. That guy, 505 00:23:54,024 --> 00:23:56,936 to your point, Nick Anderson was spent at that point. 506 00:23:57,088 --> 00:23:59,944 But the point here is I remember someone and I 507 00:23:59,952 --> 00:24:02,904 can't remember his name. It was Connor. Somebody did a bit of a 508 00:24:02,912 --> 00:24:05,592 study on this. He went back and he found all of 509 00:24:05,616 --> 00:24:08,504 the starts that were similar in innings 510 00:24:08,552 --> 00:24:11,288 pitched, uh, out Scott and 0 earned runs. Whatever 511 00:24:11,304 --> 00:24:14,184 Snell had done that day, he found very similar starts. These are obviously 512 00:24:14,232 --> 00:24:17,132 extremely good starts by extremely good pitchers. 513 00:24:17,276 --> 00:24:20,060 And he looked okay. What did those guys do after 514 00:24:20,100 --> 00:24:23,036 that, the ones who were left in the game and the outcomes 515 00:24:23,068 --> 00:24:25,900 were bad. It was like an average ERA of, I don't know, four 516 00:24:25,940 --> 00:24:28,796 and a half or whatever. It's not going to work every time. 517 00:24:28,868 --> 00:24:31,804 Nothing's going to work every time. I wouldn't have taken him out right 518 00:24:31,812 --> 00:24:34,476 then and there, but I probably wouldn't have waited very much longer 519 00:24:34,508 --> 00:24:37,404 either dealing or not, because the numbers 520 00:24:37,452 --> 00:24:40,332 were pretty clear. If you leave him in, it's not going to end 521 00:24:40,356 --> 00:24:42,892 well. You're sort of pushing your luck until that 522 00:24:42,916 --> 00:24:45,840 happens. But that's not going to make any Tampa Bay fan feel 523 00:24:45,880 --> 00:24:48,592 better. All they're going to remember is they lost the World Series. 524 00:24:48,776 --> 00:24:51,580 >> Speaker B: Mike, one of the things you, me, 525 00:24:52,120 --> 00:24:53,700 we may enjoy, 526 00:24:54,680 --> 00:24:56,780 statistics and 527 00:24:57,400 --> 00:25:00,368 how analytics is making the game 528 00:25:00,424 --> 00:25:03,312 and teams smarter. That's something that I find fun, that 529 00:25:03,336 --> 00:25:05,744 I enjoy. I think there are, there's a 530 00:25:05,752 --> 00:25:08,660 conventionalism and even you working for Major League Baseball, 531 00:25:09,250 --> 00:25:11,962 Major League Baseball has implemented some rule 532 00:25:12,026 --> 00:25:14,810 changes to try and do things to 533 00:25:14,930 --> 00:25:17,920 speed up the game, add action to the game. Are, 534 00:25:17,920 --> 00:25:20,634 uh, there ways in which making all the teams 535 00:25:20,682 --> 00:25:23,322 smarter, leveling that playing field when 536 00:25:23,346 --> 00:25:25,866 everybody has data has taken 537 00:25:25,938 --> 00:25:28,842 away action or had a negative impact 538 00:25:28,946 --> 00:25:29,962 on the game? 539 00:25:30,146 --> 00:25:33,002 >> Mike Petriello: I think the first thing I would say is that's not a baseball specific 540 00:25:33,066 --> 00:25:36,042 issue. I'm not the world's biggest basketball fan, but I do hear the 541 00:25:36,066 --> 00:25:39,064 complaining about three pointers like all the time. So 542 00:25:39,072 --> 00:25:41,832 this is happening across a lot of sports. Uh, in baseball, I think the 543 00:25:41,856 --> 00:25:44,792 biggest issue is that, uh, the pitchers have gotten so good 544 00:25:44,896 --> 00:25:47,864 because of the pitch design, the pitch labs, the emphasis on velocity, 545 00:25:47,912 --> 00:25:50,552 that there's just not as much contact as there used to be. Too many 546 00:25:50,576 --> 00:25:53,352 strikeouts. Right. This has been an issue for 20 years and 547 00:25:53,376 --> 00:25:56,344 nobody's really cracked that code yet. I, uh, do think some of 548 00:25:56,352 --> 00:25:59,144 the rule changes that have been put in place have worked out well because 549 00:25:59,232 --> 00:26:02,152 baseball has long been seen as maybe the old school, 550 00:26:02,256 --> 00:26:05,032 sometimes dinosaur of sports, maybe slow to adapt. 551 00:26:05,096 --> 00:26:08,088 That's probably a deserved label for a long time and that's changed 552 00:26:08,104 --> 00:26:11,092 over the last couple years. The pitch clock, which came in two 553 00:26:11,116 --> 00:26:14,068 years ago, which everybody lost their minds about, they can't have a clock 554 00:26:14,084 --> 00:26:16,900 in baseball. Well, you can and it worked great. 555 00:26:17,020 --> 00:26:19,956 It's been fantastic. The ratings have been up, the fan 556 00:26:19,988 --> 00:26:22,900 attendance has been up. So I think that the 557 00:26:22,940 --> 00:26:25,492 sport has done a better job now where it didn't 558 00:26:25,556 --> 00:26:28,548 previously of going out and doing fan surveys, listening 559 00:26:28,564 --> 00:26:31,412 to fans trying to get a handle on what kind of action 560 00:26:31,516 --> 00:26:34,324 they like to see. And you can get into some real wild 561 00:26:34,372 --> 00:26:37,172 rule changes. Someone wrote the other day we should have smaller gloves 562 00:26:37,236 --> 00:26:40,180 for outfielders. Which I thought was pretty funny. The other thing is people 563 00:26:40,220 --> 00:26:43,152 hate change. Time you pro propose a rule change, you'll see 564 00:26:43,176 --> 00:26:45,952 everybody on social media saying, ah, uh, the game is perfect, don't 565 00:26:46,016 --> 00:26:48,704 change it as though the game hasn't changed a 566 00:26:48,712 --> 00:26:51,664 hundred times over the last 150 years. So 567 00:26:51,672 --> 00:26:54,560 it's that you got to make changes, but you've also got to not make too many 568 00:26:54,600 --> 00:26:56,064 changes or people get upset. 569 00:26:56,192 --> 00:26:58,880 >> Speaker B: Yeah. And so with the 570 00:26:59,000 --> 00:27:01,968 increase of information, it added 571 00:27:02,024 --> 00:27:03,820 into the game that 572 00:27:04,760 --> 00:27:07,568 hitting home runs, launch angle, getting the ball 573 00:27:07,624 --> 00:27:10,160 into the air was going to be more valuable for 574 00:27:10,200 --> 00:27:13,152 players then hitting a single, hitting it 575 00:27:13,176 --> 00:27:15,968 on the ground, more likely to hit it to a fielder. With 576 00:27:16,024 --> 00:27:18,992 that an outcome ended up being you 577 00:27:19,016 --> 00:27:21,760 and I would know that what they call the three true outcomes where 578 00:27:21,800 --> 00:27:24,672 hitters would tend to focus on I want to hit a home run or 579 00:27:24,696 --> 00:27:27,632 I want to walk high, um, on base percentage and if 580 00:27:27,656 --> 00:27:30,480 I strike out, I'm not as worried about it because I'm going to get paid for 581 00:27:30,520 --> 00:27:33,472 hitting those home runs or getting on base, having a high on 582 00:27:33,496 --> 00:27:36,448 base percentage. As teams and 583 00:27:36,504 --> 00:27:39,144 players get smarter with the use of data, 584 00:27:39,272 --> 00:27:42,248 what I think has been interesting with baseball is then that's 585 00:27:42,264 --> 00:27:45,000 where and to your point, it's not just baseball, it's across the world 586 00:27:45,120 --> 00:27:48,072 is when you want a change in behavior, you 587 00:27:48,096 --> 00:27:50,920 can legislate that. You can change the rules. As 588 00:27:50,960 --> 00:27:53,912 the example being larger bases and 589 00:27:53,936 --> 00:27:56,872 you can't throw over to first base as much. You limit the 590 00:27:56,896 --> 00:27:59,608 number of times a pitcher can throw to first base. Now suddenly stolen 591 00:27:59,624 --> 00:28:02,328 bases are up, stolen base success is 592 00:28:02,384 --> 00:28:05,192 up. And as it's easier to steal bases, players steal more 593 00:28:05,216 --> 00:28:08,072 bases and that becomes a part of the game. And now you've got more action in the 594 00:28:08,096 --> 00:28:10,942 game. So you can do things or the shift you 595 00:28:10,966 --> 00:28:13,822 can limit. If defenses get so smart with 596 00:28:13,846 --> 00:28:16,302 the use of data and where you're placing people 597 00:28:16,486 --> 00:28:19,422 that it gets really hard to get a 598 00:28:19,446 --> 00:28:22,398 ball, uh, through an infield, then you can 599 00:28:22,454 --> 00:28:25,294 legislate and change the rules on those things. 600 00:28:25,382 --> 00:28:28,366 You mentioned the NBA, what you're referencing is that ah, the data 601 00:28:28,438 --> 00:28:31,310 tells us that the least efficient 602 00:28:31,390 --> 00:28:34,244 shot in the NBA is A long two point shot. 603 00:28:34,342 --> 00:28:37,272 You basically should never take a long two point shot. You 604 00:28:37,296 --> 00:28:40,152 either want to take a three or take a in the 605 00:28:40,176 --> 00:28:42,936 paint short shot. Something that has a much higher 606 00:28:43,008 --> 00:28:45,912 percentage of success. But that long two is 607 00:28:45,936 --> 00:28:48,792 like a stupid shot. If the NBA 608 00:28:48,856 --> 00:28:51,800 wants to see changes in the game and more playmaking and 609 00:28:51,840 --> 00:28:54,792 not as many guys sitting there popping away from three all 610 00:28:54,816 --> 00:28:57,800 the time, they're gonna have to change the rules. You can't ask people 611 00:28:57,840 --> 00:29:00,472 to once they understand something and it is smart and 612 00:29:00,496 --> 00:29:02,420 efficient to go back and be stupid. 613 00:29:02,600 --> 00:29:05,180 >> Mike Petriello: Yeah, I think that's right. At the end of the day, all of these sports are 614 00:29:05,220 --> 00:29:08,012 entertainment products. Listen, I am a big hockey fan and I 615 00:29:08,036 --> 00:29:11,036 vividly remember when I used to live in Boston, I went to this Bruins wild 616 00:29:11,068 --> 00:29:13,980 game in like 2006 and I'm like, this is awful. This is 617 00:29:14,020 --> 00:29:16,844 no fun to watch. There's no offense. It's all clutching and 618 00:29:16,852 --> 00:29:19,692 grabbing. They changed a lot of the rules and then the offense came back 619 00:29:19,716 --> 00:29:22,636 and it's been a lot more fun to watch. Basically everybody wants 620 00:29:22,668 --> 00:29:25,596 the game to be like it was when they were 14 years old and just want to like 621 00:29:25,668 --> 00:29:28,588 freeze it in amber. You go back to like 19, 20 622 00:29:28,644 --> 00:29:31,564 and I'll tell you, the game looks a little bit different. Aside from the fact that it 623 00:29:31,572 --> 00:29:34,472 was segregated, the players did not look the same, they did not 624 00:29:34,496 --> 00:29:37,384 act the same. Pitchers would pitch nine innings every third day. 625 00:29:37,472 --> 00:29:40,248 The sport has always, always, always changed. Night 626 00:29:40,304 --> 00:29:43,304 games cross continental flights. So I do think that the sport is going 627 00:29:43,312 --> 00:29:46,152 to continue to evolve. And like I said, when 628 00:29:46,176 --> 00:29:48,648 people say that baseball didn't evolve, that was a totally fair 629 00:29:48,704 --> 00:29:51,480 criticism. I think the last couple years they've really 630 00:29:51,520 --> 00:29:54,440 gotten their heads around the fact that the world is changing. The sport needs to change 631 00:29:54,480 --> 00:29:55,060 too. 632 00:29:55,360 --> 00:29:58,168 >> Speaker B: Yeah, I always think, uh, give it a chance. 633 00:29:58,264 --> 00:30:01,160 For instance, the new roles of baseball, I ended up really liking one. 634 00:30:01,200 --> 00:30:03,992 Although since you work with Major League baseball in the league office, one 635 00:30:04,016 --> 00:30:06,812 change I would make with limiting how many times a 636 00:30:06,836 --> 00:30:09,404 pitcher can throw over to first base instead 637 00:30:09,452 --> 00:30:12,364 of once they've thrown over twice. The rule is then if you 638 00:30:12,372 --> 00:30:15,100 throw a third time, then that runner is entitled to a free 639 00:30:15,140 --> 00:30:18,012 base. I was thinking that should be more like a balk. 640 00:30:18,156 --> 00:30:21,132 If you've thrown twice instead of rewarding with a 641 00:30:21,156 --> 00:30:23,900 free base, seems like such a big penalty 642 00:30:23,980 --> 00:30:26,812 versus giving away. Okay, now it's giving the 643 00:30:26,836 --> 00:30:27,884 batter a ball. 644 00:30:28,052 --> 00:30:30,838 >> Mike Petriello: One thing. You can throw over a third time, but you have to get them. 645 00:30:30,924 --> 00:30:33,602 So it's only if you, if you don't get them, if you don't get. 646 00:30:33,626 --> 00:30:36,562 >> Speaker B: Them, then they get a free base. So you better be right on 647 00:30:36,586 --> 00:30:39,506 that third time or you're giving away a three base. I'm 648 00:30:39,538 --> 00:30:42,370 saying the penalty should be if you don't get them that third time. 649 00:30:42,410 --> 00:30:44,050 Make it a ball anyway. Make a change. 650 00:30:44,090 --> 00:30:46,594 >> Mike Petriello: Make it a ball. Yeah, fair enough. I think that would lessen the 651 00:30:46,602 --> 00:30:49,394 penalty and, uh, change behaviors. So I think that'd be an 652 00:30:49,402 --> 00:30:50,066 interesting experiment. 653 00:30:50,098 --> 00:30:52,802 >> Speaker B: Yeah, that's the only one I would tweak. But otherwise, I love the new 654 00:30:52,826 --> 00:30:55,474 rules. So one thing that we're seeing, 655 00:30:55,522 --> 00:30:58,386 certainly with prompt in our line of work, but the whole 656 00:30:58,418 --> 00:31:01,208 world is, of course, is embracing AI 657 00:31:01,304 --> 00:31:04,200 and augmented intelligence. So in terms 658 00:31:04,240 --> 00:31:07,240 of baseball, how is Major League 659 00:31:07,280 --> 00:31:10,248 Baseball using AI? Whether 660 00:31:10,304 --> 00:31:13,064 that is in terms of fan experience 661 00:31:13,232 --> 00:31:15,480 or in terms of teams and 662 00:31:15,520 --> 00:31:18,200 predictive intelligence. Leveraging 663 00:31:18,280 --> 00:31:20,808 data. How is AI a part of the game? 664 00:31:20,944 --> 00:31:23,736 >> Mike Petriello: Yeah, I like to think that there's multiple kinds of AI. There's 665 00:31:23,768 --> 00:31:26,616 smart AI, which is using technology to 666 00:31:26,688 --> 00:31:29,602 consume large data sets and help you get to patterns and 667 00:31:29,626 --> 00:31:32,450 answers you wouldn't have, and, uh, obnoxious AI, which 668 00:31:32,490 --> 00:31:35,378 is like my mom having to see AI attached to every brand 669 00:31:35,434 --> 00:31:38,226 that she's ever seen in commercials, which I find wildly 670 00:31:38,258 --> 00:31:40,946 unnecessary. As far as how any 671 00:31:41,018 --> 00:31:43,970 baseball or really any company uses AI, it is to try 672 00:31:44,010 --> 00:31:46,930 to get to those informed decisions maybe a little bit 673 00:31:46,970 --> 00:31:49,810 faster, uh, especially as the size of these data 674 00:31:49,850 --> 00:31:52,754 sets, uh, increases, I'm pretty sure. And I can't 675 00:31:52,802 --> 00:31:55,378 speak to this in first person because I don't know, but I would be shocked if 676 00:31:55,434 --> 00:31:58,162 MLB isn't using ad optimize ticket sales and 677 00:31:58,186 --> 00:32:01,122 marketing in some way because that would just make sense as far as on 678 00:32:01,146 --> 00:32:03,826 the field stuff goes. I know that some of the pitching 679 00:32:03,858 --> 00:32:06,690 labs are using AI to, you know, 680 00:32:06,730 --> 00:32:09,730 you think about all of the biomechanical data that comes in 681 00:32:09,770 --> 00:32:12,434 when you've got all of these pitchers throwing all these pitches. That's 682 00:32:12,482 --> 00:32:15,442 huge data. And that helps you get to what combination of 683 00:32:15,466 --> 00:32:18,402 these things leads to more optimal outcomes. And whether 684 00:32:18,426 --> 00:32:21,010 you want to think about it as AI or just the 685 00:32:21,050 --> 00:32:24,002 Googling that we've been doing for 25 years, I'm not sure it matters that much 686 00:32:24,026 --> 00:32:26,880 to most people. You don't see it under the hood. But if that 687 00:32:26,920 --> 00:32:29,776 kind of tool can help you get to better answers faster, that's 688 00:32:29,808 --> 00:32:31,536 the entire point of any of this, really. 689 00:32:31,688 --> 00:32:34,400 >> Speaker B: In terms of looking ahead, what sort of 690 00:32:34,440 --> 00:32:37,056 innovations in analytics, in 691 00:32:37,128 --> 00:32:39,808 information are you most 692 00:32:39,864 --> 00:32:41,500 excited about for the future? 693 00:32:41,960 --> 00:32:44,816 >> Mike Petriello: Well, I think the Holy grail, if anybody can figure 694 00:32:44,848 --> 00:32:47,824 this out, they will be the Richest person in baseball is how do you 695 00:32:47,832 --> 00:32:50,240 keep pitchers healthy? This has been an 696 00:32:50,360 --> 00:32:53,250 ongoing issue as pitchers got bigger and 697 00:32:53,290 --> 00:32:56,018 stronger and worked on maximizing velocity. 698 00:32:56,114 --> 00:32:59,074 It turns out it's really hard to strengthen that little ligament in 699 00:32:59,082 --> 00:33:02,082 your elbow. Guys keep getting hurt. It's bad for the game. You 700 00:33:02,106 --> 00:33:04,882 want the stars in the field, it's bad for the players. Nobody wants to get hurt. 701 00:33:04,946 --> 00:33:07,842 So that is something the entire industry is thinking about 702 00:33:07,946 --> 00:33:10,754 how to do in terms of metrics and stuff. We're continuing 703 00:33:10,802 --> 00:33:13,746 to push forward because the technology on the field keeps getting better. 704 00:33:13,818 --> 00:33:16,706 Up until last year, you could never really tell anything 705 00:33:16,738 --> 00:33:19,554 about the way the bat moves. You knew a lot about the pitch, a lot about the 706 00:33:19,562 --> 00:33:22,362 ball, but nobody could track the bat because it moves at 707 00:33:22,386 --> 00:33:24,922 like 100ft per second. Now the technology got 708 00:33:24,946 --> 00:33:27,850 upgraded. All of a sudden that's a thing we can measure. More 709 00:33:27,890 --> 00:33:30,522 and more metrics on that are coming out. I, uh, bring that up because it's really 710 00:33:30,546 --> 00:33:33,498 interesting. When you start to measure something 711 00:33:33,554 --> 00:33:36,506 that you couldn't measure before, it's not just a curiosity, 712 00:33:36,618 --> 00:33:39,514 then it becomes something you can quantify and value. 713 00:33:39,682 --> 00:33:42,474 And when you can value it, then players start working towards 714 00:33:42,522 --> 00:33:45,466 it because teams start paying money for it. For example, the bat 715 00:33:45,498 --> 00:33:48,322 speed. I don't think it's revolutionary to say if you swing the 716 00:33:48,346 --> 00:33:51,282 bat faster, you'll hit the ball harder. That's something you can see 717 00:33:51,306 --> 00:33:54,018 with your eyes back to Babe Ruth's time. But now that you can 718 00:33:54,154 --> 00:33:56,994 measure it and say, hey, every extra miles an hour in your 719 00:33:57,002 --> 00:33:59,842 bat speed gets you this much distance and this many points of 720 00:33:59,866 --> 00:34:02,754 slug. And we value that. Now you got these guys who are coming up from 721 00:34:02,762 --> 00:34:05,666 the Miners saying, yeah, I spent my winter not working on my defense, 722 00:34:05,698 --> 00:34:08,594 but trying to improve my bat speed. And I think that's what's going to 723 00:34:08,602 --> 00:34:09,430 keep happening. 724 00:34:10,250 --> 00:34:12,866 >> Speaker B: So bat speed is a really interesting one. 725 00:34:12,938 --> 00:34:15,612 And spin rate on pitches, things like that, those are 726 00:34:15,636 --> 00:34:18,604 interesting ones. Then there's results outcome based. And 727 00:34:18,612 --> 00:34:21,516 there are fans who've been around the game forever and they look at and 728 00:34:21,588 --> 00:34:23,932 wanted batting average, home runs, 729 00:34:23,996 --> 00:34:26,972 RBIs, counting stats, things like that. 730 00:34:27,156 --> 00:34:30,108 When you look at stats, is there 731 00:34:30,244 --> 00:34:33,116 one metric that you 732 00:34:33,268 --> 00:34:36,268 personally would find the most valuable? If I said to you, 733 00:34:36,324 --> 00:34:39,116 I want to compare players or know how good a player 734 00:34:39,188 --> 00:34:42,156 is, are you looking at WAR wins above 735 00:34:42,188 --> 00:34:44,764 replacement? Are you looking at, if we're talking about position 736 00:34:44,852 --> 00:34:47,594 players, not pitchers, are you looking at OPS 737 00:34:47,642 --> 00:34:50,234 plus, are you looking at weighted runs created 738 00:34:50,282 --> 00:34:53,162 plus? There's so many different good statistics that, uh, are out 739 00:34:53,186 --> 00:34:55,322 there. Is there one that you have, that's a favorite. 740 00:34:55,466 --> 00:34:58,362 >> Mike Petriello: Those are all different answers to different questions. So if 741 00:34:58,386 --> 00:35:01,114 I just want a quick at a glance, who are the most valuable 742 00:35:01,162 --> 00:35:03,866 players all in like hitting, defense, running. Yes, wins above 743 00:35:03,898 --> 00:35:06,682 replacement. Uh, that's the, the best we have. It's not perfect, but 744 00:35:06,706 --> 00:35:09,370 it's really good. A lot of the other stuff you mentioned is very 745 00:35:09,410 --> 00:35:12,138 specific just to hitting. So if I want to see who the best hitters are, 746 00:35:12,194 --> 00:35:15,070 Parker jocks said yes, I'll go to weighted roads created. Plus 747 00:35:15,210 --> 00:35:17,654 if I want to see who, uh, hits the ball the 748 00:35:17,662 --> 00:35:20,502 hardest, go look up statcast, go look at hard hit rate. But 749 00:35:20,526 --> 00:35:23,462 there's a lot of different ways to answer those questions. It just depends on 750 00:35:23,486 --> 00:35:26,278 what you're looking at. But I look at all of them as a starting point 751 00:35:26,334 --> 00:35:29,222 and not necessarily an ending point. If I look at the leaders in 752 00:35:29,246 --> 00:35:31,862 hard hit rate, I'm probably going to find Aaron Judge and I'm going to find 753 00:35:31,886 --> 00:35:34,838 Giancarlo Stanton. I'm going to find guys who hit the ball really, 754 00:35:34,894 --> 00:35:37,686 really hard. Doesn't necessarily guarantee I'm 755 00:35:37,718 --> 00:35:40,694 finding the best hitters in baseball because Luisa Rice does not 756 00:35:40,702 --> 00:35:43,548 hit the ball hard and he always has a very good batting average. So 757 00:35:43,604 --> 00:35:46,348 it all comes with a certain amount of contextual knowledge 758 00:35:46,444 --> 00:35:48,412 to make any of these numbers useful. 759 00:35:48,556 --> 00:35:51,212 >> Speaker B: As we finish a couple last questions here, what 760 00:35:51,236 --> 00:35:54,060 advice would you give to marketers looking to 761 00:35:54,100 --> 00:35:56,940 adapt a more data driven approach and what 762 00:35:56,980 --> 00:35:59,372 can they learn from baseball that would be 763 00:35:59,396 --> 00:36:00,236 applicable? 764 00:36:00,428 --> 00:36:03,324 >> Mike Petriello: Number one, listen to the fans or your audience or whoever. I 765 00:36:03,332 --> 00:36:06,140 don't think baseball has always done that, as I said, and now that's really 766 00:36:06,180 --> 00:36:09,112 helped a lot to understand what the audience wants. If 767 00:36:09,136 --> 00:36:11,992 you have a sort of complicated and dense data set, make sure 768 00:36:12,016 --> 00:36:14,840 you explain it in a way that people can enjoy or 769 00:36:14,880 --> 00:36:17,752 understand, even be entertained by. Uh, because if 770 00:36:17,776 --> 00:36:20,552 not, everybody's going to tune it out, no matter how valuable it might 771 00:36:20,576 --> 00:36:20,904 be. 772 00:36:20,992 --> 00:36:23,704 >> Speaker B: And then to close, we like to ask a fun question 773 00:36:23,792 --> 00:36:26,472 here. And so if you could pick any 774 00:36:26,576 --> 00:36:29,368 player from any sport, past 775 00:36:29,424 --> 00:36:32,184 or present, to join you for a Mike 776 00:36:32,232 --> 00:36:34,834 Petriello dream dinner 777 00:36:34,882 --> 00:36:37,730 party, who would you want to sit there and talk to 778 00:36:37,770 --> 00:36:38,514 and why? 779 00:36:38,682 --> 00:36:41,634 >> Mike Petriello: I would like to say that I have an extremely deep 780 00:36:41,682 --> 00:36:44,674 cut and a really thought out answer, but I'm going to give you one of the 781 00:36:44,682 --> 00:36:47,666 most famous people of all time. Uh, but for a good reason. The answer 782 00:36:47,698 --> 00:36:50,514 would be Ted Williams, who had a fascinating 783 00:36:50,562 --> 00:36:53,458 life, obviously served in two wars, you know, all around 784 00:36:53,594 --> 00:36:56,098 amazing life and career. He was 785 00:36:56,234 --> 00:36:59,042 maybe the first real baseball nerd. He literally 786 00:36:59,106 --> 00:37:01,842 wrote a book on this called the Science of hitting in 787 00:37:01,866 --> 00:37:04,770 1971. And he didn't actually say exophilosity and launch 788 00:37:04,810 --> 00:37:07,570 angle, but you go read it and he basically did. He 789 00:37:07,610 --> 00:37:10,610 drew charts and diagrams to the inch of 790 00:37:10,650 --> 00:37:13,618 saying, here's where I'm good when I hit the ball here and there. And it's 791 00:37:13,634 --> 00:37:16,482 funny because we'll bring out a lot of the new nerd stuff and people be 792 00:37:16,506 --> 00:37:19,362 like, oh, Lou Gehrig, Ted Williams, they'd have hated this stuff. And I'm like, 793 00:37:19,386 --> 00:37:22,386 no, no, Ted Williams would have loved this stuff. 794 00:37:22,418 --> 00:37:25,170 And I would just love to take them all through it and see what he'd have to say about 795 00:37:25,210 --> 00:37:25,698 it. 796 00:37:25,834 --> 00:37:28,674 >> Speaker B: We think about old school, new school Ted 797 00:37:28,722 --> 00:37:31,714 Williams using all of that information and science 798 00:37:31,762 --> 00:37:34,568 of hitting to hit.400, a.400 799 00:37:34,624 --> 00:37:37,544 batting average, an old school stat. But he's leveraging data to be 800 00:37:37,552 --> 00:37:40,376 able to accomplish something that is a feat that we haven't 801 00:37:40,408 --> 00:37:41,720 seen in the sport in a while. 802 00:37:41,760 --> 00:37:43,496 >> Mike Petriello: I'd ask him about that. Uh, that'd be great. 803 00:37:43,648 --> 00:37:46,456 >> Speaker B: All right, well, invite me. I'd like to sit and listen to you and Ted 804 00:37:46,488 --> 00:37:49,256 Williams and lob in a question or two on that. Mike 805 00:37:49,288 --> 00:37:52,200 Petriello, really, really appreciate the 806 00:37:52,240 --> 00:37:55,096 time. It's been fun talking to you and thinking 807 00:37:55,128 --> 00:37:58,040 about some of the ways in which fans and 808 00:37:58,080 --> 00:38:00,952 media and others. There's a certain resistance at 809 00:38:00,976 --> 00:38:03,884 times to data and analytics. And yet when you wake up 810 00:38:03,892 --> 00:38:06,780 and realize over time, whether it's with 811 00:38:06,820 --> 00:38:09,660 the hall of Fame inductees and others, how it has 812 00:38:09,700 --> 00:38:12,620 become so embraced and so much a part of the game, uh, 813 00:38:12,620 --> 00:38:15,596 that information and increased information will only 814 00:38:15,668 --> 00:38:18,572 just increase over time. So anyway, 815 00:38:18,636 --> 00:38:20,700 really enjoy talking to you. Thanks so much. 816 00:38:20,820 --> 00:38:22,080 >> Mike Petriello: Thanks a lot, Laurie. 817 00:38:27,780 --> 00:38:30,716 >> Lori Rubinson: Thank you for listening to this episode of the Frictionless Marketing 818 00:38:30,748 --> 00:38:33,692 Podcast. For a complete transcript of this 819 00:38:33,716 --> 00:38:36,332 conversation or more information on Prompt, 820 00:38:36,476 --> 00:38:39,240 please visit us at meetprompt. Co. 821 00:38:40,420 --> 00:38:43,276 If you found this episode insightful, share it with your connections 822 00:38:43,308 --> 00:38:46,236 on LinkedIn to learn 823 00:38:46,268 --> 00:38:49,164 more about how to make marketing Frictionless Purchase Friction 824 00:38:49,212 --> 00:38:52,204 Fatigue by Prompt CEO Paul Dyer online 825 00:38:52,292 --> 00:38:54,080 and at Booksellers Worldwide 826 00:38:55,950 --> 00:38:58,806 Frictionless Marketing is a production from Prompt, the leading 827 00:38:58,838 --> 00:39:01,766 earned first creative marketing and communications agency 828 00:39:01,878 --> 00:39:04,534 grounded in the present, yet attuned to the future. 829 00:39:04,702 --> 00:39:07,190 Produced and distributed by Simpler Media Productions.