WEBVTT - The Data Coach

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<v Speaker 1>Pushkin. For roughly five thousand years, people call themselves doctors

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<v Speaker 1>and pretended to know all sorts of things that they

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<v Speaker 1>didn't know, and were as likely to kill you as

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<v Speaker 1>to cure you. These doctors existed because sick people desperately

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<v Speaker 1>wanted to believe in them. Coaching feels the same way

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<v Speaker 1>to me. For decades, people just sort of hoped that

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<v Speaker 1>if a man was hollering at them, he must be

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<v Speaker 1>helping them to win. Maybe he was sometimes, but that's

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<v Speaker 1>not my point. My point is that even if coaches

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<v Speaker 1>have no effect on performance, even if they're doing more

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<v Speaker 1>harm than good, we might still insist on having them

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<v Speaker 1>because we need someone on our side to believe in.

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<v Speaker 1>But coaching's changing the same way medicine changed one hundred

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<v Speaker 1>years ago. Coaches are discovering science, and science is discovering them.

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<v Speaker 1>I'm Michael Lewis and This is Against the Rules, a

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<v Speaker 1>show about various authority figures in American life. This season

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<v Speaker 1>is about the rise of coaches, and this episode is

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<v Speaker 1>about data and pitching. A while back in two thousand

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<v Speaker 1>and three, I published a book called Moneyball. It's about

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<v Speaker 1>how the Oakland A's baseball team had used data analysis

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<v Speaker 1>to get an edge on everyone else. They were a

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<v Speaker 1>poorly funded team in a small market. They had no

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<v Speaker 1>money to spend on players, but their new and better

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<v Speaker 1>statistics enable them to value baseball players more accurately, so

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<v Speaker 1>they could sell the players that were overvalued and trade

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<v Speaker 1>for the ones that were undervalued. I remember at the

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<v Speaker 1>time being shocked at the notion that baseball players could

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<v Speaker 1>be misvalued. I mean, baseball players have been doing the

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<v Speaker 1>same job for a century, out in the open, in

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<v Speaker 1>front of millions of people. But suddenly, all over a

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<v Speaker 1>thing that had been done a certain way forever was

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<v Speaker 1>now being done a totally different way. Everyone in baseball

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<v Speaker 1>started using data and getting all sorts of insights from it,

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<v Speaker 1>and the insights led not just to better valuations of

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<v Speaker 1>baseball players, it eventually led to a new kind of coaching.

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<v Speaker 1>In the past, it used to be that many coaching

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<v Speaker 1>positions were almost a sine cure. That's Ben Lindberg, co

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<v Speaker 1>author of a book called The MVP Machine. It's about

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<v Speaker 1>a revolution in how the world's best baseball players get coached.

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<v Speaker 1>It was the coaches who were the manager's palace, his

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<v Speaker 1>drinking buddies would become the coaches, and there wasn't that

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<v Speaker 1>much coaching going on at the major league level. It

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<v Speaker 1>was sort of reinforcing lessons that had already been taught

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<v Speaker 1>and keeping guys in line, but there wasn't that much

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<v Speaker 1>expectation that coaches would improve players once they got to

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<v Speaker 1>that level. But that was about to change big time.

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<v Speaker 1>Ben was part of a huge and growing crowd of

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<v Speaker 1>data geeks outside of baseball who spent lots and lots

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<v Speaker 1>of time analyzing players and building models to try to

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<v Speaker 1>predict their performance. As sophisticated as the statistical projection models are,

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<v Speaker 1>they'll only really look at the player's past performance and

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<v Speaker 1>his age and maybe some comparable players from the past,

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<v Speaker 1>and they'll spit out a projection that say, well, he

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<v Speaker 1>was this good in the past few years, and we'll

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<v Speaker 1>adjust for the ballpark, and here's how old he is,

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<v Speaker 1>and so here's our median projection for him, and so

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<v Speaker 1>all of a sudden, there players who are just busting

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<v Speaker 1>out of those projections exactly, And a projection system would

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<v Speaker 1>never forecast that. It might say that someone's going to

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<v Speaker 1>get a bit better, a bit worse, but typically it

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<v Speaker 1>won't say that someone is going to do something that's

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<v Speaker 1>completely out of line with their past performance. In other words,

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<v Speaker 1>teams had gotten really good, or at least a lot

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<v Speaker 1>better at evaluating the potential of all their players, but

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<v Speaker 1>the players were still playing better than expected, so much

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<v Speaker 1>better that the analysts were a bit suspicious. But the

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<v Speaker 1>last time you saw this was with the steroids era,

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<v Speaker 1>that all of a sudden, people players were performing in

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<v Speaker 1>ways that the projection models would never have guessed. And

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<v Speaker 1>you're so you're kind of seeing it, but without an

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<v Speaker 1>explanation as obvious as steroids exactly. Yeah, then started looking

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<v Speaker 1>into what these players were doing. The ones who were

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<v Speaker 1>dramatically exceeding the analysts expectations, the overperformers, all had something

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<v Speaker 1>in common. Coaches who use new technology. One of the

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<v Speaker 1>really big innovations has been the high speed camera. So

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<v Speaker 1>a company called Edgrotronic, which developed these cameras for scientific purposes,

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<v Speaker 1>has found that much of its business has come from

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<v Speaker 1>baseball teams because baseball teams have found that if you

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<v Speaker 1>train these high speed cameras on players, you can perceive

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<v Speaker 1>things about players movement that they didn't know about themselves.

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<v Speaker 1>And the coaches using these cameras were very different from

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<v Speaker 1>the old school baseball coaches, the Sinecure guys. For a start,

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<v Speaker 1>the new coaches weren't former big league players. In some cases,

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<v Speaker 1>they didn't even know any big league players. My name

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<v Speaker 1>is Kyle Bodie. Like this guy, I was twenty two

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<v Speaker 1>years old started coaching little league and I realized quickly

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<v Speaker 1>that I just didn't know anything about coaching. Kyle had

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<v Speaker 1>just moved from Ohio to Seattle, where he landed a

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<v Speaker 1>part time job as a little league coach. Yep, that's

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<v Speaker 1>how he started in life as a little league baseball coach.

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<v Speaker 1>Ed played some my father was a coach, but I

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<v Speaker 1>figured I owed it to the kids to learn just

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<v Speaker 1>a little bit more about keeping their arms healthy. And

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<v Speaker 1>he had some questions like how many pitches should a

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<v Speaker 1>kid be allowed to throw? And what was the best

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<v Speaker 1>way to throw him? I mean, I was once a pitcher,

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<v Speaker 1>and I spent half my life with my arm in

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<v Speaker 1>an ice bucket. To this day, I can't sleep on

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<v Speaker 1>my right shoulder throwing a ball overhand. It might look

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<v Speaker 1>like a natural and healthy thing to do, but it's not.

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<v Speaker 1>Kyle body looked around for research on the subject, but

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<v Speaker 1>unfortunately it was all very nonspecific kind of very academic research,

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<v Speaker 1>and then the training programs and the coaching programs that

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<v Speaker 1>were out there were very bland, not based on any

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<v Speaker 1>sort of evidence. It really shocked me, So Kyle started

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<v Speaker 1>to do research on his own. Then he got a

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<v Speaker 1>promotion from Little league to the freshman team at a

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<v Speaker 1>Seattle high school. But he found himself at war with

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<v Speaker 1>the JV and varsity coaches at the high school. They

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<v Speaker 1>were coaching the old fashioned way, telling players what to do,

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<v Speaker 1>hollering at them when they screwed up, praising them when

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<v Speaker 1>they didn't. Actively coaching athletes just typically makes them worse.

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<v Speaker 1>Just intervention is typically one of the worst things you

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<v Speaker 1>can do. What are the points of friction with the

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<v Speaker 1>old coaching model, Like, what specifically kind of things would

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<v Speaker 1>you do that were heretical. So informing the athlete that, like,

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<v Speaker 1>whatever they're doing is not good enough and then just

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<v Speaker 1>seeing how they change over time and how they will

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<v Speaker 1>self organize was a real heretical idea, right because most

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<v Speaker 1>coaches think that they have a lot to give to

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<v Speaker 1>the athlete, and my view on it still is today

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<v Speaker 1>is that they're good enough, like we just need to

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<v Speaker 1>give them the right direction and let them figure it

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<v Speaker 1>out for the most part. You ever read a book

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<v Speaker 1>called The Inner Game of Tennis? I have, Yeah, it's

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<v Speaker 1>one of my favorites. Anyway, In his first year coaching,

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<v Speaker 1>Kyle's freshman team won as many games as they lost,

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<v Speaker 1>which was actually pretty great. That season, the school's varsity

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<v Speaker 1>and JV teams were losing most of their games. But

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<v Speaker 1>at the end of the year, the head coach fired

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<v Speaker 1>Kyle because the other coaches hated his methods. They thought

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<v Speaker 1>he had no clue how to coach. That didn't stop Kyle.

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<v Speaker 1>He doubled down on his approach, and he wound up

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<v Speaker 1>building what amounted to a baseball bionic manfactory drive line Baseball,

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<v Speaker 1>he called it. So describe to me this laboratory you build.

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<v Speaker 1>In its current incarnation, it's about fifteen high speed motion

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<v Speaker 1>tracking cameras. There's force plates in there to measure ground

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<v Speaker 1>reaction forces. Attracts every movement two hundred forty times per second,

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<v Speaker 1>and it's submillimeter accurate, so every movement can be tracked

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<v Speaker 1>down to at least one millimeter of accuracy, and usually

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<v Speaker 1>much better. Let's go one hundred point four ninety three

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<v Speaker 1>point seven the high speed cameras allow Kyle to measure

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<v Speaker 1>the speed of a pitcher's arm, among other things, the

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<v Speaker 1>faster a pitcher's arm, the faster the ball comes out

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<v Speaker 1>of it in theory one hundred three point zero. In practice,

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<v Speaker 1>not all pictures are able to translate their arm speed

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<v Speaker 1>into ball speed. Two guys with the exact same arm

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<v Speaker 1>speeds might throw very different fastballs. If someone's arm speed

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<v Speaker 1>is extremely high and the ball comes out at like

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<v Speaker 1>a lower predictive velocity based on like a regression algorithm,

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<v Speaker 1>that we know that there's some inefficiencies there that we

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<v Speaker 1>should be able to easily clean up. That is, you

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<v Speaker 1>could identify people with the god given talent to throw

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<v Speaker 1>a baseball because you had new insight into where that

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<v Speaker 1>talent came from. So when that happens, are you thinking,

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<v Speaker 1>there's this pool of players out there who have high

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<v Speaker 1>arm speed, low velocity that we can just fix. That's

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<v Speaker 1>all that I think about pretty much every day. And

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<v Speaker 1>could you like drive around with a little truck and

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<v Speaker 1>put people in the back of your truck and test

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<v Speaker 1>their arm speed? Is that what you would do? Yeah,

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<v Speaker 1>that's actually funny. We almost butt an RV to do

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<v Speaker 1>exactly that to drive around the country with our lab.

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<v Speaker 1>It turned out he didn't need to drive around looking

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<v Speaker 1>for people with this weird arm talent. They found him.

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<v Speaker 1>I real that I needed to make a change going

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<v Speaker 1>into the season. I was helping out my dad's team

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<v Speaker 1>and trying to make money in every way. That's Matt

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<v Speaker 1>Boyd Back in twenty seventeen. He was an unknown minor

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<v Speaker 1>league pitcher coming off a terrible season. He had a

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<v Speaker 1>below average fastball around eighty nine miles an hour. Oh,

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<v Speaker 1>and he had an arm injury. The way he was going,

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<v Speaker 1>he's about to be spending a lot more time with

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<v Speaker 1>his mom and dad. Over to the Christmas break, one

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<v Speaker 1>of my dad's players came back and he was ninety

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<v Speaker 1>two guy in high school and he was at Oregon State,

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<v Speaker 1>and after the fall he was ninety eight miles per hour. Oh,

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<v Speaker 1>I went, whoa, whoa, whoa, I go, what were you

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<v Speaker 1>doing down there? He's like, I did the drive Line program.

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<v Speaker 1>Drive Line. That's Kyle Bodie's lab. So at that point,

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<v Speaker 1>Kyle would have been kind of a local secret. So

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<v Speaker 1>someone all of a sudden has this kind of miraculous

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<v Speaker 1>jump in velocity. You hear about it, and you go see,

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<v Speaker 1>you call Kyle had had you ever heard of him

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<v Speaker 1>at that point? No? I hadn't heard of him. Matt

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<v Speaker 1>goes to seek Kyle in his Bionic manfactory and here

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<v Speaker 1>we have cameras, great our guns. I go up a

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<v Speaker 1>little stairwell and here's you know, a little eight foot

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<v Speaker 1>wide by ten foot high probably pitching lane tunnel created

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<v Speaker 1>with all this technology in there. And there's Kyle behind

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<v Speaker 1>a computer and he runs me through the program. At

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<v Speaker 1>that point, had you ever seen the technology that was there? No?

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<v Speaker 1>And I honestly I didn't. I couldn't even told you

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<v Speaker 1>right today what I saw in there. I couldn't even

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<v Speaker 1>told you what was going on a bunch of fucket

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<v Speaker 1>lids for some for balldrills. It looks like this, a

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<v Speaker 1>lot of gizmos, a lot of gizmos, a lot of wires,

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<v Speaker 1>a lot of interesting looking baseballs, a lot of lines

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<v Speaker 1>on a pitching mound and stuff. And you know, Kyle

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<v Speaker 1>explained to me what the concept of what he does,

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<v Speaker 1>and we talked about it, and then we just started

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<v Speaker 1>the program. Kyle put Matt Boy through a bunch of tests.

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<v Speaker 1>The big one was to test his arm speed, but

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<v Speaker 1>he never used the phrase arm speed. Kyle actually never

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<v Speaker 1>told Matt what he was testing for. He in our

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<v Speaker 1>lab tested higher than pretty much everyone and still almost

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<v Speaker 1>everyone to this day. He's just an excellent athlete. And

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<v Speaker 1>yet the ball velocity wasn't where it needed to be

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<v Speaker 1>or where it predicted it should be. So what was

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<v Speaker 1>the inefficiency? Like? What was what was he doing that

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<v Speaker 1>caused the ball to come out more slowly than what

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<v Speaker 1>you would have predicted? Right, it's really hard. We don't

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<v Speaker 1>know the rude cause yet. That's that's the actual interesting

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<v Speaker 1>thing is we're still studying, like why this happens. At

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<v Speaker 1>the lab, Kyle hands Matt these really heavy balls to throw.

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<v Speaker 1>He's found that when people throw a heavy ball, their

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<v Speaker 1>body naturally finds the most efficient way to do it

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<v Speaker 1>because it's so painful and uncomfortable to throw it inefficiently.

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<v Speaker 1>Matt basically moves into the Bionic manfactory and throws heavy

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<v Speaker 1>balls the entire off season. Then rejoins his minor league

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<v Speaker 1>team and you know, I get down there, I tell

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<v Speaker 1>him I'm I'm full progression off the mount and they're like, okay,

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<v Speaker 1>well let's see it. My first bullpen I'm I'm ninety

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<v Speaker 1>two to ninety five. Oh, and I think everyone's going,

0:13:16.476 --> 0:13:19.996
<v Speaker 1>what the heck happened? You know, and even I am,

0:13:20.036 --> 0:13:22.476
<v Speaker 1>I'm going, man, I'm throwing the baseball up in the zone. Now,

0:13:22.636 --> 0:13:24.596
<v Speaker 1>this is amazing. This is so cool. Like when all

0:13:24.636 --> 0:13:26.716
<v Speaker 1>of a sudden, you've got this new weapon. Yeah, when

0:13:26.756 --> 0:13:28.516
<v Speaker 1>you got this fastball that's coming out of your hand

0:13:28.516 --> 0:13:30.636
<v Speaker 1>three or four miles an hour faster than it usually does.

0:13:31.036 --> 0:13:33.956
<v Speaker 1>What do you notice in what happened and how hitters

0:13:33.996 --> 0:13:37.636
<v Speaker 1>respond and how the effectiveness hitters have against you. I

0:13:37.676 --> 0:13:40.956
<v Speaker 1>remember going in to Double A that season and I did.

0:13:40.956 --> 0:13:42.796
<v Speaker 1>We have a catcher named Jack Murphy who was about

0:13:42.836 --> 0:13:45.116
<v Speaker 1>four or five years older than me, and he went

0:13:45.196 --> 0:13:47.476
<v Speaker 1>up and told me, he goes, Maddie, you have a

0:13:47.476 --> 0:13:50.036
<v Speaker 1>new fastball. You need to pitch off it. I remember,

0:13:50.076 --> 0:13:52.276
<v Speaker 1>I was kind of scared. I was like, what, I've

0:13:52.276 --> 0:13:54.436
<v Speaker 1>never done that in my life. I'm always fastball, change up.

0:13:54.436 --> 0:13:56.116
<v Speaker 1>And then I mixed my curveball, and you know, but

0:13:56.676 --> 0:13:58.356
<v Speaker 1>he challenged me, and all of a sudden, I'm like

0:13:58.396 --> 0:14:00.676
<v Speaker 1>striking guys out on three fastballs in Double A, and

0:14:00.716 --> 0:14:03.636
<v Speaker 1>I'm going like, is it this easy? Like this is

0:14:03.676 --> 0:14:06.836
<v Speaker 1>all it took. By the end of that season, Matt

0:14:06.836 --> 0:14:08.876
<v Speaker 1>Boyd had been called up to the major leagues to

0:14:08.956 --> 0:14:12.516
<v Speaker 1>the Detroit Tigers to be a starting pitcher. Paints in

0:14:12.676 --> 0:14:16.876
<v Speaker 1>and then paints away both fastballs. Want to pitch to

0:14:17.076 --> 0:14:23.076
<v Speaker 1>Bird another strikeout for Matthew boy to start the form.

0:14:23.116 --> 0:14:25.236
<v Speaker 1>They now pay him five point three million dollars a

0:14:25.276 --> 0:14:29.076
<v Speaker 1>year and feel like they're getting a deal. And Kyle Body, well,

0:14:29.116 --> 0:14:31.156
<v Speaker 1>now he has a new job too. I'm the president

0:14:31.156 --> 0:14:33.436
<v Speaker 1>and founder of drive Land Baseball and the director of

0:14:33.436 --> 0:14:41.956
<v Speaker 1>Pitching Initiatives of the Cincinnati Reds. Somebody asks a question,

0:14:42.476 --> 0:14:46.276
<v Speaker 1>how do you throw a ball faster? They gathered data.

0:14:46.556 --> 0:14:48.996
<v Speaker 1>They measure everything that the human body does when it

0:14:49.036 --> 0:14:52.756
<v Speaker 1>throws a ball. They test theories and find answers rooted

0:14:52.836 --> 0:14:55.756
<v Speaker 1>in science. All of a sudden, there's a new way

0:14:55.796 --> 0:15:00.036
<v Speaker 1>to coach, and it's getting adopted in sports. But not

0:15:00.196 --> 0:15:03.556
<v Speaker 1>just in sports, because there are people all over who

0:15:03.556 --> 0:15:05.636
<v Speaker 1>don't know why they're good at something or how to

0:15:05.676 --> 0:15:08.876
<v Speaker 1>get better. About five years ago, I was CEO and

0:15:09.116 --> 0:15:13.876
<v Speaker 1>at a software company and we're growing pretty quickly. This

0:15:14.076 --> 0:15:16.956
<v Speaker 1>is a meet bendoff. Five years ago, he was just

0:15:16.996 --> 0:15:20.396
<v Speaker 1>another Silicon Valley entrepreneur trying to get his product out

0:15:20.396 --> 0:15:25.236
<v Speaker 1>the door by using salespeople. But I was puzzled why

0:15:27.036 --> 0:15:30.996
<v Speaker 1>some of our people were more successful than others. And

0:15:31.076 --> 0:15:34.916
<v Speaker 1>every time we wanted to understand why, we had to

0:15:34.956 --> 0:15:40.036
<v Speaker 1>go and interview people and see what they think. And

0:15:40.196 --> 0:15:45.036
<v Speaker 1>he didn't want just a collection of stories. He wanted data.

0:15:45.196 --> 0:15:51.116
<v Speaker 1>Why did some sales pitches work while others didn't? Think

0:15:51.116 --> 0:15:56.436
<v Speaker 1>about like football right or baseball right? If the coach

0:15:56.876 --> 0:15:59.876
<v Speaker 1>never sees the game and the only thing is to

0:16:00.036 --> 0:16:02.076
<v Speaker 1>understand how to get better is by interviewing some of

0:16:02.076 --> 0:16:06.036
<v Speaker 1>the players what they think has happened. So I wanted

0:16:06.076 --> 0:16:08.196
<v Speaker 1>to have like something like a game tape and game

0:16:08.236 --> 0:16:13.676
<v Speaker 1>stats for sales and AI to understand what really separates

0:16:13.676 --> 0:16:17.396
<v Speaker 1>the top performers from everybody else. You know that message

0:16:17.436 --> 0:16:20.716
<v Speaker 1>that you get on customer service calls, This call may

0:16:20.756 --> 0:16:24.876
<v Speaker 1>be recorded for quality assurance. We appreciate your patience. Calls

0:16:25.436 --> 0:16:28.476
<v Speaker 1>for quality. Companies were recording their sales calls, but no

0:16:28.476 --> 0:16:30.916
<v Speaker 1>one was really listening to them. A met wanted to

0:16:30.956 --> 0:16:33.756
<v Speaker 1>listen to all the game tape and analyze it. And

0:16:33.876 --> 0:16:36.676
<v Speaker 1>I then started asking a bunch of other people, you know,

0:16:36.716 --> 0:16:38.716
<v Speaker 1>if we build a system kind of shine a light

0:16:38.796 --> 0:16:41.396
<v Speaker 1>on conversation and give you insights from that. Would you

0:16:41.396 --> 0:16:44.036
<v Speaker 1>buy how much you're willing to pay a meat? Created

0:16:44.076 --> 0:16:47.716
<v Speaker 1>a new company he called it Gong. He went to

0:16:47.756 --> 0:16:51.036
<v Speaker 1>people and said, hand me all the recordings of all

0:16:51.116 --> 0:16:54.356
<v Speaker 1>your sales calls, plus a list of your salespeople in

0:16:54.476 --> 0:16:59.316
<v Speaker 1>order of how good they are. We'll analyze it. And

0:16:59.396 --> 0:17:01.596
<v Speaker 1>so if you'd gone to just one of those top

0:17:01.596 --> 0:17:05.396
<v Speaker 1>sales people and ask what you're doing, it works. No, no,

0:17:05.716 --> 0:17:10.076
<v Speaker 1>absolutely not, because they don't know. People think it's an art, right.

0:17:10.196 --> 0:17:12.756
<v Speaker 1>It's like they're not aware that's something that they're doing

0:17:12.796 --> 0:17:14.356
<v Speaker 1>because they don't know what the other people are doing,

0:17:14.356 --> 0:17:16.836
<v Speaker 1>so they don't know what the differences are. The Gong

0:17:17.036 --> 0:17:20.316
<v Speaker 1>artificial intelligence, had no theory about what worked and what

0:17:20.436 --> 0:17:24.156
<v Speaker 1>didn't in sales. It just had millions of sales pitches.

0:17:24.716 --> 0:17:27.636
<v Speaker 1>It searched for patterns in both the calls they got

0:17:27.676 --> 0:17:32.876
<v Speaker 1>results and the calls that didn't. The Gong actually learns

0:17:32.876 --> 0:17:35.596
<v Speaker 1>like it looks at the salespeople and says, like, which

0:17:35.596 --> 0:17:38.596
<v Speaker 1>one is closing more deals, and then it starts to

0:17:38.596 --> 0:17:41.276
<v Speaker 1>analyze the difference. So the software learns automatically. You just

0:17:41.476 --> 0:17:46.596
<v Speaker 1>connect to the calls. If there was an art to

0:17:46.676 --> 0:17:48.956
<v Speaker 1>any of this, it was in the questions that Gong

0:17:49.116 --> 0:17:51.476
<v Speaker 1>asked if the phone call data. I mean, the very

0:17:51.516 --> 0:17:54.676
<v Speaker 1>simple one is like percent of talk time? Right, You

0:17:54.716 --> 0:17:57.356
<v Speaker 1>and I are talking right now, and by the end

0:17:57.396 --> 0:17:59.356
<v Speaker 1>of the call, Gong say, well, a meat was talking

0:17:59.396 --> 0:18:03.236
<v Speaker 1>fifty six percent of the time. It turns out there's

0:18:03.316 --> 0:18:05.916
<v Speaker 1>lots of things that separated great sales pitch from a

0:18:05.956 --> 0:18:09.716
<v Speaker 1>bad one. The simple virtues. They're sort of obvious, but

0:18:09.796 --> 0:18:13.796
<v Speaker 1>they could now be quantified. On average, forty six percent

0:18:14.396 --> 0:18:21.436
<v Speaker 1>talk time is ideal. Thirteen questions is optimal? Right? Not less?

0:18:21.836 --> 0:18:25.636
<v Speaker 1>No more so, this alone is food for thought that

0:18:25.756 --> 0:18:28.996
<v Speaker 1>there's a maximum amount of time to be talking. If

0:18:29.036 --> 0:18:31.356
<v Speaker 1>I were to talk only forty six percent of the time,

0:18:31.956 --> 0:18:34.076
<v Speaker 1>would my wife stop telling me that I don't listen?

0:18:34.796 --> 0:18:36.916
<v Speaker 1>What if I counted the number of questions I asked

0:18:36.916 --> 0:18:40.956
<v Speaker 1>in a particular conversation and stopped myself at thirteen. I mean,

0:18:40.996 --> 0:18:43.556
<v Speaker 1>it's not that if you ask fourteen you lose that deal.

0:18:43.676 --> 0:18:47.916
<v Speaker 1>But more than that, people might lose their patience, and

0:18:48.796 --> 0:18:53.116
<v Speaker 1>too less it means you're talking too much. Patience factor,

0:18:53.116 --> 0:18:55.276
<v Speaker 1>that's like a pretty big challenge for a lot of people,

0:18:55.396 --> 0:19:00.236
<v Speaker 1>they're too quick to respond, so it's a good practice

0:19:00.276 --> 0:19:03.996
<v Speaker 1>to pause think in any reply. We all know that

0:19:04.716 --> 0:19:07.196
<v Speaker 1>in theory, at least, Gone could act just like a

0:19:07.276 --> 0:19:11.676
<v Speaker 1>game coach, just last instructions into a person's ear as

0:19:11.716 --> 0:19:15.596
<v Speaker 1>they pitched the product. Shut the hell up, ask another question.

0:19:16.036 --> 0:19:19.516
<v Speaker 1>You're at seventy three percent, and Meek decided it was

0:19:19.556 --> 0:19:21.356
<v Speaker 1>better if he didn't do this. If he offered the

0:19:21.396 --> 0:19:25.156
<v Speaker 1>feedback after the call instead, you know, will alert the

0:19:25.276 --> 0:19:32.316
<v Speaker 1>coaches to hear like three conversations that have room for improvement,

0:19:32.476 --> 0:19:35.996
<v Speaker 1>and then the coach will open them up just like

0:19:36.036 --> 0:19:40.196
<v Speaker 1>a game tape and start breaking it down to start commenting, Oh,

0:19:40.516 --> 0:19:43.116
<v Speaker 1>minute three ZH six, I really love the way you

0:19:43.196 --> 0:19:47.356
<v Speaker 1>phrased that question. Have you considered X man? If I

0:19:47.396 --> 0:19:49.676
<v Speaker 1>were in sales, all of this would drive me batshit,

0:19:50.636 --> 0:19:52.876
<v Speaker 1>which is why I'm not in sales and also probably

0:19:52.916 --> 0:19:55.316
<v Speaker 1>why everyone but my mother is still making fun of

0:19:55.356 --> 0:19:59.036
<v Speaker 1>my podcast ad Reads. But for people who actually make

0:19:59.076 --> 0:20:03.396
<v Speaker 1>their living selling stuff, well, Gong is their new best coach.

0:20:03.756 --> 0:20:07.076
<v Speaker 1>So when I'm engaging with a customer, especially in introductory calls,

0:20:07.676 --> 0:20:09.476
<v Speaker 1>I want them to do as much of the talking

0:20:09.556 --> 0:20:12.876
<v Speaker 1>is possible. That's Megan Dorner, who sells software for a

0:20:12.916 --> 0:20:16.196
<v Speaker 1>Canadian company called AVIC. I have no idea what the

0:20:16.196 --> 0:20:18.876
<v Speaker 1>software it does, but that's not important. The key fact

0:20:18.876 --> 0:20:21.156
<v Speaker 1>about it is that it needs to be sold. The

0:20:21.196 --> 0:20:24.516
<v Speaker 1>more the customers talking, the more that I'm learning, especially

0:20:24.516 --> 0:20:27.516
<v Speaker 1>like I said on that first call. And so the

0:20:27.596 --> 0:20:31.516
<v Speaker 1>longest customer story again is how long they're telling a

0:20:32.036 --> 0:20:35.476
<v Speaker 1>you know, an interactive I guess one long tail. So

0:20:35.556 --> 0:20:39.436
<v Speaker 1>that the customers monologuing, that's good, that's correct, that's correct.

0:20:39.516 --> 0:20:43.436
<v Speaker 1>But if you're monologuing, it's bad. Megan now pays attention

0:20:43.436 --> 0:20:49.716
<v Speaker 1>to who's monologuing, because Gong forces her too. Five minutes

0:20:49.756 --> 0:20:52.556
<v Speaker 1>after every call, she gets a note from Gong filled

0:20:52.556 --> 0:20:56.116
<v Speaker 1>with stats and a color coded report card. One of

0:20:56.116 --> 0:20:58.396
<v Speaker 1>the grades is based on the length of her monologues.

0:20:58.676 --> 0:21:02.436
<v Speaker 1>When the customer monologues, Gong doesn't call it that, They

0:21:02.476 --> 0:21:07.396
<v Speaker 1>call it a story. If you've got a woman of

0:21:07.516 --> 0:21:09.196
<v Speaker 1>few words on the other end of the line and

0:21:09.436 --> 0:21:11.236
<v Speaker 1>there's not much you can do to tease a big

0:21:11.236 --> 0:21:13.796
<v Speaker 1>story out of her, you totally right. And that's when

0:21:13.836 --> 0:21:18.316
<v Speaker 1>I find that my longest monologue, longest customer story, and

0:21:18.396 --> 0:21:23.276
<v Speaker 1>talk ratio could be all in yellow. Yellow is Gong's

0:21:23.356 --> 0:21:28.156
<v Speaker 1>color grade for a C. Green is an A red

0:21:28.396 --> 0:21:31.716
<v Speaker 1>is an F. Megan's never in the red. She's one

0:21:31.716 --> 0:21:35.476
<v Speaker 1>of Avic's top salespeople and also almost by definition, a

0:21:35.596 --> 0:21:39.596
<v Speaker 1>salesperson that Gong thinks is great. The only yellow card

0:21:39.636 --> 0:21:41.956
<v Speaker 1>she's ever likely to get from Gong is for interrupting.

0:21:42.516 --> 0:21:45.636
<v Speaker 1>I'm somebody who likes other people. I like talking, I

0:21:45.796 --> 0:21:49.356
<v Speaker 1>like engaging. I also like to be right. I like

0:21:49.556 --> 0:21:54.316
<v Speaker 1>to help. I like to give the information I think

0:21:54.396 --> 0:21:56.316
<v Speaker 1>is going to be helpful, and so I'm eager to

0:21:56.356 --> 0:21:58.476
<v Speaker 1>do that. So, in a funny way, you had to

0:21:58.476 --> 0:22:00.836
<v Speaker 1>be a different kind of person when you were a

0:22:00.836 --> 0:22:04.876
<v Speaker 1>salesperson than you were just out in the world. That's correct.

0:22:08.476 --> 0:22:11.716
<v Speaker 1>All this lead It's to an obvious thought. If Gone's

0:22:11.756 --> 0:22:19.756
<v Speaker 1>report cards work for sales, why stop there? So after

0:22:20.556 --> 0:22:23.276
<v Speaker 1>I'll be with my friends or with family, I'll think

0:22:23.316 --> 0:22:25.836
<v Speaker 1>to myself, after I leave, God, I interrupted a lot,

0:22:26.436 --> 0:22:28.716
<v Speaker 1>and it's just it's always in the back of my

0:22:28.756 --> 0:22:30.796
<v Speaker 1>mind now because I see it so often that my

0:22:30.836 --> 0:22:33.916
<v Speaker 1>patience is low. Is it Gone that made you aware

0:22:33.956 --> 0:22:38.716
<v Speaker 1>of it more so than before? Absolutely? Does anybody around

0:22:38.756 --> 0:22:41.876
<v Speaker 1>you sense that your approach is changing at all? Is

0:22:41.916 --> 0:22:44.836
<v Speaker 1>anybody noted to you that you know, you're all of

0:22:44.876 --> 0:22:49.036
<v Speaker 1>a sudden listening better to me, my spousehays does that count?

0:22:49.596 --> 0:22:53.116
<v Speaker 1>Oh my God, tell me about that. Yes. So I

0:22:53.996 --> 0:22:56.356
<v Speaker 1>We've had the conversation after we've left a social setting

0:22:56.716 --> 0:23:00.476
<v Speaker 1>where I've said, god, I did I was interrupting a

0:23:00.476 --> 0:23:04.316
<v Speaker 1>lot at dinner or whatever, and she has said to me,

0:23:04.676 --> 0:23:08.996
<v Speaker 1>you know you've been actually better than you have been previous.

0:23:09.276 --> 0:23:11.196
<v Speaker 1>You know you're getting better at listening, You're getting better

0:23:11.236 --> 0:23:15.756
<v Speaker 1>at not interrupting. So she has noticed that, Yeah, I don't,

0:23:15.876 --> 0:23:18.636
<v Speaker 1>I don't do it quite as often. Did you then

0:23:18.716 --> 0:23:21.996
<v Speaker 1>explain to her why you weren't doing it as often? No,

0:23:22.156 --> 0:23:26.956
<v Speaker 1>absolutely not. Life is a market. Everyone's always looking for

0:23:27.076 --> 0:23:29.996
<v Speaker 1>edges that other people don't have. Even a nice Canadian

0:23:30.036 --> 0:23:33.396
<v Speaker 1>woman knows better than to give her secrets away. Do

0:23:33.436 --> 0:23:35.276
<v Speaker 1>you think the world would be a better place if

0:23:35.316 --> 0:23:40.236
<v Speaker 1>we were all gone in all of our conversations. I

0:23:40.276 --> 0:23:43.276
<v Speaker 1>think it depends on the level of you know, give

0:23:43.276 --> 0:23:45.676
<v Speaker 1>a shit that the person has to want to do

0:23:45.796 --> 0:23:48.836
<v Speaker 1>better or be better, right. But I think a lot

0:23:48.876 --> 0:23:51.036
<v Speaker 1>of people who spend a lot of time just talking

0:23:51.036 --> 0:23:55.276
<v Speaker 1>about themselves are unaware how much time they're spending talking

0:23:55.316 --> 0:23:59.516
<v Speaker 1>about themselves. That's very true. Yeah, there's no question that

0:23:59.596 --> 0:24:02.596
<v Speaker 1>the future of this is gone trying to figure out

0:24:03.756 --> 0:24:08.156
<v Speaker 1>and reduced to little color coded bars. What it is

0:24:08.196 --> 0:24:12.636
<v Speaker 1>that causes a uson to like another person? Oh yeah,

0:24:12.676 --> 0:24:16.476
<v Speaker 1>which is scary when you think about it, but incredibly useful.

0:24:17.556 --> 0:24:21.076
<v Speaker 1>Gong's now being used by fifty thousand salespeople at almost

0:24:21.076 --> 0:24:24.756
<v Speaker 1>a thousand different companies. We grab Megan sort of at random,

0:24:25.036 --> 0:24:27.556
<v Speaker 1>and she's a sample size of one. But it's not

0:24:27.596 --> 0:24:30.476
<v Speaker 1>hard to see how this new coaching tool might shape behavior.

0:24:30.996 --> 0:24:35.116
<v Speaker 1>It creates new stats, Gong stats. The stats capture something

0:24:35.196 --> 0:24:37.436
<v Speaker 1>true about the performance, the same way that I don't

0:24:37.436 --> 0:24:40.516
<v Speaker 1>know on base percentage captures something true about a baseball

0:24:40.556 --> 0:24:43.716
<v Speaker 1>hitter's performance. The management pays more to the people with

0:24:43.756 --> 0:24:47.356
<v Speaker 1>good stats, and so soon everyone's just adjusting their game.

0:24:48.196 --> 0:24:51.836
<v Speaker 1>Baseball hitters are learning plate discipline, and salespeople are learning patients.

0:24:52.276 --> 0:24:55.516
<v Speaker 1>Most salespeople would say like Gong is like change my

0:24:55.676 --> 0:24:58.756
<v Speaker 1>game forever. This is a meet bendoff. Again, they use

0:24:58.836 --> 0:25:02.356
<v Speaker 1>those were like a game changer. It is possible if

0:25:03.436 --> 0:25:06.956
<v Speaker 1>if your product isn't good or a competitive in the market,

0:25:07.156 --> 0:25:09.956
<v Speaker 1>it doesn't matter if he has like thirds question, right.

0:25:09.956 --> 0:25:12.036
<v Speaker 1>I mean, it's like, so there's only so much that

0:25:12.076 --> 0:25:14.396
<v Speaker 1>you can do. I mean there's like, uh, this is

0:25:14.436 --> 0:25:17.756
<v Speaker 1>not some kind of magic, it's just like facts. But

0:25:17.836 --> 0:25:19.956
<v Speaker 1>there's something else that happens with data. In the right

0:25:19.996 --> 0:25:23.396
<v Speaker 1>analysis of it, it causes all kinds of folk beliefs

0:25:23.436 --> 0:25:26.196
<v Speaker 1>to just disappear. Let me give you an anecdote. So,

0:25:26.236 --> 0:25:30.036
<v Speaker 1>a lot of the managers and the coaches are often

0:25:30.076 --> 0:25:33.316
<v Speaker 1>obsessed with the filler words. When we introduce the product, Oh,

0:25:33.316 --> 0:25:35.396
<v Speaker 1>can you track like fuller words, you know, like oombs

0:25:35.436 --> 0:25:39.396
<v Speaker 1>and arms and like and so and like. It drives

0:25:39.396 --> 0:25:43.396
<v Speaker 1>them nuts. But you know what, we ran the research

0:25:44.276 --> 0:25:48.556
<v Speaker 1>and turns out there's zero correlation between like usage of

0:25:48.996 --> 0:25:54.556
<v Speaker 1>filler words and success. Huh, So, so that parameter doesn't

0:25:54.556 --> 0:25:57.476
<v Speaker 1>really matter. The managers were thinking that the more filler

0:25:57.556 --> 0:26:00.716
<v Speaker 1>words the worse. Yes, right, it is annoying to hear

0:26:00.756 --> 0:26:03.396
<v Speaker 1>them when you listen to recording and there's and people

0:26:03.476 --> 0:26:07.836
<v Speaker 1>say like a hundred times it drives you nuts, right right.

0:26:07.996 --> 0:26:11.156
<v Speaker 1>The fact is it doesn't matter. The fact is you

0:26:11.276 --> 0:26:14.316
<v Speaker 1>never know any of this without all these facts. Gong

0:26:14.476 --> 0:26:19.036
<v Speaker 1>is screwing up everybody's assumptions. Like the recent one was

0:26:19.156 --> 0:26:24.276
<v Speaker 1>using swear words on calls, does it help, does it

0:26:24.316 --> 0:26:27.316
<v Speaker 1>get in a way or does it even matter? And

0:26:27.316 --> 0:26:31.876
<v Speaker 1>what's the answer. Um, So it depends what the correlation,

0:26:32.116 --> 0:26:35.036
<v Speaker 1>what the research shows. I said, it depends on what

0:26:35.076 --> 0:26:39.156
<v Speaker 1>you're selling, Like if you're selling bibles, it probably doesn't help. Yeah, yeah,

0:26:39.196 --> 0:26:42.956
<v Speaker 1>we didn't have any Bible sellers over there in the stats.

0:26:42.996 --> 0:26:46.036
<v Speaker 1>But it's run over like, you know, fifty thousand people,

0:26:46.116 --> 0:26:51.036
<v Speaker 1>so it's it's a very large number. But the corollary

0:26:51.396 --> 0:26:55.036
<v Speaker 1>is that it depends who starts first. If the buyer

0:26:55.396 --> 0:26:59.676
<v Speaker 1>starts with curse words and you match that that that's

0:26:59.676 --> 0:27:03.716
<v Speaker 1>actually works better. But if you start it it doesn't help.

0:27:04.316 --> 0:27:06.556
<v Speaker 1>You might fucking believe that, or you might fucking not.

0:27:07.036 --> 0:27:09.316
<v Speaker 1>The point is that it doesn't matter what you believe.

0:27:10.076 --> 0:27:13.356
<v Speaker 1>The data generates the knowledge, and the knowledge allows you

0:27:13.396 --> 0:27:15.676
<v Speaker 1>to coach people how to do better at the most

0:27:15.676 --> 0:27:23.556
<v Speaker 1>basic human activity talking. Can you imagine a future like

0:27:23.996 --> 0:27:28.356
<v Speaker 1>that distant future where human interactions will have changed pretty

0:27:28.396 --> 0:27:31.556
<v Speaker 1>meaningfully because we'll all have been coached up in how

0:27:31.596 --> 0:27:36.836
<v Speaker 1>to have conversations. Absolutely, the device that we're using today,

0:27:37.116 --> 0:27:41.236
<v Speaker 1>like namely like speech or the English language hasn't changed

0:27:41.476 --> 0:27:45.316
<v Speaker 1>much for thousands of years, where the amount of information

0:27:45.356 --> 0:27:47.796
<v Speaker 1>that we need to exchange today and the amount of

0:27:47.876 --> 0:27:51.676
<v Speaker 1>noise and clutter and the environment is so different that

0:27:51.876 --> 0:27:57.316
<v Speaker 1>it could definitely use an upgrade. An upgrade. Well, one

0:27:57.356 --> 0:27:59.996
<v Speaker 1>thing hasn't changed. If they're better ways to manipulate people,

0:28:00.516 --> 0:28:02.916
<v Speaker 1>salespeople will find them first and the rest of us

0:28:02.916 --> 0:28:06.836
<v Speaker 1>will just follow. We focus on things that are teachable,

0:28:07.076 --> 0:28:09.476
<v Speaker 1>are coachable, all right, we can't teach anybody how to

0:28:09.556 --> 0:28:11.596
<v Speaker 1>be funny. Let me stop, Let me stop, let me

0:28:11.596 --> 0:28:14.956
<v Speaker 1>stop you for a second. I'm sorry, I know I interrupted,

0:28:15.236 --> 0:28:18.076
<v Speaker 1>but this is important. You don't think you could teach

0:28:18.076 --> 0:28:20.796
<v Speaker 1>people to be funny. Well, we haven't been able to

0:28:20.836 --> 0:28:24.716
<v Speaker 1>crack that code yet. Maybe a meat hasn't cracked that code,

0:28:25.156 --> 0:28:36.396
<v Speaker 1>But that doesn't mean that it can't be cracked. Well,

0:28:36.396 --> 0:28:39.396
<v Speaker 1>you can measure precise things contained in the conversation, the

0:28:39.436 --> 0:28:42.636
<v Speaker 1>words that people are saying to each other, and how

0:28:42.636 --> 0:28:46.876
<v Speaker 1>it influences outcomes. Allison wood Brooks is an associate professor

0:28:46.876 --> 0:28:49.316
<v Speaker 1>at the Harvard Business School. Like, we're on a date,

0:28:49.356 --> 0:28:50.916
<v Speaker 1>do you want to go out with me again? We're

0:28:50.916 --> 0:28:53.116
<v Speaker 1>on a sales call? Did it convert to a sale?

0:28:53.796 --> 0:28:56.236
<v Speaker 1>All kinds of things that you can connect the content

0:28:56.276 --> 0:28:59.076
<v Speaker 1>of the conversation with things that really matter, with outcomes

0:28:59.076 --> 0:29:02.716
<v Speaker 1>that really matter. Professor Brooks takes all these conversations and

0:29:02.836 --> 0:29:06.396
<v Speaker 1>analyzes them. She's using the same new machine learning that

0:29:06.476 --> 0:29:09.756
<v Speaker 1>Gong uses, but she's looking for different things. Give me

0:29:09.796 --> 0:29:12.996
<v Speaker 1>an example, one example of something that you can you

0:29:13.116 --> 0:29:16.196
<v Speaker 1>learn from this technology, Like something someone says that leads

0:29:16.236 --> 0:29:18.356
<v Speaker 1>the other person to want to go on a date.

0:29:18.836 --> 0:29:22.316
<v Speaker 1>Let's give you that exact example. We have a data

0:29:22.316 --> 0:29:25.436
<v Speaker 1>set of people doing speed dating. Each person went on

0:29:25.476 --> 0:29:28.636
<v Speaker 1>like twenty dates, okay, quick four to five minute speed

0:29:28.716 --> 0:29:32.316
<v Speaker 1>dates in round robin fashion. At the end of each date,

0:29:32.356 --> 0:29:33.796
<v Speaker 1>you say are you willing to go out with that

0:29:33.876 --> 0:29:37.956
<v Speaker 1>person again? And they record and transcribe all the interactions.

0:29:38.236 --> 0:29:40.556
<v Speaker 1>So now we see exactly what people are saying on

0:29:40.556 --> 0:29:42.316
<v Speaker 1>their dates, and we can measure what are the things

0:29:42.356 --> 0:29:44.516
<v Speaker 1>that people are doing and saying that make them more

0:29:44.556 --> 0:29:47.956
<v Speaker 1>likely to be more datable in the future. And one

0:29:47.956 --> 0:29:50.796
<v Speaker 1>thing that really matters is question asking. So asking more

0:29:50.876 --> 0:29:54.156
<v Speaker 1>questions for both men and women on these heterosexual dates

0:29:54.236 --> 0:29:58.996
<v Speaker 1>leads to better dating outcomes. Really, especially follow up questions.

0:29:59.356 --> 0:30:01.116
<v Speaker 1>How do you know that the people who are asking

0:30:01.116 --> 0:30:03.276
<v Speaker 1>the questions aren't just naturally more attracted to the people

0:30:03.276 --> 0:30:05.956
<v Speaker 1>who they're asking the questions of. Because so two things.

0:30:06.036 --> 0:30:09.956
<v Speaker 1>One we can control for other aspects of attractiveness observational

0:30:10.036 --> 0:30:13.116
<v Speaker 1>data too. We then come back to Harvard and run

0:30:13.156 --> 0:30:16.636
<v Speaker 1>experiments where we tell people ask a lot of questions

0:30:16.636 --> 0:30:18.916
<v Speaker 1>in one condition or another condition where we say we

0:30:18.916 --> 0:30:22.036
<v Speaker 1>don't tell them anything. In the condition where they're asking

0:30:22.036 --> 0:30:24.916
<v Speaker 1>a lot of questions, they also are more attractive and likable.

0:30:25.636 --> 0:30:28.556
<v Speaker 1>This new line of research has uncovered the various ways

0:30:28.596 --> 0:30:32.956
<v Speaker 1>that people gain power and authority in conversation. This professor

0:30:32.996 --> 0:30:35.396
<v Speaker 1>can prove that they work, and it's just made her

0:30:35.476 --> 0:30:39.116
<v Speaker 1>want to ask even more questions. Even if you know

0:30:40.156 --> 0:30:45.836
<v Speaker 1>conceptually what a charismatic, smooth, productive conversationalist looks like. Is

0:30:45.876 --> 0:30:48.756
<v Speaker 1>there any way that you can train people to actually

0:30:49.036 --> 0:30:52.676
<v Speaker 1>get better at executing it? Brooks has all this data,

0:30:52.796 --> 0:30:55.796
<v Speaker 1>some of it from Gong, but also from doctor's appointments

0:30:55.796 --> 0:30:58.996
<v Speaker 1>and work meetings and parole interviews and speed dating sessions

0:30:58.996 --> 0:31:01.596
<v Speaker 1>and on and on and on. She uses the data

0:31:01.676 --> 0:31:05.556
<v Speaker 1>to test theories about conversations, and one of those theories

0:31:05.676 --> 0:31:09.236
<v Speaker 1>is about humor. There's this great work that sends of

0:31:09.356 --> 0:31:15.396
<v Speaker 1>play right is like the key to psychological safety and

0:31:15.956 --> 0:31:19.516
<v Speaker 1>thriving and creativity. It's the only way that you can

0:31:19.516 --> 0:31:21.636
<v Speaker 1>really be creative in the presence of others is if

0:31:21.636 --> 0:31:24.556
<v Speaker 1>you feel safe to say something stupid and silly. That

0:31:24.636 --> 0:31:26.676
<v Speaker 1>all sounds sensible to me, But do you have any

0:31:27.756 --> 0:31:30.876
<v Speaker 1>data to back up absolutely? What's the data? Let me

0:31:30.876 --> 0:31:33.996
<v Speaker 1>tell you about one paper. It's a bidirectional finding, meaning

0:31:34.316 --> 0:31:38.596
<v Speaker 1>people who have high status tend to be more free

0:31:38.636 --> 0:31:41.396
<v Speaker 1>and use humor more freely. But the more interesting direction

0:31:41.556 --> 0:31:44.956
<v Speaker 1>is if you are of low status, if you can

0:31:45.076 --> 0:31:49.116
<v Speaker 1>land a joke, people perceive you as higher status. A

0:31:49.156 --> 0:31:51.916
<v Speaker 1>lot of polite laughter happens. So but if other people

0:31:51.956 --> 0:31:55.676
<v Speaker 1>think what you said is actually funny, appropriate for the circumstances,

0:31:55.796 --> 0:31:58.956
<v Speaker 1>and at least one person laughs at it, your status

0:31:58.996 --> 0:32:05.036
<v Speaker 1>takes this huge jump. Totally true, right, So yes, it

0:32:05.116 --> 0:32:08.316
<v Speaker 1>pays to be funny. Funny gets you status, and status

0:32:08.316 --> 0:32:13.036
<v Speaker 1>get you money. But the mystery remains, can funny be coached?

0:32:14.156 --> 0:32:16.636
<v Speaker 1>There's a part of me that wonders if there's a

0:32:16.756 --> 0:32:20.876
<v Speaker 1>version of what's going on in baseball coaching that doesn't

0:32:20.916 --> 0:32:23.516
<v Speaker 1>apply to what you're doing. And what's going on baseball

0:32:23.556 --> 0:32:27.716
<v Speaker 1>coaching is the technology has generated all this data about

0:32:27.716 --> 0:32:32.556
<v Speaker 1>how a pitcher's body moves, and they're able to identify

0:32:33.156 --> 0:32:36.796
<v Speaker 1>people who have an aptitude for doing things that is

0:32:36.876 --> 0:32:40.556
<v Speaker 1>untapped because they have whatever. The fundamental attribute is arm

0:32:40.596 --> 0:32:44.956
<v Speaker 1>speed that you can't teach, but translating the arm speed

0:32:44.996 --> 0:32:47.196
<v Speaker 1>into a speed on a fastball is a different thing.

0:32:47.556 --> 0:32:50.276
<v Speaker 1>And I wonder if there's an equivalent in conversation where

0:32:50.956 --> 0:32:56.676
<v Speaker 1>you could identify the core traits that lead to conversational

0:32:56.876 --> 0:33:00.596
<v Speaker 1>excellence and you can figure out who's sort of maxing

0:33:00.596 --> 0:33:06.916
<v Speaker 1>out and who's not and why totally. And not only that,

0:33:07.636 --> 0:33:10.716
<v Speaker 1>Allison's taken everything she's learned about human speech, humor, and

0:33:10.756 --> 0:33:13.116
<v Speaker 1>the rest and built it into a new course at

0:33:13.116 --> 0:33:16.676
<v Speaker 1>the Harvard Business School. It's called how to Talk Gooder

0:33:17.156 --> 0:33:20.356
<v Speaker 1>in Business and in Life. It's trying to turn conversation

0:33:20.556 --> 0:33:23.436
<v Speaker 1>into a science. It's like having the right arm for

0:33:23.476 --> 0:33:26.316
<v Speaker 1>the fastball and not doing it quite right. You've got

0:33:26.396 --> 0:33:28.556
<v Speaker 1>the brain space and the ability to do it, you

0:33:28.596 --> 0:33:31.716
<v Speaker 1>just didn't think to do it. That's the dream scenario,

0:33:31.756 --> 0:33:35.196
<v Speaker 1>and that's really the hope with these HBS students, right

0:33:35.236 --> 0:33:39.476
<v Speaker 1>These are super smart people who maybe just haven't heard

0:33:39.516 --> 0:33:43.076
<v Speaker 1>the right strategies. Hundreds of Harvard students tried to get

0:33:43.116 --> 0:33:47.236
<v Speaker 1>into Alison's new class. She accepted seventy seventy of the

0:33:47.276 --> 0:33:51.236
<v Speaker 1>world's most ambitious people, hoping that the science of conversation

0:33:51.676 --> 0:33:55.316
<v Speaker 1>will offer them yet another edge in life. Many people

0:33:55.356 --> 0:33:58.516
<v Speaker 1>would find it odd to approach a conversation seeking to

0:33:58.596 --> 0:34:03.196
<v Speaker 1>maximize the profits of it. Exactly, They'll love it. They

0:34:03.196 --> 0:34:06.156
<v Speaker 1>want to perform optimally in every way in their lives,

0:34:06.436 --> 0:34:08.796
<v Speaker 1>and this is the moment to moment way that you

0:34:08.836 --> 0:34:13.196
<v Speaker 1>would achieve that moment to moment, and in each moment,

0:34:13.236 --> 0:34:15.876
<v Speaker 1>there's now data which can be used by a coach.

0:34:16.316 --> 0:34:19.676
<v Speaker 1>I'll be checking in with Alison's students next week and

0:34:19.796 --> 0:34:22.996
<v Speaker 1>asking a new question. When you start to get an

0:34:23.076 --> 0:34:27.436
<v Speaker 1>edge like this, a data edge, does the coaching start

0:34:27.476 --> 0:34:31.916
<v Speaker 1>to shade into something else, something that's maybe against the rules.

0:34:38.396 --> 0:34:41.556
<v Speaker 1>I'm Michael Lewis, Thanks for listening to Against the Rules.

0:34:42.516 --> 0:34:45.076
<v Speaker 1>Against the Rules is brought to you by Pushkin Industries.

0:34:45.276 --> 0:34:49.756
<v Speaker 1>The show's produced by Audrey Dilling and Catherine girodo Or

0:34:49.876 --> 0:34:53.996
<v Speaker 1>Girardo or girodot Or Girardo I've Never Gotten It right,

0:34:54.276 --> 0:34:58.636
<v Speaker 1>with research assistance from Lydia Genecott and Zooe Wynn Our

0:34:58.796 --> 0:35:03.036
<v Speaker 1>editor is the magnificent Julia Barton, who finds my every mistake.

0:35:03.396 --> 0:35:06.676
<v Speaker 1>Mia Loebell is our executive producer, and she disapproves of

0:35:06.716 --> 0:35:10.596
<v Speaker 1>me half the time. Our theme was composed by Nick Brittell,

0:35:10.716 --> 0:35:14.116
<v Speaker 1>who is really slumming it working here, with additional scoring

0:35:14.196 --> 0:35:18.596
<v Speaker 1>by Stellwagon Symphonette. We got fact checked by Beth Johnson,

0:35:18.636 --> 0:35:22.036
<v Speaker 1>which was totally unnecessary because our facts are always right.

0:35:22.356 --> 0:35:25.236
<v Speaker 1>And our show was recorded by tofur Ruth and Trey

0:35:25.316 --> 0:35:29.476
<v Speaker 1>Schiltz in spite of enduring the coronavirus in the studio,

0:35:29.836 --> 0:35:32.836
<v Speaker 1>which is the Northgate Studios in Berkeley, as always thanks

0:35:32.876 --> 0:35:35.996
<v Speaker 1>to Pushkin's founders Jacob Weisberg, who I think of as

0:35:36.076 --> 0:35:38.756
<v Speaker 1>my brain, and Malcolm Gladwell, who I think of as

0:35:38.796 --> 0:35:51.956
<v Speaker 1>my goal. Do you sense that the challenge with men

0:35:52.076 --> 0:35:54.556
<v Speaker 1>is different from the challenge with women. We do have

0:35:54.676 --> 0:36:00.636
<v Speaker 1>a good amount of research on gender in conversation. For example,

0:36:00.836 --> 0:36:03.756
<v Speaker 1>we know Matthias Melt the University of Arizona has this

0:36:03.876 --> 0:36:07.116
<v Speaker 1>great paper showing that men and women are equally talkative

0:36:07.436 --> 0:36:10.156
<v Speaker 1>and even though women have the stereo type of being chattier,

0:36:10.516 --> 0:36:12.596
<v Speaker 1>what he finds is that men and women both speak

0:36:12.636 --> 0:36:16.276
<v Speaker 1>about sixteen thousand words per day, so that's about similar.

0:36:16.316 --> 0:36:18.396
<v Speaker 1>But what we do know from other researches that men

0:36:18.396 --> 0:36:22.436
<v Speaker 1>and women speak at different times, especially in the workplace.

0:36:22.556 --> 0:36:24.996
<v Speaker 1>You know, I find I speak when a woman needs

0:36:24.996 --> 0:36:30.116
<v Speaker 1>something explained to her, exactly fifteen and a half thousand

0:36:30.156 --> 0:36:32.996
<v Speaker 1>and of my words every day just to do that,

0:36:33.316 --> 0:36:37.036
<v Speaker 1>and for some reason, for some reason, they don't appreciate it.

0:36:38.636 --> 0:36:41.316
<v Speaker 1>This just is Could I learn how to get them

0:36:41.316 --> 0:36:45.476
<v Speaker 1>to appreciate it by coming to your class? No, No,

0:36:46.916 --> 0:36:50.236
<v Speaker 1>what kind of class is this? Oh my god, it's

0:36:50.316 --> 0:36:52.916
<v Speaker 1>too funny.