June 2, 2020 • 36 mins
We explore the quantitative, scientific, and data-driven new frontier of coaching.
- Major League baseball is undergoing a coaching revolution from old-school to new tech. We talk to players whose careers were turned around not by a charismatic coach, but by data, and the techies who coach them.
- We see how data coaching is creeping into the workspace with a computerized conversation coach that has pinned the successful sales pitch down to a science.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:15):
Pushkin. For roughly five thousand years, people call themselves doctors
and pretended to know all sorts of things that they
didn't know, and were as likely to kill you as
to cure you. These doctors existed because sick people desperately
(00:35):
wanted to believe in them. Coaching feels the same way
to me. For decades, people just sort of hoped that
if a man was hollering at them, he must be
helping them to win. Maybe he was sometimes, but that's
not my point. My point is that even if coaches
have no effect on performance, even if they're doing more
(00:56):
harm than good, we might still insist on having them
because we need someone on our side to believe in.
But coaching's changing the same way medicine changed one hundred
years ago. Coaches are discovering science, and science is discovering them.
(01:18):
I'm Michael Lewis and This is Against the Rules, a
show about various authority figures in American life. This season
is about the rise of coaches, and this episode is
about data and pitching. A while back in two thousand
(01:45):
and three, I published a book called Moneyball. It's about
how the Oakland A's baseball team had used data analysis
to get an edge on everyone else. They were a
poorly funded team in a small market. They had no
money to spend on players, but their new and better
statistics enable them to value baseball players more accurately, so
they could sell the players that were overvalued and trade
(02:07):
for the ones that were undervalued. I remember at the
time being shocked at the notion that baseball players could
be misvalued. I mean, baseball players have been doing the
same job for a century, out in the open, in
front of millions of people. But suddenly, all over a
(02:27):
thing that had been done a certain way forever was
now being done a totally different way. Everyone in baseball
started using data and getting all sorts of insights from it,
and the insights led not just to better valuations of
baseball players, it eventually led to a new kind of coaching.
In the past, it used to be that many coaching
(02:49):
positions were almost a sine cure. That's Ben Lindberg, co
author of a book called The MVP Machine. It's about
a revolution in how the world's best baseball players get coached.
It was the coaches who were the manager's palace, his
drinking buddies would become the coaches, and there wasn't that
much coaching going on at the major league level. It
(03:11):
was sort of reinforcing lessons that had already been taught
and keeping guys in line, but there wasn't that much
expectation that coaches would improve players once they got to
that level. But that was about to change big time.
Ben was part of a huge and growing crowd of
data geeks outside of baseball who spent lots and lots
(03:31):
of time analyzing players and building models to try to
predict their performance. As sophisticated as the statistical projection models are,
they'll only really look at the player's past performance and
his age and maybe some comparable players from the past,
and they'll spit out a projection that say, well, he
was this good in the past few years, and we'll
adjust for the ballpark, and here's how old he is,
(03:53):
and so here's our median projection for him, and so
all of a sudden, there players who are just busting
out of those projections exactly, And a projection system would
never forecast that. It might say that someone's going to
get a bit better, a bit worse, but typically it
won't say that someone is going to do something that's
completely out of line with their past performance. In other words,
teams had gotten really good, or at least a lot
(04:15):
better at evaluating the potential of all their players, but
the players were still playing better than expected, so much
better that the analysts were a bit suspicious. But the
last time you saw this was with the steroids era,
that all of a sudden, people players were performing in
ways that the projection models would never have guessed. And
(04:36):
you're so you're kind of seeing it, but without an
explanation as obvious as steroids exactly. Yeah, then started looking
into what these players were doing. The ones who were
dramatically exceeding the analysts expectations, the overperformers, all had something
in common. Coaches who use new technology. One of the
(04:57):
really big innovations has been the high speed camera. So
a company called Edgrotronic, which developed these cameras for scientific purposes,
has found that much of its business has come from
baseball teams because baseball teams have found that if you
train these high speed cameras on players, you can perceive
things about players movement that they didn't know about themselves.
(05:20):
And the coaches using these cameras were very different from
the old school baseball coaches, the Sinecure guys. For a start,
the new coaches weren't former big league players. In some cases,
they didn't even know any big league players. My name
is Kyle Bodie. Like this guy, I was twenty two
years old started coaching little league and I realized quickly
(05:42):
that I just didn't know anything about coaching. Kyle had
just moved from Ohio to Seattle, where he landed a
part time job as a little league coach. Yep, that's
how he started in life as a little league baseball coach.
Ed played some my father was a coach, but I
figured I owed it to the kids to learn just
a little bit more about keeping their arms healthy. And
(06:02):
he had some questions like how many pitches should a
kid be allowed to throw? And what was the best
way to throw him? I mean, I was once a pitcher,
and I spent half my life with my arm in
an ice bucket. To this day, I can't sleep on
my right shoulder throwing a ball overhand. It might look
like a natural and healthy thing to do, but it's not.
Kyle body looked around for research on the subject, but
(06:25):
unfortunately it was all very nonspecific kind of very academic research,
and then the training programs and the coaching programs that
were out there were very bland, not based on any
sort of evidence. It really shocked me, So Kyle started
to do research on his own. Then he got a
promotion from Little league to the freshman team at a
(06:46):
Seattle high school. But he found himself at war with
the JV and varsity coaches at the high school. They
were coaching the old fashioned way, telling players what to do,
hollering at them when they screwed up, praising them when
they didn't. Actively coaching athletes just typically makes them worse.
Just intervention is typically one of the worst things you
(07:07):
can do. What are the points of friction with the
old coaching model, Like, what specifically kind of things would
you do that were heretical. So informing the athlete that, like,
whatever they're doing is not good enough and then just
seeing how they change over time and how they will
self organize was a real heretical idea, right because most
(07:28):
coaches think that they have a lot to give to
the athlete, and my view on it still is today
is that they're good enough, like we just need to
give them the right direction and let them figure it
out for the most part. You ever read a book
called The Inner Game of Tennis? I have, Yeah, it's
one of my favorites. Anyway, In his first year coaching,
(07:49):
Kyle's freshman team won as many games as they lost,
which was actually pretty great. That season, the school's varsity
and JV teams were losing most of their games. But
at the end of the year, the head coach fired
Kyle because the other coaches hated his methods. They thought
he had no clue how to coach. That didn't stop Kyle.
He doubled down on his approach, and he wound up
(08:12):
building what amounted to a baseball bionic manfactory drive line Baseball,
he called it. So describe to me this laboratory you build.
In its current incarnation, it's about fifteen high speed motion
tracking cameras. There's force plates in there to measure ground
reaction forces. Attracts every movement two hundred forty times per second,
(08:35):
and it's submillimeter accurate, so every movement can be tracked
down to at least one millimeter of accuracy, and usually
much better. Let's go one hundred point four ninety three
point seven the high speed cameras allow Kyle to measure
the speed of a pitcher's arm, among other things, the
(08:56):
faster a pitcher's arm, the faster the ball comes out
of it in theory one hundred three point zero. In practice,
not all pictures are able to translate their arm speed
into ball speed. Two guys with the exact same arm
speeds might throw very different fastballs. If someone's arm speed
is extremely high and the ball comes out at like
(09:18):
a lower predictive velocity based on like a regression algorithm,
that we know that there's some inefficiencies there that we
should be able to easily clean up. That is, you
could identify people with the god given talent to throw
a baseball because you had new insight into where that
talent came from. So when that happens, are you thinking,
there's this pool of players out there who have high
(09:41):
arm speed, low velocity that we can just fix. That's
all that I think about pretty much every day. And
could you like drive around with a little truck and
put people in the back of your truck and test
their arm speed? Is that what you would do? Yeah,
that's actually funny. We almost butt an RV to do
exactly that to drive around the country with our lab.
It turned out he didn't need to drive around looking
(10:02):
for people with this weird arm talent. They found him.
I real that I needed to make a change going
into the season. I was helping out my dad's team
and trying to make money in every way. That's Matt
Boyd Back in twenty seventeen. He was an unknown minor
league pitcher coming off a terrible season. He had a
below average fastball around eighty nine miles an hour. Oh,
(10:25):
and he had an arm injury. The way he was going,
he's about to be spending a lot more time with
his mom and dad. Over to the Christmas break, one
of my dad's players came back and he was ninety
two guy in high school and he was at Oregon State,
and after the fall he was ninety eight miles per hour. Oh,
I went, whoa, whoa, whoa, I go, what were you
doing down there? He's like, I did the drive Line program.
(10:49):
Drive Line. That's Kyle Bodie's lab. So at that point,
Kyle would have been kind of a local secret. So
someone all of a sudden has this kind of miraculous
jump in velocity. You hear about it, and you go see,
you call Kyle had had you ever heard of him
at that point? No? I hadn't heard of him. Matt
goes to seek Kyle in his Bionic manfactory and here
(11:10):
we have cameras, great our guns. I go up a
little stairwell and here's you know, a little eight foot
wide by ten foot high probably pitching lane tunnel created
with all this technology in there. And there's Kyle behind
a computer and he runs me through the program. At
that point, had you ever seen the technology that was there? No?
(11:31):
And I honestly I didn't. I couldn't even told you
right today what I saw in there. I couldn't even
told you what was going on a bunch of fucket
lids for some for balldrills. It looks like this, a
lot of gizmos, a lot of gizmos, a lot of wires,
a lot of interesting looking baseballs, a lot of lines
on a pitching mound and stuff. And you know, Kyle
explained to me what the concept of what he does,
(11:51):
and we talked about it, and then we just started
the program. Kyle put Matt Boy through a bunch of tests.
The big one was to test his arm speed, but
he never used the phrase arm speed. Kyle actually never
told Matt what he was testing for. He in our
(12:12):
lab tested higher than pretty much everyone and still almost
everyone to this day. He's just an excellent athlete. And
yet the ball velocity wasn't where it needed to be
or where it predicted it should be. So what was
the inefficiency? Like? What was what was he doing that
caused the ball to come out more slowly than what
you would have predicted? Right, it's really hard. We don't
know the rude cause yet. That's that's the actual interesting
(12:32):
thing is we're still studying, like why this happens. At
the lab, Kyle hands Matt these really heavy balls to throw.
He's found that when people throw a heavy ball, their
body naturally finds the most efficient way to do it
because it's so painful and uncomfortable to throw it inefficiently.
(12:52):
Matt basically moves into the Bionic manfactory and throws heavy
balls the entire off season. Then rejoins his minor league
team and you know, I get down there, I tell
him I'm I'm full progression off the mount and they're like, okay,
well let's see it. My first bullpen I'm I'm ninety
two to ninety five. Oh, and I think everyone's going,
(13:16):
what the heck happened? You know, and even I am,
I'm going, man, I'm throwing the baseball up in the zone. Now,
this is amazing. This is so cool. Like when all
of a sudden, you've got this new weapon. Yeah, when
you got this fastball that's coming out of your hand
three or four miles an hour faster than it usually does.
What do you notice in what happened and how hitters
respond and how the effectiveness hitters have against you. I
(13:37):
remember going in to Double A that season and I did.
We have a catcher named Jack Murphy who was about
four or five years older than me, and he went
up and told me, he goes, Maddie, you have a
new fastball. You need to pitch off it. I remember,
I was kind of scared. I was like, what, I've
never done that in my life. I'm always fastball, change up.
And then I mixed my curveball, and you know, but
he challenged me, and all of a sudden, I'm like
(13:58):
striking guys out on three fastballs in Double A, and
I'm going like, is it this easy? Like this is
all it took. By the end of that season, Matt
Boyd had been called up to the major leagues to
the Detroit Tigers to be a starting pitcher. Paints in
and then paints away both fastballs. Want to pitch to
Bird another strikeout for Matthew boy to start the form.
(14:23):
They now pay him five point three million dollars a
year and feel like they're getting a deal. And Kyle Body, well,
now he has a new job too. I'm the president
and founder of drive Land Baseball and the director of
Pitching Initiatives of the Cincinnati Reds. Somebody asks a question,
how do you throw a ball faster? They gathered data.
(14:46):
They measure everything that the human body does when it
throws a ball. They test theories and find answers rooted
in science. All of a sudden, there's a new way
to coach, and it's getting adopted in sports. But not
just in sports, because there are people all over who
don't know why they're good at something or how to
get better. About five years ago, I was CEO and
(15:09):
at a software company and we're growing pretty quickly. This
is a meet bendoff. Five years ago, he was just
another Silicon Valley entrepreneur trying to get his product out
the door by using salespeople. But I was puzzled why
some of our people were more successful than others. And
(15:31):
every time we wanted to understand why, we had to
go and interview people and see what they think. And
he didn't want just a collection of stories. He wanted data.
Why did some sales pitches work while others didn't? Think
(15:51):
about like football right or baseball right? If the coach
never sees the game and the only thing is to
understand how to get better is by interviewing some of
the players what they think has happened. So I wanted
to have like something like a game tape and game
stats for sales and AI to understand what really separates
(16:13):
the top performers from everybody else. You know that message
that you get on customer service calls, This call may
be recorded for quality assurance. We appreciate your patience. Calls
for quality. Companies were recording their sales calls, but no
one was really listening to them. A met wanted to
listen to all the game tape and analyze it. And
(16:33):
I then started asking a bunch of other people, you know,
if we build a system kind of shine a light
on conversation and give you insights from that. Would you
buy how much you're willing to pay a meat? Created
a new company he called it Gong. He went to
people and said, hand me all the recordings of all
your sales calls, plus a list of your salespeople in
(16:54):
order of how good they are. We'll analyze it. And
so if you'd gone to just one of those top
sales people and ask what you're doing, it works. No, no,
absolutely not, because they don't know. People think it's an art, right.
It's like they're not aware that's something that they're doing
because they don't know what the other people are doing,
so they don't know what the differences are. The Gong
(17:17):
artificial intelligence, had no theory about what worked and what
didn't in sales. It just had millions of sales pitches.
It searched for patterns in both the calls they got
results and the calls that didn't. The Gong actually learns
like it looks at the salespeople and says, like, which
one is closing more deals, and then it starts to
(17:38):
analyze the difference. So the software learns automatically. You just
connect to the calls. If there was an art to
any of this, it was in the questions that Gong
asked if the phone call data. I mean, the very
simple one is like percent of talk time? Right, You
and I are talking right now, and by the end
of the call, Gong say, well, a meat was talking
(17:59):
fifty six percent of the time. It turns out there's
lots of things that separated great sales pitch from a
bad one. The simple virtues. They're sort of obvious, but
they could now be quantified. On average, forty six percent
talk time is ideal. Thirteen questions is optimal? Right? Not less?
(18:21):
No more so, this alone is food for thought that
there's a maximum amount of time to be talking. If
I were to talk only forty six percent of the time,
would my wife stop telling me that I don't listen?
What if I counted the number of questions I asked
in a particular conversation and stopped myself at thirteen. I mean,
it's not that if you ask fourteen you lose that deal.
(18:43):
But more than that, people might lose their patience, and
too less it means you're talking too much. Patience factor,
that's like a pretty big challenge for a lot of people,
they're too quick to respond, so it's a good practice
to pause think in any reply. We all know that
(19:04):
in theory, at least, Gone could act just like a
game coach, just last instructions into a person's ear as
they pitched the product. Shut the hell up, ask another question.
You're at seventy three percent, and Meek decided it was
better if he didn't do this. If he offered the
feedback after the call instead, you know, will alert the
(19:25):
coaches to hear like three conversations that have room for improvement,
and then the coach will open them up just like
a game tape and start breaking it down to start commenting, Oh,
minute three ZH six, I really love the way you
phrased that question. Have you considered X man? If I
(19:47):
were in sales, all of this would drive me batshit,
which is why I'm not in sales and also probably
why everyone but my mother is still making fun of
my podcast ad Reads. But for people who actually make
their living selling stuff, well, Gong is their new best coach.
So when I'm engaging with a customer, especially in introductory calls,
(20:07):
I want them to do as much of the talking
is possible. That's Megan Dorner, who sells software for a
Canadian company called AVIC. I have no idea what the
software it does, but that's not important. The key fact
about it is that it needs to be sold. The
more the customers talking, the more that I'm learning, especially
like I said on that first call. And so the
longest customer story again is how long they're telling a
(20:32):
you know, an interactive I guess one long tail. So
that the customers monologuing, that's good, that's correct, that's correct.
But if you're monologuing, it's bad. Megan now pays attention
to who's monologuing, because Gong forces her too. Five minutes
after every call, she gets a note from Gong filled
(20:52):
with stats and a color coded report card. One of
the grades is based on the length of her monologues.
When the customer monologues, Gong doesn't call it that, They
call it a story. If you've got a woman of
few words on the other end of the line and
there's not much you can do to tease a big
story out of her, you totally right. And that's when
(21:13):
I find that my longest monologue, longest customer story, and
talk ratio could be all in yellow. Yellow is Gong's
color grade for a C. Green is an A red
is an F. Megan's never in the red. She's one
of Avic's top salespeople and also almost by definition, a
(21:35):
salesperson that Gong thinks is great. The only yellow card
she's ever likely to get from Gong is for interrupting.
I'm somebody who likes other people. I like talking, I
like engaging. I also like to be right. I like
to help. I like to give the information I think
is going to be helpful, and so I'm eager to
(21:56):
do that. So, in a funny way, you had to
be a different kind of person when you were a
salesperson than you were just out in the world. That's correct.
All this lead It's to an obvious thought. If Gone's
report cards work for sales, why stop there? So after
(22:20):
I'll be with my friends or with family, I'll think
to myself, after I leave, God, I interrupted a lot,
and it's just it's always in the back of my
mind now because I see it so often that my
patience is low. Is it Gone that made you aware
of it more so than before? Absolutely? Does anybody around
you sense that your approach is changing at all? Is
(22:41):
anybody noted to you that you know, you're all of
a sudden listening better to me, my spousehays does that count?
Oh my God, tell me about that. Yes. So I
We've had the conversation after we've left a social setting
where I've said, god, I did I was interrupting a
lot at dinner or whatever, and she has said to me,
(23:04):
you know you've been actually better than you have been previous.
You know you're getting better at listening, You're getting better
at not interrupting. So she has noticed that, Yeah, I don't,
I don't do it quite as often. Did you then
explain to her why you weren't doing it as often? No,
absolutely not. Life is a market. Everyone's always looking for
(23:27):
edges that other people don't have. Even a nice Canadian
woman knows better than to give her secrets away. Do
you think the world would be a better place if
we were all gone in all of our conversations. I
think it depends on the level of you know, give
a shit that the person has to want to do
better or be better, right. But I think a lot
(23:48):
of people who spend a lot of time just talking
about themselves are unaware how much time they're spending talking
about themselves. That's very true. Yeah, there's no question that
the future of this is gone trying to figure out
and reduced to little color coded bars. What it is
that causes a uson to like another person? Oh yeah,
(24:12):
which is scary when you think about it, but incredibly useful.
Gong's now being used by fifty thousand salespeople at almost
a thousand different companies. We grab Megan sort of at random,
and she's a sample size of one. But it's not
hard to see how this new coaching tool might shape behavior.
It creates new stats, Gong stats. The stats capture something
(24:35):
true about the performance, the same way that I don't
know on base percentage captures something true about a baseball
hitter's performance. The management pays more to the people with
good stats, and so soon everyone's just adjusting their game.
Baseball hitters are learning plate discipline, and salespeople are learning patients.
Most salespeople would say like Gong is like change my
(24:55):
game forever. This is a meet bendoff. Again, they use
those were like a game changer. It is possible if
if your product isn't good or a competitive in the market,
it doesn't matter if he has like thirds question, right.
I mean, it's like, so there's only so much that
you can do. I mean there's like, uh, this is
not some kind of magic, it's just like facts. But
(25:17):
there's something else that happens with data. In the right
analysis of it, it causes all kinds of folk beliefs
to just disappear. Let me give you an anecdote. So,
a lot of the managers and the coaches are often
obsessed with the filler words. When we introduce the product, Oh,
can you track like fuller words, you know, like oombs
and arms and like and so and like. It drives
(25:39):
them nuts. But you know what, we ran the research
and turns out there's zero correlation between like usage of
filler words and success. Huh, So, so that parameter doesn't
really matter. The managers were thinking that the more filler
words the worse. Yes, right, it is annoying to hear
(26:00):
them when you listen to recording and there's and people
say like a hundred times it drives you nuts, right right.
The fact is it doesn't matter. The fact is you
never know any of this without all these facts. Gong
is screwing up everybody's assumptions. Like the recent one was
using swear words on calls, does it help, does it
(26:24):
get in a way or does it even matter? And
what's the answer. Um, So it depends what the correlation,
what the research shows. I said, it depends on what
you're selling, Like if you're selling bibles, it probably doesn't help. Yeah, yeah,
we didn't have any Bible sellers over there in the stats.
But it's run over like, you know, fifty thousand people,
(26:46):
so it's it's a very large number. But the corollary
is that it depends who starts first. If the buyer
starts with curse words and you match that that that's
actually works better. But if you start it it doesn't help.
You might fucking believe that, or you might fucking not.
(27:07):
The point is that it doesn't matter what you believe.
The data generates the knowledge, and the knowledge allows you
to coach people how to do better at the most
basic human activity talking. Can you imagine a future like
that distant future where human interactions will have changed pretty
(27:28):
meaningfully because we'll all have been coached up in how
to have conversations. Absolutely, the device that we're using today,
like namely like speech or the English language hasn't changed
much for thousands of years, where the amount of information
that we need to exchange today and the amount of
noise and clutter and the environment is so different that
(27:51):
it could definitely use an upgrade. An upgrade. Well, one
thing hasn't changed. If they're better ways to manipulate people,
salespeople will find them first and the rest of us
will just follow. We focus on things that are teachable,
are coachable, all right, we can't teach anybody how to
be funny. Let me stop, Let me stop, let me
stop you for a second. I'm sorry, I know I interrupted,
(28:15):
but this is important. You don't think you could teach
people to be funny. Well, we haven't been able to
crack that code yet. Maybe a meat hasn't cracked that code,
But that doesn't mean that it can't be cracked. Well,
(28:36):
you can measure precise things contained in the conversation, the
words that people are saying to each other, and how
it influences outcomes. Allison wood Brooks is an associate professor
at the Harvard Business School. Like, we're on a date,
do you want to go out with me again? We're
on a sales call? Did it convert to a sale?
All kinds of things that you can connect the content
of the conversation with things that really matter, with outcomes
(28:59):
that really matter. Professor Brooks takes all these conversations and
analyzes them. She's using the same new machine learning that
Gong uses, but she's looking for different things. Give me
an example, one example of something that you can you
learn from this technology, Like something someone says that leads
the other person to want to go on a date.
Let's give you that exact example. We have a data
(29:22):
set of people doing speed dating. Each person went on
like twenty dates, okay, quick four to five minute speed
dates in round robin fashion. At the end of each date,
you say are you willing to go out with that
person again? And they record and transcribe all the interactions.
So now we see exactly what people are saying on
their dates, and we can measure what are the things
(29:42):
that people are doing and saying that make them more
likely to be more datable in the future. And one
thing that really matters is question asking. So asking more
questions for both men and women on these heterosexual dates
leads to better dating outcomes. Really, especially follow up questions.
How do you know that the people who are asking
the questions aren't just naturally more attracted to the people
(30:03):
who they're asking the questions of. Because so two things.
One we can control for other aspects of attractiveness observational
data too. We then come back to Harvard and run
experiments where we tell people ask a lot of questions
in one condition or another condition where we say we
don't tell them anything. In the condition where they're asking
a lot of questions, they also are more attractive and likable.
(30:25):
This new line of research has uncovered the various ways
that people gain power and authority in conversation. This professor
can prove that they work, and it's just made her
want to ask even more questions. Even if you know
conceptually what a charismatic, smooth, productive conversationalist looks like. Is
(30:45):
there any way that you can train people to actually
get better at executing it? Brooks has all this data,
some of it from Gong, but also from doctor's appointments
and work meetings and parole interviews and speed dating sessions
and on and on and on. She uses the data
to test theories about conversations, and one of those theories
is about humor. There's this great work that sends of
(31:09):
play right is like the key to psychological safety and
thriving and creativity. It's the only way that you can
really be creative in the presence of others is if
you feel safe to say something stupid and silly. That
all sounds sensible to me, But do you have any
data to back up absolutely? What's the data? Let me
(31:30):
tell you about one paper. It's a bidirectional finding, meaning
people who have high status tend to be more free
and use humor more freely. But the more interesting direction
is if you are of low status, if you can
land a joke, people perceive you as higher status. A
lot of polite laughter happens. So but if other people
(31:51):
think what you said is actually funny, appropriate for the circumstances,
and at least one person laughs at it, your status
takes this huge jump. Totally true, right, So yes, it
pays to be funny. Funny gets you status, and status
get you money. But the mystery remains, can funny be coached?
(32:14):
There's a part of me that wonders if there's a
version of what's going on in baseball coaching that doesn't
apply to what you're doing. And what's going on baseball
coaching is the technology has generated all this data about
how a pitcher's body moves, and they're able to identify
people who have an aptitude for doing things that is
(32:36):
untapped because they have whatever. The fundamental attribute is arm
speed that you can't teach, but translating the arm speed
into a speed on a fastball is a different thing.
And I wonder if there's an equivalent in conversation where
you could identify the core traits that lead to conversational
excellence and you can figure out who's sort of maxing
(33:00):
out and who's not and why totally. And not only that,
Allison's taken everything she's learned about human speech, humor, and
the rest and built it into a new course at
the Harvard Business School. It's called how to Talk Gooder
in Business and in Life. It's trying to turn conversation
into a science. It's like having the right arm for
(33:23):
the fastball and not doing it quite right. You've got
the brain space and the ability to do it, you
just didn't think to do it. That's the dream scenario,
and that's really the hope with these HBS students, right
These are super smart people who maybe just haven't heard
the right strategies. Hundreds of Harvard students tried to get
into Alison's new class. She accepted seventy seventy of the
(33:47):
world's most ambitious people, hoping that the science of conversation
will offer them yet another edge in life. Many people
would find it odd to approach a conversation seeking to
maximize the profits of it. Exactly, They'll love it. They
want to perform optimally in every way in their lives,
and this is the moment to moment way that you
(34:08):
would achieve that moment to moment, and in each moment,
there's now data which can be used by a coach.
I'll be checking in with Alison's students next week and
asking a new question. When you start to get an
edge like this, a data edge, does the coaching start
to shade into something else, something that's maybe against the rules.
(34:38):
I'm Michael Lewis, Thanks for listening to Against the Rules.
Against the Rules is brought to you by Pushkin Industries.
The show's produced by Audrey Dilling and Catherine girodo Or
Girardo or girodot Or Girardo I've Never Gotten It right,
with research assistance from Lydia Genecott and Zooe Wynn Our
(34:58):
editor is the magnificent Julia Barton, who finds my every mistake.
Mia Loebell is our executive producer, and she disapproves of
me half the time. Our theme was composed by Nick Brittell,
who is really slumming it working here, with additional scoring
by Stellwagon Symphonette. We got fact checked by Beth Johnson,
which was totally unnecessary because our facts are always right.
(35:22):
And our show was recorded by tofur Ruth and Trey
Schiltz in spite of enduring the coronavirus in the studio,
which is the Northgate Studios in Berkeley, as always thanks
to Pushkin's founders Jacob Weisberg, who I think of as
my brain, and Malcolm Gladwell, who I think of as
my goal. Do you sense that the challenge with men
(35:52):
is different from the challenge with women. We do have
a good amount of research on gender in conversation. For example,
we know Matthias Melt the University of Arizona has this
great paper showing that men and women are equally talkative
and even though women have the stereo type of being chattier,
what he finds is that men and women both speak
(36:12):
about sixteen thousand words per day, so that's about similar.
But what we do know from other researches that men
and women speak at different times, especially in the workplace.
You know, I find I speak when a woman needs
something explained to her, exactly fifteen and a half thousand
and of my words every day just to do that,
(36:33):
and for some reason, for some reason, they don't appreciate it.
This just is Could I learn how to get them
to appreciate it by coming to your class? No, No,
what kind of class is this? Oh my god, it's
too funny.
Pushkin. For roughly five thousand years, people call themselves doctors
and pretended to know all sorts of things that they
didn't know, and were as likely to kill you as
to cure you. These doctors existed because sick people desperately
(00:35):
wanted to believe in them. Coaching feels the same way
to me. For decades, people just sort of hoped that
if a man was hollering at them, he must be
helping them to win. Maybe he was sometimes, but that's
not my point. My point is that even if coaches
have no effect on performance, even if they're doing more
(00:56):
harm than good, we might still insist on having them
because we need someone on our side to believe in.
But coaching's changing the same way medicine changed one hundred
years ago. Coaches are discovering science, and science is discovering them.
(01:18):
I'm Michael Lewis and This is Against the Rules, a
show about various authority figures in American life. This season
is about the rise of coaches, and this episode is
about data and pitching. A while back in two thousand
(01:45):
and three, I published a book called Moneyball. It's about
how the Oakland A's baseball team had used data analysis
to get an edge on everyone else. They were a
poorly funded team in a small market. They had no
money to spend on players, but their new and better
statistics enable them to value baseball players more accurately, so
they could sell the players that were overvalued and trade
(02:07):
for the ones that were undervalued. I remember at the
time being shocked at the notion that baseball players could
be misvalued. I mean, baseball players have been doing the
same job for a century, out in the open, in
front of millions of people. But suddenly, all over a
(02:27):
thing that had been done a certain way forever was
now being done a totally different way. Everyone in baseball
started using data and getting all sorts of insights from it,
and the insights led not just to better valuations of
baseball players, it eventually led to a new kind of coaching.
In the past, it used to be that many coaching
(02:49):
positions were almost a sine cure. That's Ben Lindberg, co
author of a book called The MVP Machine. It's about
a revolution in how the world's best baseball players get coached.
It was the coaches who were the manager's palace, his
drinking buddies would become the coaches, and there wasn't that
much coaching going on at the major league level. It
(03:11):
was sort of reinforcing lessons that had already been taught
and keeping guys in line, but there wasn't that much
expectation that coaches would improve players once they got to
that level. But that was about to change big time.
Ben was part of a huge and growing crowd of
data geeks outside of baseball who spent lots and lots
(03:31):
of time analyzing players and building models to try to
predict their performance. As sophisticated as the statistical projection models are,
they'll only really look at the player's past performance and
his age and maybe some comparable players from the past,
and they'll spit out a projection that say, well, he
was this good in the past few years, and we'll
adjust for the ballpark, and here's how old he is,
(03:53):
and so here's our median projection for him, and so
all of a sudden, there players who are just busting
out of those projections exactly, And a projection system would
never forecast that. It might say that someone's going to
get a bit better, a bit worse, but typically it
won't say that someone is going to do something that's
completely out of line with their past performance. In other words,
teams had gotten really good, or at least a lot
(04:15):
better at evaluating the potential of all their players, but
the players were still playing better than expected, so much
better that the analysts were a bit suspicious. But the
last time you saw this was with the steroids era,
that all of a sudden, people players were performing in
ways that the projection models would never have guessed. And
(04:36):
you're so you're kind of seeing it, but without an
explanation as obvious as steroids exactly. Yeah, then started looking
into what these players were doing. The ones who were
dramatically exceeding the analysts expectations, the overperformers, all had something
in common. Coaches who use new technology. One of the
(04:57):
really big innovations has been the high speed camera. So
a company called Edgrotronic, which developed these cameras for scientific purposes,
has found that much of its business has come from
baseball teams because baseball teams have found that if you
train these high speed cameras on players, you can perceive
things about players movement that they didn't know about themselves.
(05:20):
And the coaches using these cameras were very different from
the old school baseball coaches, the Sinecure guys. For a start,
the new coaches weren't former big league players. In some cases,
they didn't even know any big league players. My name
is Kyle Bodie. Like this guy, I was twenty two
years old started coaching little league and I realized quickly
(05:42):
that I just didn't know anything about coaching. Kyle had
just moved from Ohio to Seattle, where he landed a
part time job as a little league coach. Yep, that's
how he started in life as a little league baseball coach.
Ed played some my father was a coach, but I
figured I owed it to the kids to learn just
a little bit more about keeping their arms healthy. And
(06:02):
he had some questions like how many pitches should a
kid be allowed to throw? And what was the best
way to throw him? I mean, I was once a pitcher,
and I spent half my life with my arm in
an ice bucket. To this day, I can't sleep on
my right shoulder throwing a ball overhand. It might look
like a natural and healthy thing to do, but it's not.
Kyle body looked around for research on the subject, but
(06:25):
unfortunately it was all very nonspecific kind of very academic research,
and then the training programs and the coaching programs that
were out there were very bland, not based on any
sort of evidence. It really shocked me, So Kyle started
to do research on his own. Then he got a
promotion from Little league to the freshman team at a
(06:46):
Seattle high school. But he found himself at war with
the JV and varsity coaches at the high school. They
were coaching the old fashioned way, telling players what to do,
hollering at them when they screwed up, praising them when
they didn't. Actively coaching athletes just typically makes them worse.
Just intervention is typically one of the worst things you
(07:07):
can do. What are the points of friction with the
old coaching model, Like, what specifically kind of things would
you do that were heretical. So informing the athlete that, like,
whatever they're doing is not good enough and then just
seeing how they change over time and how they will
self organize was a real heretical idea, right because most
(07:28):
coaches think that they have a lot to give to
the athlete, and my view on it still is today
is that they're good enough, like we just need to
give them the right direction and let them figure it
out for the most part. You ever read a book
called The Inner Game of Tennis? I have, Yeah, it's
one of my favorites. Anyway, In his first year coaching,
(07:49):
Kyle's freshman team won as many games as they lost,
which was actually pretty great. That season, the school's varsity
and JV teams were losing most of their games. But
at the end of the year, the head coach fired
Kyle because the other coaches hated his methods. They thought
he had no clue how to coach. That didn't stop Kyle.
He doubled down on his approach, and he wound up
(08:12):
building what amounted to a baseball bionic manfactory drive line Baseball,
he called it. So describe to me this laboratory you build.
In its current incarnation, it's about fifteen high speed motion
tracking cameras. There's force plates in there to measure ground
reaction forces. Attracts every movement two hundred forty times per second,
(08:35):
and it's submillimeter accurate, so every movement can be tracked
down to at least one millimeter of accuracy, and usually
much better. Let's go one hundred point four ninety three
point seven the high speed cameras allow Kyle to measure
the speed of a pitcher's arm, among other things, the
(08:56):
faster a pitcher's arm, the faster the ball comes out
of it in theory one hundred three point zero. In practice,
not all pictures are able to translate their arm speed
into ball speed. Two guys with the exact same arm
speeds might throw very different fastballs. If someone's arm speed
is extremely high and the ball comes out at like
(09:18):
a lower predictive velocity based on like a regression algorithm,
that we know that there's some inefficiencies there that we
should be able to easily clean up. That is, you
could identify people with the god given talent to throw
a baseball because you had new insight into where that
talent came from. So when that happens, are you thinking,
there's this pool of players out there who have high
(09:41):
arm speed, low velocity that we can just fix. That's
all that I think about pretty much every day. And
could you like drive around with a little truck and
put people in the back of your truck and test
their arm speed? Is that what you would do? Yeah,
that's actually funny. We almost butt an RV to do
exactly that to drive around the country with our lab.
It turned out he didn't need to drive around looking
(10:02):
for people with this weird arm talent. They found him.
I real that I needed to make a change going
into the season. I was helping out my dad's team
and trying to make money in every way. That's Matt
Boyd Back in twenty seventeen. He was an unknown minor
league pitcher coming off a terrible season. He had a
below average fastball around eighty nine miles an hour. Oh,
(10:25):
and he had an arm injury. The way he was going,
he's about to be spending a lot more time with
his mom and dad. Over to the Christmas break, one
of my dad's players came back and he was ninety
two guy in high school and he was at Oregon State,
and after the fall he was ninety eight miles per hour. Oh,
I went, whoa, whoa, whoa, I go, what were you
doing down there? He's like, I did the drive Line program.
(10:49):
Drive Line. That's Kyle Bodie's lab. So at that point,
Kyle would have been kind of a local secret. So
someone all of a sudden has this kind of miraculous
jump in velocity. You hear about it, and you go see,
you call Kyle had had you ever heard of him
at that point? No? I hadn't heard of him. Matt
goes to seek Kyle in his Bionic manfactory and here
(11:10):
we have cameras, great our guns. I go up a
little stairwell and here's you know, a little eight foot
wide by ten foot high probably pitching lane tunnel created
with all this technology in there. And there's Kyle behind
a computer and he runs me through the program. At
that point, had you ever seen the technology that was there? No?
(11:31):
And I honestly I didn't. I couldn't even told you
right today what I saw in there. I couldn't even
told you what was going on a bunch of fucket
lids for some for balldrills. It looks like this, a
lot of gizmos, a lot of gizmos, a lot of wires,
a lot of interesting looking baseballs, a lot of lines
on a pitching mound and stuff. And you know, Kyle
explained to me what the concept of what he does,
(11:51):
and we talked about it, and then we just started
the program. Kyle put Matt Boy through a bunch of tests.
The big one was to test his arm speed, but
he never used the phrase arm speed. Kyle actually never
told Matt what he was testing for. He in our
(12:12):
lab tested higher than pretty much everyone and still almost
everyone to this day. He's just an excellent athlete. And
yet the ball velocity wasn't where it needed to be
or where it predicted it should be. So what was
the inefficiency? Like? What was what was he doing that
caused the ball to come out more slowly than what
you would have predicted? Right, it's really hard. We don't
know the rude cause yet. That's that's the actual interesting
(12:32):
thing is we're still studying, like why this happens. At
the lab, Kyle hands Matt these really heavy balls to throw.
He's found that when people throw a heavy ball, their
body naturally finds the most efficient way to do it
because it's so painful and uncomfortable to throw it inefficiently.
(12:52):
Matt basically moves into the Bionic manfactory and throws heavy
balls the entire off season. Then rejoins his minor league
team and you know, I get down there, I tell
him I'm I'm full progression off the mount and they're like, okay,
well let's see it. My first bullpen I'm I'm ninety
two to ninety five. Oh, and I think everyone's going,
(13:16):
what the heck happened? You know, and even I am,
I'm going, man, I'm throwing the baseball up in the zone. Now,
this is amazing. This is so cool. Like when all
of a sudden, you've got this new weapon. Yeah, when
you got this fastball that's coming out of your hand
three or four miles an hour faster than it usually does.
What do you notice in what happened and how hitters
respond and how the effectiveness hitters have against you. I
(13:37):
remember going in to Double A that season and I did.
We have a catcher named Jack Murphy who was about
four or five years older than me, and he went
up and told me, he goes, Maddie, you have a
new fastball. You need to pitch off it. I remember,
I was kind of scared. I was like, what, I've
never done that in my life. I'm always fastball, change up.
And then I mixed my curveball, and you know, but
he challenged me, and all of a sudden, I'm like
(13:58):
striking guys out on three fastballs in Double A, and
I'm going like, is it this easy? Like this is
all it took. By the end of that season, Matt
Boyd had been called up to the major leagues to
the Detroit Tigers to be a starting pitcher. Paints in
and then paints away both fastballs. Want to pitch to
Bird another strikeout for Matthew boy to start the form.
(14:23):
They now pay him five point three million dollars a
year and feel like they're getting a deal. And Kyle Body, well,
now he has a new job too. I'm the president
and founder of drive Land Baseball and the director of
Pitching Initiatives of the Cincinnati Reds. Somebody asks a question,
how do you throw a ball faster? They gathered data.
(14:46):
They measure everything that the human body does when it
throws a ball. They test theories and find answers rooted
in science. All of a sudden, there's a new way
to coach, and it's getting adopted in sports. But not
just in sports, because there are people all over who
don't know why they're good at something or how to
get better. About five years ago, I was CEO and
(15:09):
at a software company and we're growing pretty quickly. This
is a meet bendoff. Five years ago, he was just
another Silicon Valley entrepreneur trying to get his product out
the door by using salespeople. But I was puzzled why
some of our people were more successful than others. And
(15:31):
every time we wanted to understand why, we had to
go and interview people and see what they think. And
he didn't want just a collection of stories. He wanted data.
Why did some sales pitches work while others didn't? Think
(15:51):
about like football right or baseball right? If the coach
never sees the game and the only thing is to
understand how to get better is by interviewing some of
the players what they think has happened. So I wanted
to have like something like a game tape and game
stats for sales and AI to understand what really separates
(16:13):
the top performers from everybody else. You know that message
that you get on customer service calls, This call may
be recorded for quality assurance. We appreciate your patience. Calls
for quality. Companies were recording their sales calls, but no
one was really listening to them. A met wanted to
listen to all the game tape and analyze it. And
(16:33):
I then started asking a bunch of other people, you know,
if we build a system kind of shine a light
on conversation and give you insights from that. Would you
buy how much you're willing to pay a meat? Created
a new company he called it Gong. He went to
people and said, hand me all the recordings of all
your sales calls, plus a list of your salespeople in
(16:54):
order of how good they are. We'll analyze it. And
so if you'd gone to just one of those top
sales people and ask what you're doing, it works. No, no,
absolutely not, because they don't know. People think it's an art, right.
It's like they're not aware that's something that they're doing
because they don't know what the other people are doing,
so they don't know what the differences are. The Gong
(17:17):
artificial intelligence, had no theory about what worked and what
didn't in sales. It just had millions of sales pitches.
It searched for patterns in both the calls they got
results and the calls that didn't. The Gong actually learns
like it looks at the salespeople and says, like, which
one is closing more deals, and then it starts to
(17:38):
analyze the difference. So the software learns automatically. You just
connect to the calls. If there was an art to
any of this, it was in the questions that Gong
asked if the phone call data. I mean, the very
simple one is like percent of talk time? Right, You
and I are talking right now, and by the end
of the call, Gong say, well, a meat was talking
(17:59):
fifty six percent of the time. It turns out there's
lots of things that separated great sales pitch from a
bad one. The simple virtues. They're sort of obvious, but
they could now be quantified. On average, forty six percent
talk time is ideal. Thirteen questions is optimal? Right? Not less?
(18:21):
No more so, this alone is food for thought that
there's a maximum amount of time to be talking. If
I were to talk only forty six percent of the time,
would my wife stop telling me that I don't listen?
What if I counted the number of questions I asked
in a particular conversation and stopped myself at thirteen. I mean,
it's not that if you ask fourteen you lose that deal.
(18:43):
But more than that, people might lose their patience, and
too less it means you're talking too much. Patience factor,
that's like a pretty big challenge for a lot of people,
they're too quick to respond, so it's a good practice
to pause think in any reply. We all know that
(19:04):
in theory, at least, Gone could act just like a
game coach, just last instructions into a person's ear as
they pitched the product. Shut the hell up, ask another question.
You're at seventy three percent, and Meek decided it was
better if he didn't do this. If he offered the
feedback after the call instead, you know, will alert the
(19:25):
coaches to hear like three conversations that have room for improvement,
and then the coach will open them up just like
a game tape and start breaking it down to start commenting, Oh,
minute three ZH six, I really love the way you
phrased that question. Have you considered X man? If I
(19:47):
were in sales, all of this would drive me batshit,
which is why I'm not in sales and also probably
why everyone but my mother is still making fun of
my podcast ad Reads. But for people who actually make
their living selling stuff, well, Gong is their new best coach.
So when I'm engaging with a customer, especially in introductory calls,
(20:07):
I want them to do as much of the talking
is possible. That's Megan Dorner, who sells software for a
Canadian company called AVIC. I have no idea what the
software it does, but that's not important. The key fact
about it is that it needs to be sold. The
more the customers talking, the more that I'm learning, especially
like I said on that first call. And so the
longest customer story again is how long they're telling a
(20:32):
you know, an interactive I guess one long tail. So
that the customers monologuing, that's good, that's correct, that's correct.
But if you're monologuing, it's bad. Megan now pays attention
to who's monologuing, because Gong forces her too. Five minutes
after every call, she gets a note from Gong filled
(20:52):
with stats and a color coded report card. One of
the grades is based on the length of her monologues.
When the customer monologues, Gong doesn't call it that, They
call it a story. If you've got a woman of
few words on the other end of the line and
there's not much you can do to tease a big
story out of her, you totally right. And that's when
(21:13):
I find that my longest monologue, longest customer story, and
talk ratio could be all in yellow. Yellow is Gong's
color grade for a C. Green is an A red
is an F. Megan's never in the red. She's one
of Avic's top salespeople and also almost by definition, a
(21:35):
salesperson that Gong thinks is great. The only yellow card
she's ever likely to get from Gong is for interrupting.
I'm somebody who likes other people. I like talking, I
like engaging. I also like to be right. I like
to help. I like to give the information I think
is going to be helpful, and so I'm eager to
(21:56):
do that. So, in a funny way, you had to
be a different kind of person when you were a
salesperson than you were just out in the world. That's correct.
All this lead It's to an obvious thought. If Gone's
report cards work for sales, why stop there? So after
(22:20):
I'll be with my friends or with family, I'll think
to myself, after I leave, God, I interrupted a lot,
and it's just it's always in the back of my
mind now because I see it so often that my
patience is low. Is it Gone that made you aware
of it more so than before? Absolutely? Does anybody around
you sense that your approach is changing at all? Is
(22:41):
anybody noted to you that you know, you're all of
a sudden listening better to me, my spousehays does that count?
Oh my God, tell me about that. Yes. So I
We've had the conversation after we've left a social setting
where I've said, god, I did I was interrupting a
lot at dinner or whatever, and she has said to me,
(23:04):
you know you've been actually better than you have been previous.
You know you're getting better at listening, You're getting better
at not interrupting. So she has noticed that, Yeah, I don't,
I don't do it quite as often. Did you then
explain to her why you weren't doing it as often? No,
absolutely not. Life is a market. Everyone's always looking for
(23:27):
edges that other people don't have. Even a nice Canadian
woman knows better than to give her secrets away. Do
you think the world would be a better place if
we were all gone in all of our conversations. I
think it depends on the level of you know, give
a shit that the person has to want to do
better or be better, right. But I think a lot
(23:48):
of people who spend a lot of time just talking
about themselves are unaware how much time they're spending talking
about themselves. That's very true. Yeah, there's no question that
the future of this is gone trying to figure out
and reduced to little color coded bars. What it is
that causes a uson to like another person? Oh yeah,
(24:12):
which is scary when you think about it, but incredibly useful.
Gong's now being used by fifty thousand salespeople at almost
a thousand different companies. We grab Megan sort of at random,
and she's a sample size of one. But it's not
hard to see how this new coaching tool might shape behavior.
It creates new stats, Gong stats. The stats capture something
(24:35):
true about the performance, the same way that I don't
know on base percentage captures something true about a baseball
hitter's performance. The management pays more to the people with
good stats, and so soon everyone's just adjusting their game.
Baseball hitters are learning plate discipline, and salespeople are learning patients.
Most salespeople would say like Gong is like change my
(24:55):
game forever. This is a meet bendoff. Again, they use
those were like a game changer. It is possible if
if your product isn't good or a competitive in the market,
it doesn't matter if he has like thirds question, right.
I mean, it's like, so there's only so much that
you can do. I mean there's like, uh, this is
not some kind of magic, it's just like facts. But
(25:17):
there's something else that happens with data. In the right
analysis of it, it causes all kinds of folk beliefs
to just disappear. Let me give you an anecdote. So,
a lot of the managers and the coaches are often
obsessed with the filler words. When we introduce the product, Oh,
can you track like fuller words, you know, like oombs
and arms and like and so and like. It drives
(25:39):
them nuts. But you know what, we ran the research
and turns out there's zero correlation between like usage of
filler words and success. Huh, So, so that parameter doesn't
really matter. The managers were thinking that the more filler
words the worse. Yes, right, it is annoying to hear
(26:00):
them when you listen to recording and there's and people
say like a hundred times it drives you nuts, right right.
The fact is it doesn't matter. The fact is you
never know any of this without all these facts. Gong
is screwing up everybody's assumptions. Like the recent one was
using swear words on calls, does it help, does it
(26:24):
get in a way or does it even matter? And
what's the answer. Um, So it depends what the correlation,
what the research shows. I said, it depends on what
you're selling, Like if you're selling bibles, it probably doesn't help. Yeah, yeah,
we didn't have any Bible sellers over there in the stats.
But it's run over like, you know, fifty thousand people,
(26:46):
so it's it's a very large number. But the corollary
is that it depends who starts first. If the buyer
starts with curse words and you match that that that's
actually works better. But if you start it it doesn't help.
You might fucking believe that, or you might fucking not.
(27:07):
The point is that it doesn't matter what you believe.
The data generates the knowledge, and the knowledge allows you
to coach people how to do better at the most
basic human activity talking. Can you imagine a future like
that distant future where human interactions will have changed pretty
(27:28):
meaningfully because we'll all have been coached up in how
to have conversations. Absolutely, the device that we're using today,
like namely like speech or the English language hasn't changed
much for thousands of years, where the amount of information
that we need to exchange today and the amount of
noise and clutter and the environment is so different that
(27:51):
it could definitely use an upgrade. An upgrade. Well, one
thing hasn't changed. If they're better ways to manipulate people,
salespeople will find them first and the rest of us
will just follow. We focus on things that are teachable,
are coachable, all right, we can't teach anybody how to
be funny. Let me stop, Let me stop, let me
stop you for a second. I'm sorry, I know I interrupted,
(28:15):
but this is important. You don't think you could teach
people to be funny. Well, we haven't been able to
crack that code yet. Maybe a meat hasn't cracked that code,
But that doesn't mean that it can't be cracked. Well,
(28:36):
you can measure precise things contained in the conversation, the
words that people are saying to each other, and how
it influences outcomes. Allison wood Brooks is an associate professor
at the Harvard Business School. Like, we're on a date,
do you want to go out with me again? We're
on a sales call? Did it convert to a sale?
All kinds of things that you can connect the content
of the conversation with things that really matter, with outcomes
(28:59):
that really matter. Professor Brooks takes all these conversations and
analyzes them. She's using the same new machine learning that
Gong uses, but she's looking for different things. Give me
an example, one example of something that you can you
learn from this technology, Like something someone says that leads
the other person to want to go on a date.
Let's give you that exact example. We have a data
(29:22):
set of people doing speed dating. Each person went on
like twenty dates, okay, quick four to five minute speed
dates in round robin fashion. At the end of each date,
you say are you willing to go out with that
person again? And they record and transcribe all the interactions.
So now we see exactly what people are saying on
their dates, and we can measure what are the things
(29:42):
that people are doing and saying that make them more
likely to be more datable in the future. And one
thing that really matters is question asking. So asking more
questions for both men and women on these heterosexual dates
leads to better dating outcomes. Really, especially follow up questions.
How do you know that the people who are asking
the questions aren't just naturally more attracted to the people
(30:03):
who they're asking the questions of. Because so two things.
One we can control for other aspects of attractiveness observational
data too. We then come back to Harvard and run
experiments where we tell people ask a lot of questions
in one condition or another condition where we say we
don't tell them anything. In the condition where they're asking
a lot of questions, they also are more attractive and likable.
(30:25):
This new line of research has uncovered the various ways
that people gain power and authority in conversation. This professor
can prove that they work, and it's just made her
want to ask even more questions. Even if you know
conceptually what a charismatic, smooth, productive conversationalist looks like. Is
(30:45):
there any way that you can train people to actually
get better at executing it? Brooks has all this data,
some of it from Gong, but also from doctor's appointments
and work meetings and parole interviews and speed dating sessions
and on and on and on. She uses the data
to test theories about conversations, and one of those theories
is about humor. There's this great work that sends of
(31:09):
play right is like the key to psychological safety and
thriving and creativity. It's the only way that you can
really be creative in the presence of others is if
you feel safe to say something stupid and silly. That
all sounds sensible to me, But do you have any
data to back up absolutely? What's the data? Let me
(31:30):
tell you about one paper. It's a bidirectional finding, meaning
people who have high status tend to be more free
and use humor more freely. But the more interesting direction
is if you are of low status, if you can
land a joke, people perceive you as higher status. A
lot of polite laughter happens. So but if other people
(31:51):
think what you said is actually funny, appropriate for the circumstances,
and at least one person laughs at it, your status
takes this huge jump. Totally true, right, So yes, it
pays to be funny. Funny gets you status, and status
get you money. But the mystery remains, can funny be coached?
(32:14):
There's a part of me that wonders if there's a
version of what's going on in baseball coaching that doesn't
apply to what you're doing. And what's going on baseball
coaching is the technology has generated all this data about
how a pitcher's body moves, and they're able to identify
people who have an aptitude for doing things that is
(32:36):
untapped because they have whatever. The fundamental attribute is arm
speed that you can't teach, but translating the arm speed
into a speed on a fastball is a different thing.
And I wonder if there's an equivalent in conversation where
you could identify the core traits that lead to conversational
excellence and you can figure out who's sort of maxing
(33:00):
out and who's not and why totally. And not only that,
Allison's taken everything she's learned about human speech, humor, and
the rest and built it into a new course at
the Harvard Business School. It's called how to Talk Gooder
in Business and in Life. It's trying to turn conversation
into a science. It's like having the right arm for
(33:23):
the fastball and not doing it quite right. You've got
the brain space and the ability to do it, you
just didn't think to do it. That's the dream scenario,
and that's really the hope with these HBS students, right
These are super smart people who maybe just haven't heard
the right strategies. Hundreds of Harvard students tried to get
into Alison's new class. She accepted seventy seventy of the
(33:47):
world's most ambitious people, hoping that the science of conversation
will offer them yet another edge in life. Many people
would find it odd to approach a conversation seeking to
maximize the profits of it. Exactly, They'll love it. They
want to perform optimally in every way in their lives,
and this is the moment to moment way that you
(34:08):
would achieve that moment to moment, and in each moment,
there's now data which can be used by a coach.
I'll be checking in with Alison's students next week and
asking a new question. When you start to get an
edge like this, a data edge, does the coaching start
to shade into something else, something that's maybe against the rules.
(34:38):
I'm Michael Lewis, Thanks for listening to Against the Rules.
Against the Rules is brought to you by Pushkin Industries.
The show's produced by Audrey Dilling and Catherine girodo Or
Girardo or girodot Or Girardo I've Never Gotten It right,
with research assistance from Lydia Genecott and Zooe Wynn Our
(34:58):
editor is the magnificent Julia Barton, who finds my every mistake.
Mia Loebell is our executive producer, and she disapproves of
me half the time. Our theme was composed by Nick Brittell,
who is really slumming it working here, with additional scoring
by Stellwagon Symphonette. We got fact checked by Beth Johnson,
which was totally unnecessary because our facts are always right.
(35:22):
And our show was recorded by tofur Ruth and Trey
Schiltz in spite of enduring the coronavirus in the studio,
which is the Northgate Studios in Berkeley, as always thanks
to Pushkin's founders Jacob Weisberg, who I think of as
my brain, and Malcolm Gladwell, who I think of as
my goal. Do you sense that the challenge with men
(35:52):
is different from the challenge with women. We do have
a good amount of research on gender in conversation. For example,
we know Matthias Melt the University of Arizona has this
great paper showing that men and women are equally talkative
and even though women have the stereo type of being chattier,
what he finds is that men and women both speak
(36:12):
about sixteen thousand words per day, so that's about similar.
But what we do know from other researches that men
and women speak at different times, especially in the workplace.
You know, I find I speak when a woman needs
something explained to her, exactly fifteen and a half thousand
and of my words every day just to do that,
(36:33):
and for some reason, for some reason, they don't appreciate it.
This just is Could I learn how to get them
to appreciate it by coming to your class? No, No,
what kind of class is this? Oh my god, it's
too funny.
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