00:00:00
Dan Loney: Well, certainly the world of
sports has changed quite a bit
00:00:03
in the last couple of decades
with the advent of big data and
00:00:07
the massive sums of money being
brought in. Now, we are obviously
00:00:11
in the advent of AI, which is
already having an impact. How
00:00:15
much and how much might sports
continue to develop? We bring in
00:00:19
Cade Massey, who's a Practice
Professor in the Operations,
00:00:23
Information and Decisions
department at the Wharton
00:00:25
School. You also hear him as
host of the Wharton Moneyball
00:00:29
podcast. Hi Cade, how are you, sir?
Cade Massey: Good, Dan. Good to see you.
00:00:33
Thanks for having me. - You know,
you and I are obviously big
00:00:36
sports lovers, and so
you've watched it a lot, this
00:00:40
side of it, a lot closer than I,
but it has been just amazing to
00:00:43
see how sports, in general, the
operation, the playing on the
00:00:47
field, has changed in the last
couple of decades because of
00:00:50
big data, and now, in part,
because of AI. - Yeah, I
00:00:54
mean,
something's obvious. People
00:00:57
complain even about how much it's
changed baseball, how much it's
00:01:01
changed basketball. It's not
obvious that it's always changed
00:01:04
it for the better. It's just
accelerating optimization. And
00:01:08
sometimes, when that happens,
you realize maybe the rules need
00:01:10
to be tweaked, maybe
optimization for these
00:01:13
particular rules isn't what we
actually want. And so, you know,
00:01:15
basketball is thinking about
changing. Baseball is always
00:01:18
experimenting with changes. But
big data has definitely
00:01:21
accelerated that in multiple
sports over recent years.
00:01:24
- Is there a moment in
time that you can think of that
00:01:28
really kind of started to make
the turn occur?You know,
00:01:31
whatever the sport was.
But the involvement, I guess
00:01:35
maybe the moneyball kind of
philosophy kind of coming into
00:01:38
baseball kind of started this
process.
00:01:40
- Yeah, I mean, you know, all
sports, they say this about the
00:01:43
NFL, but it's really true about
all leagues, are copycat
00:01:46
leagues. And so when somebody
sees something that works,
00:01:49
really kind of whether it's by
chance or not, it gets copied.
00:01:53
And then in some sports, that
doesn't last very long, you
00:01:56
know? People got a little
curious about going for two
00:01:59
until Belichick went for two on
his own 28, or whatever it was,
00:02:03
and didn't get it, and all of a
sudden set the whole thing back
00:02:05
for years. But with baseball,
the moment I think about is
00:02:08
with the shifts. You know,
people had shifted for decades,
00:02:12
but whenever
advanced analytics started
00:02:16
suggesting, well, we can really
figure out player tendencies,
00:02:19
and we can position our
defensive players in a way that
00:02:21
substantively changes their
success getting on base, and one
00:02:25
team adopts it, they
adopt it kind of extremely,
00:02:28
right? So you see people do
things they hadn't done in
00:02:30
decades. They have some success,
and it just runs through the
00:02:34
league. And I don't know what
year that was. IYou know,
00:02:37
2017 or something like that,
but you saw this was an
00:02:40
analytics driven innovation, and
as soon as it was proven to be
00:02:44
advantageous, it was contagious
in the league and other teams
00:02:48
adopted it.
00:02:49
- So I guess is it any surprise to
you then that, with the advent of
00:02:53
a lot of this data and how it's
being used in terms of coaching
00:02:58
and playing the game, that
obviously, front offices
00:03:02
are using it as kind of a
marker in terms of how they're
00:03:06
paying out contracts and the
length of contracts, and how,
00:03:09
you know, these business
decisions are being made by
00:03:13
front offices around the world
of sports?
00:03:16
- Well, you know,
probably the single biggest
00:03:19
example, we just had a game last
night, Dan, that was a real high
00:03:23
watermark for the analytics
world with Florida under Todd
00:03:27
Golden winning the NCAA Men's
Championship. We haven't had
00:03:31
that many moments like that
around sports. So obviously, Theo
00:03:34
Epstein with the Red Sox and the
Cubs in baseball. There's not
00:03:38
been one in NHL. Arguably, there
really hasn't been one in the
00:03:42
NBA. I mean, Brad Stevens is not
really an analytics guy, per se,
00:03:47
with the Celtics. Daryl Morey,
he's famously never won.
00:03:51
In football, it's kind of an
open question, because Howie
00:03:55
Roseman is not strictly
analytics. He's a salary cap
00:03:58
guy, right? But they've really
been, obviously, successful now
00:04:03
with almost two different teams.
And Howie's approach to salary
00:04:07
cap is what you're talking
about, is much more analytics-y,
00:04:10
Moneyball approach to salary
cap, and that has really run
00:04:14
through the NFL, and that has
made big changes across the NFL.
00:04:17
And it's not quite AI, but it's
a predecessor to AI, and it's in
00:04:22
a different part of the
building.
00:04:24
- So how is AI having an impact in
the world of sports right now?
00:04:29
- Well, it's still an open
question. That's the big thing,
00:04:31
Dan. It reminds me of when
motion tracking hit football,
00:04:35
you know, probably six, seven
years ago, and you'd talk to teams
00:04:38
and they'd say, "What's going to happen?"
Like, I don't know, but you need
00:04:41
to be involved, because things
are going to change as a result.
00:04:43
So get the data, start getting
used to metabolizing it and
00:04:47
using it, because it's going to
matter. It feels the same way
00:04:49
with AI right now. We don't know
exactly what the consequences
00:04:52
are going to be, but
they will be there. So if you
00:04:55
think about sports broadly, we
divide it from an analytics
00:04:58
world, we divide it into a game
day, in game strategy. And then
00:05:02
there's the personnel side, all
the scouting. And then there's
00:05:06
high performance, like
individual player and injury
00:05:09
prevention, that kind of thing.
And if I had to put my chips on
00:05:12
one of those three areas, Dan, I
would put it on the latter. I'd
00:05:15
put it on high performance. And
the thinking, my thinking is
00:05:18
the in game strategy, it's not
quite optimized yet, but it's
00:05:22
pretty close. I mean, these
teams have been playing under
00:05:24
these rules for a long time. I
don't think there's any huge
00:05:26
gains that AI is going to
generate there. On personnel,
00:05:29
there's a different kind of
problem. Personnel, yeah, we're
00:05:32
going to get some gains out of
it, for sure, and we could dive
00:05:35
into that, but you're never
going to forecast personnel
00:05:39
perfectly. It's just, we
can't expect perfect forecast.
00:05:43
And so there's a limit to the
advantage of this kind of data.
00:05:46
But on
00:05:46
performance -- go ahead. - That's
the dynamic, because when you
00:05:50
talk about personnel, there are
so many other components that
00:05:53
could come into play that can
change the path of a player in
00:05:57
terms of how he performs, he or
she performs on the field, not
00:06:00
just the data itself.
00:06:01
- That's right. I mean,
there's a lot of path dependence
00:06:04
there from his life or her
life, but also teammates, from
00:06:09
chance, from coaching staff. I
mean, there's
00:06:13
irreducible uncertainty. This is
the fancy phrase we use in my
00:06:16
world, irreducible uncertainty.
And as forecasters, as human
00:06:20
beings, we hate that, we're loath
to accept it. But on the
00:06:23
personnel side, you're never
going to perfectly forecast,
00:06:26
we're just not. I mean, maybe in
a thousand years, I don't know,
00:06:29
whenever we think -- what, right now,
we think is random, we figure
00:06:32
out what's not random about it.
Right now, we just lump all the
00:06:34
things we don't understand into
randomness. That's going to be
00:06:37
that way for a while. The domain
that is more susceptible, I'd
00:06:41
say, is the performance side.
Just take injury prevention, for
00:06:44
example. It's probably the
biggest frontier, the biggest
00:06:48
margin that we can make
improvements on in sports, and
00:06:51
it's still pretty wide open. I
mean, people are still figuring
00:06:55
out, how can we anticipate
injuries better? How can we
00:06:58
prevent them better? And the
data that can go into that are
00:07:02
almost unlimited, and the models
we need to pulling signal out of
00:07:06
those data are yet to be
invented. And I think that's
00:07:09
probably the biggest frontier,
biggest margin.
00:07:11
- One final thing, because I also
saw an article that talked about
00:07:14
how AI is going to have an
impact on the refereeing or the
00:07:18
judging of sports as well, which
I think is very interesting,
00:07:22
because there's obviously,
depending on the sport, there's
00:07:24
a lot of conversation about, you
know, are the referees good? Are
00:07:28
the umpires good, or are they
not? Should they be replaced by
00:07:30
technology? You know, I
mean, obviously that's kind of
00:07:33
a next area to watch as well.
- Well,
00:07:35
you can think about the -- I think
that's interesting, Dan. There's
00:07:38
a few different things that
strike me straight away. One is
00:07:41
like the baseball umpires, of
course. I mean, those guys could
00:07:43
be almost perfectly replaced.
Obviously, there's some limits,
00:07:46
but calling balls and strikes is
pretty straightforward. What if
00:07:49
we had models, though, that
evaluated referees in soccer,
00:07:52
for example, and had a sense of,
here's what this ref is
00:07:55
attending to, here's what this
ref is missing, here's what this
00:07:59
ref is good at, this is what the
ref needs to work on. Could we
00:08:02
evaluate refs in that way? Could
it be a tool to make them better?
00:08:05
In a way that we use -- right now,
we're using, you know, data on
00:08:09
pitches to improve umpires over
time. Could we do something with
00:08:12
AI to improve referees? But
then, obviously, the easy one to
00:08:16
think about is, all these sports
that have judges, like Olympic
00:08:20
sports that have judges, right?
That dive. How do we know who's
00:08:24
really saying which dive is a
better dive in the Olympics? And
00:08:29
could a model help us do that a
little more precisely? Even if
00:08:31
it's just an assistant, you
know, kind of an advisor to an
00:08:35
actual judge.
00:08:36
- Cade, great insight. Thanks very
much. - Thank you, Dan. Any time.
00:08:40
- You got it. Cade Massey, who's
Practice Professor in the
00:08:43
Operations, Information and
Decision Department here at the
00:08:46
Wharton School, and you also
hear him as host of the
00:08:49
Moneyball podcast.
