June 2, 2026 • 47 mins
Most of what happens inside the UFC Octagon is too fast for the human eye to follow. Enter Alon Cohen, Executive Vice President of Innovation for TKO, who has spent 15 years building the data and AI systems that expose the hidden moments that help decide a match. Malcolm Gladwell sits down with Alon to uncover how UFC’s partnership with IBM turns chaos into clarity, giving fans and commentators a deeper story behind every bout.
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Episode Transcript
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Speaker 1 (00:01):
I'm Malcolm Gladwell and you're listening to smart talks with IBM.
Most of what happens in a UFC fight is too
fast to see. A fighter drops their shoulder for a
split second. Did you catch it? A shift in position
that looks insignificant but changes everything? Were you watching alone?
(00:22):
Cohen is the head of R and D for UFC.
His job is to help people see those moments, not
by slowing things down, but by knowing what to point
to after it happens. By the time you realize something mattered,
his data systems have already figured out why it's taken
fifteen years to get here, first with paper scorecards and
a TVO. Now in partnership with IBM, and along the way,
(00:47):
a loan's learned something that applies far beyond fighting. The
best technology is the kind you don't notice at all.
It just helps you see. I thought you were going
to be like tattoos muscle shirt. I thought you're gonna
like I thought you were going to represent the brand
(01:08):
in that respect that you yourself would be. It would
be a kind of, you know, mixed martial arts type.
You were not, in fact a mixed martial arts type.
Speaker 2 (01:17):
I did a little taekwondo in my past. I am
I am not in the way that you put that,
not a mixed martial arts type. But I think if
you if you come through the building in Vegas, if
you come through the building when we do a show,
you're gonna see a lot of the mixed martial arts type.
It even surprised me. I came out of the tech world,
the young tech world in the two thousands, and everybody
(01:39):
was talking about, you know, we have to have a
mix of viewpoints and a mix of you know, people
from all walks of life, and all of these other
things that evolved into into something else. But yeah, that
didn't happen to me until I went to work for UFC,
which is like a weird way to get there.
Speaker 1 (01:54):
Were you in a tech world before? What were you doing?
Speaker 2 (01:56):
So when I left school, I went to a start
up up that today we would call a big data company,
and it was there at a moment where everybody was
doing relational databases and relational databases from micro Strategy and
from Cognos and from whoever. So we did that two
thousand and one hits after I graduate nine to eleven
(02:17):
and the dot com bust and all that kind of stuff,
and we all looked at each other and we said,
no one, no one of these institutions that needs this
kind of information is going to take a risk on
a small company right now. They're going to retrench. And
I went to law school.
Speaker 1 (02:30):
Oh, I see, you started tech. You briefly have a
foot in the twenty first century, and then you decide no,
I'm going to go get a lot. Agree I did.
Speaker 2 (02:40):
People said you feel like somebody who would benefit from this,
and I wanted to want it. And the first attorney
I worked for out of law school, he looked at
me and goes, you need to go be in business,
Like you're not an attorney.
Speaker 1 (02:55):
Yeah, so you're you're a failed lawyer. They kick you out.
Where do you go next?
Speaker 2 (03:01):
So that's what happened. So in two thousand and eight,
there were a bunch of lawyers who were doing just fine,
but the bottom has fallen out of the legal market.
I was helping a friend off the side of my
desk who had come to me with a banker's box
of paper and said, I have been watching The Ultimate Fighter,
the reality show where they pick new entrance to the UFC,
and I had been watching. He was a sports writer before,
(03:22):
and he very much. This gentleman's name is Romic. An hour,
Romy comes to me and he says, there's no stats
for this stuff. I'm used to writing about baseball. So
I did a full regression analysis after watching one hundred
hours of fight time. And now we're at closer to
two hundred hours of fight time, and I've been scoring
these fights. And the UFC's talent had been reading my
(03:43):
blog and then want me to do analysis for them,
and I can't be doing it on paper. And so
he came to a technologist and said, what do I do?
And we talked about double king the data in Indonesia
and building a database and APIs. A year later, we
were making small salaries and we had gotten this thing
up off the ground and going, and the UFC was
starting to use our statistics and we had turned it
into a real data product. And you know, we were
(04:05):
able to score electronically at that point. And the rest
is at this point seventeen or eighteen years of history.
Speaker 1 (04:11):
So wait, back up, this is really interesting. Pretend I
know nothing about UFC. Okay, So Romi comes to you,
and Rommy's issue is what that the way in which
these matches are scored is too subjective.
Speaker 2 (04:25):
The way that these matches are scored is inscrutable to
the average person. So his analogy, and it's the one
that he experienced, is when you write a summary of
last night's baseball game, right, what do you do? You say,
here's my thesis statement, so and so had a great outing,
and middle relief collapsed or whatever it is. And last
(04:46):
night at seven pm at this stadium, the following happened.
Here was the final score. And then this middle paragraph is,
you know, middle relief had this as a cra and
so on and so forth, and you'd run an analysis
and the and you talk about, you know, so and
so has been performing well, and he had this outing
and that outing, and this is part of a trend.
Speaker 1 (05:01):
Tell you tell the story of the game in the
context of a core of statistical.
Speaker 2 (05:06):
Knowledge, exactly right. And this is at least how he wrote.
And you get to that paragraph in an MMA story,
and it's non existent. It's so and so is a
strong striker. So and so is coming off of two losses.
So and so is on a three fight win streak.
And that is the totality of what's currently available. And
Romy's approach is we can count everything, but everything's not important,
(05:30):
and you can't count everything practically because by the time
he gets to the end of this first iteration, we
have sixty seven data points per fight er per round.
So it's a ton of stuff.
Speaker 1 (05:39):
At this point you're collecting the data.
Speaker 2 (05:41):
How just visually, he has a piece of paper and
he is on a TVO remember TV's. We had TVO's
until long after they were dead because we needed them.
And he is playing and pausing and rewinding to be able.
Speaker 1 (05:53):
To He's doing it the way people in the olders
in baseball, remember they would score. Sure, they would take
the piece of paper and they would score the game
as they were. Well, it's going to matches.
Speaker 2 (06:02):
His history is I go to baseball games with dad.
Dad has a scorecard, and you, as a kid, are
both drawn to it and averse to it because you're
like dad, you're missing the game, and at some point
you find yourself marking down the scorecard. And he brings
a lot of that to this.
Speaker 1 (06:17):
So you say, in his first iteration he had seventy
two data points, say that again, sixty seven sixty seven
data points per fighter fighter per round per round. Yeah,
so that's consistently there.
Speaker 2 (06:30):
Or this as an average of Well, so at any
points on the sheet, there are sixty seven different things
that we are tracking. So there may be more head
strikes landed, powerhead strikes landed, and there may be some
other head strikes landed, but there's probably not necessarily tight
submission or a camorra or something like that. But these
are different kinds of submission types that we would track,
(06:50):
and so all of them are available on the sheet
so that you can just tick those off and move on. Right,
So those are the sixty seven independent boxes on the
sheet that we could mark something down on.
Speaker 1 (07:00):
Give me examples of things that non obvious things that
you would learn if you did a formal statistical analysis
of a match, like as a viewer or as a fan.
How is this enhancing his experience of what he's watching.
Speaker 2 (07:13):
A couple of things are happening inside of an MMA
match that are worthy of note to the average fan.
It is scored per round. And one of the problems
that you have when you score per round is that
people don't think per round, right. They don't get to
the end of round one and say round one is over,
I will make a decision about round one. Let us
now go to They don't do that. They look at
(07:34):
the totality of the match. The shorthand I used for
this for a long time is you make the invisible
visible to people like what exactly did I see? The
other thing that the last thing that the numbers do
for us is they tend to combat recency because you
just forget what happened in round one or it just
doesn't leave a strong an impression. And one of the
and of course fighters fight to that. What do you
(07:56):
do right at the end of the round. You take
a dude and you take him down because you were
going to finish the fight now, because you wanted to
leave in the impression, the impression in the mind of
the judges like that dude controlled that round, And so
you were trying to score these big shots at the
end of a round. And the numbers allow us to
battle some of that for our fans to say, like
how well did he actually do? What did she actually do?
(08:16):
In this fan So that's one of the big learnings
is you're able to go back in time and not
have to watch the video. You just get a spot
to be like, oh, yeah, there were two takedowns in
that first round, and so.
Speaker 1 (08:27):
You're allowing people to construct a much more complete and
accurate narrative of the match.
Speaker 2 (08:33):
I think so yeah, And I think for new fans
there's a different calculus because new fans don't know how
to watch the sport, and when you it's I joke
that when we all watch the Olympics, like, I don't
know anything about synchronized swimming, and I shouldn't pretend to,
but the Olympic broadcast allows me to pretend to, right,
(08:53):
And that's a little bit of what's happening here, is
that they can look at this and say, oh, I
get it right, Like I'm watching all this action and
then we condensed that down to this is the number
of leg kicks this person landed, and they're like, oh,
I did see. Okay, Now I know to focus on that.
This is part of the storyline and it is a
signpost and that's a big deal.
Speaker 1 (09:12):
So in the beginning, you're just a contractor with UFC.
Speaker 2 (09:16):
We are a contractor entity. Yeah, for the first seven
years of our existence, we are independent and are the
official data feed of the.
Speaker 1 (09:23):
UFC, and and at a certain point UFC says, come
and join the.
Speaker 2 (09:30):
Ari emmanual at wm IMG. At the time buys UFC
and he says, I want to sell the data. Where's
the data? And they said, well, these two nice kids
in the Georgetown waterfront they have the data. And he goes,
what do you mean they have the data? Like, it's
our data, and that started an acquisition. Conver We were
a fold into that larger acquisition.
Speaker 1 (09:48):
Going back to the fights, the data, how is it?
How is the scoring working at this point, so.
Speaker 2 (09:53):
The tvo I brought up earlier, you're able to watch
it with slow mo and rewind and we have at
the time tablet or a touch screen, and we have
taken our piece of paper and we have split it
in twain and you're able to mark what you see
as you see it. Save around score.
Speaker 1 (10:08):
You've got trained scores.
Speaker 2 (10:10):
Yes, yeah, they are some of my favorite people. We
have had trained scorers in our group that go back
thirteen years with us, folks that we plucked out of
the ether. Right, you put something online looking for people
who are interested in learning this stuff, and then you
have to whittle out the people who can learn how
to do it and then do it quickly. The first
time you score something like this and try to make
sense of the scoring page. It's going to take you
(10:32):
fifteen minutes to do a five minute round. I take
you more. And that means that at the end of
the fight, you're probably thirty five minutes late. And so
you have to do this for six or seven months
on a regular basis, paying you make sure that you
know you're fairly compensated to get you to the point
where you're able to make decisions rapidly. It just comes
as second nature to you. And this is the dirty
little secret of UFC. But we joke we've been trying
(10:54):
to put ourselves out of that business for at this
point eleven or twelve and we aren't close yet.
Speaker 1 (11:02):
Put yourself out of the business of having to rely
on a human score a human score.
Speaker 2 (11:07):
Yeah, And we said that joshingly. But there's a sense
of which there's a sense in which at some point
you understand that compute ought to catch up with you, right,
And we said we should be out ahead of this
and sort of the Netflix model, right, If DVDs are
going to be a problem, you should figure out what's
coming next, and you should do that also, and disrupt yourself.
(11:27):
And you know, we wanted initially we have used it
as additive data, so we wanted to do motion tracking.
So eleven twelve years ago we bought a motion tracking
software and it couldn't make sense of any of what
it was seeing inside the octagon, like it really couldn't understand.
And this wasn't even striking. This is just like red
is here, blue is here, and the rough is here.
And so we taught a human system how to do it.
(11:49):
But we keep going back to the to the machine well,
hoping it will get better. And today, in fact we
are we are closer than ever, but nowhere near close
to overtaking us as scores. It's weird. I have been playing,
as I said, with my motion tracking computer vision experiments.
We're twelve years ago. Ten years ago I was hanging
(12:10):
cameras to attempt to do skeletal tracking to see what
that could give us, and it didn't work. You know,
we have been on a ten year journey to discover
what it can do and being fostering of it. Right,
That's what R and D is supposed to say when
something fails, to say, well is there something of this
that I can retain. Does this add some value enough
(12:32):
that it really makes sense to just continue to invest
in it some so if it can ever make good
on its promises, we'll get to see exponentially.
Speaker 1 (12:40):
What striving you here is what you want to get
your data quicker? Or do you want more data? Do
you want qualitatively different data?
Speaker 2 (12:48):
What is it?
Speaker 1 (12:49):
What's what's what's in the back of your head?
Speaker 2 (12:51):
So, man, so many motivations are pushing this for us.
One motivation is faster data. So imagine the very simple.
If I see a significant strike landed in the middle,
and so let's say the first person to one hundred
significant strikes landed in this fight, that is potentially betting data.
Somebody could bet who could get to that first. Right now,
(13:13):
I can only offer that data unofficially because my humans
need to wait for my official data to come in.
If computer vision could really get that right, then I
could stand behind that number and say that is betting ready,
and you could resolve that bet at the moment it happens.
And so you open up all of these markets. For
one like, that's a big motivator for this kind of stuff.
(13:34):
The second is I am when I'm adding these AI technologies.
I'm often looking at them and saying, like, what else
can I do with this? What else is it telling
me about the fight that I'm not already getting. Motion
was a big one of them. Right, where are people
in the octagon? What I mentioned earlier that one of
the one of the criteria for judging a fight is
(13:55):
control of the octagon space? Well, okay, everything I was
tracking with my scores before, it doesn't talk about that.
Speaker 1 (14:01):
Right.
Speaker 2 (14:01):
If I'm hitting you more and you're you know, defending
poorly or whatever it is, maybe that's control. But what
if you're a counterpuncher, right, famous fighters who are counterpunchers.
I draw you in and then I hit you. Does
that mean I'm seating control to be able to take
back control? What does that mean? And so we didn't
have any any numbers on where people are. That's an
immediate use of AI that is additive to what I
(14:22):
am doing. And all of this is always in the
service of storyline. Right, what is not being captured in
the fight? And what should the commentators be talking about?
And how can I support what they're talking about? How
can I give them something that makes them think, Oh, actually,
the thing I want to be talking about here. Is
this right? That's ultimately the goal. The goal here is
(14:45):
I mean, let's go back to the first thing you said.
You said, I don't look like the kind of person
who might be an MMA fan. There is a point
at which if I throw up a number on the
screen and it's like, this person is twenty seven point
two four, and people are like nerd, like, what are
you doing? And there is a space for that in
other sports, but sometimes we're doing it just to seem technical.
(15:06):
I think if you're doing the tech right, you should
forget about the tech. You should forget about the fact
that there's a I behind that. You should forget about
all of this. The thing that hits the screen is
so and so has never lost a fight if they
have landed four takedowns. We're on number three. Right now,
we know what we're watching. Yeah, right now you have
(15:27):
a binary simple thing to look at. So and so
has a takedown accuracy of ninety four percent. So and
SO has a takedown defense of seventy five percent. Let's
see how they match up against their classic averages. And
now you're getting into some more tech and number. You're like,
this guy is below his average and that guy's above
his average. Okay, Now we have a way of measuring
very quickly who's winning and who's losing across at least
(15:48):
this part of the fight. And so the goal is
to get to storyline as fast as possible in human
understandable terms. Right, This is in some ways the great
moment of the LM as well. Right, Who cares how
many weights and how many parameters and how many whatever?
When I talk to it, it feels like I'm talking
to a human. That's the great unlock. The technology suddenly
(16:11):
disappears and the experience stays. And that's at our level,
at our level of sophistication, that's what we're doing.
Speaker 1 (16:19):
Yeah, I want to go back to make sure I
understand when you talk about control of the octagon of
the octagon conceptually, what is control the octagon mean?
Speaker 2 (16:31):
It is the last of the criteria in the judging rules,
and it should mean that in a fight, one of
the things you're trying to do is assert dominance the
most basic level. It is left vague. Different commissions around
the country will tell you slightly different things about what
it means. Some of them will boil it down to
(16:52):
if I hit you more, I control the fight. But
a lot of others of them will say it is
really there to be that sixth cent feel of was
I able to put you where I wanted you to
be when I wanted you to be there? And in
our statistics, one of the ways you can talk about
that is advancing or center control. If I always own
(17:15):
the center, you always have the wall at your back,
so I control the space. Right. There are ways of
finding small analogus directional pointers to tell you that. But
when you talk about what a judge is looking for,
they're looking for that ephemeral sense of you know, who's
(17:35):
the big.
Speaker 1 (17:35):
Dog, and that is if you're strictly kind of scoring
the match, that's one thing you're not picking up because
you're too focused on position, who's striking whom that kind
of larger gestal you're talking about a gestalt of right.
Speaker 2 (17:50):
Sure, yeah, somehow the Nevada State Athletic Commission, I got
to get to criteria five and go gestalt. That's what
we're looking for. We're trying to deal as as a
as a matter of course, we want to bring people
into our arena, have this experience be devoid of Veltschmertz
and focus on the GESTALTA and it just didn't. It
didn't make it. That's what I'm saying.
Speaker 1 (18:11):
So you've got your motivations are one, I can enhance
the viewer experience in terms of story, yep, I can.
I can facilitate things like betting on the match. And
I can aid the judges in getting a sense of
who the dominant party is.
Speaker 2 (18:29):
One and two, Yes, three, we actually scrub all of
our all of our video feeds into the arena are
scrubbed of our statistics. I do not want the judges.
Their job is to follow the criteria set by their commission.
So we're in this weird spot where like my sport has,
my fighters are independent contractors. It's a regulated sport by
(18:52):
the state commission that we're dealing with, and my job
is to is to bring the event together. And as
a result, I don't want anyone pointing at us saying
you said. And so I decided We're like, look, you
understand your criteria. You're a fully trained judge. You need
to decide who won this fight. Our statistics will tell
the story our statistics tell And by the way, we
can have an occasionally antagonistic relationship where we thought that
(19:14):
was a poor decision, right, Yeah, that makes it interesting exactly.
Speaker 1 (19:17):
Yeah. So okay, let's go back to the AI. So
you're you start experimenting with this a while ago, oh yeah,
and it and you're not you're not entirely happy with
the with the results. Is that a fair or you're oh,
you understand it's embryotic.
Speaker 2 (19:33):
Both, right? You? Why should I start down the project
of making this work if I don't believe it's going
to work? Right, I try? If I'm going to bother
dedicating the time and the effort and potentially a lot
of money, I wanted to work. Uh, and I was
disappointed in time and again, and I have found things
to bring along right, So early skeletal tracking has become
(19:54):
the basis for.
Speaker 1 (19:55):
Todaya tracking is defined the term.
Speaker 2 (19:59):
Skeletal tre is when you hang cameras. Nowadays you are
very quickly able to discern what isn't as not a
human inside of your space, and then you're able to
identify points on their body, nowadays more points than we
used to. It used to be like shoulders, head, torso, hips, knees, feet, right,
and nowadays you have two additional points on the feet,
so you can see their orientation, hands, you can see
(20:21):
the orientation and face, you can see where I'm looking.
Those are basic skeletal models and you need that to
be able to understand from an AI perspective, who is
striking and was that a miss? Was that a hit?
We can get into collision detection, which is very difficult
because skeletal tracking isn't it's not the outside of your arm,
(20:42):
it's the inside. And then beyond was there a collision,
we get into problems of intent. So intent is the
hardest thing actually to solve. There's a lot of times
where two guys are fighting and one guy's going like this,
he's range finding, just putting a hand out there to
see is you know, how far away are you and
sort of to bat you away? And that is not
(21:03):
a strike attempt. How do you teach the computer that
that's not a strike attempt? Because sometimes it looks like it,
sometimes it doesn't. And then same thing with kick to
the head, it makes contact with the outset of my arm,
which I brought up in our world, that's a blocked strike. Yeah,
but teaching a computer what exactly that means and when
(21:24):
and how like when it's up here, when my arm
is up, that's a block. When my arm is down
and hits my shoulder, that's not It's those nuances that
proved incredibly difficult for machines to be able to handle
for a very very long time. And the acceleration in
the last few years which has allowed us to onboard
this technology has solved so many of these problems. At
(21:44):
the same time, we no longer need fixed cameras. I
now do it off the broadcast cameras, which are on
people's shoulders and they shake and they rotate and whatever.
Doesn't matter. It can still figure out what it's looking at.
This is the octagon, These are the fighters. Here are
their bodies, that's the ref He doesn't matter all of
that stuff. It can now figure out. It can begin
to understand what real landed missed is, and it can
(22:05):
do so on much less perfect data than we needed
the first time. That acceleration has been incredible. If I
didn't know step one and two and three and four,
I'm not sure how ready I would have been for
where we are now.
Speaker 1 (22:17):
Yeah, so let's just dig into where we are now
a little bit. So you go. I'm interested in the
partnership with IBM and the kind of transition from this
period of experimentation to where you are now. So where
at the time, you, guys, you're aware of the development
of AI yep, and it's getting better and better and
(22:39):
better and better and better, and you clearly have an
intuition that, oh, this is this could potentially really open
some opportunities for us yep. What's the point in time
where you start to think, oh, this could be real.
Speaker 2 (22:54):
Our relationship with IBM is born of a little bit
of suffering on our behalf, is the best way to
put it. We had a partner as part of the
larger business that really wanted to focus on AI with us,
and we said, we would really like to do an
insights engine. We think it's really additive. These storylines in
real time would change the way that we talk about
(23:15):
the sport in broadcast. And for two years we went
down that road and it crashed and burned. We go
into the reasons, but they didn't understand the sport. They
tried to brute force the problem. And if you try
to brute force this problem, it's going to kill you.
And because there are just so many permutations out there,
(23:36):
and IBM starts a conversation with us, and very quickly
as we engage, a couple of things emerge, and they're
not about the tech. They say to us, we think
we can do it with the tech. We say, we
have to believe you that you can do it with
the tech. We don't know. We're not experts in it.
They can bring people who understand sports analysis to the table,
(23:58):
so all of a sudden, when my call ends, when
somebody at IBM has a question, they can talk to
somebody better. Yet, within the first couple of weeks, I said,
it would really be helpful to have somebody who's an
MMA fan, because some of your questions are specific to
the sport and we could answer them all the time.
But I think that the velocity here goes up if
you can just say, hey, Brad, what's happening here? Can
(24:20):
you explain what is the culture, what's important, what's not important? Right,
because you can come up with a lot of ideas
and fans will be like, I don't care, and it
gets worse from there. So we quickly eliminated that. With IBM.
They had people on the bench who understood just the
area and then the specifics of our sport, and they
were able to fill in some of the gaps for us,
and so we could move at pace. And it was
(24:42):
all about the people at the beginning, and obviously beyond
that they had to make good on the artech and
actually do this. We can use the AI. It's not
so much that the AI needs to run the final product, right,
That's not what we're talking about. It's can the AI
speed up the creation of the product? Can AI help
us expand its reach? Can AI suggest to us the
(25:02):
things around the corner that we didn't see. That's where
the AI came in. That's really the genius of this.
Once it's all hard coded down, the AI does take
sort of a backseat to the code because once you
have to go fast, once you need to be efficient,
you know. That's the way that you do it is
you run a whole bunch of traditional code and then
you run it through an interpretation model to be able
(25:23):
to give you natural language at the end. And that's
super efficient in AI. But it's an understanding of how
to deploy both of those things in concert and have
the right kind of people.
Speaker 1 (25:32):
So walk me through here, IBM, what are they doing.
They're taking the video.
Speaker 2 (25:36):
Feed, they're taking the data feed, the data feed, correct,
it's instead of trying to have them extract everything out
of the video feed, we've already done a lot of
that stuff, and we're doing it at speed. So we
have my data feeds, and then we also have any
augmented information that we may be getting from external AI
computer vision systems, and that's coming in as the source
(25:57):
data to IBM. So now they're geting, they're getting a
data feed, and they're getting it at speed. Their job
is to bust open that data feed into all of
the various questions that somebody might ask and think about
the interrelationships between data. So sometimes it's as simple as
this person is on pace to set the largest number
(26:19):
of takedown attempts we've seen the last twelve months in
this division. It's like a really nice talking point. Relatively
simple all the way too. Here are two comparative metrics
for these two fighters, one of which is this is
his strength and this is his strength, and here's how
they're matching up right now and everything in between. And
to do that at speed.
Speaker 1 (26:37):
Yeah, so they're taking two things. They're taking the data
stream from your scores and then also, so do you
have these vision cameras.
Speaker 2 (26:48):
Yeah, it's a computer vision AI STAT system that we're
using to augment our system.
Speaker 1 (26:53):
And how how does that work?
Speaker 2 (26:55):
So the way that works is we've finally found somebody
who could do two things for us accurate counts, and
be able to do so without us attaching a whole
bunch of cameras to the octagon, right, So they can
do it directly off of my handhelds, and they're able
to do things like give us combo percentage. They can
(27:15):
do punch and CounterPunch because they can assess that, they
could talk about style. That's a cross, that's a roundhouse,
that's right, those kinds of things, and all of that
is additive. Plus, by the way, they brought back all
the motion stuff because now my AI computer vision can
actually do motion for real, and so they brought all
that back in as well into the.
Speaker 1 (27:35):
Way I can now do just because the tech is better.
Speaker 2 (27:38):
Text a lot better, the ability to just know there's
blue tape on the blue fighter's wrists and red tape
on the red fighter's wrist, and we've gotten to the
point now where skeletal tracking and computer vision is able
to identify people by that and also take additional notes
that dude's in green shorts and that dud's in yellow shorts, right,
and it'll do those kinds of things to just figure
out who's who. And the hardest thing for a computer
(28:00):
vision system to do as it does motion, is when
we become the eight legged beast and we come apart,
it can know that we're purple, right, we're red and
blue at the same time, but when we come apart,
it has to rapidly understand who's red and who's blue. Again,
that's where a lot of these motion systems had a
lot of difficulty. Refs in black pretty easy to figure out,
like somebody's dressed in black head to toe.
Speaker 1 (28:21):
You've got these data streams, and presumably there is some
overlap between them, but that's fine. You're just feeding as
much data as you can from these two different sources
into the IBM black box, and the black box is
generating stories for us, ideas, observations, insights.
Speaker 2 (28:45):
I can give you a little more detail on how
it happens. Yeah, I think that the beauty of unpacking
how AI works is there are moments of magic and
moments of drudgery, and they have to work perfectly together, right, AI.
The data comes in to IBM and it has a
storage system called Iceberg, and it runs a classic data
process called ETL. It's extract transform load. We care about
(29:08):
our data at the fight level or the round level.
IBM is trying to tell us things about our fighters,
so it is consolidating all of its information in these
big fighter tables that it can then sick the AI on,
and then the data moves from this ETL Iceberg level
up a level. They actually, I think they do it
by metals, So you start at bronze and you move
(29:28):
all the way up to gold and you move up
a level. So now your data is mezzanined basically for
the actual AI system to be able to draw on
it and efficiently process this data into insights, and then
it moves up into insight delivery, where we have an
interface that allows us to not just look at insights.
But then I have a human factors problem. My human
(29:50):
needs to see the insights and he can't see or
she can't see all of them. Right, that's way too many.
So you have to have a scoring system that says
this insight is more relevant than this insight and surface
only those so that a human Actually this was true
of Watson a long time ago, right when it did
medical diagnostics. It couldn't give you the answer. It could
(30:11):
give you seven answers, and a doctor would look through
them and say it, got it, got it. That's silly,
that's silly. Hadn't thought of that one? And that was
the value right here. And we have a similar thing
right where we'll get a whole list of them and
you get to pick one of them and be like go.
And so that's how the data comes in. And to
everybody else that should be a black box. But I'd
prefer it not be a black box to us.
Speaker 1 (30:33):
In the course my possibility of question, in the course
of a fight, how many insights are being generated?
Speaker 2 (30:41):
Yeah, that's an excellent question. So in an active let's
say we'll pick what I would consider to be the
Cadillac of fights. Right, So it's five rounds, it's a
championship fight. So you have two fighters with long history,
good records in that fight. I mean it could be
it could be one thousand, a thousand, because every permutation
(31:04):
is possible, right, So the system is generating just an
enormous amount, and the real the intelligence is in filtering
it out so that my humans don't see a thousand,
my humans see fifteen fifteen. Yeah, because remember the moment
that in a fight, there's not a lot of chances, right,
I'm not going to spit out all fifteen onto the screen.
(31:26):
In traditional statistics, I've got two stats at around. Maybe
we don't have any stats at the first half of
the first round. If something as amazing as happening, my
job is to move out of the way and just
let that sit on the screen, And so my insights
are there to be the most valuable thing I can
put on screen at that moment. And that's going to
happen in a fight twice and over the course of
(31:47):
the night ten times. But when it happens, everybody should
have been talking about this fight like this, and now
they should be talking about this fight like that.
Speaker 1 (31:56):
Give me an example of the kind of insight you
get that you probably would never have gotten in the
kind of previous universe.
Speaker 2 (32:06):
Sure, I think compound insights are the most complicated right
where it's we used to get basic, basic, but difficult
to answer questions. When was the last time someone want
to fight after being down one hundred strikes? Like that's
a hard thing to throw into a sequel database at
the Latin a moment's notice and get that answer. But
you can do that kind of thing with INSIGHT's engine, right,
(32:27):
was like you're like, oh, it is looking for it's
one of its pipelines. It's looking for those kinds of
disconnects between the data and the reality. This is the
largest differential striking differential between two fighters, you know, in
a middleweight title fight since and here's this other iconic
fight that everybody remembers. Right, It's those kinds of things
(32:48):
that you might in the back of your head think
to yourself like, huh, this feels like I don't think
I've seen this in a while. But you can't. You
can't give voice to it. And in a live fight,
there's very time. So what will happen is two fighters,
Let's say it's a striking statistic. Two fighters have not
had this much of a striking differential in a fight
since X point. It is currently three minutes into the
(33:12):
round of round three, let's say, and the commentators are talking,
We're watching the action, everything's happening. You get this sense
in your head I need to get in the next
two minutes the last thirty seconds are already we're going
out to commercial, like we're not interrupting that the fight
could end at any moment, So there's time pressure. I've
got to hit a promo at some point in the
(33:33):
middle of this thing, so you see how my time
is getting shorter and shorter. To be able to deliver this,
I've got to have my producer be able to communicate
it to the producer, it's got to communicate it to commentators,
who's got to be able to get it out of
a system into a graphic and then sold in to
be able to put on screen. All of that has
to happen in call it between thirty. I said to you, like,
I have three minutes. I have two minutes left in
(33:54):
my round, probably only have about forty seconds window, and
I'm going to lose it starting the mo and I
think of it until the moment it hits screen. So
I've got to have about a fifteen second turnaround. And
so it's not you don't have to ask a very
complex question for you to exceed the fifteen second turnaround
unless it's at your fingertips. And that's what insights engine
is for. It's it's to kind of come in and
(34:15):
be like, I know you have some cool ideas, but
we're never going to be able to get him into
fight time unless something is is pushing not pulling.
Speaker 1 (34:24):
You need to have buy in from the commentators.
Speaker 2 (34:26):
Yes, we do so. First of all, being a commentator
is a very sophisticated undertaking almost anything you see. And
it's one of those things that you watch happen and
you say, well, that doesn't look that difficult. But a
commentator has an IFB, a little ear piece in and
I've watched them do this and I can't wrap my
head around doing it, which is you must be looking
at the camera and presenting information in coherent pros while
(34:49):
the producer is talking in your ear, and then when
you stop talking, he starts talking. No, no, no, it's talking
in your ear while you're talking. And you've got to
be able to process that information and two sentences hence
make a part of your delivery. And that information processing
capability is by in large sophisticated. Now, people are people.
Some people are more pro stat some people are more
(35:12):
anti static. It doesn't really matter. You just have to
know who that is. But they have a relationship of
trust with their producer and if the producer says I
want to go here, they can push back. But those
two people work together on the regular producer's going to
sell you the things he thinks you're going to buy, right,
or at least the things he thinks. This is as
(35:32):
far as I can push it, and you'll really go
there because he wants you to sound authentic and he
certainly doesn't want you to be fighting with him on air, right,
So that very human process is taking place. And by
and large, one of the reasons Insights Engine produces human
readable stories is because I would have pushed back if
somebody said, IBM, Insights Engine says that so and so
(35:55):
is fifty seven point two four six percent likely to
win the fight. Okay, and that doesn't feel authentic. But
so like we've noticed that he's advancing and he's a counterpuncher.
This is off and so now hopefully the commentator is
going like, wow, the movement in this fight is different
(36:15):
than we would expect to see, and let me give
you some things to bolster that that point. Or and
people have done this, We have given them an insight
and they're like, oh, yeah, you're right, Like that's what's weird. Yeah,
about this fight.
Speaker 1 (36:29):
Yeah, it's fascinating because genuine engagement with the insights he's
actually from the viewers standpoint. The most interesting thing the
idea that they would be there'd be some tension between
what we're seeing and what's actually happening. That is one
of the most interesting things in sport, right Oh. I thought,
(36:49):
I thought, you know, Steph Curry had a great game,
and then I look after at the stat line afterwards
and I'm like, or the opposite is even better. He
looked terrible out there and look outline. I was like,
oh my god. He was like, I find my shot
seventy percent from the field.
Speaker 2 (37:04):
I feel like I do that more often than not,
where you're like, that looks terrible. So a couple of
things that are true in fighting that are I think
true elsewhere is if people have a favorite, they tend
to watch their guy, and so their guy always unless
he looks terrible, he looks great because you're not watching
the other guy, You're just watching your guy, and that
stats help bridge that gap a lot. Our commentators in
(37:25):
general don't fall into that trap, and so when people
fight with the commentators, when when fans fight with the commentators,
they're watching one guy, They're not watching both guys. I
will watch fights and I will say, you know, I'm
not watching I'm not seeing the fight you're seeing. Some
of that is because a lot of our commentators are practitioners.
They're seeing something I'm not seeing and they but it
is then their job to explain to me what I'm
not seeing, and that's fine. I think that The other
(37:47):
place that I would want to take this, and where
insights I think are very useful, is our fans watching
a lot of contexts a lot and how many times
have you walked by a game? Right? The classic is
I'm at a sports bar and maybe the audio is on,
but maybe the audio is not. Maybe it doesn't matter
because it's loud. I want to be able to provide
an experience for that person that is meaningful and potentially
(38:09):
more meaningful. So, yes, my insights are going to interact
with my commentators. But if I can't hear the commentators,
how nice to be able to put a storyline, not
just a number, but a storyline directly on screen. Right,
we'll have this with milestone. So and so has exceeded
their best ever whatever, and it's just a moment where
you're watching it like that's cool, and you'll go back
to watching. And that's really that's what we're going for.
Speaker 1 (38:30):
Are we fundamentally changing the relationship between the fan and
the sport?
Speaker 2 (38:34):
Interesting. I didn't set out to have insights engine fundamentally
change the relationship of the fan and the sport. I
I wanted something to fit like a glove. Right if
if you were sitting next to someone who had an
encyclopedic understanding of the sport and they were in and
(38:55):
they were fun, right, that was a big part of it.
What are the kinds of things they could tell you
where you're like, oh cool, and it's it's more fun
to watch because of that and not I think where
a lot of the push was in adding tech to
the sport, which is we're gonna make it nerdier, Like
that was the thing I don't want to do. I'm
(39:16):
interested in leaning in to what makes this great and
letting you see write the classic, the stats, to make
the invisible visible, letting you see those things but in
a narrative arc so that you can contextualize them. You
can say something more intelligent about sport. But like it,
(39:36):
I wasn't here to make you feel like I changed
the sport. I was here to make you feel like
you were spoken to more crisply about it. You were
able to speak more crisply about it, and the fight
just looked a little shinier, a little brighter because of
this kind of thing. And like I said, when this
tech does its best work, it just disappears.
Speaker 1 (40:00):
As hearing you describe it, it strikes me that what
you're doing is you're moving people up a level.
Speaker 2 (40:05):
You're right, we are bringing it to a place where
you feel like you can you could even have an
opinion because you understand enough of what's going on. You
are one hundred percent right. We're moving everybody right right
up the scale on a daily basis. I want to
go back to INSIGHT's engine is not here to feel technical,
and that's the genius of it. It's simplicity. It's narrative.
(40:28):
Is the thing that allows you to bring people forward
because you're not using jargon, because you're not just into
the numbers, right, That's really that's really the key here.
Speaker 1 (40:37):
When you look back at your experience with fight data,
are there a few categories that are essential to winning?
Speaker 2 (40:45):
I think the easiest way to answer that is, the
entire statistical system that we built is based on only
one question. We don't track style. For example, I don't
track whether somebody hit you with a cross or a jab.
Even more ridiculous, in the official statistics, we don't track
(41:08):
whether we hit you with an arm or a leg.
And that's because the entire statistical system is built on
this question what moved you closer to winning the fight?
And it turns out that what moves you closer to
winning a fight is where you take damage, right, and
how much damage you take. So if I hit you
in the head, arm, leg doesn't really matter, Style doesn't
(41:29):
really matter, it's did it land? So the whole statistical
system is built on that question of how much of
each of these things did you do? These things which
we have directly correlated to victory, And with live statistics
and the augments, we can do lefts and rights and
things like that people want to see. But that's the
fundamental question. So for the question is what's the most
(41:52):
important thing you could do? Land strikes? Land significant strikes
to the head that's going to end most fights. Can
you end a fight with significant strikes to the body
that massively outpace your opponent. You can, they're at a
second level down can And then there are things that
are a little harder to talk about. That way, if
you have more ground control, that's putting you closer to
(42:14):
completing a submission. So that again is the kind of thing.
And obviously once we get to the number of submission
attempts you attempt, are going to make it more likely
that one of those will be successful. But it is
all built from this basic question of all of our stats,
does it get you closer to winning the fight? Yeah?
Speaker 1 (42:31):
One last question. I know you've thought about this a lot.
Which is next, Like if I, if I, if I
invite you back, we have this conversation five years soon. Now,
what is the fun wrinkle that you're working on.
Speaker 2 (42:44):
We have a couple of things that are in the works.
Some of them are very sideways to this, but there
so one of them, which we're prototyping is the ability
to bring additional voices into the mix seamlessly. So I'll
give you a perfect example. If the coaches who are
(43:06):
on the side and they're yelling in whatever language they
are yelling, they are attempting to influence the fight, and
it is and we do this. Occasionally we'll cut to
the coaches camp and we'll listen to them for a moment,
but they're doing it constantly. Wouldn't it be interesting to
understand what they're trying to do and what it means. So,
(43:26):
for example, if I'm saying you're doing great, but you're
not doing great by being encouraging. If I'm saying you
are doing great and you are doing great, what does
that mean? What is my tone of voice, what's my
level of stress? All of these kinds of things, So
that you could very subtly be able to distill that
information and bring it in and say, like, by the way,
if you want to know what the coaches think, feels,
(43:47):
do interesting. So that's like a really nice one that's
relatively close.
Speaker 1 (43:50):
Now I have a sense of the psychological context of
the coach and the athlete.
Speaker 2 (43:54):
Let's go further away, right, you said five years in
the future. I think five years in the future. I
have two beliefs. I don't believe people want to lean
forward experience, as much as everybody would love them to
want to lean forward experience. If you could watch a
Steph Curry performance and I could let you control the camera.
Speaker 1 (44:13):
No, I don't want to go O.
Speaker 2 (44:14):
I don't believe that people do. Yeah, but that doesn't
mean that those technologies don't have a place. So if
people are talking about vogumetric capture or GAUSSI and splatting,
all of these kinds of things that allow you to
create three dimensional space and move through it, I am
working on. I have a belief that one of the
places I can't put a camera imagine the two of
us are fighting, is right here, right in front of
(44:35):
my face and watching your face. And yet the explanation
you want to give of I hit Malcolm in the
shoulder because he had dropped his hand and it was open.
Show me, make it available to me. Speed up that
process fast enough, create the telestration tools fast enough. And
I think that in five years those things are going
to become rote and we will expect impossible angles, all
(44:56):
of these kinds of things to become part of our world.
We are going to have to rethink what it means
to watch to be a fan of something. Right, if
you only watch a fifteen minute cutdown of a NASCAR race,
are you a fan or not a fan? I don't know.
Do you watch fifteen minute cutdown of every race, and
how do I service that person and how do I
(45:17):
engage with that person? If you do have this very
for you said earlier that people only watch a half
a show, for example, if you have a fragmented set
of attention, I can either fight it and rage against
the dying of the light, or I could lean into
it and try to service that person and make them
a real fan and bring them into the rest of
our ecosystem. And I think that that is going to
continue to happen. I think we're going to continue to
make an argument for live events and the pull of
(45:39):
that one only needs to go. As I'm sure you've seen,
when you're in the room, there's nowhere you'd rather be,
and it is so interesting, even though it is so
much less good than the angles you see on television,
you can't help but look away. We're bought in on
that thesis. But the moment you step out of that room,
how do I give you the greatest experience possible?
Speaker 1 (46:01):
Yeah? Yeah, alone, Thank you very much.
Speaker 2 (46:04):
Thank you.
Speaker 1 (46:05):
You maybe want to become a UFC fan, which is
a statement I never thought I would ever say.
Speaker 2 (46:10):
I'll tell you what, Come watch one with us, Come
watch one with me. Yeah, I bet you walk away
and you're like, I'm not sure. I watching every week,
but holy moly, is this amazing.
Speaker 1 (46:20):
Smart Talks with IBM is produced by Matt Ramano, Amy Gains, McQuaid,
Trina Menino, and Jake Harper. Were edited by Lacy Roberts.
Engineering by Nina Bird Lawrence, mastering by Sarah Buguerre, music
by Gramoscope, Strategy by Cassidy Meyer and Sophia Derlin. Smart
Talks with IBM is a production of Pushkin Industries and
(46:41):
Ruby Studio at iHeartMedia. To find more Pushkin podcasts, listen
on the iHeartRadio app, Apple Podcasts, or wherever you listen
to podcasts. I'm Malcolm Glappwell. This is a paid advertisement
from IBM. The conversations on this podcast don't necessarily represent
IBM's positions, strategies, our opinions.
I'm Malcolm Gladwell and you're listening to smart talks with IBM.
Most of what happens in a UFC fight is too
fast to see. A fighter drops their shoulder for a
split second. Did you catch it? A shift in position
that looks insignificant but changes everything? Were you watching alone?
(00:22):
Cohen is the head of R and D for UFC.
His job is to help people see those moments, not
by slowing things down, but by knowing what to point
to after it happens. By the time you realize something mattered,
his data systems have already figured out why it's taken
fifteen years to get here, first with paper scorecards and
a TVO. Now in partnership with IBM, and along the way,
(00:47):
a loan's learned something that applies far beyond fighting. The
best technology is the kind you don't notice at all.
It just helps you see. I thought you were going
to be like tattoos muscle shirt. I thought you're gonna
like I thought you were going to represent the brand
(01:08):
in that respect that you yourself would be. It would
be a kind of, you know, mixed martial arts type.
You were not, in fact a mixed martial arts type.
Speaker 2 (01:17):
I did a little taekwondo in my past. I am
I am not in the way that you put that,
not a mixed martial arts type. But I think if
you if you come through the building in Vegas, if
you come through the building when we do a show,
you're gonna see a lot of the mixed martial arts type.
It even surprised me. I came out of the tech world,
the young tech world in the two thousands, and everybody
(01:39):
was talking about, you know, we have to have a
mix of viewpoints and a mix of you know, people
from all walks of life, and all of these other
things that evolved into into something else. But yeah, that
didn't happen to me until I went to work for UFC,
which is like a weird way to get there.
Speaker 1 (01:54):
Were you in a tech world before? What were you doing?
Speaker 2 (01:56):
So when I left school, I went to a start
up up that today we would call a big data company,
and it was there at a moment where everybody was
doing relational databases and relational databases from micro Strategy and
from Cognos and from whoever. So we did that two
thousand and one hits after I graduate nine to eleven
(02:17):
and the dot com bust and all that kind of stuff,
and we all looked at each other and we said,
no one, no one of these institutions that needs this
kind of information is going to take a risk on
a small company right now. They're going to retrench. And
I went to law school.
Speaker 1 (02:30):
Oh, I see, you started tech. You briefly have a
foot in the twenty first century, and then you decide no,
I'm going to go get a lot. Agree I did.
Speaker 2 (02:40):
People said you feel like somebody who would benefit from this,
and I wanted to want it. And the first attorney
I worked for out of law school, he looked at
me and goes, you need to go be in business,
Like you're not an attorney.
Speaker 1 (02:55):
Yeah, so you're you're a failed lawyer. They kick you out.
Where do you go next?
Speaker 2 (03:01):
So that's what happened. So in two thousand and eight,
there were a bunch of lawyers who were doing just fine,
but the bottom has fallen out of the legal market.
I was helping a friend off the side of my
desk who had come to me with a banker's box
of paper and said, I have been watching The Ultimate Fighter,
the reality show where they pick new entrance to the UFC,
and I had been watching. He was a sports writer before,
(03:22):
and he very much. This gentleman's name is Romic. An hour,
Romy comes to me and he says, there's no stats
for this stuff. I'm used to writing about baseball. So
I did a full regression analysis after watching one hundred
hours of fight time. And now we're at closer to
two hundred hours of fight time, and I've been scoring
these fights. And the UFC's talent had been reading my
(03:43):
blog and then want me to do analysis for them,
and I can't be doing it on paper. And so
he came to a technologist and said, what do I do?
And we talked about double king the data in Indonesia
and building a database and APIs. A year later, we
were making small salaries and we had gotten this thing
up off the ground and going, and the UFC was
starting to use our statistics and we had turned it
into a real data product. And you know, we were
(04:05):
able to score electronically at that point. And the rest
is at this point seventeen or eighteen years of history.
Speaker 1 (04:11):
So wait, back up, this is really interesting. Pretend I
know nothing about UFC. Okay, So Romi comes to you,
and Rommy's issue is what that the way in which
these matches are scored is too subjective.
Speaker 2 (04:25):
The way that these matches are scored is inscrutable to
the average person. So his analogy, and it's the one
that he experienced, is when you write a summary of
last night's baseball game, right, what do you do? You say,
here's my thesis statement, so and so had a great outing,
and middle relief collapsed or whatever it is. And last
(04:46):
night at seven pm at this stadium, the following happened.
Here was the final score. And then this middle paragraph is,
you know, middle relief had this as a cra and
so on and so forth, and you'd run an analysis
and the and you talk about, you know, so and
so has been performing well, and he had this outing
and that outing, and this is part of a trend.
Speaker 1 (05:01):
Tell you tell the story of the game in the
context of a core of statistical.
Speaker 2 (05:06):
Knowledge, exactly right. And this is at least how he wrote.
And you get to that paragraph in an MMA story,
and it's non existent. It's so and so is a
strong striker. So and so is coming off of two losses.
So and so is on a three fight win streak.
And that is the totality of what's currently available. And
Romy's approach is we can count everything, but everything's not important,
(05:30):
and you can't count everything practically because by the time
he gets to the end of this first iteration, we
have sixty seven data points per fight er per round.
So it's a ton of stuff.
Speaker 1 (05:39):
At this point you're collecting the data.
Speaker 2 (05:41):
How just visually, he has a piece of paper and
he is on a TVO remember TV's. We had TVO's
until long after they were dead because we needed them.
And he is playing and pausing and rewinding to be able.
Speaker 1 (05:53):
To He's doing it the way people in the olders
in baseball, remember they would score. Sure, they would take
the piece of paper and they would score the game
as they were. Well, it's going to matches.
Speaker 2 (06:02):
His history is I go to baseball games with dad.
Dad has a scorecard, and you, as a kid, are
both drawn to it and averse to it because you're
like dad, you're missing the game, and at some point
you find yourself marking down the scorecard. And he brings
a lot of that to this.
Speaker 1 (06:17):
So you say, in his first iteration he had seventy
two data points, say that again, sixty seven sixty seven
data points per fighter fighter per round per round. Yeah,
so that's consistently there.
Speaker 2 (06:30):
Or this as an average of Well, so at any
points on the sheet, there are sixty seven different things
that we are tracking. So there may be more head
strikes landed, powerhead strikes landed, and there may be some
other head strikes landed, but there's probably not necessarily tight
submission or a camorra or something like that. But these
are different kinds of submission types that we would track,
(06:50):
and so all of them are available on the sheet
so that you can just tick those off and move on. Right,
So those are the sixty seven independent boxes on the
sheet that we could mark something down on.
Speaker 1 (07:00):
Give me examples of things that non obvious things that
you would learn if you did a formal statistical analysis
of a match, like as a viewer or as a fan.
How is this enhancing his experience of what he's watching.
Speaker 2 (07:13):
A couple of things are happening inside of an MMA
match that are worthy of note to the average fan.
It is scored per round. And one of the problems
that you have when you score per round is that
people don't think per round, right. They don't get to
the end of round one and say round one is over,
I will make a decision about round one. Let us
now go to They don't do that. They look at
(07:34):
the totality of the match. The shorthand I used for
this for a long time is you make the invisible
visible to people like what exactly did I see? The
other thing that the last thing that the numbers do
for us is they tend to combat recency because you
just forget what happened in round one or it just
doesn't leave a strong an impression. And one of the
and of course fighters fight to that. What do you
(07:56):
do right at the end of the round. You take
a dude and you take him down because you were
going to finish the fight now, because you wanted to
leave in the impression, the impression in the mind of
the judges like that dude controlled that round, And so
you were trying to score these big shots at the
end of a round. And the numbers allow us to
battle some of that for our fans to say, like
how well did he actually do? What did she actually do?
(08:16):
In this fan So that's one of the big learnings
is you're able to go back in time and not
have to watch the video. You just get a spot
to be like, oh, yeah, there were two takedowns in
that first round, and so.
Speaker 1 (08:27):
You're allowing people to construct a much more complete and
accurate narrative of the match.
Speaker 2 (08:33):
I think so yeah, And I think for new fans
there's a different calculus because new fans don't know how
to watch the sport, and when you it's I joke
that when we all watch the Olympics, like, I don't
know anything about synchronized swimming, and I shouldn't pretend to,
but the Olympic broadcast allows me to pretend to, right,
(08:53):
And that's a little bit of what's happening here, is
that they can look at this and say, oh, I
get it right, Like I'm watching all this action and
then we condensed that down to this is the number
of leg kicks this person landed, and they're like, oh,
I did see. Okay, Now I know to focus on that.
This is part of the storyline and it is a
signpost and that's a big deal.
Speaker 1 (09:12):
So in the beginning, you're just a contractor with UFC.
Speaker 2 (09:16):
We are a contractor entity. Yeah, for the first seven
years of our existence, we are independent and are the
official data feed of the.
Speaker 1 (09:23):
UFC, and and at a certain point UFC says, come
and join the.
Speaker 2 (09:30):
Ari emmanual at wm IMG. At the time buys UFC
and he says, I want to sell the data. Where's
the data? And they said, well, these two nice kids
in the Georgetown waterfront they have the data. And he goes,
what do you mean they have the data? Like, it's
our data, and that started an acquisition. Conver We were
a fold into that larger acquisition.
Speaker 1 (09:48):
Going back to the fights, the data, how is it?
How is the scoring working at this point, so.
Speaker 2 (09:53):
The tvo I brought up earlier, you're able to watch
it with slow mo and rewind and we have at
the time tablet or a touch screen, and we have
taken our piece of paper and we have split it
in twain and you're able to mark what you see
as you see it. Save around score.
Speaker 1 (10:08):
You've got trained scores.
Speaker 2 (10:10):
Yes, yeah, they are some of my favorite people. We
have had trained scorers in our group that go back
thirteen years with us, folks that we plucked out of
the ether. Right, you put something online looking for people
who are interested in learning this stuff, and then you
have to whittle out the people who can learn how
to do it and then do it quickly. The first
time you score something like this and try to make
sense of the scoring page. It's going to take you
(10:32):
fifteen minutes to do a five minute round. I take
you more. And that means that at the end of
the fight, you're probably thirty five minutes late. And so
you have to do this for six or seven months
on a regular basis, paying you make sure that you
know you're fairly compensated to get you to the point
where you're able to make decisions rapidly. It just comes
as second nature to you. And this is the dirty
little secret of UFC. But we joke we've been trying
(10:54):
to put ourselves out of that business for at this
point eleven or twelve and we aren't close yet.
Speaker 1 (11:02):
Put yourself out of the business of having to rely
on a human score a human score.
Speaker 2 (11:07):
Yeah, And we said that joshingly. But there's a sense
of which there's a sense in which at some point
you understand that compute ought to catch up with you, right,
And we said we should be out ahead of this
and sort of the Netflix model, right, If DVDs are
going to be a problem, you should figure out what's
coming next, and you should do that also, and disrupt yourself.
(11:27):
And you know, we wanted initially we have used it
as additive data, so we wanted to do motion tracking.
So eleven twelve years ago we bought a motion tracking
software and it couldn't make sense of any of what
it was seeing inside the octagon, like it really couldn't understand.
And this wasn't even striking. This is just like red
is here, blue is here, and the rough is here.
And so we taught a human system how to do it.
(11:49):
But we keep going back to the to the machine well,
hoping it will get better. And today, in fact we
are we are closer than ever, but nowhere near close
to overtaking us as scores. It's weird. I have been playing,
as I said, with my motion tracking computer vision experiments.
We're twelve years ago. Ten years ago I was hanging
(12:10):
cameras to attempt to do skeletal tracking to see what
that could give us, and it didn't work. You know,
we have been on a ten year journey to discover
what it can do and being fostering of it. Right,
That's what R and D is supposed to say when
something fails, to say, well is there something of this
that I can retain. Does this add some value enough
(12:32):
that it really makes sense to just continue to invest
in it some so if it can ever make good
on its promises, we'll get to see exponentially.
Speaker 1 (12:40):
What striving you here is what you want to get
your data quicker? Or do you want more data? Do
you want qualitatively different data?
Speaker 2 (12:48):
What is it?
Speaker 1 (12:49):
What's what's what's in the back of your head?
Speaker 2 (12:51):
So, man, so many motivations are pushing this for us.
One motivation is faster data. So imagine the very simple.
If I see a significant strike landed in the middle,
and so let's say the first person to one hundred
significant strikes landed in this fight, that is potentially betting data.
Somebody could bet who could get to that first. Right now,
(13:13):
I can only offer that data unofficially because my humans
need to wait for my official data to come in.
If computer vision could really get that right, then I
could stand behind that number and say that is betting ready,
and you could resolve that bet at the moment it happens.
And so you open up all of these markets. For
one like, that's a big motivator for this kind of stuff.
(13:34):
The second is I am when I'm adding these AI technologies.
I'm often looking at them and saying, like, what else
can I do with this? What else is it telling
me about the fight that I'm not already getting. Motion
was a big one of them. Right, where are people
in the octagon? What I mentioned earlier that one of
the one of the criteria for judging a fight is
(13:55):
control of the octagon space? Well, okay, everything I was
tracking with my scores before, it doesn't talk about that.
Speaker 1 (14:01):
Right.
Speaker 2 (14:01):
If I'm hitting you more and you're you know, defending
poorly or whatever it is, maybe that's control. But what
if you're a counterpuncher, right, famous fighters who are counterpunchers.
I draw you in and then I hit you. Does
that mean I'm seating control to be able to take
back control? What does that mean? And so we didn't
have any any numbers on where people are. That's an
immediate use of AI that is additive to what I
(14:22):
am doing. And all of this is always in the
service of storyline. Right, what is not being captured in
the fight? And what should the commentators be talking about?
And how can I support what they're talking about? How
can I give them something that makes them think, Oh, actually,
the thing I want to be talking about here. Is
this right? That's ultimately the goal. The goal here is
(14:45):
I mean, let's go back to the first thing you said.
You said, I don't look like the kind of person
who might be an MMA fan. There is a point
at which if I throw up a number on the
screen and it's like, this person is twenty seven point
two four, and people are like nerd, like, what are
you doing? And there is a space for that in
other sports, but sometimes we're doing it just to seem technical.
(15:06):
I think if you're doing the tech right, you should
forget about the tech. You should forget about the fact
that there's a I behind that. You should forget about
all of this. The thing that hits the screen is
so and so has never lost a fight if they
have landed four takedowns. We're on number three. Right now,
we know what we're watching. Yeah, right now you have
(15:27):
a binary simple thing to look at. So and so
has a takedown accuracy of ninety four percent. So and
SO has a takedown defense of seventy five percent. Let's
see how they match up against their classic averages. And
now you're getting into some more tech and number. You're like,
this guy is below his average and that guy's above
his average. Okay, Now we have a way of measuring
very quickly who's winning and who's losing across at least
(15:48):
this part of the fight. And so the goal is
to get to storyline as fast as possible in human
understandable terms. Right, This is in some ways the great
moment of the LM as well. Right, Who cares how
many weights and how many parameters and how many whatever?
When I talk to it, it feels like I'm talking
to a human. That's the great unlock. The technology suddenly
(16:11):
disappears and the experience stays. And that's at our level,
at our level of sophistication, that's what we're doing.
Speaker 1 (16:19):
Yeah, I want to go back to make sure I
understand when you talk about control of the octagon of
the octagon conceptually, what is control the octagon mean?
Speaker 2 (16:31):
It is the last of the criteria in the judging rules,
and it should mean that in a fight, one of
the things you're trying to do is assert dominance the
most basic level. It is left vague. Different commissions around
the country will tell you slightly different things about what
it means. Some of them will boil it down to
(16:52):
if I hit you more, I control the fight. But
a lot of others of them will say it is
really there to be that sixth cent feel of was
I able to put you where I wanted you to
be when I wanted you to be there? And in
our statistics, one of the ways you can talk about
that is advancing or center control. If I always own
(17:15):
the center, you always have the wall at your back,
so I control the space. Right. There are ways of
finding small analogus directional pointers to tell you that. But
when you talk about what a judge is looking for,
they're looking for that ephemeral sense of you know, who's
(17:35):
the big.
Speaker 1 (17:35):
Dog, and that is if you're strictly kind of scoring
the match, that's one thing you're not picking up because
you're too focused on position, who's striking whom that kind
of larger gestal you're talking about a gestalt of right.
Speaker 2 (17:50):
Sure, yeah, somehow the Nevada State Athletic Commission, I got
to get to criteria five and go gestalt. That's what
we're looking for. We're trying to deal as as a
as a matter of course, we want to bring people
into our arena, have this experience be devoid of Veltschmertz
and focus on the GESTALTA and it just didn't. It
didn't make it. That's what I'm saying.
Speaker 1 (18:11):
So you've got your motivations are one, I can enhance
the viewer experience in terms of story, yep, I can.
I can facilitate things like betting on the match. And
I can aid the judges in getting a sense of
who the dominant party is.
Speaker 2 (18:29):
One and two, Yes, three, we actually scrub all of
our all of our video feeds into the arena are
scrubbed of our statistics. I do not want the judges.
Their job is to follow the criteria set by their commission.
So we're in this weird spot where like my sport has,
my fighters are independent contractors. It's a regulated sport by
(18:52):
the state commission that we're dealing with, and my job
is to is to bring the event together. And as
a result, I don't want anyone pointing at us saying
you said. And so I decided We're like, look, you
understand your criteria. You're a fully trained judge. You need
to decide who won this fight. Our statistics will tell
the story our statistics tell And by the way, we
can have an occasionally antagonistic relationship where we thought that
(19:14):
was a poor decision, right, Yeah, that makes it interesting exactly.
Speaker 1 (19:17):
Yeah. So okay, let's go back to the AI. So
you're you start experimenting with this a while ago, oh yeah,
and it and you're not you're not entirely happy with
the with the results. Is that a fair or you're oh,
you understand it's embryotic.
Speaker 2 (19:33):
Both, right? You? Why should I start down the project
of making this work if I don't believe it's going
to work? Right, I try? If I'm going to bother
dedicating the time and the effort and potentially a lot
of money, I wanted to work. Uh, and I was
disappointed in time and again, and I have found things
to bring along right, So early skeletal tracking has become
(19:54):
the basis for.
Speaker 1 (19:55):
Todaya tracking is defined the term.
Speaker 2 (19:59):
Skeletal tre is when you hang cameras. Nowadays you are
very quickly able to discern what isn't as not a
human inside of your space, and then you're able to
identify points on their body, nowadays more points than we
used to. It used to be like shoulders, head, torso, hips, knees, feet, right,
and nowadays you have two additional points on the feet,
so you can see their orientation, hands, you can see
(20:21):
the orientation and face, you can see where I'm looking.
Those are basic skeletal models and you need that to
be able to understand from an AI perspective, who is
striking and was that a miss? Was that a hit?
We can get into collision detection, which is very difficult
because skeletal tracking isn't it's not the outside of your arm,
(20:42):
it's the inside. And then beyond was there a collision,
we get into problems of intent. So intent is the
hardest thing actually to solve. There's a lot of times
where two guys are fighting and one guy's going like this,
he's range finding, just putting a hand out there to
see is you know, how far away are you and
sort of to bat you away? And that is not
(21:03):
a strike attempt. How do you teach the computer that
that's not a strike attempt? Because sometimes it looks like it,
sometimes it doesn't. And then same thing with kick to
the head, it makes contact with the outset of my arm,
which I brought up in our world, that's a blocked strike. Yeah,
but teaching a computer what exactly that means and when
(21:24):
and how like when it's up here, when my arm
is up, that's a block. When my arm is down
and hits my shoulder, that's not It's those nuances that
proved incredibly difficult for machines to be able to handle
for a very very long time. And the acceleration in
the last few years which has allowed us to onboard
this technology has solved so many of these problems. At
(21:44):
the same time, we no longer need fixed cameras. I
now do it off the broadcast cameras, which are on
people's shoulders and they shake and they rotate and whatever.
Doesn't matter. It can still figure out what it's looking at.
This is the octagon, These are the fighters. Here are
their bodies, that's the ref He doesn't matter all of
that stuff. It can now figure out. It can begin
to understand what real landed missed is, and it can
(22:05):
do so on much less perfect data than we needed
the first time. That acceleration has been incredible. If I
didn't know step one and two and three and four,
I'm not sure how ready I would have been for
where we are now.
Speaker 1 (22:17):
Yeah, so let's just dig into where we are now
a little bit. So you go. I'm interested in the
partnership with IBM and the kind of transition from this
period of experimentation to where you are now. So where
at the time, you, guys, you're aware of the development
of AI yep, and it's getting better and better and
(22:39):
better and better and better, and you clearly have an
intuition that, oh, this is this could potentially really open
some opportunities for us yep. What's the point in time
where you start to think, oh, this could be real.
Speaker 2 (22:54):
Our relationship with IBM is born of a little bit
of suffering on our behalf, is the best way to
put it. We had a partner as part of the
larger business that really wanted to focus on AI with us,
and we said, we would really like to do an
insights engine. We think it's really additive. These storylines in
real time would change the way that we talk about
(23:15):
the sport in broadcast. And for two years we went
down that road and it crashed and burned. We go
into the reasons, but they didn't understand the sport. They
tried to brute force the problem. And if you try
to brute force this problem, it's going to kill you.
And because there are just so many permutations out there,
(23:36):
and IBM starts a conversation with us, and very quickly
as we engage, a couple of things emerge, and they're
not about the tech. They say to us, we think
we can do it with the tech. We say, we
have to believe you that you can do it with
the tech. We don't know. We're not experts in it.
They can bring people who understand sports analysis to the table,
(23:58):
so all of a sudden, when my call ends, when
somebody at IBM has a question, they can talk to
somebody better. Yet, within the first couple of weeks, I said,
it would really be helpful to have somebody who's an
MMA fan, because some of your questions are specific to
the sport and we could answer them all the time.
But I think that the velocity here goes up if
you can just say, hey, Brad, what's happening here? Can
(24:20):
you explain what is the culture, what's important, what's not important? Right,
because you can come up with a lot of ideas
and fans will be like, I don't care, and it
gets worse from there. So we quickly eliminated that. With IBM.
They had people on the bench who understood just the
area and then the specifics of our sport, and they
were able to fill in some of the gaps for us,
and so we could move at pace. And it was
(24:42):
all about the people at the beginning, and obviously beyond
that they had to make good on the artech and
actually do this. We can use the AI. It's not
so much that the AI needs to run the final product, right,
That's not what we're talking about. It's can the AI
speed up the creation of the product? Can AI help
us expand its reach? Can AI suggest to us the
(25:02):
things around the corner that we didn't see. That's where
the AI came in. That's really the genius of this.
Once it's all hard coded down, the AI does take
sort of a backseat to the code because once you
have to go fast, once you need to be efficient,
you know. That's the way that you do it is
you run a whole bunch of traditional code and then
you run it through an interpretation model to be able
(25:23):
to give you natural language at the end. And that's
super efficient in AI. But it's an understanding of how
to deploy both of those things in concert and have
the right kind of people.
Speaker 1 (25:32):
So walk me through here, IBM, what are they doing.
They're taking the video.
Speaker 2 (25:36):
Feed, they're taking the data feed, the data feed, correct,
it's instead of trying to have them extract everything out
of the video feed, we've already done a lot of
that stuff, and we're doing it at speed. So we
have my data feeds, and then we also have any
augmented information that we may be getting from external AI
computer vision systems, and that's coming in as the source
(25:57):
data to IBM. So now they're geting, they're getting a
data feed, and they're getting it at speed. Their job
is to bust open that data feed into all of
the various questions that somebody might ask and think about
the interrelationships between data. So sometimes it's as simple as
this person is on pace to set the largest number
(26:19):
of takedown attempts we've seen the last twelve months in
this division. It's like a really nice talking point. Relatively
simple all the way too. Here are two comparative metrics
for these two fighters, one of which is this is
his strength and this is his strength, and here's how
they're matching up right now and everything in between. And
to do that at speed.
Speaker 1 (26:37):
Yeah, so they're taking two things. They're taking the data
stream from your scores and then also, so do you
have these vision cameras.
Speaker 2 (26:48):
Yeah, it's a computer vision AI STAT system that we're
using to augment our system.
Speaker 1 (26:53):
And how how does that work?
Speaker 2 (26:55):
So the way that works is we've finally found somebody
who could do two things for us accurate counts, and
be able to do so without us attaching a whole
bunch of cameras to the octagon, right, So they can
do it directly off of my handhelds, and they're able
to do things like give us combo percentage. They can
(27:15):
do punch and CounterPunch because they can assess that, they
could talk about style. That's a cross, that's a roundhouse,
that's right, those kinds of things, and all of that
is additive. Plus, by the way, they brought back all
the motion stuff because now my AI computer vision can
actually do motion for real, and so they brought all
that back in as well into the.
Speaker 1 (27:35):
Way I can now do just because the tech is better.
Speaker 2 (27:38):
Text a lot better, the ability to just know there's
blue tape on the blue fighter's wrists and red tape
on the red fighter's wrist, and we've gotten to the
point now where skeletal tracking and computer vision is able
to identify people by that and also take additional notes
that dude's in green shorts and that dud's in yellow shorts, right,
and it'll do those kinds of things to just figure
out who's who. And the hardest thing for a computer
(28:00):
vision system to do as it does motion, is when
we become the eight legged beast and we come apart,
it can know that we're purple, right, we're red and
blue at the same time, but when we come apart,
it has to rapidly understand who's red and who's blue. Again,
that's where a lot of these motion systems had a
lot of difficulty. Refs in black pretty easy to figure out,
like somebody's dressed in black head to toe.
Speaker 1 (28:21):
You've got these data streams, and presumably there is some
overlap between them, but that's fine. You're just feeding as
much data as you can from these two different sources
into the IBM black box, and the black box is
generating stories for us, ideas, observations, insights.
Speaker 2 (28:45):
I can give you a little more detail on how
it happens. Yeah, I think that the beauty of unpacking
how AI works is there are moments of magic and
moments of drudgery, and they have to work perfectly together, right, AI.
The data comes in to IBM and it has a
storage system called Iceberg, and it runs a classic data
process called ETL. It's extract transform load. We care about
(29:08):
our data at the fight level or the round level.
IBM is trying to tell us things about our fighters,
so it is consolidating all of its information in these
big fighter tables that it can then sick the AI on,
and then the data moves from this ETL Iceberg level
up a level. They actually, I think they do it
by metals, So you start at bronze and you move
(29:28):
all the way up to gold and you move up
a level. So now your data is mezzanined basically for
the actual AI system to be able to draw on
it and efficiently process this data into insights, and then
it moves up into insight delivery, where we have an
interface that allows us to not just look at insights.
But then I have a human factors problem. My human
(29:50):
needs to see the insights and he can't see or
she can't see all of them. Right, that's way too many.
So you have to have a scoring system that says
this insight is more relevant than this insight and surface
only those so that a human Actually this was true
of Watson a long time ago, right when it did
medical diagnostics. It couldn't give you the answer. It could
(30:11):
give you seven answers, and a doctor would look through
them and say it, got it, got it. That's silly,
that's silly. Hadn't thought of that one? And that was
the value right here. And we have a similar thing
right where we'll get a whole list of them and
you get to pick one of them and be like go.
And so that's how the data comes in. And to
everybody else that should be a black box. But I'd
prefer it not be a black box to us.
Speaker 1 (30:33):
In the course my possibility of question, in the course
of a fight, how many insights are being generated?
Speaker 2 (30:41):
Yeah, that's an excellent question. So in an active let's
say we'll pick what I would consider to be the
Cadillac of fights. Right, So it's five rounds, it's a
championship fight. So you have two fighters with long history,
good records in that fight. I mean it could be
it could be one thousand, a thousand, because every permutation
(31:04):
is possible, right, So the system is generating just an
enormous amount, and the real the intelligence is in filtering
it out so that my humans don't see a thousand,
my humans see fifteen fifteen. Yeah, because remember the moment
that in a fight, there's not a lot of chances, right,
I'm not going to spit out all fifteen onto the screen.
(31:26):
In traditional statistics, I've got two stats at around. Maybe
we don't have any stats at the first half of
the first round. If something as amazing as happening, my
job is to move out of the way and just
let that sit on the screen, And so my insights
are there to be the most valuable thing I can
put on screen at that moment. And that's going to
happen in a fight twice and over the course of
(31:47):
the night ten times. But when it happens, everybody should
have been talking about this fight like this, and now
they should be talking about this fight like that.
Speaker 1 (31:56):
Give me an example of the kind of insight you
get that you probably would never have gotten in the
kind of previous universe.
Speaker 2 (32:06):
Sure, I think compound insights are the most complicated right
where it's we used to get basic, basic, but difficult
to answer questions. When was the last time someone want
to fight after being down one hundred strikes? Like that's
a hard thing to throw into a sequel database at
the Latin a moment's notice and get that answer. But
you can do that kind of thing with INSIGHT's engine, right,
(32:27):
was like you're like, oh, it is looking for it's
one of its pipelines. It's looking for those kinds of
disconnects between the data and the reality. This is the
largest differential striking differential between two fighters, you know, in
a middleweight title fight since and here's this other iconic
fight that everybody remembers. Right, It's those kinds of things
(32:48):
that you might in the back of your head think
to yourself like, huh, this feels like I don't think
I've seen this in a while. But you can't. You
can't give voice to it. And in a live fight,
there's very time. So what will happen is two fighters,
Let's say it's a striking statistic. Two fighters have not
had this much of a striking differential in a fight
since X point. It is currently three minutes into the
(33:12):
round of round three, let's say, and the commentators are talking,
We're watching the action, everything's happening. You get this sense
in your head I need to get in the next
two minutes the last thirty seconds are already we're going
out to commercial, like we're not interrupting that the fight
could end at any moment, So there's time pressure. I've
got to hit a promo at some point in the
(33:33):
middle of this thing, so you see how my time
is getting shorter and shorter. To be able to deliver this,
I've got to have my producer be able to communicate
it to the producer, it's got to communicate it to commentators,
who's got to be able to get it out of
a system into a graphic and then sold in to
be able to put on screen. All of that has
to happen in call it between thirty. I said to you, like,
I have three minutes. I have two minutes left in
(33:54):
my round, probably only have about forty seconds window, and
I'm going to lose it starting the mo and I
think of it until the moment it hits screen. So
I've got to have about a fifteen second turnaround. And
so it's not you don't have to ask a very
complex question for you to exceed the fifteen second turnaround
unless it's at your fingertips. And that's what insights engine
is for. It's it's to kind of come in and
(34:15):
be like, I know you have some cool ideas, but
we're never going to be able to get him into
fight time unless something is is pushing not pulling.
Speaker 1 (34:24):
You need to have buy in from the commentators.
Speaker 2 (34:26):
Yes, we do so. First of all, being a commentator
is a very sophisticated undertaking almost anything you see. And
it's one of those things that you watch happen and
you say, well, that doesn't look that difficult. But a
commentator has an IFB, a little ear piece in and
I've watched them do this and I can't wrap my
head around doing it, which is you must be looking
at the camera and presenting information in coherent pros while
(34:49):
the producer is talking in your ear, and then when
you stop talking, he starts talking. No, no, no, it's talking
in your ear while you're talking. And you've got to
be able to process that information and two sentences hence
make a part of your delivery. And that information processing
capability is by in large sophisticated. Now, people are people.
Some people are more pro stat some people are more
(35:12):
anti static. It doesn't really matter. You just have to
know who that is. But they have a relationship of
trust with their producer and if the producer says I
want to go here, they can push back. But those
two people work together on the regular producer's going to
sell you the things he thinks you're going to buy, right,
or at least the things he thinks. This is as
(35:32):
far as I can push it, and you'll really go
there because he wants you to sound authentic and he
certainly doesn't want you to be fighting with him on air, right,
So that very human process is taking place. And by
and large, one of the reasons Insights Engine produces human
readable stories is because I would have pushed back if
somebody said, IBM, Insights Engine says that so and so
(35:55):
is fifty seven point two four six percent likely to
win the fight. Okay, and that doesn't feel authentic. But
so like we've noticed that he's advancing and he's a counterpuncher.
This is off and so now hopefully the commentator is
going like, wow, the movement in this fight is different
(36:15):
than we would expect to see, and let me give
you some things to bolster that that point. Or and
people have done this, We have given them an insight
and they're like, oh, yeah, you're right, Like that's what's weird. Yeah,
about this fight.
Speaker 1 (36:29):
Yeah, it's fascinating because genuine engagement with the insights he's
actually from the viewers standpoint. The most interesting thing the
idea that they would be there'd be some tension between
what we're seeing and what's actually happening. That is one
of the most interesting things in sport, right Oh. I thought,
(36:49):
I thought, you know, Steph Curry had a great game,
and then I look after at the stat line afterwards
and I'm like, or the opposite is even better. He
looked terrible out there and look outline. I was like,
oh my god. He was like, I find my shot
seventy percent from the field.
Speaker 2 (37:04):
I feel like I do that more often than not,
where you're like, that looks terrible. So a couple of
things that are true in fighting that are I think
true elsewhere is if people have a favorite, they tend
to watch their guy, and so their guy always unless
he looks terrible, he looks great because you're not watching
the other guy, You're just watching your guy, and that
stats help bridge that gap a lot. Our commentators in
(37:25):
general don't fall into that trap, and so when people
fight with the commentators, when when fans fight with the commentators,
they're watching one guy, They're not watching both guys. I
will watch fights and I will say, you know, I'm
not watching I'm not seeing the fight you're seeing. Some
of that is because a lot of our commentators are practitioners.
They're seeing something I'm not seeing and they but it
is then their job to explain to me what I'm
not seeing, and that's fine. I think that The other
(37:47):
place that I would want to take this, and where
insights I think are very useful, is our fans watching
a lot of contexts a lot and how many times
have you walked by a game? Right? The classic is
I'm at a sports bar and maybe the audio is on,
but maybe the audio is not. Maybe it doesn't matter
because it's loud. I want to be able to provide
an experience for that person that is meaningful and potentially
(38:09):
more meaningful. So, yes, my insights are going to interact
with my commentators. But if I can't hear the commentators,
how nice to be able to put a storyline, not
just a number, but a storyline directly on screen. Right,
we'll have this with milestone. So and so has exceeded
their best ever whatever, and it's just a moment where
you're watching it like that's cool, and you'll go back
to watching. And that's really that's what we're going for.
Speaker 1 (38:30):
Are we fundamentally changing the relationship between the fan and
the sport?
Speaker 2 (38:34):
Interesting. I didn't set out to have insights engine fundamentally
change the relationship of the fan and the sport. I
I wanted something to fit like a glove. Right if
if you were sitting next to someone who had an
encyclopedic understanding of the sport and they were in and
(38:55):
they were fun, right, that was a big part of it.
What are the kinds of things they could tell you
where you're like, oh cool, and it's it's more fun
to watch because of that and not I think where
a lot of the push was in adding tech to
the sport, which is we're gonna make it nerdier, Like
that was the thing I don't want to do. I'm
(39:16):
interested in leaning in to what makes this great and
letting you see write the classic, the stats, to make
the invisible visible, letting you see those things but in
a narrative arc so that you can contextualize them. You
can say something more intelligent about sport. But like it,
(39:36):
I wasn't here to make you feel like I changed
the sport. I was here to make you feel like
you were spoken to more crisply about it. You were
able to speak more crisply about it, and the fight
just looked a little shinier, a little brighter because of
this kind of thing. And like I said, when this
tech does its best work, it just disappears.
Speaker 1 (40:00):
As hearing you describe it, it strikes me that what
you're doing is you're moving people up a level.
Speaker 2 (40:05):
You're right, we are bringing it to a place where
you feel like you can you could even have an
opinion because you understand enough of what's going on. You
are one hundred percent right. We're moving everybody right right
up the scale on a daily basis. I want to
go back to INSIGHT's engine is not here to feel technical,
and that's the genius of it. It's simplicity. It's narrative.
(40:28):
Is the thing that allows you to bring people forward
because you're not using jargon, because you're not just into
the numbers, right, That's really that's really the key here.
Speaker 1 (40:37):
When you look back at your experience with fight data,
are there a few categories that are essential to winning?
Speaker 2 (40:45):
I think the easiest way to answer that is, the
entire statistical system that we built is based on only
one question. We don't track style. For example, I don't
track whether somebody hit you with a cross or a jab.
Even more ridiculous, in the official statistics, we don't track
(41:08):
whether we hit you with an arm or a leg.
And that's because the entire statistical system is built on
this question what moved you closer to winning the fight?
And it turns out that what moves you closer to
winning a fight is where you take damage, right, and
how much damage you take. So if I hit you
in the head, arm, leg doesn't really matter, Style doesn't
(41:29):
really matter, it's did it land? So the whole statistical
system is built on that question of how much of
each of these things did you do? These things which
we have directly correlated to victory, And with live statistics
and the augments, we can do lefts and rights and
things like that people want to see. But that's the
fundamental question. So for the question is what's the most
(41:52):
important thing you could do? Land strikes? Land significant strikes
to the head that's going to end most fights. Can
you end a fight with significant strikes to the body
that massively outpace your opponent. You can, they're at a
second level down can And then there are things that
are a little harder to talk about. That way, if
you have more ground control, that's putting you closer to
(42:14):
completing a submission. So that again is the kind of thing.
And obviously once we get to the number of submission
attempts you attempt, are going to make it more likely
that one of those will be successful. But it is
all built from this basic question of all of our stats,
does it get you closer to winning the fight? Yeah?
Speaker 1 (42:31):
One last question. I know you've thought about this a lot.
Which is next, Like if I, if I, if I
invite you back, we have this conversation five years soon. Now,
what is the fun wrinkle that you're working on.
Speaker 2 (42:44):
We have a couple of things that are in the works.
Some of them are very sideways to this, but there
so one of them, which we're prototyping is the ability
to bring additional voices into the mix seamlessly. So I'll
give you a perfect example. If the coaches who are
(43:06):
on the side and they're yelling in whatever language they
are yelling, they are attempting to influence the fight, and
it is and we do this. Occasionally we'll cut to
the coaches camp and we'll listen to them for a moment,
but they're doing it constantly. Wouldn't it be interesting to
understand what they're trying to do and what it means. So,
(43:26):
for example, if I'm saying you're doing great, but you're
not doing great by being encouraging. If I'm saying you
are doing great and you are doing great, what does
that mean? What is my tone of voice, what's my
level of stress? All of these kinds of things, So
that you could very subtly be able to distill that
information and bring it in and say, like, by the way,
if you want to know what the coaches think, feels,
(43:47):
do interesting. So that's like a really nice one that's
relatively close.
Speaker 1 (43:50):
Now I have a sense of the psychological context of
the coach and the athlete.
Speaker 2 (43:54):
Let's go further away, right, you said five years in
the future. I think five years in the future. I
have two beliefs. I don't believe people want to lean
forward experience, as much as everybody would love them to
want to lean forward experience. If you could watch a
Steph Curry performance and I could let you control the camera.
Speaker 1 (44:13):
No, I don't want to go O.
Speaker 2 (44:14):
I don't believe that people do. Yeah, but that doesn't
mean that those technologies don't have a place. So if
people are talking about vogumetric capture or GAUSSI and splatting,
all of these kinds of things that allow you to
create three dimensional space and move through it, I am
working on. I have a belief that one of the
places I can't put a camera imagine the two of
us are fighting, is right here, right in front of
(44:35):
my face and watching your face. And yet the explanation
you want to give of I hit Malcolm in the
shoulder because he had dropped his hand and it was open.
Show me, make it available to me. Speed up that
process fast enough, create the telestration tools fast enough. And
I think that in five years those things are going
to become rote and we will expect impossible angles, all
(44:56):
of these kinds of things to become part of our world.
We are going to have to rethink what it means
to watch to be a fan of something. Right, if
you only watch a fifteen minute cutdown of a NASCAR race,
are you a fan or not a fan? I don't know.
Do you watch fifteen minute cutdown of every race, and
how do I service that person and how do I
(45:17):
engage with that person? If you do have this very
for you said earlier that people only watch a half
a show, for example, if you have a fragmented set
of attention, I can either fight it and rage against
the dying of the light, or I could lean into
it and try to service that person and make them
a real fan and bring them into the rest of
our ecosystem. And I think that that is going to
continue to happen. I think we're going to continue to
make an argument for live events and the pull of
(45:39):
that one only needs to go. As I'm sure you've seen,
when you're in the room, there's nowhere you'd rather be,
and it is so interesting, even though it is so
much less good than the angles you see on television,
you can't help but look away. We're bought in on
that thesis. But the moment you step out of that room,
how do I give you the greatest experience possible?
Speaker 1 (46:01):
Yeah? Yeah, alone, Thank you very much.
Speaker 2 (46:04):
Thank you.
Speaker 1 (46:05):
You maybe want to become a UFC fan, which is
a statement I never thought I would ever say.
Speaker 2 (46:10):
I'll tell you what, Come watch one with us, Come
watch one with me. Yeah, I bet you walk away
and you're like, I'm not sure. I watching every week,
but holy moly, is this amazing.
Speaker 1 (46:20):
Smart Talks with IBM is produced by Matt Ramano, Amy Gains, McQuaid,
Trina Menino, and Jake Harper. Were edited by Lacy Roberts.
Engineering by Nina Bird Lawrence, mastering by Sarah Buguerre, music
by Gramoscope, Strategy by Cassidy Meyer and Sophia Derlin. Smart
Talks with IBM is a production of Pushkin Industries and
(46:41):
Ruby Studio at iHeartMedia. To find more Pushkin podcasts, listen
on the iHeartRadio app, Apple Podcasts, or wherever you listen
to podcasts. I'm Malcolm Glappwell. This is a paid advertisement
from IBM. The conversations on this podcast don't necessarily represent
IBM's positions, strategies, our opinions.
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