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Chapter 1: What is the main topic discussed in this episode?
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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? Alon Cohen is the head of R&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 15 years to get here, first with paper scorecards and a TiVo, now in partnership with IBM. And along the way, Alones 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 were going to represent the brand in that respect. That you yourself would be a kind of mixed martial arts type. You are not, in fact, a mixed martial arts type.
I did a little taekwondo in my past. I am not, in the way that you put that, not a mixed martial arts type.
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Chapter 2: What challenges do UFC fans face in following fast-paced fights?
But I think if you come through the building... In Vegas, if you come through the building when we do a show, you're going to 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 2000s, and everybody 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 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.
Where were you in the tech world before? What were you doing?
So when I left school, I went to a startup 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 MicroStrategy and from Cognos and from whoever. So we did that. 2001 hits after I graduated. 9-11 and the dot-com bust and all that kind of stuff.
And we all looked at each other and we said, 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.
Oh, I see. You start in tech. You briefly have a foot in the 21st century, and then you decide, no, I'm going to go and get a law degree.
I did. 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. Yeah.
So you're a failed lawyer. They kick you out.
Where do you go next? So in 2008, 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, you know, new entrants to the UFC. And I had been watching.
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Chapter 3: How has technology evolved in UFC match analysis?
And he... Very much. This gentleman's name is Rami Ganar. Rami 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 100 hours of fight time. And now we're at closer to 200 hours of fight time. And I've been scoring these fights and the UFC's.
talent have been reading my blog and they want me to do analysis for them. And I, 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 keying the data in Indonesia and building a database and APIs year later, we were making small salaries and we had gotten this thing up off the ground and going, uh, and the UFC was starting to use our statistics and we had turned it into a real data product. And, you know, we were able to score electronically at that point. And
And the rest is, at this point, 17 or 18 years of history.
So wait, back up. This is really interesting. Pretend I know nothing about UFC. Okay. So Rami comes to you. And Rami's issue is what? That the way in which these matches are scored is too subjective?
Yes.
The way that these matches are scored is inscrutable to the average person. Yeah. 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 night at 7 p.m. at this stadium, the following happened.
Here's 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 then you talk about, you know, so-and-so has been performing well and he had this outing and that outing and blah, blah, blah. This is part of a trend.
You tell the story of the game in the context of a core of statistical knowledge.
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Chapter 4: What role does data play in understanding UFC fights?
And Rami's approach is... We can count everything, but everything's not important and you can't count everything practically because by the time he gets to the end of this first iteration, we have 67 data points per fighter per round. So it's a ton of stuff. At this point, you're collecting the data how? Just visually? He has a piece of paper. Yeah. And he is on a TiVo. Remember TiVos? Yeah.
We had TiVos until long after they were dead because we needed them. And he is playing and pausing and rewinding to be able to.
He's doing it the way people in the old days in baseball, remember, they would score. Sure. They would take their piece of paper and they would score the game as they were watching.
100%.
He's scoring the matches.
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.
Yeah. So you say in his first iteration, he had 72 data points. Say that again. 67. 67 data points per. Fighter. Fighter.
Per round.
Per round. Yeah. So that's consistently there or this as an average of.
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Chapter 5: How does Alon Cohen's background influence his work in UFC?
Oh, I see. So there may be more head strikes landed, power head strikes landed, and there may be some other head strikes landed. But there's probably not a tight submission or a Kimura or something like that. But these are different kinds of submission types that we would track. 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 67 independent boxes on the sheet that we could mark something down on.
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?
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 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 impression. And of course, fighters fight to that.
What do you 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? No, because you wanted to leave 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? Did she actually do in this? 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 on.
So you're allowing people to construct a much more complete and accurate narrative of the match.
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... I joke that when we all watch the Olympics, I don't know anything about synchronized swimming. And I shouldn't pretend to. But the Olympic... This broadcast allows me to pretend to.
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Chapter 6: What insights can AI provide to enhance UFC viewing experiences?
So we we quickly eliminated that with IBM. They had people on the bench who understood everything. 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 all about the people at the beginning. And obviously beyond that, they had to make good on the art tech 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 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 AI
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 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.
So walk me through here. IBM, what are they doing? They're taking the video feed. Is that how it starts?
They're taking the data feed. The data feed? Correct. 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 data to IBM.
Okay.
So now 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 of takedown attempts we've seen in the last 12 months in this division.
It's like a really nice talking point. Relatively simple. All the way to 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. Yeah.
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Chapter 7: How does the scoring system work in UFC matches?
And I'm going to lose it starting the moment I think of it until the moment it hits screen. So I've got to have about a 15 second turnaround. And so it's not, you don't have to ask a very complex question for you to exceed the 15 second turnaround unless it's at your fingertips. And that's what Insights Engine is for.
It's to kind of come in and be like, I know you have some cool ideas, but we're never going to be able to get them into fight time unless something is pushing, not pulling.
You need to have buy-in from the commentators.
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 earpiece 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 prose while 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 it part of your delivery. And that information processing capability is by and large sophisticated. Now, people are people. Some people are more pro-stat. Some people are more anti-stat. 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. Yeah. Right? Or at least the things he thinks, this is as 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. Yeah. 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 is 57.246% likely to win the fight. You're like, okay.
And that doesn't feel authentic. But 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 than we would expect to see. And let me give you some things to bolster that idea.
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