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  • My colleagues and I are fascinated by the science of moving dots.

  • So what are these dots?

  • Well, it's all of us.

  • And we're moving in our homes, in our offices, as we shop and travel

  • throughout our cities and around the world.

  • And wouldn't it be great if we could understand all this movement?

  • If we could find patterns and meaning and insight in it.

  • And luckily for us, we live in a time

  • where we're incredibly good at capturing information about ourselves.

  • So whether it's through sensors or videos, or apps,

  • we can track our movement with incredibly fine detail.

  • So it turns out one of the places where we have the best data about movement

  • is sports.

  • So whether it's basketball or baseball, or football or the other football,

  • we're instrumenting our stadiums and our players to track their movements

  • every fraction of a second.

  • So what we're doing is turning our athletes into --

  • you probably guessed it --

  • moving dots.

  • So we've got mountains of moving dots and like most raw data,

  • it's hard to deal with and not that interesting.

  • But there are things that, for example, basketball coaches want to know.

  • And the problem is they can't know them because they'd have to watch every second

  • of every game, remember it and process it.

  • And a person can't do that,

  • but a machine can.

  • The problem is a machine can't see the game with the eye of a coach.

  • At least they couldn't until now.

  • So what have we taught the machine to see?

  • So, we started simply.

  • We taught it things like passes, shots and rebounds.

  • Things that most casual fans would know.

  • And then we moved on to things slightly more complicated.

  • Events like post-ups, and pick-and-rolls, and isolations.

  • And if you don't know them, that's okay. Most casual players probably do.

  • Now, we've gotten to a point where today, the machine understands complex events

  • like down screens and wide pins.

  • Basically things only professionals know.

  • So we have taught a machine to see with the eyes of a coach.

  • So how have we been able to do this?

  • If I asked a coach to describe something like a pick-and-roll,

  • they would give me a description,

  • and if I encoded that as an algorithm, it would be terrible.

  • The pick-and-roll happens to be this dance in basketball between four players,

  • two on offense and two on defense.

  • And here's kind of how it goes.

  • So there's the guy on offense without the ball

  • the ball and he goes next to the guy guarding the guy with the ball,

  • and he kind of stays there

  • and they both move and stuff happens, and ta-da, it's a pick-and-roll.

  • (Laughter)

  • So that is also an example of a terrible algorithm.

  • So, if the player who's the interferer -- he's called the screener --

  • goes close by, but he doesn't stop,

  • it's probably not a pick-and-roll.

  • Or if he does stop, but he doesn't stop close enough,

  • it's probably not a pick-and-roll.

  • Or, if he does go close by and he does stop

  • but they do it under the basket, it's probably not a pick-and-roll.

  • Or I could be wrong, they could all be pick-and-rolls.

  • It really depends on the exact timing, the distances, the locations,

  • and that's what makes it hard.

  • So, luckily, with machine learning, we can go beyond our own ability

  • to describe the things we know.

  • So how does this work? Well, it's by example.

  • So we go to the machine and say, "Good morning, machine.

  • Here are some pick-and-rolls, and here are some things that are not.

  • Please find a way to tell the difference."

  • And the key to all of this is to find features that enable it to separate.

  • So if I was going to teach it the difference

  • between an apple and orange,

  • I might say, "Why don't you use color or shape?"

  • And the problem that we're solving is, what are those things?

  • What are the key features

  • that let a computer navigate the world of moving dots?

  • So figuring out all these relationships with relative and absolute location,

  • distance, timing, velocities --

  • that's really the key to the science of moving dots, or as we like to call it,

  • spatiotemporal pattern recognition, in academic vernacular.

  • Because the first thing is, you have to make it sound hard --

  • because it is.

  • The key thing is, for NBA coaches, it's not that they want to know

  • whether a pick-and-roll happened or not.

  • It's that they want to know how it happened.

  • And why is it so important to them? So here's a little insight.

  • It turns out in modern basketball,

  • this pick-and-roll is perhaps the most important play.

  • And knowing how to run it, and knowing how to defend it,

  • is basically a key to winning and losing most games.

  • So it turns out that this dance has a great many variations

  • and identifying the variations is really the thing that matters,

  • and that's why we need this to be really, really good.

  • So, here's an example.

  • There are two offensive and two defensive players,

  • getting ready to do the pick-and-roll dance.

  • So the guy with ball can either take, or he can reject.

  • His teammate can either roll or pop.

  • The guy guarding the ball can either go over or under.

  • His teammate can either show or play up to touch, or play soft

  • and together they can either switch or blitz

  • and I didn't know most of these things when I started

  • and it would be lovely if everybody moved according to those arrows.

  • It would make our lives a lot easier, but it turns out movement is very messy.

  • People wiggle a lot and getting these variations identified

  • with very high accuracy,

  • both in precision and recall, is tough

  • because that's what it takes to get a professional coach to believe in you.

  • And despite all the difficulties with the right spatiotemporal features

  • we have been able to do that.

  • Coaches trust our ability of our machine to identify these variations.

  • We're at the point where almost every single contender

  • for an NBA championship this year

  • is using our software, which is built on a machine that understands

  • the moving dots of basketball.

  • So not only that, we have given advice that has changed strategies

  • that have helped teams win very important games,

  • and it's very exciting because you have coaches who've been in the league

  • for 30 years that are willing to take advice from a machine.

  • And it's very exciting, it's much more than the pick-and-roll.

  • Our computer started out with simple things

  • and learned more and more complex things

  • and now it knows so many things.

  • Frankly, I don't understand much of what it does,

  • and while it's not that special to be smarter than me,

  • we were wondering, can a machine know more than a coach?

  • Can it know more than person could know?

  • And it turns out the answer is yes.

  • The coaches want players to take good shots.

  • So if I'm standing near the basket

  • and there's nobody near me, it's a good shot.

  • If I'm standing far away surrounded by defenders, that's generally a bad shot.

  • But we never knew how good "good" was, or how bad "bad" was quantitatively.

  • Until now.

  • So what we can do, again, using spatiotemporal features,

  • we looked at every shot.

  • We can see: Where is the shot? What's the angle to the basket?

  • Where are the defenders standing? What are their distances?

  • What are their angles?

  • For multiple defenders, we can look at how the player's moving

  • and predict the shot type.

  • We can look at all their velocities and we can build a model that predicts

  • what is the likelihood that this shot would go in under these circumstances?

  • So why is this important?

  • We can take something that was shooting,

  • which was one thing before, and turn it into two things:

  • the quality of the shot and the quality of the shooter.

  • So here's a bubble chart, because what's TED without a bubble chart?

  • (Laughter)

  • Those are NBA players.

  • The size is the size of the player and the color is the position.

  • On the x-axis, we have the shot probability.

  • People on the left take difficult shots,

  • on the right, they take easy shots.

  • On the [y-axis] is their shooting ability.

  • People who are good are at the top, bad at the bottom.

  • So for example, if there was a player

  • who generally made 47 percent of their shots,

  • that's all you knew before.

  • But today, I can tell you that player takes shots that an average NBA player

  • would make 49 percent of the time,

  • and they are two percent worse.

  • And the reason that's important is that there are lots of 47s out there.

  • And so it's really important to know

  • if the 47 that you're considering giving 100 million dollars to

  • is a good shooter who takes bad shots

  • or a bad shooter who takes good shots.

  • Machine understanding doesn't just change how we look at players,

  • it changes how we look at the game.

  • So there was this very exciting game a couple of years ago, in the NBA finals.

  • Miami was down by three, there was 20 seconds left.

  • They were about to lose the championship.

  • A gentleman named LeBron James came up and he took a three to tie.

  • He missed.

  • His teammate Chris Bosh got a rebound,

  • passed it to another teammate named Ray Allen.

  • He sank a three. It went into overtime.

  • They won the game. They won the championship.

  • It was one of the most exciting games in basketball.

  • And our ability to know the shot probability for every player

  • at every second,

  • and the likelihood of them getting a rebound at every second

  • can illuminate this moment in a way that we never could before.

  • Now unfortunately, I can't show you that video.

  • But for you, we recreated that moment

  • at our weekly basketball game about 3 weeks ago.

  • (Laughter)

  • And we recreated the tracking that led to the insights.

  • So, here is us. This is Chinatown in Los Angeles,

  • a park we play at every week,

  • and that's us recreating the Ray Allen moment

  • and all the tracking that's associated with it.

  • So, here's the shot.

  • I'm going to show you that moment

  • and all the insights of that moment.

  • The only difference is, instead of the professional players, it's us,

  • and instead of a professional announcer, it's me.

  • So, bear with me.

  • Miami.

  • Down three.

  • Twenty seconds left.

  • Jeff brings up the ball.

  • Josh catches, puts up a three!

  • [Calculating shot probability]

  • [Shot quality]

  • [Rebound probability]

  • Won't go!

  • [Rebound probability]

  • Rebound, Noel.

  • Back to Daria.

  • [Shot quality]

  • Her three-pointer -- bang!

  • Tie game with five seconds left.

  • The crowd goes wild.

  • (Laughter)

  • That's roughly how it happened.

  • (Applause)

  • Roughly.

  • (Applause)

  • That moment had about a nine percent chance of happening in the NBA

  • and we know that and a great many other things.

  • I'm not going to tell you how many times it took us to make that happen.

  • (Laughter)

  • Okay, I will! It was four.

  • (Laughter)

  • Way to go, Daria.

  • But the important thing about that video

  • and the insights we have for every second of every NBA game -- it's not that.

  • It's the fact you don't have to be a professional team to track movement.

  • You do not have to be a professional player to get insights about movement.

  • In fact, it doesn't even have to be about sports because we're moving everywhere.

  • We're moving in our homes,

  • in our offices,

  • as we shop and we travel

  • throughout our cities

  • and around our world.

  • What will we know? What will we learn?

  • Perhaps, instead of identifying pick-and-rolls,

  • a machine can identify the moment and let me know

  • when my daughter takes her first steps.

  • Which could literally be happening any second now.

  • Perhaps we can learn to better use our buildings, better plan our cities.

  • I believe that with the development of the science of moving dots,

  • we will move better, we will move smarter, we will move forward.

  • Thank you very much.

  • (Applause)

My colleagues and I are fascinated by the science of moving dots.

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A2 BEG US nba teammate basketball probability machine roll

【TED】The Math Behind Basketball's Wildest Moves | Rajiv Maheswaran | TED Talks

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    陳秀如   posted on 2015/09/04
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