The Math Behind Basketball's Wildest Moves | Rajiv Maheswaran | TED Talks
1,107,248 views ・ 2015-07-06
请双击下面的英文字幕来播放视频。
翻译人员: Li Li
校对人员: Jing Peng
00:12
My colleagues and I are fascinated
by the science of moving dots.
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我和我的同事对移动圆点
背后的科学非常着迷。
00:16
So what are these dots?
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那么这些小圆点是什么呢?
00:18
Well, it's all of us.
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就是我们自己。
00:19
And we're moving in our homes,
in our offices, as we shop and travel
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我们在家里,办公室里来回走动,
00:24
throughout our cities
and around the world.
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也在世界各地旅行和购物。
00:26
And wouldn't it be great
if we could understand all this movement?
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如果我们能弄清这些移动,
00:30
If we could find patterns and meaning
and insight in it.
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并从中发现规律,意义并提出见解,
不是一件很棒的事吗?
00:34
And luckily for us, we live in a time
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很幸运的是,
我们生活在这么一个时代,
00:36
where we're incredibly good
at capturing information about ourselves.
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我们非常擅长捕捉关于自身的信息。
00:40
So whether it's through
sensors or videos, or apps,
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不管是通过传感器,视频,或软件应用,
00:44
we can track our movement
with incredibly fine detail.
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我们都能详尽地追踪到个人移动的轨迹。
00:48
So it turns out one of the places
where we have the best data about movement
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这就让我们发现,
最佳的数据来源之一
00:53
is sports.
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就是体育运动。
00:54
So whether it's basketball or baseball,
or football or the other football,
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因此无论是篮球、棒球、橄榄球或足球,
我们都可以在场馆内,
甚至运动员身上装上设备来追踪
01:00
we're instrumenting our stadiums
and our players to track their movements
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01:04
every fraction of a second.
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他们每个时刻的运动数据。
01:05
So what we're doing
is turning our athletes into --
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所以我们要做的
——你们大概已经猜到了——
01:10
you probably guessed it --
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就是把运动员的移动
01:12
moving dots.
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转化成圆点的移动。
01:13
So we've got mountains of moving dots
and like most raw data,
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所以我们收集了不计其数的移动小圆点,
01:18
it's hard to deal with
and not that interesting.
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就像多数原始数据一样,
难以处理,也没什么趣味。
01:21
But there are things that, for example,
basketball coaches want to know.
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但数据里面蕴藏着,
比如篮球教练想知道的事情。
01:25
And the problem is they can't know them
because they'd have to watch every second
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但问题是,除非教练们把每场比赛里
每一秒数据都记下来再去思考,
01:29
of every game, remember it and process it.
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否则他们没法从中得到想要的信息。
01:31
And a person can't do that,
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人类大脑无法做到这件事,
01:33
but a machine can.
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但机器没问题。
01:35
The problem is a machine can't see
the game with the eye of a coach.
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然而,机器没办法自己
以教练的视角去看一场比赛。
01:39
At least they couldn't until now.
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直到现在,我们做到了。
01:42
So what have we taught the machine to see?
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那么,
我们让机器去观察些什么呢?
01:45
So, we started simply.
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先从简单的开始。
01:47
We taught it things like passes,
shots and rebounds.
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我们先教会它传球、投篮和篮板球,
01:51
Things that most casual fans would know.
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这类普通球迷也知道的事。
01:53
And then we moved on to things
slightly more complicated.
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然后我们开始教它一些
稍复杂点的事情,
01:56
Events like post-ups,
and pick-and-rolls, and isolations.
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比如落位背打、挡拆和拉开单打。
02:01
And if you don't know them, that's okay.
Most casual players probably do.
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你们如果不了解这些名词,
没关系。打球的人大都了如指掌。
02:05
Now, we've gotten to a point where today,
the machine understands complex events
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迄今为止,我们已经能够让机器理解
02:10
like down screens and wide pins.
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下掩护和无球掩护这类复杂的,
02:14
Basically things only professionals know.
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只有专业人士才懂的战术。
02:16
So we have taught a machine to see
with the eyes of a coach.
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于是我们已经教会电脑用
教练的视角去观察数据了。
02:22
So how have we been able to do this?
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我们是怎么做到的呢?
02:24
If I asked a coach to describe
something like a pick-and-roll,
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如果我让一个教练讲解挡拆,
02:27
they would give me a description,
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我会得到一个定义,
02:29
and if I encoded that as an algorithm,
it would be terrible.
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如果我把这个定义编码成一个算法
估计会惨不忍睹。
02:33
The pick-and-roll happens to be this dance
in basketball between four players,
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挡拆就是四个球员之间的舞蹈,
02:37
two on offense and two on defense.
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两人进攻,两人防守。
02:39
And here's kind of how it goes.
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大概是这么个过程:
02:41
So there's the guy on offense
without the ball
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一个没有带球的进攻球员
02:43
the ball and he goes next to the guy
guarding the guy with the ball,
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跑向持球的防守队员,
02:46
and he kind of stays there
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站在那里待一会儿,
02:48
and they both move and stuff happens,
and ta-da, it's a pick-and-roll.
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然后他们一起移动(制造机会),
嗒哒,这就是挡拆。
02:51
(Laughter)
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(笑声)
02:53
So that is also an example
of a terrible algorithm.
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这也是个糟糕的算法实例。
02:56
So, if the player who's the interferer --
he's called the screener --
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如果那个干扰的球员——
或者叫掩护者——
03:01
goes close by, but he doesn't stop,
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只是跑过来干扰一下而不停下,
03:04
it's probably not a pick-and-roll.
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这可能就不是挡拆了。
03:06
Or if he does stop,
but he doesn't stop close enough,
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就算他停下来,
但停的位置不够接近,
03:10
it's probably not a pick-and-roll.
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那也不算是挡拆。
03:12
Or, if he does go close by
and he does stop
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或者,就算他足够近,而且停下来,
03:15
but they do it under the basket,
it's probably not a pick-and-roll.
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但他是在篮下完成的
那也不算挡拆。
03:19
Or I could be wrong,
they could all be pick-and-rolls.
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或者我可能错了,
这些都是挡拆。
是否是挡拆要根据发生的时间、
球员间距、位置而定,
03:22
It really depends on the exact timing,
the distances, the locations,
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03:26
and that's what makes it hard.
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这些都很难去界定。
03:28
So, luckily, with machine learning,
we can go beyond our own ability
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幸运的是,有了机器学习技术,
我们就能超越自身的能力
03:33
to describe the things we know.
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来描述我们已知的事物。
03:35
So how does this work?
Well, it's by example.
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这个技术要如何实现呢?
举个例子:
03:37
So we go to the machine and say,
"Good morning, machine.
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我们对机器说,
“早上好,机器。
03:41
Here are some pick-and-rolls,
and here are some things that are not.
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这儿有些挡拆例子,还有一些不是。
03:44
Please find a way to tell the difference."
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你来找出不同点吧。”
03:47
And the key to all of this is to find
features that enable it to separate.
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这其中的关键是电脑能找出
区别两者的特征来。
03:50
So if I was going
to teach it the difference
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所以如果我要教会机器
03:52
between an apple and orange,
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辨别苹果和橘子,
我可能会说:
“不妨用颜色和形状来区分吧?”
03:54
I might say, "Why don't you
use color or shape?"
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03:56
And the problem that we're solving is,
what are those things?
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而目前要解决的问题就是,
要区分事物的特征是什么?
03:59
What are the key features
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电脑需要掌握的整个
04:00
that let a computer navigate
the world of moving dots?
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移动圆点世界的关键特征是什么?
04:04
So figuring out all these relationships
with relative and absolute location,
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搞清楚所有这些相对位置、
绝对位置、距离、时机、
04:09
distance, timing, velocities --
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速率之间的关系——
04:11
that's really the key to the science
of moving dots, or as we like to call it,
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就是移动圆点科学的真正关键所在,
换成专业术语,
04:16
spatiotemporal pattern recognition,
in academic vernacular.
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我们喜欢称之为:时空模式识别。
04:19
Because the first thing is,
you have to make it sound hard --
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因为首先,你要让它听起来
很难懂,很专业——
04:22
because it is.
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因为事实的确如此。
04:24
The key thing is, for NBA coaches,
it's not that they want to know
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对于NBA教练们来说,判断是否是
04:27
whether a pick-and-roll happened or not.
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挡拆并不是关键,
04:29
It's that they want to know
how it happened.
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而这个挡拆是怎么发生的
才是他们关注的。
04:31
And why is it so important to them?
So here's a little insight.
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为何教练们如此关心这一点?
这儿我要解释一下。
04:34
It turns out in modern basketball,
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在现代的篮球比赛中,
04:36
this pick-and-roll is perhaps
the most important play.
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挡拆几乎是最重要的战术。
04:39
And knowing how to run it,
and knowing how to defend it,
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了解如何使用以及怎样防守挡拆,
04:41
is basically a key to winning
and losing most games.
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基本上是比赛输赢的关键。
04:44
So it turns out that this dance
has a great many variations
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因此挡拆的步伐多种多样,
04:48
and identifying the variations
is really the thing that matters,
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能够识别这些不同的形式
是非常重要的,
04:51
and that's why we need this
to be really, really good.
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这就是为什么我们对
机器的智能性要求相当高。
举个例子。
04:55
So, here's an example.
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04:56
There are two offensive
and two defensive players,
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这儿有两个进攻队员和
两个防守队员,
他们准备开始实施挡拆。
04:58
getting ready to do
the pick-and-roll dance.
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那么持球人既可以选择利用挡拆,
也可以放弃挡拆,
05:01
So the guy with ball
can either take, or he can reject.
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05:04
His teammate can either roll or pop.
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他的队友可以拆向篮下,
或撤到一个无人盯防的空位。
05:07
The guy guarding the ball
can either go over or under.
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防守持球者的人可以上前绕过掩护,
或者从后方绕过掩护。
05:10
His teammate can either show
or play up to touch, or play soft
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而他的队友则可以探出补防,或保持
近距离防守,亦或者向后消极防守。
05:14
and together they can
either switch or blitz
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两个防守球员也可以换防,或者包夹。
05:17
and I didn't know
most of these things when I started
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一开始的时候我也不是很懂这些,
05:20
and it would be lovely if everybody moved
according to those arrows.
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如果每个人都能沿着箭头方向移动,
事情就好办多了。
05:23
It would make our lives a lot easier,
but it turns out movement is very messy.
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这会让我们的工作简单很多,
但往往这些移动非常杂乱。
球场上会发生很多突然的变动,
要在查准率和查全率方面
05:28
People wiggle a lot and getting
these variations identified
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准确识别这些变化
05:33
with very high accuracy,
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05:34
both in precision and recall, is tough
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是相当困难的,
05:36
because that's what it takes to get
a professional coach to believe in you.
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但只有这样,
才能让专业教练相信你的技术。
05:40
And despite all the difficulties
with the right spatiotemporal features
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尽管在准确的时空特性识别上
困难重重,
05:43
we have been able to do that.
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我们还是成功地做到了。
05:45
Coaches trust our ability of our machine
to identify these variations.
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教练相信我们的机器
能够识别这些变化。
05:49
We're at the point where
almost every single contender
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目前,我们已经推出了
相关的识别软件,几乎每个
觊觎今年NBA总冠军的球队,
05:53
for an NBA championship this year
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05:54
is using our software, which is built
on a machine that understands
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都在使用我们的这款软件,
其功能就是通过机器
05:59
the moving dots of basketball.
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识别篮球领域的移动。
06:01
So not only that, we have given advice
that has changed strategies
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不仅如此,
我们还对如何改善战术提供建议,
并帮助球队赢得过重要的比赛。
06:07
that have helped teams win
very important games,
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06:10
and it's very exciting because you have
coaches who've been in the league
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能够让联盟中执教30年的
老教练愿意听取
机器提供的意见,这太让人激动了。
06:14
for 30 years that are willing to take
advice from a machine.
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06:17
And it's very exciting,
it's much more than the pick-and-roll.
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不仅仅局限于挡拆,
更让我们兴奋的是
06:20
Our computer started out
with simple things
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我们让电脑从简单的事情着手,
06:22
and learned more and more complex things
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逐渐学会了更复杂的事物,
06:24
and now it knows so many things.
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如今它已经掌握了丰富的知识。
06:26
Frankly, I don't understand
much of what it does,
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老实说,我不大明白它是怎么做到的,
06:29
and while it's not that special
to be smarter than me,
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不过就算比我聪明也没什么特别的,
06:33
we were wondering,
can a machine know more than a coach?
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但我们在想,
机器能否比教练懂得更多呢?
06:36
Can it know more than person could know?
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它能比人类懂得更多吗?
06:38
And it turns out the answer is yes.
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事实上,答案是肯定的。
06:40
The coaches want players
to take good shots.
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教练想让球员投出好球。
06:43
So if I'm standing near the basket
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所以如果我站在篮筐旁边,
06:44
and there's nobody near me,
it's a good shot.
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周围没人,这就是好的投篮时机。
如果我站得远,而且被对方包围住,
通常来讲这球投不进。
06:47
If I'm standing far away surrounded
by defenders, that's generally a bad shot.
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06:51
But we never knew how good "good" was,
or how bad "bad" was quantitatively.
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但我们无法定量衡量这个“好”有多好,
“差”有多差,
06:56
Until now.
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但现在不同了。
06:57
So what we can do, again,
using spatiotemporal features,
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同样,我们能做的就是利用时空特性
07:00
we looked at every shot.
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来分析每次投篮。
07:02
We can see: Where is the shot?
What's the angle to the basket?
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我们可以看到:在哪里投篮?
投篮的角度是多少?
07:05
Where are the defenders standing?
What are their distances?
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防守方的站位?
他们间的距离,
以及角度如何?
07:08
What are their angles?
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07:09
For multiple defenders, we can look
at how the player's moving
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防守球员不止一名的情况下,
我们能够通过观察球员的移动
07:12
and predict the shot type.
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来预测投篮类型。
07:13
We can look at all their velocities
and we can build a model that predicts
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我们可以根据他们的速度
建立一个模型,
07:17
what is the likelihood that this shot
would go in under these circumstances?
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预测在这些情况下,进球的可能性。
07:22
So why is this important?
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为什么这一点很重要?
07:24
We can take something that was shooting,
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因为我们可以通过分析投篮
这一单一行为得到
07:26
which was one thing before,
and turn it into two things:
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不同以往的两种信息:
07:29
the quality of the shot
and the quality of the shooter.
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投篮的质量,以及投手的质量。
07:33
So here's a bubble chart,
because what's TED without a bubble chart?
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我们可以看一下这个气泡图,
没有气泡图,还算什么TED呢?
07:36
(Laughter)
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(笑声)
07:38
Those are NBA players.
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这些气泡都是NBA球员。
07:39
The size is the size of the player
and the color is the position.
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大小代表球员的体型,
颜色代表他们的位置。
07:42
On the x-axis,
we have the shot probability.
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x轴代表投篮的命中率。
07:44
People on the left take difficult shots,
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靠左的球员偏向勉强投篮,
07:46
on the right, they take easy shots.
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靠右的球员会在有空当时才出手。
07:49
On the [y-axis] is their shooting ability.
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Y轴代表的是投篮质量。
07:51
People who are good are at the top,
bad at the bottom.
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好投手在上面,较差的在下面。
07:53
So for example, if there was a player
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举个例子,有一个球员的
07:55
who generally made
47 percent of their shots,
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投篮命中率是47%,
07:57
that's all you knew before.
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以前你只能知道这么多。
07:59
But today, I can tell you that player
takes shots that an average NBA player
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但如今,我能告诉你NBA球员投篮的
08:04
would make 49 percent of the time,
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平均命中率是49%,
08:06
and they are two percent worse.
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他还低了两个百分点。
08:08
And the reason that's important
is that there are lots of 47s out there.
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因为我们要在众多47%的
球员中选择一个。
08:13
And so it's really important to know
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那么重点就在于要搞清楚
08:16
if the 47 that you're considering
giving 100 million dollars to
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让你支付了一大笔美金的人
08:20
is a good shooter who takes bad shots
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到底是个经常勉强投篮的神投手,
08:23
or a bad shooter who takes good shots.
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还是一个愿意空位出手的差投手。
08:27
Machine understanding doesn't just change
how we look at players,
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机器分析不只改变了
我们对球员的看法,
08:30
it changes how we look at the game.
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也改变了我们看待比赛的方式。
08:32
So there was this very exciting game
a couple of years ago, in the NBA finals.
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几年前有一场很激烈的NBA总决赛,
08:36
Miami was down by three,
there was 20 seconds left.
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迈阿密落后三分,只剩20秒了。
08:39
They were about to lose the championship.
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他们将要失去总冠军了。
08:41
A gentleman named LeBron James
came up and he took a three to tie.
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一位叫勒布朗詹姆斯的年轻人
上去想投个三分追平。
08:44
He missed.
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但他没投中。
他的队友克里斯波什拿到篮板,
08:46
His teammate Chris Bosh got a rebound,
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08:47
passed it to another teammate
named Ray Allen.
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传给另一个队友雷阿伦。
他投中了个三分,比赛进入加时。
08:50
He sank a three. It went into overtime.
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08:52
They won the game.
They won the championship.
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最后他们赢了比赛,得了总冠军。
08:54
It was one of the most exciting
games in basketball.
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这是篮球比赛中
最激动人心的时刻之一。
08:57
And our ability to know
the shot probability for every player
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而我们能知道每个球员在每一刻的
09:00
at every second,
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投篮命中率
以及抢到篮板的可能性,
09:02
and the likelihood of them getting
a rebound at every second
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这种能力是前所未有的。
09:05
can illuminate this moment in a way
that we never could before.
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09:09
Now unfortunately,
I can't show you that video.
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有点可惜,
我无法给大家展示这个精彩片段。
09:12
But for you, we recreated that moment
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但为了在座的各位,我们在三周前的
09:16
at our weekly basketball game
about 3 weeks ago.
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篮球周赛上重塑了那经典一刻。
09:19
(Laughter)
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(笑声)
09:21
And we recreated the tracking
that led to the insights.
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我们也重新加入了
电脑追踪数据的演示。
09:25
So, here is us.
This is Chinatown in Los Angeles,
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这就是我和同事们,
在洛杉矶的唐人街,
09:29
a park we play at every week,
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我们每周都会去打球的公园,
09:31
and that's us recreating
the Ray Allen moment
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我们在重塑雷阿伦时刻,
09:33
and all the tracking
that's associated with it.
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所有的轨迹都与之相关。
09:36
So, here's the shot.
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就是这个投篮。
09:38
I'm going to show you that moment
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你们会看到这一经典时刻,
09:40
and all the insights of that moment.
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以及这一刻背后都发生了什么。
09:43
The only difference is, instead
of the professional players, it's us,
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唯一的不同就是
我们取代了专业球员,
09:47
and instead of a professional
announcer, it's me.
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而我取代了专业讲解员。
09:49
So, bear with me.
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大家请见谅。
09:53
Miami.
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迈阿密。
09:54
Down three.
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落后三分。
09:56
Twenty seconds left.
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还有20秒。
09:59
Jeff brings up the ball.
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杰夫带球。
10:02
Josh catches, puts up a three!
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约什接球,三分出手!
10:04
[Calculating shot probability]
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[计算命中率]
10:07
[Shot quality]
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[投篮质量]
10:09
[Rebound probability]
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[篮板球概率]
10:12
Won't go!
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进不了!
10:13
[Rebound probability]
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[篮板球概率]
10:15
Rebound, Noel.
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诺尔的篮板。
传回给达丽亚。
10:17
Back to Daria.
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10:18
[Shot quality]
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[投篮质量]
10:22
Her three-pointer -- bang!
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球进了——三分!
10:24
Tie game with five seconds left.
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打平了,还剩5秒。
10:26
The crowd goes wild.
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观众们沸腾了!
10:28
(Laughter)
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(笑声)
10:30
That's roughly how it happened.
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真实情况大概就是这样。
10:31
(Applause)
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(掌声)
10:32
Roughly.
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差不多。
(掌声)
10:34
(Applause)
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10:36
That moment had about a nine percent
chance of happening in the NBA
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在NBA有9%的概率
会发生这样的时刻,
10:41
and we know that
and a great many other things.
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我们知道的还有很多。
10:43
I'm not going to tell you how many times
it took us to make that happen.
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我是不会告诉你们
我们尝试了多少次才成功的。
10:47
(Laughter)
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(笑声)
10:49
Okay, I will! It was four.
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好吧,我还是说吧,四次。
10:51
(Laughter)
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(笑声)
10:52
Way to go, Daria.
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达丽亚,三分球还得努力啊。
10:53
But the important thing about that video
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但那段视频以及我们对
10:57
and the insights we have for every second
of every NBA game -- it's not that.
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每场NBA比赛的细微观察
并不是重点。
11:02
It's the fact you don't have to be
a professional team to track movement.
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事实上,你无需组建
一个专业团队才能追踪移动。
11:07
You do not have to be a professional
player to get insights about movement.
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你也无需成为专业运动员
去理解那些移动。
11:10
In fact, it doesn't even have to be about
sports because we're moving everywhere.
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而且,这不仅限于运动,
因为我们无时不刻不在移动。
11:15
We're moving in our homes,
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我们在家里,
11:21
in our offices,
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在办公室里来回走动,
11:24
as we shop and we travel
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我们也在世界各地
11:29
throughout our cities
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各个城市
11:32
and around our world.
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购物旅行。
11:35
What will we know? What will we learn?
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我们能发现什么?
我们能学到什么?
11:37
Perhaps, instead of identifying
pick-and-rolls,
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或许,除了识别挡拆,
11:39
a machine can identify
the moment and let me know
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机器还能识别某些时刻,
11:42
when my daughter takes her first steps.
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让我知道我女儿何时
迈出她的第一步。
11:45
Which could literally be happening
any second now.
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她现在随时都有可能学会走路。
11:48
Perhaps we can learn to better use
our buildings, better plan our cities.
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或许我们能合理地利用我们的建筑物,
更加好地规划我们的城市。
11:52
I believe that with the development
of the science of moving dots,
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我相信随着移动圆点这一科学的发展,
11:56
we will move better, we will move smarter,
we will move forward.
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我们能更好地移动,
更智能地移动,一路向前。
12:00
Thank you very much.
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谢谢大家。
12:01
(Applause)
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(掌声)
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Original video on YouTube.com
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