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 sensor,s 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 the stance 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 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,
you know, 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.