
Google Research Football - a_imho
https://research-football.dev/
======
mennis16
I'd be interested in seeing something like this where each individual player
has skill levels for different actions, varying across both positions and
teams. Not sure how to make the competition fully fair then (perhaps a sum
skill level "salary cap") but it would be cool to see what strategies might
arise when a team knows the strengths and weaknesses of their own players and
can learn (maybe starting with a reasonable ballpark guess from "scouting")
the weaker points of a given opposing team.

To make a ridiculous analogy to the other football, in NCAAF video games the
mascot mode had every "player" on every team have 99 ability in all possible
skills. Which means that any player standing on the field could throw as well
as the quarterback, punt as well as the punter, etc. Opening up many
unrealistic trick plays.

But even adjusting for positional differences, I think a lot of the cool
sports stories have involved seeing how a particular team strategizes against
another particular team, which goes away when all teams start with equal
across the board abilities.

~~~
rjtobin
There is an upcoming contest to write an AI for an (American) football based
board game, Bloodbowl. The players have different stats as you describe, and
the teams can be asymmetrical (though for the competition, both teams select
11 players from a fixed 12 player roster, so not too asymmetrical).

Here is a link to the competition: [https://njustesen.github.io/ffai/bot-bowl-
ii](https://njustesen.github.io/ffai/bot-bowl-ii)

(As an aside, just wanted to mention that the original article is actually
about soccer, rather American Football. The comment I'm replying to is
certainly about American football, so I feel my comment here is fair.)

~~~
mennis16
Thanks for sharing, I've never played Bloodbowl but heard about it a few times
now, will have to look into this!

(I honestly don't know enough about soccer to know how much positional
variation or asymmetric skills matter. I assume they do but probably to a
lesser extent than football, which I also know way more about, hence going on
a bit of a tangent when trying to make the idea concrete.)

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pvg
The mentioned blog post is mislinked in the article but it's been on HN before
with a bit of discussion. It has some video and a better overview.

[https://ai.googleblog.com/2019/06/introducing-google-
researc...](https://ai.googleblog.com/2019/06/introducing-google-research-
football.html)

Thread at:

[https://news.ycombinator.com/item?id=20135726](https://news.ycombinator.com/item?id=20135726)

~~~
RAVEman
Thanks for pointing out. Fixed the link on the page.

~~~
pvg
With all (human) competitions cut or postponed, watching a bit of zany machine
football was especially enjoyable, thanks!

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sduff
RoboCup was founded almost 23 years ago. While the physical robot leagues
often garner most attention, there has always existed a simulation league, of
both 2d and 3d varieties.
[https://ssim.robocup.org/](https://ssim.robocup.org/)

~~~
claar
Yeah, but in RoboCup 3D the robots fall all over themselves and can barely
play:
[https://www.youtube.com/watch?v=7T1ElDs5eSQ](https://www.youtube.com/watch?v=7T1ElDs5eSQ)
\- RoboCup seems focused more on simulating robot movement than the game
itself.

This new Google competition appears to be more about ML and playing the game,
judging from videos:
[https://www.youtube.com/watch?v=F8DcgFDT9sc](https://www.youtube.com/watch?v=F8DcgFDT9sc)

~~~
nickswalker
That's a fair characterization; the 3D league is meant to incorporate physical
constraints, and learning "through" those to the higher level aspects of the
game is challenging. That's what the 2D league exists for though.

The Google simulator lets you learn from pixels if you want to, but the agent
that you're controlling only has 8 actions [1] available to it, so the
learning problem here really has no bearing on a robotics application or
anything in the real world that I can think of.

[1]
[https://arxiv.org/pdf/1907.11180.pdf](https://arxiv.org/pdf/1907.11180.pdf)

------
DevKoala
Awesome. Finally we'll know if possession is the true answer.

After looking at the video, I get the feeling I could beat the winning team;
it definitely misses tons of clear passing opportunities that would open the
play.

------
cambalache
Happy to see it is about "actual" football. I am embarrassed to say that
football is one of my life-long passions, especially now that I am older than
most players. But I would love to work in any area even tangentially related
to it.

~~~
mennis16
I don't know much about this football so it is probably way more nuanced than
I'm giving it credit for, but I'm actually disappointed this is not about
American Football. I think it is an interesting problem in that there is both
the play calling decision made by the coach, and then the choices each (highly
specialized) agent makes during execution. In addition to learning the physics
to actually execute well of course.

There is also a nice hierarchical structure i.e. there is a clear outcome to
each play, to each set of downs, to each possession.

Obviously this mostly has to do with personal preference, and I know that's
all you were commenting on. But I've encountered enough people that seem to
think football is a mindless game that I wanted to get a comment out there
when I saw the topic come up.

~~~
govg
I think one difference between American Football and Football is that American
football tends to have specialized events happening. You have field goals, you
have turnovers, you have punts, and so on. Each of them require specialized
instructions, someone who is a wide receiver will not share the same skill as
a QB. In Football, by and large the skillsets that everyone develops is the
same, i.e. good ball control and fitness. There is specialization when it
comes to specific positions, but it is not as drastic a change. This could
allow you to get away with simpler player models in Football, everyone can
basically be modeled by a simple "footballer", instead of different people for
"kicker", "quarter back", "line back" and so on. Potentially, this can make
sport simulation environments for RL like this easier for football.

------
IMAYousaf
Disney did some absolutely fascinating research here a few years ago. I
suggest anyone interested in Deep Learning and Soccer checks out the following
video:

[https://www.youtube.com/watch?v=WI-
WL2cj0CA](https://www.youtube.com/watch?v=WI-WL2cj0CA)

"Data-Driven Ghosting using Deep Imitation Learning"

~~~
dluan
Supposedly Liverpool are using this "ghost" technique as well. in training to
change player behavior, using the "expected goals" stat as the main metric or
representation of the field state. It works well with Klopp's system.

~~~
anjc
Are there any regulations against transmitting data to wearables during play?
I only see a regulation saying that data _from_ wearables can't be used during
a game. Is there anything preventing players from wearing e.g. a device that
vibrates when they're not optimally positioned, according to camera tracking

~~~
dluan
I'm pretty sure it would violate the "spirit of the game" rules by FIFA. Not
sure if during halftime teams using digital analysis would even be allowed.

------
thom
I've played with this a bit (I work in football analytics and play with
tracking data a lot) and it's disappointingly unlike real football. Defenders
are mostly useless ghosts and shooting seems wildly more accurate than in real
life. Also a shame that the multi-agent track of the tournament is only 5
players - most of the off-the-ball behaviour of agents with the existing
checkpoints is terrible.

If you wanted to think about this stuff with real football in mind, there are
worse places to start than here:

[https://www.youtube.com/channel/UCUBFJYcag8j2rm_9HkrrA7w](https://www.youtube.com/channel/UCUBFJYcag8j2rm_9HkrrA7w)

~~~
ctchocula
I haven't played around with this system, but judging by the videos it seems
they should have vetted their physics system against some priors from real-
life such as defender's advantage (attacking is harder than defending) and
relative difficulty of actions (defending shouldn't be so much harder than
shooting, dribbling and passing). It might also be good to have basic
structure for off-the-ball behavior to make it more realistic and organized
(e.g. maintain 4-4-2 shape), so that there is always 1 defender behind each
opposing player when the team doesn't have possession. That way, it won't look
so much like a schoolyard game.

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agent008t
So the state includes the positions of all players and of the ball? That
doesn't sound like solving football at all.

Why not build on the RoboCup 2D Football simulation league? It is a more
realistic environment, where each agent has to sense the environment and
decide what to do with some limited communication with other agents and with
the coach. Furthermore, it already has some state-of-the-art teams. And
finally, it is a step on the road to actually having a team of physical
humanoid robots playing against a human team.

As is, this does not look very interesting and does not look too dissimilar
from the Atari reinforcement learning.

------
vesinisa
Interesting - this goal is scored by hacking the offside rule:
[https://youtu.be/c3dv8s2SR0c?t=54](https://youtu.be/c3dv8s2SR0c?t=54)

The scoring player was not in offside because they are on their own half of
the field when the goalie passes the ball.

~~~
toyg
That's hardly new - every 8-yo central defender in half-decent teams is taught
never to cross the midfield line precisely for that sort of situation.

~~~
dfgasdgsd
Two recent(ish) examples in real life:

Salah vs Manchester United:
[https://www.youtube.com/watch?v=STjxoGgFIzk](https://www.youtube.com/watch?v=STjxoGgFIzk)

Torres vs Barcelona:
[https://www.youtube.com/watch?v=lKOXBLTsu3s](https://www.youtube.com/watch?v=lKOXBLTsu3s)

~~~
parthdesai
Torres for Barca is because it's 90th of the minute of the game and Barca are
about to be knocked out of CL. At that point, everyone goes up. If the ball
went for a corner, the goalie would come up as well. I'm assuming it's
something similar for that Salah goal as well. Context matters as well.

~~~
mav3rick
Torres was a bit of a flop at Chelsea. This goal was kind of a saving grace.

~~~
parthdesai
What has that got anything to do with the parent comment?

~~~
mav3rick
It's a random comment on a random website. Chill.

------
nateberkopec
Neat!

Watching the games, it looks like the effectiveness of dribbling is
unrealistically high. Most scoring plays consist of dribbling around 1 player,
then dribbling straight at that back line and hoping to win the take-on.

------
SubNoize
I would love to see some sort of AI be able to play and then understand the
rules correctly. Then eventually replace/aid referee's on the field.

------
jstanley
I'd be interested to watch some video from some of the games, if anyone knows
if there is any?

~~~
claar
Here's a trailer video:
[https://www.youtube.com/watch?v=F8DcgFDT9sc](https://www.youtube.com/watch?v=F8DcgFDT9sc)

Here's an actual game:
[https://www.youtube.com/watch?v=f7KiAPtAPwg](https://www.youtube.com/watch?v=f7KiAPtAPwg)

More games at [https://research-football.dev/11vs11/ranking](https://research-
football.dev/11vs11/ranking)

------
jedberg
Google's answer to Amazon's DeepRacer?

~~~
tpetry
Deep Racer extended to mario kart „combat“ features would be a really
interesting topic. So they had to learn deiving and combating and everything
is a tradeoff between these two functionalities: Maybe driving a little bit
worse will result in a better position if you can E.g. better target your
opponents with the shells.

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microcolonel
I got confused when the Google thing wasn't in American English.

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sulam
Nitpick, but it recurs:

s/challange/challenge/g

~~~
jdm2212
FWIW this is from Zurich-based folks so they're presumably not native English
speakers... and challenge is not exactly phonetic :)

~~~
sulam
They are also presumably comfortable with improving their work with feedback,
even in relatively small ways. Some HN readers seem to believe otherwise,
though.

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csense
Looks more like soccer to me.

~~~
whoisjuan
Dude you’re either trolling or you’re just extremely oblivious.

Do you realize that football is the most popular sport in the world and almost
every country call it football? (you know, because you’re controlling a ball
with your feet)

~~~
dntbnmpls
> Do you realize that football is the most popular sport in the world and
> almost every country call it football?

So? Hacker news is an american site and we call it soccer.

> (you know, because you’re controlling a ball with your feet)

Actually, football is a class of games which involve kicking a ball to score a
goal. Everything from "football" to soccer to rugby has kicking to score
goals/points.

"Sports commonly called football include association football (known as soccer
in some countries); gridiron football (specifically American football or
Canadian football); Australian rules football; rugby football (either rugby
league or rugby union); and Gaelic football."

[https://en.wikipedia.org/wiki/Football](https://en.wikipedia.org/wiki/Football)

If you really want to be accurate, then you should call it association
football. And the word soccer is derived from assocation football.

[https://www.etymonline.com/word/soccer](https://www.etymonline.com/word/soccer)

------
nefitty
“ Google Research Football Environment is a novel Reinforcement Learning
environment where agents aim to master the world’s most popular sport --
football.”

------
dontreact
Are we done with games yet? I thought it’s been well shown that you can beat
any computer game using RL. Would like to see people move on from these types
of things and engage more with the difficulty of solving real world problems
with AI.

At this point, I’m not really sure that the progress in playing games is
carrying over to much in terms of solving real world problems, but curious to
see if anyone has good counterexamples.

~~~
SpaceManNabs
There are still lots of things that are more easily answerable in games to see
if they exist.

For example, can we get AI to play deceptive strategies without being
explicitly rewarded (Open AI hide and seek game, StarCraft fog of war plays,
etc)?

As to whether it can translate to solving real world problems is arguable
(there are some cases yes), but it definitely helps out in weeding inference
models or training strategies that are not viable in games (and probably not
the real world).

edit: Forgot to list an example. Using the same reinforcement learning
strategies not to play games, but to design them.

~~~
dontreact
Thanks for the perspective. I have been asking this question for a few years
now, and I think Deepmind's marketing has been pretty deceptive on this. What
examples do you have in mind where the research from playing games with RL has
carried over into solving a real-world problem? Probably the state of the art
has advanced as I have focused in applying supervised learning on some applied
problems that interest me.

