

Using Python and heat maps to determine the best place to aim on a dart board - middlegeek
http://thevirtuosi.blogspot.com/2011/01/darts.html

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bl4k
Dart shots are not evenly distributed. You are more likely to get the height
wrong then the width - this is because of how the dart travels.

Darts dip a lot in flight - they do not move side to side as much. As a dart
player, I guesstimate that after only a few hours practice most people will be
off 1.5cm or so in the vertical but only 0.75cm in the horizontal

Professional dart players are always aiming at 20. If they block the triple-
twenty with their first or second dart, they then aim at triple-19.with some
players preferring triple-18 since height-wise it is in similar range

~~~
srean
To adjust the model for this effect one needs to scale the axis of the
multivariate Gaussian noise differently.

But does the variation in the two different axis result from "how the dart
travels", i.e. the physics ? To me it seems to be a matter of relative
consistency of the angles with which one throws the dart. I remember reading
an article that analyzed trajectories of basket ball free throws. They found
for a given player their velocity of projection remained surprisingly
consistent. Maybe that is true for throwing darts as well.

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tmcw
Three statisticians played a game of darts. The first statistician shot, but
missed, by a meter to the left. The second statistician shot, but also missed,
by a meter to the right. The third statistician yelled "bullseye!"

\- relevant statistician joke

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vColin
Find where you should aim with this handy utility: [http://www-
stat.stanford.edu/~ryantibs/darts/dartsapplet.htm...](http://www-
stat.stanford.edu/~ryantibs/darts/dartsapplet.html) (context: <http://www-
stat.stanford.edu/~ryantibs/darts/>)

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mitko
Gaussians? Why Gaussians? Why not Cauchy for example (it has fatter tail)? And
why identity matrix for the Gaussian covariance?

I really like the way you are using your technical skills for computation but
I think you are using it on the wrong problem.

In reality, even if your dart throws are Gaussians they need not be
independent of each other.

I'm coming from math background and was observing math people(physicists as a
proxy) to the approach of making unreasonable assumptions if they will
simplify the problem and make it solvable.

It reminds me of the joke:

 _A person is walking around a street light. Another asks him_

" _What are you doing there?_ "

" _Looking for my keys._ "

" _Did you lose them there?_ "

" _No, but there is more light here._ "

Edit: formatting

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gregdetre
This is a delight!

There's only one more thing this needs to be perfect. I'd love to be able to
calibrate it to me, by aiming for the bullseye 10 times and telling it what I
hit. That way, it could assess my expertise, and suggest what I should
personally be aiming for.

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rgarcia
back in college the first problem set in my intro AI class was something very
similar to this, except instead of maximizing the points scored on the current
throw, it looked ahead and solved for the best "path" to get to zero points
given your current score (in darts you win by reducing your score down to
zero). Like the OP, I also remember being surprised by the fact that the
bullseye wasn't the highest scoring point on the dartboard, but things get
even more interesting when you look at the optimal strategy for actually
winning the game.

~~~
wahnfrieden
This would make it much more complicated, but did your algorithm take into
account the difficulty of hitting each scoring area too? That would be pretty
interesting to have as an iPhone app or something.

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sesqu
This reminds me of two things. The first is the intro to statistics exercise
on how you can't have uniform probability on a continuous domain, since you
will almost surely miss any particular point on the board. It is often
illustrated with dart boards, and caused me great consternation once.

The second is that in my experience, dart throws aren't normally distributed:
they're skewed towards the bottom, because I don't throw very hard. I wonder
if that effect goes away with practise.

