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Show HN: A basketball hoop to maximize shots that go in [video]

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Re: Show HN: A basketball hoop to maximize shots that go in [video]

#91

Post by mturmon »

shawnz wrote: post #62
contravariant wrote: post #35

Earlier quoted context omitted.

If you fix the position of the 'thrower' and just throw them at ridiculous speeds so gravity doesn't matter you'd end up with a hyperbolic backboard. So annoyingly for an 'optimal' solution you'd need to specify where people can throw from and how fast. Frankly you might as well just use a hyperbola with one foci on the hoop and the other on the middle of the court, or maybe a point slightly higher than the court its…

Don't forget about spin as well, which can significantly change the trajectory due to the Magnus effect

Better to account for gravity first, no?

Re: Show HN: A basketball hoop to maximize shots that go in [video]

#92

Post by npunt »

IgorPartola wrote: post #84
Taek wrote: post #78

Earlier quoted context omitted.

A common trap in tech is to reach for the fanciest tool instead of the simplest. The best engineering often comes from mastery of the simple tools, and the most beautiful engineering is the one that makes you say 'that's so obvious, why didn't anyone ever come up with that before?'

One of my favorite software solutions that I came up with was “human learning” powered. A few years ago I was working as the engineering manager at a small company. I had 8 other people who I was in charge of and we had moved to a new office in the middle of this. Our team took up two rooms in the office and I had to figure out who would be sitting where. I had some preconceived notions of who would be productive tog…

This is great! In a way it's like the output was a question rather than an answer.

This is quite similar to education research on Teachable Agents [1], which is an embodiment of the idea that to know something you must be able to teach it to someone else. In Teachable Agents you teach the computer rules (e.g. how ecosystems work), and then it gets a quiz where it compares the output to the right answer. When its wrong, you as the teacher must figure out whether the rules you taught it were correct and/or if it needs more rules.

Teachable Agents works for things where there's a right or wrong answer, because the computer is doing the test proctoring. But in your method the human is doing that, and I think it works quite well for things of a more qualitative nature like the arts, with the human playing the role of the critic or curator.

[1]: https://slate.com/technology/2015/04/teachable-agents-making...

Re: Show HN: A basketball hoop to maximize shots that go in [video]

#95

Post by IgorPartola »

npunt wrote: post #92
IgorPartola wrote: post #84

Earlier quoted context omitted.

One of my favorite software solutions that I came up with was “human learning” powered. A few years ago I was working as the engineering manager at a small company. I had 8 other people who I was in charge of and we had moved to a new office in the middle of this. Our team took up two rooms in the office and I had to figure out who would be sitting where. I had some preconceived notions of who would be productive tog…

This is great! In a way it's like the output was a question rather than an answer. This is quite similar to education research on Teachable Agents [1], which is an embodiment of the idea that to know something you must be able to teach it to someone else. In Teachable Agents you teach the computer rules (e.g. how ecosystems work), and then it gets a quiz where it compares the output to the right answer. When its wron…

That is super cool, thank you!

Re: Show HN: A basketball hoop to maximize shots that go in [video]

#99

Post by jasonheh »

curiousgal wrote: post #96

I mean, is it really Monte Carlo? He's iteratively trying to optimize a function which makes this more like SGD, no?

It definitely felt that way to me.

When he was finding the optimal angle for a single sampled shot, that's the partial derivative of his probability of making the basket for that shot wrt the surface normal at the backboard hit position (or something like that).

If you average all of those you get a gradient descent update.

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