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Comment by gretch | Hacker News Reader

As a tennis player, I think this is super impressive.

Their models actually do a decent job of replicating true tennis strategy, and as they pointed out, even account for the quirks like the left handedness of Nadal.

However, it's still a bit unrealistic due to the lack of full data.

There's 3 things that make a tennis shot what it is: placement (covered in the video), pace (speed of ball), and spin (rpm and direction of spin). In this method, they only use placement. Probably because pace and spin data don't exist at this scale.

But there's a big difference between a slice, flat, and top spin shot to the same placement on the court, and it directly affects the return shot. For example, it's a very common and 'safe' play to return a slice with a slice

Would like to see the full extension one day

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The lack of full data is because tournaments keep Hawk-Eye (https://en.wikipedia.org/wiki/Hawk-Eye) data to themselves right?

Hmm from the look of it, it's not clear Hawk eye does everything.

Certainly it does pace. But from the article it's hard to say if it would track spin - it's not clear to me that with the natural markings on the ball (seam lines + ballmaker logo) and the lighting that exists in the stadium, that would be enough fidelity for some algorithm to calculate the spin.

I think Hawkeye could be updated to easily do it though, and maybe if they would be willing to draw 1 or 2 black dot markers on the ball in addition to the natural markings.

It does track spin, they often show stats measuring rpm.

Additionally spin is a big part of the trajectory model - the Hawkeye cameras are only 60fps, so the trajectory is interpolated.

However, I doubt it’s the cameras measuring spin. It’s more likely spin is a free variable when they fit the trajectory.

Found this: https://commons.nmu.edu/cgi/viewcontent.cgi?article=1746&con...

Very interesting read, but I'm not knowledgeable enough to really give you an accurate tldr, sorry

There is some moonwalking to be seen. I wonder how easy it is to get an neural network to remove that.

I'd think that pace and spin could be extrapolated relatively easily if you know placement and time; particularly if you know (or can extrapolate) the height of the ball.

edit: Well, perhaps not entirely. Trajectory is a function of a number of factors including spin. However, the ball itself is a factor. More fuzz = more drag = spin has a greater effect on trajectory. That would vary from ball to ball and even over the lifetime of an individual ball.

For those not familiar with tennis:

Primarily because of aerodynamic drag created by the fuzz on tennis balls, the ball's spin greatly affects its trajectory.

A topspin makes the ball dive down more sharply. This is how players can hit the ball extremely hard and fast, yet still land in bounds, as opposed to flying out of bounds.

This "trick" of topspin is also why tennis is easier than it may first seem if you've ever tried it. It's not easy to learn topspin but once you do, it increases your margin for error.

I'm also a tennis player and fan - agreed that it's super impressive, but a bit unrealistic.

Certain smart racquets (I think Babolat's?) can track the rpm and spin direction based on the head movement. I think using this type of data too could make the difference in terms of realism.

As a non-tennis player, I'd agree, this is impressive and fun.

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