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Building an AI that watches rugby

nickjones.tech

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Re: Building an AI that watches rugby

#31

Post by bondarchuk »

Workaccount2 wrote: post #28
bondarchuk wrote: post #19

Earlier quoted context omitted.

How do you know you're seeing peoples' opinions change, and not just a change in which people express their opinions? That said I'd personally be happy if LLMs cause the death (or drastic weakening) of copyright and IP laws, however as it is now, with no copyright for AIs but the same old copyright for humans, it's the worst of both worlds.

I know people personally with strong gripes about AI "infringement" (in quotes because I believe people are just confused about how these models work), and every single one of them -100%- have a stash of pirated media they casually accumulated over the years. People are in it for themselves. When you are young everyone has righteous ideals, but then trends of society eventually ebb, and you realize that just about ev…

>I know people personally with strong gripes about AI "infringement" (in quotes because I believe people are just confused about how these models work), and every single one of them -100%- have a stash of pirated media they casually accumulated over the years.

I don't know them, of course, but it is a consistent and imho reasonable position to be against copyright yet, while we normal people live in fear of copyright, ask for it to be applied to AI as well.

It is even reasonable IMO to be against copyright for individuals but in favour of copyright for businesses. That's how it de facto works in a lot of places anyway.

Re: Building an AI that watches rugby

#33

Post by thom »

dmurray wrote: post #8
mon_ wrote: post #6

Why the focus on scorekeeping? I feel like an AI model is overkill here, when you have text-based sources readily available such as news apps, Twitter feeds, and apps such as Livescore which would be easier and cheaper to scrape. They probably cover more matches that aren't televised too. I'd be curious to see what useful insights could be gleamed from the match commentary. You have the main commentator giving play-b…

The only interesting part of the model's output was { "current_play": "ruck", } So the vision model can correctly identify that there's a ruck going on and that the ball is most likely in the ruck. Why not build on this? Which team is in possession? Who was the ball carrier at the start of the ruck, and who tackled him? Who joined the ruck, and how quickly did they get there? How quickly did the attacking team get th…

Multiple companies sell Rugby data of various levels of granularity. I don't know if rugby has all the toys (i.e. full tracking outside of wearables) that soccer or American football have because there's less money sloshing around.

Re: Building an AI that watches rugby

#34

Post by thom »

dmurray wrote: post #21
MuffinFlavored wrote: post #12

Earlier quoted context omitted.

ESPN has play by play stuff for free like this on their website for some other sports not sure if it is done by a human or not curious how “an AI can do it” yields much difference in terms of result for the casual watcher

> curious how “an AI can do it” yields much difference in terms of result for the casual watcher An AI can do it in volume, and therefore cheaper. I don't think a human could do everything I said in real time - maybe with a lot of training and custom software. A human could transcribe the scoreboard, but the article still thinks that's an interesting application of cutting-edge machine vision.

Humans can do _most_ of what you said in real time, both providers using bespoke software and club analysts using off the shelf stuff like Sportscode. For full positional data on every player, every frame then yes, computer vision is doing most of the work but the quality isn't always great. Providers with in-stadium multi-camera systems provide great data, but you don't necessarily have access to the size of dataset you'd want for recruitment, and so lower-quality broadcast tracking exists (with all the problems you can imagine like missing players, occlusions, crazy camerawork etc). Most clubs also have wearables for their own analysis. Almost every fully automated broadcast tracking solution has hit a wall (sometimes on the first day of a season) in terms of quality that is often only solved by human QA, or by just discarding some games, so this is far from a completely solved problem. Fun domain to work in, but lots of horrible edge cases.

Re: Building an AI that watches rugby

#38

Post by jollygoodshow »

thom wrote: post #33
dmurray wrote: post #8

Earlier quoted context omitted.

The only interesting part of the model's output was { "current_play": "ruck", } So the vision model can correctly identify that there's a ruck going on and that the ball is most likely in the ruck. Why not build on this? Which team is in possession? Who was the ball carrier at the start of the ruck, and who tackled him? Who joined the ruck, and how quickly did they get there? How quickly did the attacking team get th…

Multiple companies sell Rugby data of various levels of granularity. I don't know if rugby has all the toys (i.e. full tracking outside of wearables) that soccer or American football have because there's less money sloshing around.

Most pros now have the vests, but also they tend to have additional tech in their mouth guards. This is mostly for CTE monitoring, but I imagine that there's other data that can be extracted
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