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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, an

by mon_ 1y ago

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-by-play objective reporting and then a 'colour' commentator giving some subjective analysis during breaks in play. I bet there's a lot of interesting ways this could be used.

dmurray 1y ago
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 the ball back in hand, or the defending team turn over possession? What would be a good option for the outhalf if he got the ball right now? All of these except the last would be straightforward enough for a human observer with basic rugby knowledge going through the footage frame by frame, and I bet it would be really valuable to analysts. It seems like computer vision technology is at a stage where this could be automated too.
- MuffinFlavored 1y ago
  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
  - brookst 1y ago
    If this is the final product, not much difference at all. But where the human version is pretty much as far as it’s going to go, this is v0.01 of the AI version. Pretty soon the AI will be predicting what will happen next, commenting on whether this was a good idea (based on statistics), and letting the viewer ask questions about what exactly happened and why.
  - dmurray 1y ago
    > 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.
    - thom 1y ago
      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.
- thom 1y ago
  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.
  - jollygoodshow 1y ago
    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
ookdatnog 1y ago
The AI's job as described in this article is two-fold: - The relatively trivial task of extracting textual data from the screen. - The task of obfuscating that they're publishing other people's work as their own. When I clicked the article I assumed they'd try to automatically construct analysis of the game by using AI to analyze frames of the game, but that's not what they are doing. They are extracting some trivial information from the frames, and then they process the audio of the referee mic and commentary. In other words, the analysis has already been done by humans and they just want to re-publish this analysis as their own, without paying money for it. So they run it through an AI because in today's legal environment this seems to completely exempt you from copyright infringement or plagiarism laws.
- brookst 1y ago
  Perhaps the most surprising thing about the whole LLM revolution is how quickly attitudes about IP have shifted in the HN and similar communities. A few years ago, media companies were rent-seeking parasites who leveraged the jack-booted thugs of law enforcement to protect an artificial monopoly using IP laws that were massive overreach and contrary to the interests of humanity. Today, suddenly, media companies are pillars of society whose valuable contributions must be protected from the scourge of theft by everything from VC backed AI companies to armchair hackers who don’t respect the sanctity of IP. It’s amazing how mutable these principles are. I’m sure plenty of people are somewhere between the two extreme, but the shift is so dramatic that I am 100% sure many individuals have completely revised their opinions of IP companies based largely on worries about their own work being disrupted. At the very least it should create some empathy for the lawyers and business folk we all despised for their rent-seeking blah blah blah. They were just honestly espousing the positions their financial incentives aligned them to.
  - bondarchuk 1y ago
    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.

by mon_ 1y ago

dmurray 1y ago

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 the ball back in hand, or the defending team turn over possession? What would be a good option for the outhalf if he got the ball right now? All of these except the last would be straightforward enough for a human observer with basic rugby knowledge going through the footage frame by frame, and I bet it would be really valuable to analysts. It seems like computer vision technology is at a stage where this could be automated too.

MuffinFlavored 1y ago
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
- brookst 1y ago
  If this is the final product, not much difference at all. But where the human version is pretty much as far as it’s going to go, this is v0.01 of the AI version. Pretty soon the AI will be predicting what will happen next, commenting on whether this was a good idea (based on statistics), and letting the viewer ask questions about what exactly happened and why.
- dmurray 1y ago
  > 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.
  - thom 1y ago
    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.
thom 1y ago
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.
- jollygoodshow 1y ago
  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

ookdatnog 1y ago

The AI's job as described in this article is two-fold: - The relatively trivial task of extracting textual data from the screen. - The task of obfuscating that they're publishing other people's work as their own. When I clicked the article I assumed they'd try to automatically construct analysis of the game by using AI to analyze frames of the game, but that's not what they are doing. They are extracting some trivial information from the frames, and then they process the audio of the referee mic and commentary. In other words, the analysis has already been done by humans and they just want to re-publish this analysis as their own, without paying money for it. So they run it through an AI because in today's legal environment this seems to completely exempt you from copyright infringement or plagiarism laws.

brookst 1y ago
Perhaps the most surprising thing about the whole LLM revolution is how quickly attitudes about IP have shifted in the HN and similar communities. A few years ago, media companies were rent-seeking parasites who leveraged the jack-booted thugs of law enforcement to protect an artificial monopoly using IP laws that were massive overreach and contrary to the interests of humanity. Today, suddenly, media companies are pillars of society whose valuable contributions must be protected from the scourge of theft by everything from VC backed AI companies to armchair hackers who don’t respect the sanctity of IP. It’s amazing how mutable these principles are. I’m sure plenty of people are somewhere between the two extreme, but the shift is so dramatic that I am 100% sure many individuals have completely revised their opinions of IP companies based largely on worries about their own work being disrupted. At the very least it should create some empathy for the lawyers and business folk we all despised for their rent-seeking blah blah blah. They were just honestly espousing the positions their financial incentives aligned them to.
- bondarchuk 1y ago
  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.