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Just curious why you say so? Asking as someone who doesn't know more about it other than what is said in the article. I am a bit torn on using margin of victory in these things, because it rewards high-scoring run-n-gun offenses over more deliberate "Princeton" style offenses with gritty defenses. But they did cap it at ten points, which seems like a reasonable compromise.They chose the only solution worse than just keeping RPI
The cap at 10 points won’t do those games any justice. I didn’t see how it would be better. I believe the only people it helps are those in the ACC, PAC 12, Big 10 and Big 12Just curious why you say so? Asking as someone who doesn't know more about it other than what is said in the article. I am a bit torn on using margin of victory in these things, because it rewards high-scoring run-n-gun offenses over more deliberate "Princeton" style offenses with gritty defenses. But they did cap it at ten points, which seems like a reasonable compromise.
Thanks. I missed the part where they were using machine learning. I know an awful lot of machine learning researchers... I am sure they will be shocked to hear that it is being used for this purpose. Using machine learning to come up with proper weights for a static and deterministic formula would be one thing... But this just screams for opportunities to have a thumb on the scales. Not to mention that it is not really deterministic in the classical sense. Input one new game of minor consequence into the 'training set', and the entire top fifteen could get resorted completely. It should theoretically get better as the training set improves, but man... Thanks for the info. "Machine Learning" is all I needed to hear to know it was garbage.It's a black box. So far, it doesn't sound like they have any intention of releasing the formula. In fact, they're implying that it's not even a static formula, but rather a machine learning algorithm with proprietary code. This has a number of consequences.
First is that we have to trust that both their input and their code is correct. The BCS had the same problem with their computer rankings and there was at least one well-publicized instance in 2010 of an error in the Colley Matrix that caused the rankings to be incorrect. Say what you will about the RPI, but there are dozens of sites calculating those rankings every day. Any discrepancies are caught almost immediately. Instead this is what we get:
"It's not really a 'formula,' so much as it's highly sophisticated and involves machine-learning is not easily digestible. It is not like the RPI, to consider this as a formula. This is not that. This is very contemporary, forward-thinking and involves machine-learning and artificial intelligence."
Second is that we don't know how any of the factors are weighed within their secret sauce and whether they make any mathematical sense. The arbritary choice of a 10-point cutoff is a good example. What's the math behind that number? The burden of proof should be on them to show that this is an improvement. At the very least, they could provide retroactive rankings so we could see how this new system compares to the well-known publicly available metrics such as KenPom, Sagarin, etc. According to Dan Gavitt, the "committee felt like there was nothing productive by going back and comparing it."
Another problem is that it's a predictive metric. This is more of a philosophical question, but I firmly believe that a team should be judged by whether they won games this year, not whether they will win games in the tournament. Most of the time, these values converge, but not always. In predictive metrics, a team can theoretically lose every game on their schedule by 1 point and finish within the top 50. The results of games should matter. It should matter whether a team wins or loses.
So is it better than RPI? Probably is. But we knew the weaknesses of RPI. We knew the numbers were correct. We knew how to weigh them against other factors. This is just a number handed down from the NCAA gods that we're supposed to trust because Google helped them make it? How will the committee handle that? How should they? I have no idea...
I sit on a committee that awards funding to various scientific computing proposals. Can't tell you how many people just add, "And we'll do machine learning on xyz!" because it is buzzword-worthy. Don't get me wrong, it can be incredibly useful (and we've funded many projects that use it). But in the worst cases, it comes across as "We'll let the computer figure out this very difficult problem that we have no original ideas about and take its solution as gospel for no reason".Also, doesn’t ML need 100,000 of data sets of training data before it is usable?
I agree with everyone else, RPI wasn’t the best, but I’d much rather have it than whatever this is.
Seems like some people sold the NCAA on a bunch of buzzwords they don’t understand.