Deggs takes some heat on this board including from me this year. I have to tip my cap to him that’s two straight regional appearances re establishing our program where we should be. Congrats coach on the late run this year and let’s go win the damn thing in Miami. Looking at Miami they are good no doubt but they got swept by wake forest and Virginia this year. I like our chances if we can beat Texas first game no easy task.
If RPI is going to be continue to be used so heavily, perhaps a simple tweak could improve it. Change the criteria to be 35 percent your record, 33 percent your opponents record and 32 percent the record of the opponents of your opponents. Your results should always be the driving factor of how you are analyzed. That is not the case with the current 25/50/25 formula. I doubt the major schools will ever let that happen however as the current system is to their advantage. Brian, how difficult would it be to recalculate the RPI of tournament teams with my suggestion and then compare it to the current output ?
Kyle Peterson said the RPI system needs scrapping right now. Not sure what replaces though. John Cohen stated a more regionally fair system needed to be used.
Unless you have a system that is a black box, it will always be open to some sort of manipulation (by folks such as myself). But you can drive a truck through the manipulation hole of the RPI . so the bar is really low if you want to implement a non-black box system improvement. It is certainly not my preference, but an improvement is not difficult . one that goes deeper in determining real SOS.
My preference would be a true learning system . a machine learning approach (machine learning is really just statistical learning). This would be a system that would train on past outcomes while using team resumes and other features (of the learning model) as the inputs from which the system would learn . and ultimately predict (the most optimal field of participants). While I think this would yield the most optimal result, it would suffer from the same thing that sometimes plagues other machine learning systems. Explainability. These feature models are so complex that they are not understandable to the typical human. And the average fan would likely not accept this when their team is left out or they are not seeded in a manner that meets their expectation.
Even an ML approach can be subject to manipulation (not by teams . but by the ML engineers that build the system) . in the training data that is used to generate feature selection and to ultimately train the model. We see this with ChatGPT, Bard, and others. But at least here you can institute some controls that make this an honest approach to the problem. And again, it would not subject to manipulation by schedule makers.
Brian
…and we avoided the often obligatory trip across the basin.
I’m sure they are relieved not to see us or the Colonels. Although they’d never admit it of course.
Brian,
Could we just add another level to the strength of schedule such as opponent’s opponent’s opponent winning %?
Plus us a standard deviation to the RPI as such like you had mentioned earlier?
Scot, I think they are always scared of us in the postseason because of all the postseason success we have had at the box. Is that what you think also or something else?
Irvine didn't even have a game vs. the RPI top 25 and finished 4th in the 10th ranked conference.
Awwwww, too bad, not sad. Even Kent of the MAC could have a been, but they realize their numbers aren’t worthy.
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Just watched the replay. The west coast just has a geographical disadvantage. It shows in all the major sports.
Peterson is just a useful tool to juggle the system, got Texas and Oklahoma joining the SEC…need to be able to justify 12 teams in the tournament.
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