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As painfully ridiculous as this is to say...
San Jose State is not a gimme.
Hardest of the remaining 3 for sure, but it's a gimme. USU destroyed SJSU in San Jose 2 weeks ago. SJSU is currently struggling at home against Texas St.
As painfully ridiculous as this is to say...
San Jose State is not a gimme.
Utah State boat raced them. I think we're better than Utah State. The computers like SJSU because they played Stanford tough, but I think we'll see that the computers are wrong on this one.
Utah State boat raced them. I think we're better than Utah State. The computers like SJSU because they played Stanford tough, but I think we'll see that the computers are wrong on this one.
Agreed. SJSU is a distant third in the WAC and that is really all that needs to be said. They played Stanford close in the season opener. Big whoop.
I think we're better than Utah State. The computers like SJSU because they played Stanford tough, .
You may be right about San Jose State's true quality but you are wrong about the computer models (you've made similar incorrect statements about computer models in the past). The small margin of loss in the Stanford game is having only a very small effect on their rating at this point. In a pure MOV model (which would give the most weight to the close Stanford loss ... other models would give less) if you switch the MOV from 3 to 17, the SJSU rating only goes down by 7. It's not an irrelevant data point, but it is hardly driving their rating.
You may be right about San Jose State's true quality but you are wrong about the computer models (you've made similar incorrect statements about computer models). The small margin of loss in the Stanford game is having only a very small effect on their rating at this point.
That's fine, I am not some computer expert with how this stuff works.
I don't understand how the computer likes them. There's nothing in their record that stands out besides playing Stanford close. They got killed by USU at home. They have beaten up on a bunch of WAC schools. They're in third there. They beat SDSU which rates around 60 in Sagarin. They beat Navy and CSU which at both 90+. They rate at around 47 in Sagarin right now, which I think is high.
What am I missing here? Your other thread still has the probability at around 55% of a BYU win, right?
I don't see it I guess. I'd think it would be close to 80%.
You may be right about San Jose State's true quality but you are wrong about the computer models (you've made similar incorrect statements about computer models in the past). The small margin of loss in the Stanford game is having only a very small effect on their rating at this point. In a pure MOV model (which would give the most weight to the close Stanford loss ... other models would give less) if you switch the MOV from 3 to 17, the SJSU rating only goes down by 7. It's not an irrelevant data point, but it is hardly driving their rating.
Te Occidere Possunt Sed Te Edere Non Possunt Nefas Est.
I predict the Riley Nelson that was top 15 in the NCAA in PER last year will amazingly show up next week. And then everyone around here can get their PER boner again.
So Russell...what do you love about music? To begin with, everything.
I predict the Riley Nelson that was top 15 in the NCAA in PER last year will amazingly show up next week. And then everyone around here can get their PER boner again.
I'm bummed out about that ND game. Utah is always a wildcard. BSU was stupid, but we always lose a stupid game. OSU was no different from any other BYU season.
That's fine, I am not some computer expert with how this stuff works.
I don't understand how the computer likes them. There's nothing in their record that stands out besides playing Stanford close. They got killed by USU at home. They have beaten up on a bunch of WAC schools. They're in third there. They beat SDSU which rates around 60 in Sagarin. They beat Navy and CSU which at both 90+. They rate at around 47 in Sagarin right now, which I think is high.
What am I missing here?
Well there is the fun general explanation and there is the boring intuitive explanation. The fun explanation requires at least an undergraduate background in econometrics or regression analysis. Here is a little document I would use with my undergrads as an example of where regression analysis is used. This is the fun general explanation (I also posted a similar explanation on cougarguard one time):
That's the technical explanation of how computer models work. Well, at least MOV based computer models. Win/Loss computer models tend to use algos like Elo-Chess. I suppose these days Elo-Chess is besst known for being the algo they use to rank women at the beginning of the Social Network. It is actually a pretty simple iterative algo, but no W/L model is any good for college football. There are not a enough games given the noise in the outcomes; you can't afford to throw out margin of victory; it simply throws away too much information.
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