Bracketology 2016-17 Style

[Hooking]

What are the statistical odds?

[Trotsky]

Wait! the statistical fact that the '58 Cornell hockey team was not in Division I means there was a 100% chance Cornell could not win the NCAA D-1 championship

There was no D-1 in 1958 and AFAIK Cornell was eligible for the NC$$ tournament.  Not sure why they were passed over with their gaudy 3-7-1 record.

[KenP]

At some point you need to let stoopid people be stoopid and get over it.   ::smashfreak::::stupid::::bang::

Hooking, let us discuss statistics in our own, flawed way.  If you don't like it, choose another thread.  Or forum.

Everyone else, don't feed the trolls.  

We are a day away from hockey... can't wait!  ::rock::

[Hooking]

Sound advice, Ken. I will read comments about Cornell Ice Hockey and try to ignore statistical speculation, no matter how annoying it might be.

[upprdeck]

just looping thru  the college hockey news site and using the rpi tool it was pretty easy to find ways that we would still be in after losing both games this weekend and thats without factoring in that more teams will lose again after that. just root for chalk in most of the other leagues and 3 game series in the ones that have teams both behind us.

[Trotsky]

just looping thru  the college hockey news site and using the rpi tool it was pretty easy to find ways that we would still be in after losing both games this weekend and thats without factoring in that more teams will lose again after that. just root for chalk in most of the other leagues and 3 game series in the ones that have teams both behind us.

I'm thinking our ideal is all the ECAC QF (in particular, US) are 2-0 home.  That sets up a Placid field that could be stronger than some NC$$ regionals.

[upprdeck]

thats why if we got 2-0 its very hard for us to lose next week 1 game and fall out, several teams around us have to lose twice. winning 2-1 and then losing becomes much dicier.

[adamw]

So - this conversation is actually quite relevant given that Cornell's odds have dropped to 65% in the new Matrix after today's game .... I have many thoughts on this topic, but no real answers. I appreciate constructive discussion.

http://www.collegehockeynews.com/ratings/probabilityMatrix.php

* Going into the weekend, Cornell, Penn State and Providence all had roughly 95%+ odds of making the NCAAs.  Penn State and Providence both lost Friday, but barely budged. Cornell, on the other hand, dropped quite a bit. To approximately 65% ... Why is this?  I wish I definitively knew the answer to this.  My math skills are OK, but not sure I have all the tools to properly answer that question. Intuitively, I wonder if it's because one particular flaw of the Matrix model that I point out on the site ... that it doesn't recalculate the KRACH after each game in each iteration. That would be ridiculously problematic and take forever to run. But a Cornell loss/Clarkson win may have shifted things enough to drop the odds of winning Saturday by a good amount.  I don't know, just a theory.

* I don't think the KRACH model is flawless by any means, but mostly I'd attribute whatever flaws there are to small sample size and a bit of circular logic that makes it work. Of course it can't take into account all factors - what can? But that's only because of sample size. The intangible factors described above are baked into the existing results. The mathematicians among us can explain this a lot better than I can - but it's about confidence intervals and what not. No one says the data is flawless.  No one suggests the games don't have to be played on the ice.  Of course they do.  But a system doesn't have to be flawless in order to give a sound assessment of odds of finishing in a certain spot.

* KRACH is actually not meant to be predictive. It's meant to be descriptive of what's already taken place. It's much closer to ideal in that direction. I think that's probably obvious to most, but worth reminding.  I think KRACH is generally the best model I've seen. That said, I have no problem if someone has an honest opinion about not buying certain odds being what they are. It just depends on what the argument is. If it's dismissive of all math, then the argument has no merit. But there is room to argue on the edges of how these things can be better. I'm all ears.

[KGR11]

So - this conversation is actually quite relevant given that Cornell's odds have dropped to 65% in the new Matrix after today's game .... I have many thoughts on this topic, but no real answers. I appreciate constructive discussion.

http://www.collegehockeynews.com/ratings/probabilityMatrix.php

* Going into the weekend, Cornell, Penn State and Providence all had roughly 95%+ odds of making the NCAAs.  Penn State and Providence both lost Friday, but barely budged. Cornell, on the other hand, dropped quite a bit. To approximately 65% ... Why is this?  I wish I definitively knew the answer to this.  My math skills are OK, but not sure I have all the tools to properly answer that question. Intuitively, I wonder if it's because one particular flaw of the Matrix model that I point out on the site ... that it doesn't recalculate the KRACH after each game in each iteration. That would be ridiculously problematic and take forever to run. But a Cornell loss/Clarkson win may have shifted things enough to drop the odds of winning Saturday by a good amount.  I don't know, just a theory.

Glad you brought this up. I've had some thoughts for how well the matrix is doing.
Yesterday, the matrix gave us a 98% chance to make the NCAAs. Our probability of being swept was around 13% (probably a little higher since our KRACH would go down for our second game). Assuming we only don't make it if we get swept, that means 11% of the 13% we get swept, we still make it in. That's over an 80% chance of getting in while getting swept.

Today, we have a 61% chance of winning tonight's game and a 65% chance of making the NCAAs. Making the same assumption as above, we would only make the tournament 4% out of the 39% when we got swept (about 11%). Obviously, this is too high. There are probably numerous scenarios we don't make it if we lose in three.

Back to your question, why the big drop? Maybe we're going down that 2% path based on what's going on with other teams. It's tough to judge a predictive model on just one year of data. I look forward to more years of seeing how well it does.

[jkahn]

So - this conversation is actually quite relevant given that Cornell's odds have dropped to 65% in the new Matrix after today's game .... I have many thoughts on this topic, but no real answers. I appreciate constructive discussion.

http://www.collegehockeynews.com/ratings/probabilityMatrix.php

* Going into the weekend, Cornell, Penn State and Providence all had roughly 95%+ odds of making the NCAAs.  Penn State and Providence both lost Friday, but barely budged. Cornell, on the other hand, dropped quite a bit. To approximately 65% ... Why is this?  I wish I definitively knew the answer to this.  My math skills are OK, but not sure I have all the tools to properly answer that question. Intuitively, I wonder if it's because one particular flaw of the Matrix model that I point out on the site ... that it doesn't recalculate the KRACH after each game in each iteration. That would be ridiculously problematic and take forever to run. But a Cornell loss/Clarkson win may have shifted things enough to drop the odds of winning Saturday by a good amount.  I don't know, just a theory.

Glad you brought this up. I've had some thoughts for how well the matrix is doing.
Yesterday, the matrix gave us a 98% chance to make the NCAAs. Our probability of being swept was around 13% (probably a little higher since our KRACH would go down for our second game). Assuming we only don't make it if we get swept, that means 11% of the 13% we get swept, we still make it in. That's over an 80% chance of getting in while getting swept.

Today, we have a 61% chance of winning tonight's game and a 65% chance of making the NCAAs. Making the same assumption as above, we would only make the tournament 4% out of the 39% when we got swept (about 11%). Obviously, this is too high. There are probably numerous scenarios we don't make it if we lose in three.

Back to your question, why the big drop? Maybe we're going down that 2% path based on what's going on with other teams. It's tough to judge a predictive model on just one year of data. I look forward to more years of seeing how well it does.

I've felt all along that the model was giving us way too high a percentage chance, and previously posted that our with a 27.5% KRACH chance of losing in the quarters and knowing our RPI would drop way more than others with losses due to our high winning percentage, the models just didn't seem accurate.  Without really understanding the logic used in the models, I can't comment further, other than I don't think that readjusting KRACH would have made that big a difference.

[upprdeck]

pwr is a strange beast.  Prov loses and gains pwr, penn state loses to a team worse than clarkson and goes no where.

vt/bc hard to tell which is better for us but i have a feeling a 2-1 series helps us the most.

wisc over osu might help us tonight.

we win 2 and we are in good shape if chalk wins the hockey east and b10.

[Dafatone]

It's our win percentage.  A single loss hurts us way more than other teams that are around us in RPI with lower win percentages.

Blame brown, RPI, and Colgate.

[BearLover]

I don't know enough about the model/KRACH/etc. to say anything especially productive, but I will say that, as a casual observer, the 98% number, the 91% number from a different model, and the 85% number from the Matrix before the RPI game all failed the eye test.  If I had to guess why, it's because KRACH, as adamw said, is meant to be descriptive of what has occurred rather than predictive.  Thus, it doesn't account for regressions to the mean, etc.  Cornell is not going to beat RPI nine times out of ten, even if their past records would equate to such a mismatch.  And beating Clarkson is far closer to a coin flip than a sure thing, even though the models gave Cornell a very high chance of winning that too.

[Dafatone]

I don't know enough about the model/KRACH/etc. to say anything especially productive, but I will say that, as a casual observer, the 98% number, the 91% number from a different model, and the 85% number from the Matrix before the RPI game all failed the eye test.  If I had to guess why, it's because KRACH, as adamw said, is meant to be descriptive of what has occurred rather than predictive.  Thus, it doesn't account for regressions to the mean, etc.  Cornell is not going to beat RPI nine times out of ten, even if their past records would equate to such a mismatch.  And beating Clarkson is far closer to a coin flip than a sure thing, even though the models gave Cornell a very high chance of winning that too.

A lot of that is that we've overachieved this year, in terms of what our win% "should" be compared to our goal differential.  We've won a lot of close games.

At the same time, Clarkson is pretty good.  The high chances of making the tournament have more to do with the fact that we have a pretty good shot if we don't win in this round.  That being said, it looks like yesterday was a disaster for us in terms of other results, not to mention the actual disaster last night.

So, let's win a few.

[adamw]

I don't know enough about the model/KRACH/etc. to say anything especially productive, but I will say that, as a casual observer, the 98% number, the 91% number from a different model, and the 85% number from the Matrix before the RPI game all failed the eye test.  If I had to guess why, it's because KRACH, as adamw said, is meant to be descriptive of what has occurred rather than predictive.  Thus, it doesn't account for regressions to the mean, etc.  Cornell is not going to beat RPI nine times out of ten, even if their past records would equate to such a mismatch.  And beating Clarkson is far closer to a coin flip than a sure thing, even though the models gave Cornell a very high chance of winning that too.

Yes and no. I'm not sure I'd use "regression to the mean" as the right way to put it, because that assumes you know what the mean is, which you really can't do from past results. But I know what you're driving at.  Intuitively you think Cornell-Clarkson were closer.  But they key is, how do you model that?  Not just go by "feel."  I think there may be a way to use goal differential and things like PDO and Team Corsi, to come up with some sort of counter-weight on straight KRACH.  But I couldn't tell you exactly what that would be.  Goal diffs have always been a dicey thing in hockey.  Corsi may be better, but has its flaws.  There might be some balance there.

I'd also like to be able to definitively answer the question why Cornell was affected more than Penn State, for example. With an actual demonstration.