Opponent and other news and results 2025-2026

[stereax]

Two prime hatewatch opportunities tonight - Stonehill/Harvard and Alaska/Quinnipiac. If either of the non-ECAC teams win, we can brand their opponents absolute frauds.

We would prefer them to win, unfortunately.  Or I suppose we can think of it as a no lose situation.

Harvard pumps Stonehill, 6-2, but Alaska and Q tie.

[abmarks]

Adam - I'm assuming you didn't catch one of my questions earlier.

Any chance of looking at home advantage and adjusting for quality of opponent by applying krach?  The ultimate question isn't home advantage. It's whether being at home makes you more likely than you should be to win vs a specific opponent.

I think this is the math for this, someone correct me if I am wrong:

for each game result, a team is likely to win at a percentage of  Ke =Kh/(Kh+Kr)

where Ke is the expected win percent, Kh is the home teams krach and Kr is the road teams krach.

You can calculate a season total for Krach expected home wins by adding up the Ke for all home games by played by a team.

Over or under expectation is the difference between the Ke and the actual win percentage from those home games. 

Admittedly I don't know how to handle ties  when krach adjusting in this model. Krach ratios tell you how often to expect a win, but I don't think I've ever seen a mention of how to use them to predict the tie percentage.  We know that if you are 80% win probability a tie is underperforming, but not as much as a loss, but using any point system for WLT is arbitrary. Is there a theoretical way to predict the tie ratio as well as the win ratio?

[BearLover]

Adam - I'm assuming you didn't catch one of my questions earlier.

Any chance of looking at home advantage and adjusting for quality of opponent by applying krach?  The ultimate question isn't home advantage. It's whether being at home makes you more likely than you should be to win vs a specific opponent.

I think this is the math for this, someone correct me if I am wrong:

for each game result, a team is likely to win at a percentage of  Ke =Kh/(Kh+Kr)

where Ke is the expected win percent, Kh is the home teams krach and Kr is the road teams krach.

You can calculate a season total for Krach expected home wins by adding up the Ke for all home games by played by a team.

Over or under expectation is the difference between the Ke and the actual win percentage from those home games. 

Admittedly I don't know how to handle ties  when krach adjusting in this model. Krach ratios tell you how often to expect a win, but I don't think I've ever seen a mention of how to use them to predict the tie percentage.  We know that if you are 80% win probability a tie is underperforming, but not as much as a loss, but using any point system for WLT is arbitrary. Is there a theoretical way to predict the tie ratio as well as the win ratio?

Rather than relying on KRACH, which (AFAIK) is not a (remotely?) perfect measure of how likely a team is to win versus a specific opponent, can't you control for this by restricting your analysis to in-conference games so that every team plays every opponent an equal number of times home and away?

[adamw]

Adam - I'm assuming you didn't catch one of my questions earlier.

Any chance of looking at home advantage and adjusting for quality of opponent by applying krach?  The ultimate question isn't home advantage. It's whether being at home makes you more likely than you should be to win vs a specific opponent.

I think this is the math for this, someone correct me if I am wrong:

for each game result, a team is likely to win at a percentage of  Ke =Kh/(Kh+Kr)

where Ke is the expected win percent, Kh is the home teams krach and Kr is the road teams krach.

You can calculate a season total for Krach expected home wins by adding up the Ke for all home games by played by a team.

Over or under expectation is the difference between the Ke and the actual win percentage from those home games. 

Admittedly I don't know how to handle ties  when krach adjusting in this model. Krach ratios tell you how often to expect a win, but I don't think I've ever seen a mention of how to use them to predict the tie percentage.  We know that if you are 80% win probability a tie is underperforming, but not as much as a loss, but using any point system for WLT is arbitrary. Is there a theoretical way to predict the tie ratio as well as the win ratio?

Side notes first ...

- not sure who injected the remark about innumeracy into my quote to make it look like I said it, but I didn't. That said, most people are, in general.

- listen to our latest podcast, with NPI architect Tim Danehy if you want more insight

- leave it to BL to twist things around into oblivion, and if they don't fit into the narrowly defined specifications of his liking, then everyone must be terrible or doing something wrong or misleading people. No one is being misled. No one thinks they are being misled. As I said, they endlessly debate, with each other, what the right thing is. If 1.2/0.8 isn't working out, they'll change it. So far it's close enough. And if it was so easy to "game the system" by scheduling nothing but road games - why aren't teams doing it? Like I said, listen to the podcast.

- As for KRACH - saying it's not "remotely" a perfect measure, is silly (go figure). Where's John Whelan when I need him? It's actually - as far as I'm concerned (with some needed tweaks to what we publish) - the best method there is. Listen to Tim discuss this on the podcast, where he gives a counter-point to that, while also saying that, with enough data, it would be the best system.

- To finally answer the question ... certainly you can do a lot of complex formulations to make things work, and that's what I mean about tweaking KRACH for home/road. But when it comes to something like NPI, they want the criteria to be also somewhat understandable. And since it's all "close enough" - albeit arbitrary in many ways - it works out. Just as the Pairwise did.

- I have 2 math PhDs helping me tweak our KRACH calculation to do exactly things like that, and more.  At the end of the day, however, almost anything you do will have subjective components to them. For example, having a "recency bias" is doable, but a philosophical discussion. Having a "Quality Win Bonus" is done now, but that's a philosophical decision, not based on real math, per se. Same for H/R weights, and OT weights. They're not always gunning for perfect math. "Better" math, but not perfect. Because there are other considerations.  I mean, you either accept that or you don't.  Sorry if anyone believed otherwise - but I tell you the truth, and you can get all agitated about it, or not. Up to you.

repeating ... listen to the Podcast.

[BearLover]

Well, I came to this forum to tell people about the fascinating podcast I just listened to on CHN, and what do I see?

- leave it to BL to twist things around into oblivion, and if they don't fit into the narrowly defined specifications of his liking, then everyone must be terrible or doing something wrong or misleading people. No one is being misled. No one thinks they are being misled. As I said, they endlessly debate, with each other, what the right thing is. If 1.2/0.8 isn't working out, they'll change it. So far it's close enough. And if it was so easy to "game the system" by scheduling nothing but road games - why aren't teams doing it? Like I said, listen to the podcast.

Huh? I was, quite literally, misled. I thought home/away weighting was intended to be accurate. Why wouldn't I? The rankings are meant to do their best to capture the most deserving teams for the NCAAs, so obviously I would expect each of the components to serve that same purpose??? I would guess many others had the same misconception.

- As for KRACH - saying it's not "remotely" a perfect measure, is silly (go figure). Where's John Whelan when I need him? It's actually - as far as I'm concerned (with some needed tweaks to what we publish) - the best method there is. Listen to Tim discuss this on the podcast, where he gives a counter-point to that, while also saying that, with enough data, it would be the best system.

But that's the key flaw, isn't it? There's nowhere near enough data. KRACH may be mathematically elegant and it may do the best job at ranking teams at the end of a short season based on past performance, but that is VERY different from accurately predicting future outcomes. There simply are nowhere near enough games in a season and most of them are intra-conference. If one were to build a betting model to predict college hockey games, it would look wildly different from KRACH. Obviously! Because KRACH is trying to rank teams the best it can based on a tiny sample of past performance, it isn't trying to tell you who  will come out on top in the future.

Anyway, the podcast is great. I encourage everybody listen.

[ugarte]

Because KRACH is trying to rank teams the best it can based on a tiny sample of past performance, it isn't trying to tell you who  will come out on top in the future.

Is there a better model for predicting the future? If there is, and you know it, don't tell anyone just go to Vegas.

The point of KRACH (or NPI) for this discussion is to select teams to play in a postseason tournament. For that, I don't want something predictive. I want something that "ranks teams the best it can based on a tiny sample of past performance" and then let the teams slug it out on the ice and see if they can live up to their ranking. Some existential analysis of who would have the best record if they played at full-strength all year is not a thing I care about outside of a sportsbook. You earn a spot in the postseason by winning and if you can't win because your starting center had knee surgery, tell it to your grandkids when they ask about the glory days.

[Trotsky]

The point of KRACH (or NPI) for this discussion is to select teams to play in a postseason tournament. For that, I don't want something predictive. I want something that "ranks teams the best it can

You do a fine job capturing a much-abused nuance of statistics: the difference between predictive and descriptive statistics.  However, I cut you off before you got into sampling because sampling muddies the issue.  Descriptive stats are based on as much prior data as we can get.  Baseball Reference has every PA in every player's history.  The point is to describe with as much precision as possible the data set.  There is no pretense of predicting future results.  This is what Pete Alonso did.  The question of who Pete Alonso is shall be left to ontological philosophy.

Exactly as you said, the stats used to pick the NC$$ field capture what teams did to earn their place there.  There is no pretense in predicting who will win.  That is for the teams on the ice.

[The Rancor]

The point of KRACH (or NPI) for this discussion is to select teams to play in a postseason tournament. For that, I don't want something predictive. I want something that "ranks teams the best it can

You do a fine job capturing a much-abused nuance of statistics: the difference between predictive and descriptive statistics.  However, I cut you off before you got into sampling because sampling muddies the issue.  Descriptive stats are based on as much prior data as we can get.  Baseball Reference has every PA in every player's history.  The point is to describe with as much precision as possible the data set.  There is no pretense of predicting future results.  This is what Pete Alonso did.  The question of who Pete Alonso is shall be left to ontological philosophy.

Exactly as you said, the stats used to pick the NC$$ field capture what teams did to earn their place there.  There is no pretense in predicting who will win.  That is for the teams on the ice.

Why on earth would you want to know who wins the game before it's played? Why watch? Why even play? The whole point is not knowing and experiencing the triumph or heartbreak- or back in the day the cold satisfaction of a tie- and the drama of the game itself?

[Chris '03]

Because KRACH is trying to rank teams the best it can based on a tiny sample of past performance, it isn't trying to tell you who  will come out on top in the future.

The point of KRACH (or NPI) for this discussion is to select teams to play in a postseason tournament. For that, I don't want something predictive. I want something that "ranks teams the best it can based on a tiny sample of past performance" and then let the teams slug it out on the ice and see if they can live up to their ranking. 

And to the extent we want to debate what the "right" way to do that is, it's always going to be subjective choice about what the inputs are. As Adam said, the committee wants to incentivize teams to schedule road games by making those wins have a higher value.  Because the committee feels that teams that all things being equal, a team that is willing to travel is more deserving a a spot in the tournament than a team that does not.  And they think that the current weighting does a reasonable job of that.  There are, of course, infinite ways to calibrate this incentive.

Most systems of selecting teams on objective criteria will put a team with a .900 winning percentage in the field. Just win and you're in.  The debate is around what the appropriate criteria are for picking between three or four similar teams for the last couple of spots based on a body of work that's about 38 games and the degree to which the criteria need to be "accurate."  Humans prioritize and the model reflects that priority.  It will never be accurate because the sample size is too low and the game is too variable.  It would otherwise be boring.  The selection system needs to be objective, published in advance, and fairly reflect the stated priorities of the committee.  Personally, I wouldn't hate a system that puts league regular season and tournament champions in the field and calls it a day.  Will the "best" teams all make it? No.  But people will argue that the "best" teams don't make it now either. Why should the fifth place team in NCHC who lost a first round playoff game play in the NCAA tournament anyway?

The gnashing of teeth around selection criteria and the relative closeness of teams ranked 5 and 12 or whatever, is part of why I remain in the neutral site NCAA camp.  A team could host a tournament game because it had an extra road game because of the way its conference scheduled vs its opponent who had a home game instead or because its AD insisted on an extra home date to cover expenses at a small school?  Neutral sites (in general) work to mitigate that issue.

[adamw]

Well, I came to this forum to tell people about the fascinating podcast I just listened to on CHN, and what do I see?

- leave it to BL to twist things around into oblivion, and if they don't fit into the narrowly defined specifications of his liking, then everyone must be terrible or doing something wrong or misleading people. No one is being misled. No one thinks they are being misled. As I said, they endlessly debate, with each other, what the right thing is. If 1.2/0.8 isn't working out, they'll change it. So far it's close enough. And if it was so easy to "game the system" by scheduling nothing but road games - why aren't teams doing it? Like I said, listen to the podcast.

Huh? I was, quite literally, misled. I thought home/away weighting was intended to be accurate. Why wouldn't I? The rankings are meant to do their best to capture the most deserving teams for the NCAAs, so obviously I would expect each of the components to serve that same purpose??? I would guess many others had the same misconception.

- As for KRACH - saying it's not "remotely" a perfect measure, is silly (go figure). Where's John Whelan when I need him? It's actually - as far as I'm concerned (with some needed tweaks to what we publish) - the best method there is. Listen to Tim discuss this on the podcast, where he gives a counter-point to that, while also saying that, with enough data, it would be the best system.

But that's the key flaw, isn't it? There's nowhere near enough data. KRACH may be mathematically elegant and it may do the best job at ranking teams at the end of a short season based on past performance, but that is VERY different from accurately predicting future outcomes. There simply are nowhere near enough games in a season and most of them are intra-conference. If one were to build a betting model to predict college hockey games, it would look wildly different from KRACH. Obviously! Because KRACH is trying to rank teams the best it can based on a tiny sample of past performance, it isn't trying to tell you who  will come out on top in the future.

Anyway, the podcast is great. I encourage everybody listen.

hmm - thanks ... heh.  Sorry - didn't realize you were referring to KRACH in a future sense, because that was not the context of this conversation. It's OK to just admit you misspoke - because your doubling down to say you were referring to the future, is also silly, given that there is nothing that can predict the future, and nobody says it can. But it's something - and it's better than anything else. And that's all we have.

Glad I could clear up the misconceptions about the home/road weighting.

[adamw]

The gnashing of teeth around selection criteria and the relative closeness of teams ranked 5 and 12 or whatever, is part of why I remain in the neutral site NCAA camp.  A team could host a tournament game because it had an extra road game because of the way its conference scheduled vs its opponent who had a home game instead or because its AD insisted on an extra home date to cover expenses at a small school?  Neutral sites (in general) work to mitigate that issue.

Agreed. I've made this point about 5 bazillion times in recent years, including this podcast, and every "debate" with David Carle and others. People who are not very math savvy are making these decisions, and also making comments like "if the math is good enough to pick the teams, why not good enough to seed the teams for home ice," not realizing the inherent flaw in that logic. I would have a much easier and enjoyable time debating the topic, if people just admitted the math was far from perfect, and so there's no way to know how to seed teams 5-12 really, so giving 5-8 home ice advantage is a double whammy. And then just say you still want home-ice Regionals for other reasons. Don't try to defend the math of it. ... But alas that will apparently never sink in for many.

[BearLover]

Just to clarify what I mean above—

KRACH (and the Pairwise, and the NPI) are built to pick the 16 teams most deserving of the NCAA tournament (and their order). That is a very different thing than a predictive model. KRACH may well be the best model we have at choosing NCAA teams, but it's not built to be predictive and shouldn't be used that way.

KRACH et al are mostly just a function of winning percentage and strength of schedule. As they should be, because that's how we should choose who has earned an NCAA bid. But you get absurd results when you extrapolate that to future performance. With a large enough, and representative enough, sample, this would start to even out, but in college hockey this isn't possible (because seasons are very short and teams only play a small subset of other teams). 

You can't just plug in two teams' win% and SOS and try to predict which one will win. (Try doing that with college hockey games, you'd lose a ton of money.) There's wayyyyy too much luck and other factors that affect the tiny sample of games on which win% and SOS are based. Again, this is not a knock on KRACH-it's doing its job just fine. Its job is not to predict future performance.

Anyway, related to the above and also something I was wondering during the podcast—the guest mentioned that coaches and fans have put their faith in these models because its outputs seem acceptable. That is, we see the 16 teams the Pairwise spits out, they pass the smell test, we move on with our lives and don't question the model. But I'm wondering if anyone has ever made a more rigorous attempt to quantify if the model is picking teams properly. I'm not sure how this would look or if it's even possible, but it did strike me as a little spooky that all this time we've been entrusting a computer model whose outputs we don't even have a means of judging past "the smell test."

[Jim Hyla]

Well, I came to this forum to tell people about the fascinating podcast I just listened to on CHN, and what do I see?

- leave it to BL to twist things around into oblivion, and if they don't fit into the narrowly defined specifications of his liking, then everyone must be terrible or doing something wrong or misleading people. No one is being misled. No one thinks they are being misled. As I said, they endlessly debate, with each other, what the right thing is. If 1.2/0.8 isn't working out, they'll change it. So far it's close enough. And if it was so easy to "game the system" by scheduling nothing but road games - why aren't teams doing it? Like I said, listen to the podcast.

Huh? I was, quite literally, misled. I thought home/away weighting was intended to be accurate. Why wouldn't I? The rankings are meant to do their best to capture the most deserving teams for the NCAAs, so obviously I would expect each of the components to serve that same purpose??? I would guess many others had the same misconception.

You weren't misled, you made an assumption that wasn't accurate. The home ice formula has been discussed on this forum forever. Adam has explained the rational for getting large schools to move out a gazillion times. Large schools loved to play small schools at home to pad their record and pad their income. The powers that be didn't like that, so tried to do something about it.

If you weren't part of that discussion, then researching before assuming would be the correct approach.

[BearLover]

Well, I came to this forum to tell people about the fascinating podcast I just listened to on CHN, and what do I see?

- leave it to BL to twist things around into oblivion, and if they don't fit into the narrowly defined specifications of his liking, then everyone must be terrible or doing something wrong or misleading people. No one is being misled. No one thinks they are being misled. As I said, they endlessly debate, with each other, what the right thing is. If 1.2/0.8 isn't working out, they'll change it. So far it's close enough. And if it was so easy to "game the system" by scheduling nothing but road games - why aren't teams doing it? Like I said, listen to the podcast.

Huh? I was, quite literally, misled. I thought home/away weighting was intended to be accurate. Why wouldn't I? The rankings are meant to do their best to capture the most deserving teams for the NCAAs, so obviously I would expect each of the components to serve that same purpose??? I would guess many others had the same misconception.

You weren't misled, you made an assumption that wasn't accurate. The home ice formula has been discussed on this forum forever. Adam has explained the rational for getting large schools to move out a gazillion times. Large schools loved to play small schools at home to pad their record and pad their income. The powers that be didn't like that, so tried to do something about it.

If you weren't part of that discussion, then researching before assuming would be the correct approach.

Yes, the entire disagreement at this point is over the use of the word "misleading." In any event, I would guess that most fans have the same misconception of the weighting that I had (that the idea is to reflect home ice advantage).

[Jim Hyla]

Well, I came to this forum to tell people about the fascinating podcast I just listened to on CHN, and what do I see?

- leave it to BL to twist things around into oblivion, and if they don't fit into the narrowly defined specifications of his liking, then everyone must be terrible or doing something wrong or misleading people. No one is being misled. No one thinks they are being misled. As I said, they endlessly debate, with each other, what the right thing is. If 1.2/0.8 isn't working out, they'll change it. So far it's close enough. And if it was so easy to "game the system" by scheduling nothing but road games - why aren't teams doing it? Like I said, listen to the podcast.

Huh? I was, quite literally, misled. I thought home/away weighting was intended to be accurate. Why wouldn't I? The rankings are meant to do their best to capture the most deserving teams for the NCAAs, so obviously I would expect each of the components to serve that same purpose??? I would guess many others had the same misconception.

You weren't misled, you made an assumption that wasn't accurate. The home ice formula has been discussed on this forum forever. Adam has explained the rational for getting large schools to move out a gazillion times. Large schools loved to play small schools at home to pad their record and pad their income. The powers that be didn't like that, so tried to do something about it.

If you weren't part of that discussion, then researching before assuming would be the correct approach.

Yes, the entire disagreement at this point is over the use of the word "misleading." In any event, I would guess that most fans have the same misconception of the weighting that I had (that the idea is to reflect home ice advantage).

Misconception means you had the wrong idea.

Being misled implies that someone was actually leading you in that direction. That your misconception was due to others.

Misinterpreting means you made an assumption that was incorrect. So your misconception was due to your own misinterpretation.

Unless you can show where you were led astray, I think that you misinterpreted and weren't misled.