The 2025 Offseason thread.

[BigThief]

Originally Posted by jayswin

Expecting a goalie with this trajectory and potential to sign for second line forward money for max term through his prime (7.5mil aav) just isn't realistic, even if he hasnt proven to be worth more than that, yet.

Exactly...Jake Ottinger signed his new contract last year for an average of 8.25million, one year later the cap projection keeps going up and Wolf isn't getting less than that.

[Bonded]

Originally Posted by BigThief

Exactly...Jake Ottinger signed his new contract last year for an average of 8.25million, one year later the cap projection keeps going up and Wolf isn't getting less than that.

It’s pretty simple to me. Oettingers % of cap is a good guideline. The cap isn’t going to increase where 10+ makes sense for Wolf imo. Either way I doubt the Flames even engage in contract talks with him until part way through the season and see how he does. It’s more likely he stumbles than does anything pushes him into that 10-12 AAV realm.

[GioforPM]

Originally Posted by White Out 403

its the 2025 off season thread. is this for flames only? if so the title is a little misleading

No, you can post what you want. That was my reaction to the post.

[Enoch Root]

Originally Posted by PepsiFree

I think you’re totally missing the point (you’re not alone, as Enoch and a couple others aren’t getting it either). The point isn’t to create the most accurate model by eliminating inputs and reducing the sophistication of the model. The point is creating a sophisticated model with these necessary inputs that is as accurate as possible.

For example. I “fixed” your model by removing the redundant addition of adding half the difference. Got it to 10.2. I “fixed” it further by adding the redundant addition back and reducing it to 1/8 instead of 1/2. Got it to 10.0. So the question is: how useful is the model? What do we learn from it if I can change one arbitrary number we just made up and make it more accurate? The answers are probably “not at all” and “nothing.”

Go back and apply your model to the year previous. 10.7. JFresh? 9.9. You’re solely trying to reverse engineer a model with the lowest error rate by ignoring as many inputs as possible, while he has a model with a laundry list of inputs and simply hopes it’s among the least inaccurate.

The point of the whole thing is the inputs. It’s a reflection of how a team should perform based on all of the inputs you ignored or eliminated. It’s more about the “why” and less about the result. The closer the result, the more we learn about the accuracy of the why and how (not the reverse). Without any why or how there’s really nothing to learn and no point to having developed a model in the first place.

LOL. You're always so quick to suggest others aren't getting it, when it is you that is missing the point.

The 'point' of a model (as you suggested in the 2nd bolded comment) is to represent something, and the point of this model is to predict where teams will finish in the standings. jfresh has built a model with lots of inputs, but it does very little in actually predicting what it is attempting to predict.

The NHL standings are a fairly tight distribution, with about 2/3s of the teams finishing within about a 30 pt ban, each year. And the overall migration of individual teams, from year to year, is generally quite small (good teams remain good, bad teams remain bad, etc).

So a model that has an average error of roughly 10 points, is actually of very little value. And to demonstrate that to you, several people threw up EXTREMELY SIMPLE models, with only one or two inputs, and with no effort to add any actual analytical inputs to them, that were almost as accurate as jfresh's. In doing so, they clearly demonstrated that his significantly more complex model is a waste of time because it isn't getting results that are any better than the simple ones. That's the point, which obviously you completely missed to grasp.

But please, go on another rant, making an entirely different (and irrelevant) point, in an attempt to demonstrate that I and others have missed some point which you are trying to make - that always makes for fun reading!

[Enoch Root]

Originally Posted by Macindoc

I was trying to prove that an average error of 10.4 is not good, only slightly better than picking random numbers. All of those inputs in the JFresh model make it no better than some simple model that has nothing at all to do with the sport or the league. So there's something wrong with it. If the JFresh model is any good, it should be measurably better, regardless of how that result was reached. If you're inputting a whole bunch of information that you think is relevant but your results are no more accurate, then maybe what you think is relevant isn't, or isn't weighted properly. It's not the concept of the JFresh model that's the problem, it's the model's design.

Exactly. We all understand what models do, and what this one is trying to do. The point is that it is not moving the needle in the slightest.

[BigThief]

Originally Posted by Bonded

It’s pretty simple to me. Oettingers % of cap is a good guideline. The cap isn’t going to increase where 10+ makes sense for Wolf imo. Either way I doubt the Flames even engage in contract talks with him until part way through the season and see how he does. It’s more likely he stumbles than does anything pushes him into that 10-12 AAV realm.

The salary cap is 95mill this season and is projected to rise by 18 million by 2027-2028. 8.6% of a 113 million dollar cap is 9.7 million so pretty darn close to ten. If we're going to buy into a young talent let's do it with Wolf, he's proved people wrong every step of the way. And a Flames fan still using undersized to discredit him is nonsesne

[Enoch Root]

Originally Posted by PepsiFree

Then you failed. You didn’t even pick random numbers, you picked specific ones using a set formula where you already knew the result you were trying to accurately predict… and his was still measurably better.

Predicting 1312 events with a 88.6% average accuracy is pretty good or at least interesting.

Predicting 32 datas points you already actually know and only hitting 88.6% accuracy is actually kind of terrible. “I put it on easy mode for myself and knew the answers and was almost as accurate as the complex model that didn’t!” isn’t really a flex.

But as I said, I think you’re just missing the point here, which is fine, though I think my math teachers would have spit on me if I told them how you reach the result and whether you can do so consistently doesn’t matter, all that matters is getting pretty close if you already know the answer you’re trying to find lol.

Would your math teachers have given you a gold star if you created a model that wasn't any more predictive than a random guess?

[PepsiFree]

Originally Posted by Enoch Root

LOL. You're always so quick to suggest others aren't getting it, when it is you that is missing the point.

The 'point' of a model (as you suggested in the 2nd bolded comment) is to represent something, and the point of this model is to predict where teams will finish in the standings. jfresh has built a model with lots of inputs, but it does very little in actually predicting what it is attempting to predict.

The NHL standings are a fairly tight distribution, with about 2/3s of the teams finishing within about a 30 pt ban, each year. And the overall migration of individual teams, from year to year, is generally quite small (good teams remain good, bad teams remain bad, etc).

So a model that has an average error of roughly 10 points, is actually of very little value. And to demonstrate that to you, several people threw up EXTREMELY SIMPLE models, with only one or two inputs, and with no effort to add any actual analytical inputs to them, that were almost as accurate as jfresh's. In doing so, they clearly demonstrated that his significantly more complex model is a waste of time because it isn't getting results that are any better than the simple ones. That's the point, which obviously you completely missed to grasp.

But please, go on another rant, making an entirely different (and irrelevant) point, in an attempt to demonstrate that I and others have missed some point which you are trying to make - that always makes for fun reading!

Glad you're having fun, me too! But you seem mostly like you're doing your angry Enoch bit. This is a weird thing to continue arguing if you don't value it and don't care. I think it's cool and interesting, you don't. Good for you!

The JFresh model does exactly what it intends to do: predict the NHL standings for fun. Last year it was the least inaccurate (compared to betting odds, fan duel, the athletic, evolving hockey, moneypuck, or average fan submissions). On a rolling 5 year average, most of the actual models are similarly accurate, but much more accurate than hockeyviz or fan submissions.

The rolling 5-year average of Macindoc's model, for example, is 13.8. That's actually worse than the average fan submission, meaning a fan could predict the season outcome more accurately on average than Macindoc's model could. That would be an example of a useless/valueless model from an output standpoint. Your error of 11.7 or whatever you said it was would be about as accurate as an average fan submission last year, so again, useless. And because neither of your models have inputs of any value, there's nothing to learn, test, or apply, so they're completely pointless. My model, where I just took Macindoc's and made it more accurate (than any other model) for the one year, has a 5-year average of 12.5. Again, terrible and useless.

Take your suggestion that good teams remain good, bad teams remain bad. Let's do a model where we just predict that teams will all be the same in 2024/25 as 2023/24. Error rate? 12.3. Not so easy, eh?

Where you're also struggling is that you can't square the difference between a model that is a by-product and a model that is the main product. These analytical models exist as a by-product of those analytics. They aren't tracking and measuring all the analytics with the purpose of predicting the season standings, it's just something that can be produced using all of those inputs. It has value, in terms of learning, entertainment, etc. If you could create a model that was near 100% accurate in predicting results, you'd probably make a lot of money. But you can't, so it seems silly to get upset at the most accurate models that exist because they aren't achieving something you can't achieve yourself and no one has been able to achieve. If you think it's a "waste of time" then stop wasting your time on it. Seems simple, no?

Everyone (almost, I guess) understands that hockey is a game with an incredible number of variables and advanced analytics are descriptive, not predictive, but can identify trends. This means that these models, while predictions, are relying on what has already happened and how players are trending. Trying to account for as many of these variables as possible

Betting odds for a game, for example, will favour one team over another for a whole whack of reasons. That doesn't mean it's a guarantee that the favoured team will win, it simply means that the team should win, based on all the inputs. Saying it's useless or has no value ignores the fact that the team that should win most often does exactly because of those inputs. It's also why we all understand when certain things are "unsustainable" (win streaks, shooting percentage, shutout streaks, etc.). Teams and players can only defy the odds for so long.

Originally Posted by Enoch Root

Exactly. We all understand what models do, and what this one is trying to do. The point is that it is not moving the needle in the slightest.

Some of us do. Some of you don't.

Originally Posted by Enoch Root

Would your math teachers have given you a gold star if you created a model that wasn't any more predictive than a random guess?

They didn't give out gold stars in my high school. Maybe we went to different types, but I doubt you'd get a gold star at all if you didn't even understand what "random" means.

You're not the first to mention it either. If someone was interested in creating a truly randomized model and then testing its accuracy, that'd be fun, too. But choosing specific inputs and formulas is not "random." And as I have demonstrated, these models are, in fact, more accurate than your guesses.

[mile]

https://twitter.com/user/status/1963267231799763299

https://twitter.com/user/status/1963275014611878205

[stemit14]

Doubt it is this year but, at some point, I’d like to see the transition to younger players being the “first over the boards” for key situations. This season, it’s still likely to be players like Kadri, Huberdeau, Andersson, Weegar, Coleman and Backlund that are often the preferred players for powerplays, penalty kills and overtime. I’d like to see the transition to Zary, Coronato, Parekh, Frost, Posposil and Klapka (or other potential young players that step up this year). I know we are in the early stages of seeing some of this but I’d like it to keep progressing.

Overtime especially seemed like Huska deferred to the vets much more than the young players - especially as the season went along. I’d love to start seeing Zary-Coronato-Parekh as the first players on the ice for overtime at some point this season.