Nykonax

ANGLED PARKING BEST PARKING!

i cant even park straight normally you think i can do that shit backwards?

I may have mentioned it in a post or two some indiscernible time ago, but I don't think I've ever really described my initial "introduction" to sim leagues. I put "introduction" in quotes because it isn't exactly a formal sim league kind of environment- it's moreso something of my own creation that I've been doing since I was a kid.
 
The first hockey video game I ever got into was NHL 2k10/2k11 for the Nintendo Wii, which was the "major" console I had at the time. I was still a few years away from getting into the world of Playstation/XBox, so these were the only hockey sim games I really had access to-- at least out of the titles that could be considered "current" for their time. Naturally, one of the game's features caught my eye nearly immediately- the "Create a Team" section, which has obviously been a standard for sports games as far back as I can remember. After fiddling around with some uniforms, logos, and cloning a bunch of free agents onto my team, I'd created the Las Vegas Yetis- the first of what would end up being ten different created franchises, all full of unique players.
 
...Well, sort of. I definitely couldn't be bothered to create custom players for all ten rosters- that would be HUNDREDS of players to sit down and manually menu through. No, young me instead decided to clone existing NHL rosters and simply change the names of everyone on the team to fill out most of the other squads. With a few created names sprinkled in for good measure, we were off to the races on our own little sim league adventure.
 
This is the best place to shout out some of the most loved players in Yetis history. Salvador Joensuu, the first created player I ever threw together. Zachary Ternavsky, our netminder turned defenseman that held down the ship on path to multiple cup victories. Percy Dubinsky, longtime shutdown defenseman and irreplaceable rock of the team. And lastly, the first line pairing of Trevor Baier and Dwayne Torquato-- two elite goalscorers in their own right, lighting the lamp in McDrai-like fashion. Then again, I don't know if they ever outscored some of those Falcons rosters...
 
I've been saying "we" as a few other people would join me on the endeavor, including my brother and a few friends from school at the time. We would each "GM" our team and make trades, edit lines, and play with strategies to see what ended up working. As it turns out, NHL 2k11 is actually a fairly decent sim game-- some teams were consistently good, some consistently bad, and certain players would rise to the top and reliably keep their name on the scorecard. And best of all, we could watch every game live as it happened, since sims required us to play CPU vs CPU matches in the "quick play" section of the game.
 
Seasons would pass, and before long it was easy to see narratives naturally forming from season to season. Each offseason would host a draft class (which I would use "create a player" for, it was much easier to sprinkle in one to two players per team every now and then), and new draftees would spark life into struggling teams. Some went on to win multiple cups, while others would get close every season only to fizzle out at the end. Hell- the team based in my grandfather's home town went on to win back to back ups right after he passed away. Some players were incredibly dominant, some would develop reputations for racking up penalty minutes and fighting. Some would always score a goal at the most opportune time. Even though I technically had a dog in the race, I found myself rooting for every team at some point or another since I had to follow every team in order to sim.
 
All of that setup, of course, transitions into why I initially took interest in the VHL. For the longest time, I was only enjoying the QCHL within a small circle of friends. Running fake sports leagues on video games felt like such a niche in the middle school days, and I wasn't internet savvy enough at the time to stumble into message boards. An older me, however, was elated to find entire communities of people who shared that exact same passion, but this time at an even lower level. Instead of taking on a whole team, you represented one player that strove to take home glory. And how do you make that player better? By exercising creative freedom to help build the world of the league you're taking part in. Seeing so many years of history etched into the Portal's HoF page immediately reminded me of what I'd done with the QCHL, and I was immediately inclined to join and begin my journey here.
 
Many years have passed since we started the QCHL, and we've always come back to it whenever free time allows. Every time, we improve a little bit of the technology behind it- from statkeeping to streaming, and even defining more proper rules for player signings and trades. Our own personal growth can be reflected in the QCHL itself, and I think that's a really wonderful thing to have so close to me. I see so much of that same growth and love put into the VHL, and it really resonates with me. This all started as a group of some nerds on a forum, and now you have so many different yet talented people coming together to contribute toward one big idea just for the fun of it. It really is fantastic.
 
So...what spurred this honestly rambly discussion about an old passion of mine? Well, I recently experienced my first round of company layoffs, so for the past month I've been doing...very little. After the high of the Vegas cup win (which I was in the building for!) finally wore off, the reality of unemployment set in and I quickly spiraled into a sleepless mess. With nothing major to put my time towards, I've started looking back into the QCHL as a creative outlet- this time taking additional steps to see just how far I can push the league. Emulators exist now, making it incredibly easy to save data and keep the game on hand at all times. Some people have even created tools to make roster editing a bit easier- done through the use of CSV files instead of having to manually sift through the game's menu system. It's buggy, but it's also been a fun exercise in trying to interact with a game's code directly. What combinations of actions lead to bugs? How fragile was this last change? It's an amusing game of trial-and-error.
 
As a professional developer, my plan is to also launch a "portal" or website for the league as a means of centralizing data. Instead of keeping numerous spreadsheets on hand, I'd love to have player and team stats easy to access at a moment's notice. On top of that, site functionality could be expanded to allow users or "GMs" to manage and interact with their team, checking box scores and editing lines to be sent in before sims. With the existence of this CSV editing tool, I can even make player updating a breeze. Of course, these are all super long term, non-committal goals that I'd like to work on over time. After all, I'm also learning how to....
 
...Edit the game's textures directly. This has been the absolute most fun I've had while revisiting the game. Apparently, Dolphin makes it BRAINDEAD easy to dump and edit textures, so I've taken it upon myself to see just how much rebranding I can get away with. Gone are the days of limited jersey styles and logos, we can now do literally whatever we want when it comes to the branding of our teams. This has always, ALWAYS been a passion of mine, as evident by some of my old media spot/graphic ventures (the jersey initiative, just to name one). Behold, the completion of my own rebranded team-- the Galveston Terrapins.
 


 
With some really appealing jersey references that I found online, as well as a pair of logos that I was able to get made on Fiverr, I managed to figure out the game's uniform textures in order to completely overhaul my old Las Vegas Yetis. I'll always love that franchise, but since professional hockey is now exploding in Las Vegas, I feel that now is the right time to bow out and accept that we've "done our job." Plus...I just really, really like turtles. Gotta have a turtle team.
 
So yeah, since I've been working on this in my off time, I've been reminded of the VHL and why I was excited to hop on board in the first place. I've really appreciated the welcome back, I wasn't sure at all how it'd go over. A lot has changed since I was last here, both on this site and in my own life, but I'm happy to be back and interacting with the internet nerds who share my niche passion for fake hockey.
 
 
I blinked and filled 1,536 words worth of content before I even knew it, I guess I have an easy claim for the next few weeks...

wtf reno and velv and mcwolf
 
the vhl cant handle thsi

wtf reno and velv

this cant be real

Player Information
Username: McWolf
Player Name: Sunset Moth
Recruited From: Returning
Age: 20
Position: D
Height: 70 in.
Weight: 160 lbs.
Birthplace: Isle of Man

Player Page
@VHLM GM

wtf reno and velv

this cant be real

goat

Player Information
Username: Velevra
Player Name: Joshua Schwarzer
Recruited From: Returning
Age: 24
Position: LW
Height: 72 in.
Weight: 200 lbs.
Birthplace: Canada

Player Page
@VHLM GM

Hi All,

I have nothing better to do so I'm back with another statistical analyses. I have come a long way since my last one using basic bitch Z-scores, taking 2 more whole statistic classes in University since then! So I figured what better to do than to procrastinate real work and reuse code from my most recently submitted university assignment and apply it to the VHL. This time we are using decision trees!

Decision trees can be thought of as statistical flowcharts, and are usually used for classification problems (example: here is some information about a mushroom, based on all this previous information about mushrooms, is this one going to be poisonous?), but it can also be used for regression predictions. Essentially they look at a bunch of previous data and it's outcome, and then decides the most important parameters and uses those in a flowchart to determine the expected outcome.

In our case, I have scraped the last 6 seasons of VHL Hybrid player attributes (STHS ones from Portal) and the stats from that season, and will be using it to build essentially a flowchart to predict how many points players will get.

Anyways, here's the flowchart. Sorry darkmode users



This can be interpreted like a flowchart. Look at the question, if the answer is yes, go left, if the answer is no, go right. The top number in the circle is the expected number of points, the bottom number is the % of people in the category. For example, if you have a PH > 82, ST < 80, and SK > 80, you would expect to score 67 points (right, left, right). It's interesting to see what variables the model sees as important cutoff points, DF and SC don't appear at all in here, whereas its only PH/SK/ST. My theory on this is that these attributes aren't really tied to anything and have their own ratios, allowing you to increase them much higher, making your STHS attributes higher relative to your TPE compared to someone upgrading DF and SC.

Now is this accurate? Eh, kind of, depends how much of a margin of error you're willing to accept.


Here's a histogram of the differences of the actual points from the prediction. Each bar represents 5 points. So for example, about 40 predictions were 5 under, while >60 were 5 over. If you want exact numbers, 22% of predictions were within 5, 43% within 10, and 74% within 20. Which honestly I think isn't bad, considering the variability of STHS. Especially at the higher ends where you're predicted a max of 86 points but still end up in the 100's or 120's.

We can run the same analyses on goals and assists:
Goals:


27% of predictions within 3, 43% within 5, 72% within 10

Assists:

22% within 3, 36% within 5, 59% within 10

Conclusion: I can make a pretty bad prediction of how many points you'll score. But if you're willing to accept a margin of error of 40 points, I have a 98.5% chance of predicting how many points you'll get successfully. For real though, I think it's interesting to see what variables the tree chooses as important, as traditional attributes aren't really present, as it puts more emphasis on PH, SK, and ST. My theory on this is that these attributes can be upgraded with good ratios compared to SC and DF, which means if you're spending your TPE on these while someone is spending there's on SC and DF, you'll have a higher PH/SK compared to their DF/SC, which means you have more effective STHS attributes for your TPE. I think this can give some interesting insight to build paths and advice, as it may be beneficial to get as much STHS attributes as possible, even if they aren't the optimal ones, as you'll just end up statchecking other players.

I'd like to follow this up with VHLM analysis, but not sure how reliable that will be considering the more changing nature of player attributes during the season. Also if anyone is wondering why I didn't just use regression, it's cause these are cooler. (And I can't write as much on regression). But here's a regression model anyways


Adjusted R-squared = 0.5369

Also if anyone wants the dataset let me know and I can send you it

1. I'd like to get a few points
2. Our boy Aky
3. Hopefully 1st, but as long as we're in playoffs I'm happy
4. Mexico, I GM'd them back in the 60's so would like to beat them
5) I'd like to win a championship. I've lost in the finals every M season I've been in I think
6) The Canucks are probably going to suck, as long as we don't make any dumbass UFA signings I dont care about their performance

1. I'd like to get a few points
2. Our boy Aky
3. Hopefully 1st, but as long as we're in playoffs I'm happy
4. Mexico, I GM'd them back in the 60's so would like to beat them
5) I'd like to win a championship. I've lost in the finals every M season I've been in I think
6) The Canucks are probably going to suck, as long as we don't make any dumbass UFA signings I dont care about their performance

Hi All,

I have nothing better to do so I'm back with another statistical analyses. I have come a long way since my last one using basic bitch Z-scores, taking 2 more whole statistic classes in University since then! So I figured what better to do than to procrastinate real work and reuse code from my most recently submitted university assignment and apply it to the VHL. This time we are using decision trees!

Decision trees can be thought of as statistical flowcharts, and are usually used for classification problems (example: here is some information about a mushroom, based on all this previous information about mushrooms, is this one going to be poisonous?), but it can also be used for regression predictions. Essentially they look at a bunch of previous data and it's outcome, and then decides the most important parameters and uses those in a flowchart to determine the expected outcome.

In our case, I have scraped the last 6 seasons of VHL Hybrid player attributes (STHS ones from Portal) and the stats from that season, and will be using it to build essentially a flowchart to predict how many points players will get.

Anyways, here's the flowchart. Sorry darkmode users



This can be interpreted like a flowchart. Look at the question, if the answer is yes, go left, if the answer is no, go right. The top number in the circle is the expected number of points, the bottom number is the % of people in the category. For example, if you have a PH > 82, ST < 80, and SK > 80, you would expect to score 67 points (right, left, right). It's interesting to see what variables the model sees as important cutoff points, DF and SC don't appear at all in here, whereas its only PH/SK/ST. My theory on this is that these attributes aren't really tied to anything and have their own ratios, allowing you to increase them much higher, making your STHS attributes higher relative to your TPE compared to someone upgrading DF and SC.

Now is this accurate? Eh, kind of, depends how much of a margin of error you're willing to accept.


Here's a histogram of the differences of the actual points from the prediction. Each bar represents 5 points. So for example, about 40 predictions were 5 under, while >60 were 5 over. If you want exact numbers, 22% of predictions were within 5, 43% within 10, and 74% within 20. Which honestly I think isn't bad, considering the variability of STHS. Especially at the higher ends where you're predicted a max of 86 points but still end up in the 100's or 120's.

We can run the same analyses on goals and assists:
Goals:


27% of predictions within 3, 43% within 5, 72% within 10

Assists:

22% within 3, 36% within 5, 59% within 10

Conclusion: I can make a pretty bad prediction of how many points you'll score. But if you're willing to accept a margin of error of 40 points, I have a 98.5% chance of predicting how many points you'll get successfully. For real though, I think it's interesting to see what variables the tree chooses as important, as traditional attributes aren't really present, as it puts more emphasis on PH, SK, and ST. My theory on this is that these attributes can be upgraded with good ratios compared to SC and DF, which means if you're spending your TPE on these while someone is spending there's on SC and DF, you'll have a higher PH/SK compared to their DF/SC, which means you have more effective STHS attributes for your TPE. I think this can give some interesting insight to build paths and advice, as it may be beneficial to get as much STHS attributes as possible, even if they aren't the optimal ones, as you'll just end up statchecking other players.

I'd like to follow this up with VHLM analysis, but not sure how reliable that will be considering the more changing nature of player attributes during the season. Also if anyone is wondering why I didn't just use regression, it's cause these are cooler. (And I can't write as much on regression). But here's a regression model anyways


Adjusted R-squared = 0.5369

Also if anyone wants the dataset let me know and I can send you it

Hi All,

I have nothing better to do so I'm back with another statistical analyses. I have come a long way since my last one using basic bitch Z-scores, taking 2 more whole statistic classes in University since then! So I figured what better to do than to procrastinate real work and reuse code from my most recently submitted university assignment and apply it to the VHL. This time we are using decision trees!

Decision trees can be thought of as statistical flowcharts, and are usually used for classification problems (example: here is some information about a mushroom, based on all this previous information about mushrooms, is this one going to be poisonous?), but it can also be used for regression predictions. Essentially they look at a bunch of previous data and it's outcome, and then decides the most important parameters and uses those in a flowchart to determine the expected outcome.

In our case, I have scraped the last 6 seasons of VHL Hybrid player attributes (STHS ones from Portal) and the stats from that season, and will be using it to build essentially a flowchart to predict how many points players will get.

Anyways, here's the flowchart. Sorry darkmode users



This can be interpreted like a flowchart. Look at the question, if the answer is yes, go left, if the answer is no, go right. The top number in the circle is the expected number of points, the bottom number is the % of people in the category. For example, if you have a PH > 82, ST < 80, and SK > 80, you would expect to score 67 points (right, left, right). It's interesting to see what variables the model sees as important cutoff points, DF and SC don't appear at all in here, whereas its only PH/SK/ST. My theory on this is that these attributes aren't really tied to anything and have their own ratios, allowing you to increase them much higher, making your STHS attributes higher relative to your TPE compared to someone upgrading DF and SC.

Now is this accurate? Eh, kind of, depends how much of a margin of error you're willing to accept.


Here's a histogram of the differences of the actual points from the prediction. Each bar represents 5 points. So for example, about 40 predictions were 5 under, while >60 were 5 over. If you want exact numbers, 22% of predictions were within 5, 43% within 10, and 74% within 20. Which honestly I think isn't bad, considering the variability of STHS. Especially at the higher ends where you're predicted a max of 86 points but still end up in the 100's or 120's.

We can run the same analyses on goals and assists:
Goals:


27% of predictions within 3, 43% within 5, 72% within 10

Assists:

22% within 3, 36% within 5, 59% within 10

Conclusion: I can make a pretty bad prediction of how many points you'll score. But if you're willing to accept a margin of error of 40 points, I have a 98.5% chance of predicting how many points you'll get successfully. For real though, I think it's interesting to see what variables the tree chooses as important, as traditional attributes aren't really present, as it puts more emphasis on PH, SK, and ST. My theory on this is that these attributes can be upgraded with good ratios compared to SC and DF, which means if you're spending your TPE on these while someone is spending there's on SC and DF, you'll have a higher PH/SK compared to their DF/SC, which means you have more effective STHS attributes for your TPE. I think this can give some interesting insight to build paths and advice, as it may be beneficial to get as much STHS attributes as possible, even if they aren't the optimal ones, as you'll just end up statchecking other players.

I'd like to follow this up with VHLM analysis, but not sure how reliable that will be considering the more changing nature of player attributes during the season. Also if anyone is wondering why I didn't just use regression, it's cause these are cooler. (And I can't write as much on regression). But here's a regression model anyways


Adjusted R-squared = 0.5369

Also if anyone wants the dataset let me know and I can send you it

I only just realized after writing the title that this idea is pretty much just the lottery tournament, but during the season, eliminating the hassle from the sim team, and actually having meaning beyond minuscule odds changes.
I could’ve sworn I wrote about this before, but with the lottery tournament being killed and static odds being put in place I thought it would be a fun time to talk about my favourite draft odds method. First created in 2012, and then more accessibly summarized in 2020, let’s talk about the Gold Drafting method.
 
It has a few goals that guide its design. Those being:
 
Every game for every team in every season has meaning and excitement.
Particularly exciting at the end of the season.
End to tanking.
End to the lottery.
 
Instead of rewarding the team that tanks the hardest with the best picks, reward teams for winning even after being eliminated by giving the best picks to the teams with the most points after being eliminated. The worst teams will have the most time to accrue these “Gold” points, even if they do so at a worse rate. The best worst teams will have the least amount of time to accrue points, but will be frantically trying to win even after disappointing playoff elimination.
 
A particularly interesting tweak to the original idea is that teams can declare themselves eliminated starting at a certain point in the season (I would suggest the trade deadline), where regardless of their standing points at the end of the season, they can not make the playoffs, and begin accruing “Gold” points immediately. Of course one would have to cap the number of teams that can be eliminated, so that the playoff structure still exists, but it allows teams who went on a weird early run but don’t have the talent to win, or would not gain from an early playoff exit to tap out, and increase their pick odds.

I think this works nicely in a sim league format, allowing GM’s more control over their destiny, and making even the worst teams have a reason to compete every day. Every sim is exciting right up until the very end, and underdog teams can ride miraculous runs to a great pick, an exciting prospect for those on the team long term.

Another important note is that picks accrue Gold Points based on the original owner, not the current holder. Therefore there is no incentive to screw with the results, assuming the original owner makes the playoffs, and if they don’t they are no more likely to want to lose than they would under a traditional lottery system.

Oh and here’s the link to the post that I’m referencing: https://hockeyviz.com/txt/gold

Curious what others think, the article is a pretty short read and pretty clear, so I’m curious how people would feel about it.
 

I had to make sure ours stayed nice and safe

Player Information
Username: Jaja Dingdong
Player Name: Jaja Dingdong Jr
Recruited From: Returning
Age: 16
Position: D
Height: 78 in.
Weight: 208 lbs.
Birthplace: Iceland

Player Page
@VHLM GM

THE MFING NYKONAX @Nykonax

Player Information
Username: Nykonax
Player Name: Eno Velvson
Recruited From: Returning
Age: 50
Position: C
Height: 75 in.
Weight: 220 lbs.
Birthplace: Canada

Player Page
@VHLM GM

Player Information
Username: Nykonax
Player Name: Eno Velvson
Recruited From: Returning
Age: 50
Position: C
Height: 75 in.
Weight: 220 lbs.
Birthplace: Canada

Player Page
@VHLM GM

Player Information
Username: Nykonax
Player Name: Eno Velvson
Recruited From: Returning
Age: 50
Position: C
Height: 75 in.
Weight: 220 lbs.
Birthplace: Canada

Player Page
@VHLM GM

Welcome back nyko! 

Player Information
Username: Nykonax
Player Name: Eno Velvson
Recruited From: Returning
Age: 50
Position: C
Height: 75 in.
Weight: 220 lbs.
Birthplace: Canada

Player Page
@VHLM GM

Player Information
Username: Nykonax
Player Name: Eno Velvson
Recruited From: Returning
Age: 50
Position: C
Height: 75 in.
Weight: 220 lbs.
Birthplace: Canada

Player Page
@VHLM GM

Player Information
Username: Nykonax
Player Name: Eno Velvson
Recruited From: Returning
Age: 50
Position: C
Height: 75 in.
Weight: 220 lbs.
Birthplace: Canada

Player Page
@VHLM GM