#hanic

[beast-feast]

Good morning round 2 is done :)

[page-2-ids]

Buncha Purple Pangenders

This was supposed to be part of a big dump I made with some of the other dump-posts that are coming out, but I Realized it was gonna take too long to make so I’m just splitting these up.

Panbluevivolic: A gender related to all shades of blue violet Pancoolpurpalic: Being all genders related to any cooler toned shade of purple, including indigos and cooler violets Pandarpuric: Being all -darpuric suffixed genders Pandarviolic: Being all -darviolic suffixed genders Panhanic: Being all -hanic suffixed genders Panhannic: Being all -hannic suffixed genders Panheatheric: Being all -heatheric suffixed genders Panindigon: A gender related to all shades of indigo Panlipuric: Being all -lipuric suffixed genders Panlipurplaic: A gender related to all shades of light purple Panliviolic: Being all -liviolic suffixed genders Panpalepurplaic: A gender related to all shades of pale purple Panpaspuric: Being all -paspuric suffixed genders Panpaspurplic: A gender related to all shades of pastel purple Panpurplaic: Being all genders related to purple of any kind (not just ‘basic’ purple), including indigos (since half-blue, half-purple) and violets Panpuric: Being all -puric suffixed genders Panpurpuric: A gender related to all shades of purple, including indigos and violets Panredviolic: Being all -redviolic suffixed genders Panredvivolic: A gender related to all shades of red violet Panvioletic: Being all genders related to any shade of the color violet Panviolic: Being all -violic suffixed genders Panvivolic: A gender related to all shades of violet Panwarmpurplaic: Being all genders related to any warmer toned shade of purple, including warmer violets

[prenupsremade]

happy ending? i wish

[beast-feast]

Who let the 11 foot hog watch some kids

[beast-feast]

Bigger and more wild hell yeah

[beast-feast]

Not sure if I've posted this but to make sure :)

[maybeitstimetoearnmybluebead]

Hockey Analytics Stuff

So there’s a group of hockey analysts/journalist on twitter who have put together a series of panels/presentations on a weekly basis regarding Hockey Analytics (like, how to get into it, coding, etc), AKA Hockey Analytics Night in Canada (HANIC) since it’s at 7pm on Saturdays which is normally when Hockey Night in Canada airs... After 2 out of 4 weeks I have to say that it’s been fantastic so far. I missed the first week (which actually got interrupted by Zoom hackers), but a fair amount of it is on youtube so I was able to catch up. I think the second week was recorded and should also be posted? But if not the main speaker posted links to his ppt etc on his twitter.

Anyways, if you’re fairly tech inclined and are a fan of numbers, 10/10 recommend- just look on twitter for #HANIC to find the links to register for the Zoom meetings and for the posts with links to stuff from previous weeks.

On the personal side, I’ve been learning data science with python via DataCamp for work the last few weeks (working from home when my part of the plant is shut down=very little regular work to do so lots of “find a relevant training that the company already subscribes to”) but it’s a skill that will be hit-or-miss if I actually end up using it regularly in my current role. I also know from experience learning select R and SAS code for graduate school that it’s a skill that will be “use it or lose it” for me...

HANIC has motivated me to use the skills in the perfect outlet to keep from getting rusty- hockey analytics. I LOVE hockey and have had an interest in analytics for a while, even more so since I took my first graduate statistics course in 2017. I just didn’t know how/where to start... now I do. 

I downloaded open source programming applications onto my personal computer (Anaconda Navagator, which includes applications for both Python and R, the same program that is on my work computer), and got my first round of basic data from the NHL website, and did my first bit of “not being walked step-by-step through a training excersize” programming and... I was able to import a file containgng 999 players’ cumulative stats for the 2016-17, 2017-18, and 2018-19, was able to filter the data for just players with more than a certain number of goals, create a histogram of that filtered data, and was able to further filter the data to ID the 2 leading goal scorers (cumulative over those three seasons). I was also able to calculate a new stat, Goals per Game (G/PG) including rounding how many digits it when to).

(ignore the first column, that’s just what line in the dataframe that player is)

I still have to work on putting labels and a title on the histogram, but... it’s a start!

It feels really good!

[smellofrose]

COME ON! KISS! I know you two want it.