Philosophy of hockey sabermetrics: Can hockey accurately be measured?

Murky

Registered User
Jan 28, 2006
851
439
The premise of presenting dozens of people affecting the outcome of a hockey game or even 12 of the players on ice as numbers is mostly amusing.

A single human being is complicated enough. 12 of them? I don't usually do this but seriously - lol :)
 

LEAFANFORLIFE23

Registered User
Jun 17, 2010
45,601
14,463
No because you can't accurately measure injuries, etc.

That's why when people said ]" see analytics were right." When the Leafs missed the playoffs in Bernier first season I laughed because I knew that if he didn't go down we wouldn't have missed the playoffs because as much of a disgrace a Bernier is now, he wasn't when he first got here, there was a time when he was good.
 

oilerbear

Registered User
Jun 2, 2008
3,168
199
When I presented my 20+ base hockey theories largely in 05/06 & 06/07 they were built on Boolean Multivariable philosophy.
I rejected the binary linear academic world when I was 14.

My father one of the leading minds in space industry was a lead designer or the designer or lead comisioner:
Avro Arrow,
Apollo lunar lander and service module,
retrofit Hawk missle,
creation of Landsat/seasat sattelites and tracking program,
Motorola home satellite dish,
Exocet missle
Nuclear powered rocket (deep space travel ) design/testing Jackass flats Nevada,
Maglev Bullit train ( commissioning)
Canadaarm

All the great minds I was subjected too were multivariable thinkers.
They did not exclude any influences to mechanism of space travel.

The principle that lead me to reject the academic scientific and mathematic community.
Binary linear analysis cannot properly/ accurately analyze sports like Hockey.

My approach was traditional robotising of mechanism of play by looking at Sequence of Events (SOE) mapping all possible outcome s from mechanical action.
Looking for the best and worst for the GF-GA win mechanism.

I see a lot of my theories poorly presented years later by binary linear analysts on blogs and @ MIT Sloan sport institute.

A lot post 2010 4-8 years after I first presented them.
Being Boolean generated theories.
These individuals did not have the mental ability/philosophy to have been the creators of these theories.

While I have been on long term disability for bone marrow cancer.
I have tried to advise all outlets of the need to credit me for all these theories.
I have had a great 2 weeks.
The leading minds in linear binary human machine action theories have rejected there Corner stone theories.

They are now taking my Boolean multivariable aproach 40 years after I rejected linesr binary process.
10 years after I first developed hockey analytic theories in the only way possible.
Boolean multivariable.

The best in the academic world agree with me that Boolean Multivariable is the only way.

There is no magic single number.

To start with. I constantly had to correct sites likenCorsica ......
Cause they wanted to present in a offensive affect way. GF.

First thing identified.
Who wins?
Top,5 GA teams can win with a cup with bottom 25-30 GF
Top 5 GF teams need top 15 GA to win a cup.
Ga teams advance toon playoffs 20-40% more than GF teams.

All my theories were presented in the critical goal diff mechanism.
Mostly GA.

I presented my cup core roster theory 8 years ago.
The GA portion of cup core theory is
1. HD sys coach
2. Top 10 HD goalie
3. 3/4+ top 60 HD dmen.
4 elite players make you GA conf/cup finalist
5-25 years ago.
5 elite players make you GA conf/cup finalists
Last 4 years.

going back to 1996 Florida Panthers.
1. d. maclean
2. JVB ( origional Table Hockey Goalie movement (puck tracking phase 1))
3. 3+ HD dmen
Rhett Warrener
Ed jovanovski
Carkner

VGK was 100% copy of my Boolean multivariable Cup Core Theory.
All the theories you see
Home plate,
player situation usage baseline averages and performance ranges 3d matrix.
3 axis ( multi variables ) are needed.
Not Rob vollmans 2 axis (binary) inaccurate copy.
Require multivariable.

All you Academc Linear binary copiers of my work
Please stop your poor binary aproach to my work.
They need all the variables to be true.

Rickithebear/Oilerbear
Alberta Coal Power Plant conversion multivariable Data collector/ verifier and Scientific/Financial modeling analyst/advisor.
Originator of Boolean multivariable human sports action analytics.

My wife an award winning (sportspage) editor/writer for sun/ Postmedia the last 14 years allways referred to my 10 pm to 2-3 am Hockey analytics as my rainman/ Beautifulmind seccions.

My initial work was taking up to 1,000,000 pieces of data and SOE mapping of multivariable results and graphing in 3-10d matricies and arrays in my head. Beautiful mind portions.

Then converting them to paper, photographable white board, or Excel/Access data columns in the Ben Affleck “Accountant” Conference room scene sort of way.

Thier is no such thing as binary human action anslytics.
 
Last edited:

Doctor No

Registered User
Oct 26, 2005
9,250
3,971
hockeygoalies.org
A passage from a book that I wanted to preserve for the record here, because I think it's valuable.

Bill James. If you don't know James, he's the one who first really started promulgating the idea of seeking answers to things within sports data (baseball in his case). Suffice it so say - I would not be doing much of any of my professional items right now, the PhD in mathematics, the career as a health actuary, the goaltender history work - without Bill James leading me there from a very young age.

Anyhow, I was re-reading the 1982 Baseball Abstract. I found this passage insightful; I thought others seeking to do the sorts of things in this chapter might as well.

"In the same way that a monkey wrench or a hammer or a screwdriver or any other tool can be used to tear things apart or to render them inoperable, [sabermetric tools], if used carelessly, will do more harm than good, will lead to false conclusions. But they give you a chance. A monkey wrench is not a guarantee that you can fix anything; it is merely a way to get to the problem. Without it you have no chance. These tools enable you to take baseball games or teams apart.

No sabermetrician has ever discovered anything of interest by compiling large stacks of numbers and shaking them vigorously to see what happens to fall out. One thing that interviewers like to ask me sometimes is 'What is the most amazing thing that you have ever found in all these statistics?' I never know what to say. I don't 'find' things in that way. What would a mechanic say if you asked him what was the most amazing thing that he had ever seen in an engine? It seems safe to assume that, whatever it was, it didn't belong there. Anyway, there are sabermetricians who do begin with the numbers, certainly; there are people who 'analyze' the game of baseball by correlating anything with everything, who spend their time frantically rushing from one column to another looking for connections and running regression analyses till hell wouldn't have it. This is the sabermetric equivalent of kicking a television set. If it is done with intelligence, it becomes the equivalent of kicking the television set vigorously. If it is done with persistence, it becomes the equivalent of kicking the television set repeatedly. If your TV goes on the blink at a crucial moment, you may derive a certain amount of gratification from kicking it; if baseball mystifies you, you may derive some satisfaction out of correlating things willy-nilly, running regression analyses and making up more and more ways to rate the hitters. But it is not going to fix the television set. Unless you get unusually lucky."
 

Doctor No

Registered User
Oct 26, 2005
9,250
3,971
hockeygoalies.org
And this all reminds me of one of my favorite XKCD comics, about machine learning.

machine_learning.png
 

HugoSimon

Registered User
Jan 25, 2013
959
263
The premise of presenting dozens of people affecting the outcome of a hockey game or even 12 of the players on ice as numbers is mostly amusing.

A single human being is complicated enough. 12 of them? I don't usually do this but seriously - lol :)


Humans are complicated but a great simplification is that they are all driven towards the shared goal of putting pucks in net.

Unironically I'm really curious where personality and other psychometric elements are placed in this? If this isn't regularly mentioned I am dumbfounded.

Seems like the most basic starting point for these analytics are the personality profiles of each player.

Which are the extroverts that are more likely to get swept away with partying. Who are the selfish types that are gonna buck the team. Who are the mama's boy who'll fluff the bed when out of their home division.

People talk about humans being so unpredictable, but can we figure out by simply personality profiles who will be perpetually streaky and who will be solid and unmoving?

While it seems like a monumental task you can figure it out relatively quickly.

Almost all hockey players are gonna be more industrious than your average person, they're gonna be less neurotic, less agreeable, and somewhat smarter.

But when you get into the nitty gritty of those personality quirks it's obvious certain things will become apparent. I wouldn't be shocked if virtually all play makers are very intelligent and rate very high in openness.
 
Last edited:

Leksand

Registered User
Oct 30, 2013
707
358
Northern VA
A passage from a book that I wanted to preserve for the record here, because I think it's valuable.

Bill James. If you don't know James, he's the one who first really started promulgating the idea of seeking answers to things within sports data (baseball in his case). Suffice it so say - I would not be doing much of any of my professional items right now, the PhD in mathematics, the career as a health actuary, the goaltender history work - without Bill James leading me there from a very young age.

Anyhow, I was re-reading the 1982 Baseball Abstract. I found this passage insightful; I thought others seeking to do the sorts of things in this chapter might as well.

"In the same way that a monkey wrench or a hammer or a screwdriver or any other tool can be used to tear things apart or to render them inoperable, [sabermetric tools], if used carelessly, will do more harm than good, will lead to false conclusions. But they give you a chance. A monkey wrench is not a guarantee that you can fix anything; it is merely a way to get to the problem. Without it you have no chance. These tools enable you to take baseball games or teams apart.

No sabermetrician has ever discovered anything of interest by compiling large stacks of numbers and shaking them vigorously to see what happens to fall out. One thing that interviewers like to ask me sometimes is 'What is the most amazing thing that you have ever found in all these statistics?' I never know what to say. I don't 'find' things in that way. What would a mechanic say if you asked him what was the most amazing thing that he had ever seen in an engine? It seems safe to assume that, whatever it was, it didn't belong there. Anyway, there are sabermetricians who do begin with the numbers, certainly; there are people who 'analyze' the game of baseball by correlating anything with everything, who spend their time frantically rushing from one column to another looking for connections and running regression analyses till hell wouldn't have it. This is the sabermetric equivalent of kicking a television set. If it is done with intelligence, it becomes the equivalent of kicking the television set vigorously. If it is done with persistence, it becomes the equivalent of kicking the television set repeatedly. If your TV goes on the blink at a crucial moment, you may derive a certain amount of gratification from kicking it; if baseball mystifies you, you may derive some satisfaction out of correlating things willy-nilly, running regression analyses and making up more and more ways to rate the hitters. But it is not going to fix the television set. Unless you get unusually lucky."
I am not disagreeing but let's not throw out the baby with the bathwater - stylized facts are extremely useful. Eg shot attempts % ie CF/FF% correlated with winning has proven to be an important observation, though not be all end all. Admittedly coming from economics where I think stylized facts have been very very important, while for ice hockey analytics I am a true amateur.
 

morehockeystats

Unusual hockey stats
Dec 13, 2016
617
296
Columbus
morehockeystats.com
I am not disagreeing but let's not throw out the baby with the bathwater - stylized facts are extremely useful. Eg shot attempts % ie CF/FF% correlated with winning has proven to be an important observation, though not be all end all. Admittedly coming from economics where I think stylized facts have been very very important, while for ice hockey analytics I am a true amateur.
the biggest issue with the aforementioned correlation is whether we're having a byproduct, or a driver.
 
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WinUnlimited

Registered User
Mar 28, 2016
32
0
Chesterfield, MO
Good statistical models attempt to explain cause and effect relationships.
Great analytics cause effects.


Analytics shares responsibility in preserving and enforcing the quality and integrity of the game. We can create rules to change how the game is played, transforming hockey to align with demands of how intense we want the play. Hockey can not only be measured with analytics, but chaos can be introduced by data science, making the game more entertaining and unpredictable.
 

morehockeystats

Unusual hockey stats
Dec 13, 2016
617
296
Columbus
morehockeystats.com
During the OTTHAC 2019 conference I made a lightning talk introducing an observation from the domain of chess, where the analytics evolved from a disregard of mid-1850s, through rules, through adjustments, through expansions, through insights, through computer involvement to a degree where suddenly a disregard is just as popular as a deep analysis, and quite frequently more successful. Since then two years have passed, and I must admit the push away from the trodden paths, away from the analytical preparation has become even stronger.
 

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