Hang tight while we fetch the video data and transcripts. This only takes a moment.
Connecting to YouTube player…
Fetching transcript data…
We’ll display the transcript, summary, and all view options as soon as everything loads.
Next steps
Loading transcript tools…
Intro to Hockey Analytics 2 | The StatStrat Analytics Module | Part 2 | StatStrat | YouTubeToText
YouTube Transcript: Intro to Hockey Analytics 2 | The StatStrat Analytics Module | Part 2
Skip watching entire videos - get the full transcript, search for keywords, and copy with one click.
Share:
Video Transcript
Video Summary
Summary
Core Theme
This content introduces hockey analytics by using a car analogy to explain its components and then distinguishes between raw statistics and deeper analytical insights, emphasizing the importance of context and open-mindedness in interpretation.
Mind Map
Click to expand
Click to explore the full interactive mind map • Zoom, pan, and navigate
hello and welcome back to the intro to
analytics module my name is Nick lost I
am a former Brock sport management
student and I'm happy to be here today
to teach you about hockey analytics
today we're going to be covering topics
such as what analytics means to me to
understand hockey analytics at an elementary
elementary
level to begin we're going to look at a car
car
analogy so if we take a car and we
assume a car is a hockey team when
evaluate analytics we can say that the
players might be the engine that makes
up the car
as the team is the
car the reason the car functions at all
is because of this engine this is the
players and the goalie you can make it
the gas absolutely essential for the car
to run so essential that you might use
emergency backup gas once in a
while let's say the coaches are the
drivers that direct the car the best
they can but without a good engine
there's only so much that you can do
without good players there's only so
much a coach can do conversely with a
bad or uninformed driver the car risks
failure whether that be accident or
parts getting
broken the scouts are the windshield
they look at what's in front of them
they can witness other cars they are
well versed on other cars and the roads
that you travel the coaches know also
what other cars look like but they're
usually more focused on driving their
own car rather than analyzing other
cars analytics are Apple carplay the new
trendy thing you have in your car
navigation available to Modern
environments uh but always optional for
the driver the driver can use his
windshield and make his way safely on
the roads but why wouldn't you want
Apple carplay to not only enhance your
driving experience but also give you
information on other cars for the
present including accidents on the
highway efficient Lanes to take Etc and
the future including travel times so
analytics I find to be apple carplay in
the sense of this car
analogy now we're going to distinguish
statistics and analytics these are terms
that get thrown around all the time and
I'm here today to to tell you what I can
consider statistics versus what I
believe to be
analytics let's look at the keys to note
so statistics are data that comes from
current performances past actions
predictions and
more such as Asel andell and Yanni Haka
were minus two last night they were on
for zero goals for and two goals against
a conclusion that you might be able to
take from that is we should try
different de
parings analytics on the other hand is
conclusions that we can make Based on
data considering context samples and
other relevant
variables such as in 150 minutes
together Lindell hacken paa have
conceded 2.8 goals per 60 minutes while
Thomas Harley playing with hakena has
only conceded 2.1 goals per 60 in 120
Minutes together conclusion could be we
should try different de pairings you can
get the same
conclusions and assumptions from base
stats as you can from Advanced analytics
the fan bias that we have from
unintentionally bringing out score
sheets and looking at NHL base stats
will be the same bias that plagues us in
our studies where we can prove what we
want to prove such as the Dallas Stars
need new deerings it'll prove what we
want to prove no matter what if we have
this bias and this lens that we're
looking straight on just base stats and
just what we want to see so regardless
of what we're looking at we must be
open-minded and consider all angles and
viewpoints to avoid misreading small
samples and other uh deficiencies in our
stats furthermore related to statistics
they're what is available to us at face
value which is why people trust them so
much analytics on the other hand are
what we make of these stats which is why
people think that people Tinker with
them people doubt them and people think
that they just make formulas to make
things up where it's not real but
analytics can still be performance-based
uh we've just decided in this case to
use apple carplay in the sense so we can
make the same conclusions from base
stats as we can from
Analytics another area of hockey stats
analytics Etc is tactics tactics are
part of the sort of group of ideas that
come to hockey from other
sports hockey tactics are offensive or
defensive strategies with the purpose of
outclassing opponents to win games that
is what I consider tactics in hockey
that is choosing to do certain things
certain ways to win games as you can see
on the left there that pictures Total
Football Total Football is the idea of
where everybody plays sort of every
position everyone's moving around
there's a fluidity to the sport this is
more common in soccer where you can sort
of strategize your whole way to the game
it's coming to hockey in modern
times hockey tactics are also arguably
coming more creative as time goes on if
you think of power plays you think of
how things are set up in the ozone
versus the dzone more and more hockey
teams are using what they call tactics
or strategies to change the way they
play and be consistent with the way they
play so that they can outclass opponents
to win games
I call it a second coming of soccer as I
said this has previously been present in
soccer for many many years and hockey
sort of just starting to sort of
formalize the idea of tactics in hockey
as well as analytics and hockey is kind
of a second coming of baseball so hockey
sort of taking from other sports where
people are deciding let's you know let's
have this formalized idea of our hockey
breakouts or our hockey Breakin uh let's
have our inzone sequencing be somewhat
of a system that's what when they say
play a system they mean actually follow
these tactics that the coaches have
aligned for you there there have always
been instructions from coaches and there
have always been strategies from players
but more and more in the modern game
they are called tactics and they are
sort of refined into these super uh optimal
optimal
strategies and the final topic I like to
cover for the intro analytics module is situational
situational
dependency now to me situational
dependency is the the fact that every
hockey player is in unique situation no
matter if they're in the same situation
as other players you are not the same as
your teammates you're not the same as
your opponents you are a unique hockey
player with a unique scenario a unique
background and more so I like to look at
Wyatt Johnson for example this is his
player card and I've been following him
closely since the winds or Spitfires in
the OHL where he played so he played in
the OHL the Ontario league with Windsor
and then he got drafted by the Dallas
Stars of the NHL and as you can see here
this is a a lot to look at this is an
overwhelming graphic this is from the
athletic uh using data from two sources
evolving hockey and cat friendly you can
see in the bottom right of that card but
to summarize this card this is
predictions combined with present stats
Wyatt Johnson's black text the left side
the current that is his pace for the
whole season based on what he's doing
right now for goals assists points X
goals is expected goals So based on
every shot he's taken how much they're
valued differently so that could be more
or less than the singular goals that
he's scoring and then the on I goals
expected goals and power play impact so
as you can see he's his forecast net
rating is negative one while his current
net rating is negative one he's on Pace
to meet exactly what the full season
projections were at the beginning of the
season but this is troubling to me
because we look at his on Ice the bottom
half of the graphic and you see that he
is in the 10th percentile for offense
and the 25th percentile for expected
goals as well as the
36% tile for goals on
defense now this is troubling to me
because of his situation who are his
linemates who are his teammates why is
he not succeeding on a team like the
Dallas Stars who are super competitive
and near the top of the League this is
something that coaches and managers
should be looking at to consider unique
Transitions and changes if you're
looking at a guy like Wyatt Johnston who
is should be a top prospect as a young
player he's only 20 years of age but
he's putting up great numbers in the top
league in the entire world if he's
performing at at negative value you
should be looking at this and making
changes you should see oh wow on the ice
he is in the 10th percentile in the
National Hockey League all of those bars
are including all NHL players including
forwards being that there are 90% of
other forwards are better than him in
that trait so I I'm surprised to see
that he's doing so poorly first of all
but at the same same time situational
dependency is a key to analytics if
you're seeing a guy do poorly it might
not always be just his fault so that's
another key to look at when you're
looking at analytics in hockey and their
value maybe we wouldn't have known this
if we just watched the games maybe you
could see that he's playing poorly maybe
you can see he's playing fine maybe it's
sort of hidden behind the curtain that
he's actually performing pretty terribly
compared to his peers so if you want
changes you can use analytics to inform
yourself on
decisions thank you for watching and
Click on any text or timestamp to jump to that moment in the video
Share:
Most transcripts ready in under 5 seconds
One-Click Copy125+ LanguagesSearch ContentJump to Timestamps
Paste YouTube URL
Enter any YouTube video link to get the full transcript
Transcript Extraction Form
Most transcripts ready in under 5 seconds
Get Our Chrome Extension
Get transcripts instantly without leaving YouTube. Install our Chrome extension for one-click access to any video's transcript directly on the watch page.
