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
คลิกเพื่อขยาย
คลิกเพื่อสำรวจ Mind Map แบบอินเตอร์แอคทีฟฉบับเต็ม
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
