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Intro to Hockey Analytics | The StatStrat Analytics Module | Part 1 | StatStrat | YouTubeToText
YouTube Transcript: Intro to Hockey Analytics | The StatStrat Analytics Module | Part 1
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Core Theme
Hockey analytics has seen significant growth in usage and reliance across professional and junior leagues, offering valuable tools for data-driven decision-making, proactivity, and a sense of certainty, though it's crucial to balance these insights with the inherent randomness of the sport.
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hello and welcome to the intro to hockey
analytics module my name is Nick lost I
am a former Brock University sport
management student and I'm happy to be
here today to teach you about hockey
analytics now the goals of this hockey
analytics module are the following to
understand the growth of statistical
usage and Reliance in hockey to
distinguish analytics statistics and
tactics in hockey to learn how to use
and study hockey analytics tools as well
as to evaluate hockey analytics
questions in a creative and modern
manner now the table of contents for
this section of the module is the growth
of analytics usage in hockey the growth
of analytics Reliance in hockey and the
benefits of datadriven hockey as well as
the drawbacks of overreliance on
analytics let's talk about the growth of
analytics uses in hockey now over the
years analytics has become prominent
within modern organizations as well as
modern hocke processes more and more
teams at the pro and Junior levels are
using analytics to inform their
decision- making we can see this as you
look at the 2019 list provided by Shaya
Goldman of the athletic uh on Twitter
she's been collecting a lists of people
that are said to work in all these
organizations in what you can call
analytics roles whether that be a
general manager with analytics
background or positions that are
specifically devoted to analytics work
as you can see here this is from 201
and as I move forward to 2022 you can
see the list has grown tremendously in
just 3 years as well as 2023 this past
year in just four years the list of
analytics personnel has more than
doubled in the across the National
Hockey League to what we know there are
obviously some roles that may not be
mentioned because this is all from
publicly available data and and
availability on Twitter based on hiring
and and news articles that we can find
but the analyst Community has grown both
publicly on Twitter at as well as
through Pro and developed
organizations moving on to the growth of
analytics Reliance in hockey this is
further to the growth of the usage while
more and more people are getting hired
more and more people are using analytics
to inform the decision- making and this
is feeding into our broadcasts to our
media and more as you can see by this
graphic here the slot shots per game
this is something that you would never
see 10 to 15 years ago in a hockey
broadcast but with the Partnerships with
companies like sport logic these are
available for fans and viewers to see
and understand during broadcast to
become more informed fans more educated
fans you would never have seen these
Graphics previously uh it might be
throughout Twitter and and online
spheres and sort of Niche communities
but nowadays it is become common place
for analytics and Stat and data driven
uh information to be available to the
public even thirdparty companies will
use hockey analytics in this sense such
as sport logic and and and be hired out
to provide these kind of stats to
broadcasts that's how important they
have become in recent
years now moving on to the benefits of
data driven hockey I have broken these
down into three main categories data
driven decision-making proactivity and a
sense of certainty so let's dive into
each of
these the first idea that relates to the
use of data driven uh information is
data driven decision- making now on the
screen here you can see two websites
these are publicly available websites
that you can look at for free the left
is money.com and the right is natural
statc I'll be posting tutorials for both
of these websites and how to best use
them as a fan later in this analytics
series uh but as you can see I have I
have made the money.com left picture
there about defensive pairs in the
National Hockey League and the right you
can see Matthew kachuck Sam Bennett and
Carter for heagy has sort of a line tool
about all their Advanced stats when
they're played together now datadriven
decision-making I I find to be the most
primary usage of analytics in the Pro
and Junior worlds specifically you're
looking at teams use and coaches using
data like line tools or data on specific
players to inform what they do if I'm
going to build a lineup do I want to
build the lineup using players that play
the best together based on the stats or
do I want to do it based on gut feelings
based on chemistry that I can see based
on friendships based on whatever data
driven decision- making has become more
and more prominent in the modern world
and this is part of of it building
lineups building rosters even that even
a management perspective furthermore you
might see players watching replays on
the bench they might be looking at their
information they might be replaying uh
past shifts players nowadays know more
and more about their analytics than ever
before some players may choose not to uh
understand or not choose to learn what
analytics are and that's totally fine
they can go about their way uh as as
well as they can uh but data driven and
data informed players uh seem to be the
ones that are taking the next step in
their game whether they know that
certain shots are working in certain
areas or certain Lanes up the ice are
becoming open against certain teams or
exploiting certain goal tenders for that
matter there are a lot of datadriven
decisions that are made within high
level NHL organizations as well as the
junior leagues that will help teams have
a competitive advantage in their
everyday hockey
games the second main benefit of data
driven hockey is proactivity I like to
categorize proactivity as preparing for
the future while being ready for the
present proactivity consists of using
data to inform us on what could be
expected out of players based on how
they're doing currently and what they
may be able to do Lucas Raymond plays
for the Detroit rings and as on the left
here you can see his uh his sort of
player card if you will this can this
shows his individual on ice and usage
metrics on the right hand side of it
where you see he's on Pace for 27 goals
and 42 assists this season uh and this
is basic from uh Advanced Tas websites
that will calculate the the paces that
you compared to everybody else in the
Inter National rocket league as you can
see by the star number on the left the
98 that's in yellow that means that he's
in the 98th percentile of assists
meaning that there are only 2% more in
the NHL that have more assists than him
which is quite
outstanding however we see that in the
projected gsva timeline on the left
there that is game score value added
it's one of those metrics that you can
use uh to add uh wins or value over time
and and compare year by year and predict
into the future uh this gsva timeline
shows a bit of a plateau going into 2025
2026 and 2027 this implies that Lucas
Raymond may have a high floor because
three wins 3.5 wins is really good for a
National Hockey League player consider
zero to be basically a replacement level
uh like a fourth ler that would be
scratched so 3 five is tremendous we
could assume someone like Conor McDavid
would be extremely high like near the
six maybe even perhaps uh but Lucas
Herman is in a high percentile in the
league because of his stuff on the right
you can see there those current
statistics are looking really good he's
in very high percentiles out of all
National Hockey League players however
it looks like going into his uh next
contract which will be up soon uh he'll
need to prove himself even further
because of this expected Plateau we
could call this a high floor but also a
low ceing type of player so analytics
can help us whether it be managers front
office or ownership in general to
understand what players could be before
we Dole out contracts did Leon Dr settle
for example uh would would anyone have
expected him to overcome $8.5 million
per year as a forward and get 100 Point
Seasons back to back to back uh Maybe
not maybe so but with the sense of
productivity maybe they were sure in the
O organization that he would do well so
they signed him at a certain point in
this contract to a longterm versus a
short term all these decisions can be
influenced by data but they don't have
to be uh so as a manager coach or a
owner when you're doing out money to
hockey players and you're spending
millions of dollars on your front office
uh it's important to understand the
value of data where it can be used to
benefit you and gain an advantage that
you otherwise would not
have the third benefit of datadriven
hockey is a sense of certainty and it's
not enti L the idea of certainty where
you would know the future and you
understand how players are going to pan
out per se but a sense of certainty
meaning you have using data you have the
idea of how a player could pan out uh
and this is a really important tool for
managers for front offices for
ownerships even for agents if you're
going to vouch for your own player if
you're able to prove that based on their
comparables to other players they're
going to pan out a really good way you
can get them a lot more money than you
wouldn't have before if you didn't use
use data so a way to use this would be
to use a software uh such as hockey
prospecting which is a payable Service
uh made by Byron Bader this is a
prominent guy on Twitter uh who you
might know he is very involved with anal
front offices in the past where he's run
some of their drafts and part of that is
using this hockey prospecting model
basically it takes the NHL point value
and it takes a percentage of that and
says how many OHL points would be one
NHL point for
example if as you can see there in the
second year of Andrew mapani on the left
you see he's got 18 in that OHL d0 which
is your draft year D1 is the year after
your draft otherwise known as draft plus
one draft plus two draft plus three you
can see there and the D minus one is the
one before your draft year uh so as you
can see Andrew mapani put 18 and then 38
and 43 this means that in the prime of
Andrew mapi's NHL career he's expected
to put up that many points compared to
that exact season in the NHL it takes
OHL points for example if puts up 100
OHL points that might put him at 43 but
it all depends on things like age
depends on a lot of other factors that
go into this model to make it as
accurate as it can be in a prediction
type of scenario as you can see on the
right I've chosen Yuri kuluk as a decent
uh comparable as you can see with the
trend that they're both going they're
almost identical in their same age years
their D minus1 their d0 and their d+1
and their D plus2 are almost identical
in points however you can see Yuri Kulik
is already in the AHL now the AHL and
the OHL being different leagues but the
same points still kind of means the same
thing where they're both on Pace to have
in their Prime in the NHL a 45ish point
uh total regardless of what league
they're playing in in that year but this
is interesting to look at compared to
what other prospects are in the model if
you have a young up and cominging
Prospect in your organization and you're
not sure how to value him you can look
at something like this and say wow okay
compared to other players uh in their
age times when they were 18 when they
were 17 they're they did this well and
he's doing the same as they were or even
better that means he's going to be
better than them possibly it's not
exactly certain but it's a sense of
certainty analytics are not inherently
wrong no one can predict the exact
future like all sports hockey will
always be a game where legally every
game of hockey is random and there's an
opportunity for both teams to win the
game uh scandals and everything
aside former New Jersey Devil's Head of
analytics Sunny MAA said a quote that
really struck out to me it says never
underestimate the randomness of hockey
it's not impossible that we exceed
expectations it's not impossible that
players play better than what the stats
are expecting them to do it's also not
impossible that they play worse for that
matter the game of hockey is a game it's
a sport we love to watch it because
we're not sure what's going to happen so
that's a really key Point here is that
analytics can inform us on what may
happen and what could happen based on
informed data but it's not going to
predict the future
exactly now compared to other sports
like baseball as you can see in the
background there we've got Jose Brios
giving the ball uh to manager John
Schneider this was a pivotal moment in
the playoffs uh last season for the Blue
Jays where Brios was pulled early in the
game based on analytical decisions that
were made before the game had even
happened and assumptions were made that
this would be better for the bles to
pull him now and put somebody else in to
finish the job and do better
analytically of course this did not
happen and the BL just ended up losing
the series which is unfortunately sort
of plag the analytics Community being
that everyone sort of is hating on
analytics and saying that stats are not
the way to go you got to understand the
game moment and balance that with the
present moments so I I like to say that
hockey is more random than baseball
because of team play 1 V one in baseball
is super important it's a pitcher versus
a batter hockey in the sense is is very
fluid I call it the fluidity of hockey
is the reason why it's so special is
because it's 5v5 everyone has different
variables and everyone's part of the
situation at the same time you also
can't score four goals in the same play
as you could with a grand slam um so
hockey becomes you know harder to
predict in the where you can't really
understand uh how fast things are going
to go how slow things are going to go it
could be 0 it could be
76 um whereas baseball can be opened up
uh tremendously in one play via the
one-on-one scenario it wasn't the
analytics fault per se that Jos Brio
situation didn't work out it's the
people who were using analytics who made
the decisions that resulted in the
negative consequences for the Blue Jays
so this can happen in any sport this is
not just something that has never
happened before somebody has has run
every data thing they can possibly think
of and understood the situation to the
best of their ability and said I want to
do this because of this a b and c this
could be analytics driven this could be
Personnel driven they could have this
they could have won this game and then
everyone would be applauding analytics
in the sense so this one game sort of
defined the idea of you know strongly
abiding by the analytics gospel which is
not really what I'm hoping to convey
today I'm hoping to sh share that
analytics can be used and should be used
for that matter because of its ability
to help us understand the game further
than we could at face value however
sometimes things can happen you can't
predict the exact future balancing past
and present methods of evaluating
athletes involves being ready for
Randomness you have to sort of hope for
the best and prepare for the worst in
that sense prepare for injuries prepare
for what you call and hockey puck luck
prepare for any other variables that may
influence the game that what makes it
fun to watch as fans because it's so
Random you can't predict every single
game that's why betting is not 100%
perfect in the modern
schemes to conclude simply put scouting
information is context analytics is
context personal opinions are biases
this means that scouting and analytics
are both useful in the modern Sports
World analytics have become increasingly
more popular
but both scouting and analytics are
super important in your decision- making
and they can both influence decision-
making to radical or marginal degrees so
be aware of that and be aware of the
personal opinions that can be biases
when you're evaluating players and teams in
in
sports thank you for watching and I'll
see you in the next video
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