0:03 hello and welcome to the intro to hockey
0:06 analytics module my name is Nick lost I
0:08 am a former Brock University sport
0:10 management student and I'm happy to be
0:13 here today to teach you about hockey
0:16 analytics now the goals of this hockey
0:18 analytics module are the following to
0:19 understand the growth of statistical
0:21 usage and Reliance in hockey to
0:24 distinguish analytics statistics and
0:26 tactics in hockey to learn how to use
0:29 and study hockey analytics tools as well
0:30 as to evaluate hockey analytics
0:34 questions in a creative and modern
0:36 manner now the table of contents for
0:38 this section of the module is the growth
0:40 of analytics usage in hockey the growth
0:43 of analytics Reliance in hockey and the
0:45 benefits of datadriven hockey as well as
0:49 the drawbacks of overreliance on
0:51 analytics let's talk about the growth of
0:54 analytics uses in hockey now over the
0:56 years analytics has become prominent
0:59 within modern organizations as well as
1:01 modern hocke processes more and more
1:04 teams at the pro and Junior levels are
1:06 using analytics to inform their
1:08 decision- making we can see this as you
1:12 look at the 2019 list provided by Shaya
1:14 Goldman of the athletic uh on Twitter
1:16 she's been collecting a lists of people
1:18 that are said to work in all these
1:20 organizations in what you can call
1:22 analytics roles whether that be a
1:23 general manager with analytics
1:26 background or positions that are
1:28 specifically devoted to analytics work
1:30 as you can see here this is from 201
1:33 and as I move forward to 2022 you can
1:35 see the list has grown tremendously in
1:37 just 3 years as well as 2023 this past
1:41 year in just four years the list of
1:42 analytics personnel has more than
1:44 doubled in the across the National
1:46 Hockey League to what we know there are
1:47 obviously some roles that may not be
1:49 mentioned because this is all from
1:51 publicly available data and and
1:54 availability on Twitter based on hiring
1:56 and and news articles that we can find
1:58 but the analyst Community has grown both
2:00 publicly on Twitter at as well as
2:03 through Pro and developed
2:06 organizations moving on to the growth of
2:08 analytics Reliance in hockey this is
2:10 further to the growth of the usage while
2:12 more and more people are getting hired
2:14 more and more people are using analytics
2:16 to inform the decision- making and this
2:18 is feeding into our broadcasts to our
2:21 media and more as you can see by this
2:24 graphic here the slot shots per game
2:25 this is something that you would never
2:27 see 10 to 15 years ago in a hockey
2:29 broadcast but with the Partnerships with
2:31 companies like sport logic these are
2:33 available for fans and viewers to see
2:35 and understand during broadcast to
2:38 become more informed fans more educated
2:39 fans you would never have seen these
2:41 Graphics previously uh it might be
2:43 throughout Twitter and and online
2:45 spheres and sort of Niche communities
2:47 but nowadays it is become common place
2:49 for analytics and Stat and data driven
2:52 uh information to be available to the
2:55 public even thirdparty companies will
2:58 use hockey analytics in this sense such
3:00 as sport logic and and and be hired out
3:02 to provide these kind of stats to
3:03 broadcasts that's how important they
3:05 have become in recent
3:07 years now moving on to the benefits of
3:09 data driven hockey I have broken these
3:12 down into three main categories data
3:14 driven decision-making proactivity and a
3:16 sense of certainty so let's dive into
3:18 each of
3:22 these the first idea that relates to the
3:25 use of data driven uh information is
3:28 data driven decision- making now on the
3:29 screen here you can see two websites
3:31 these are publicly available websites
3:33 that you can look at for free the left
3:35 is money.com and the right is natural
3:37 statc I'll be posting tutorials for both
3:38 of these websites and how to best use
3:41 them as a fan later in this analytics
3:44 series uh but as you can see I have I
3:46 have made the money.com left picture
3:48 there about defensive pairs in the
3:50 National Hockey League and the right you
3:52 can see Matthew kachuck Sam Bennett and
3:54 Carter for heagy has sort of a line tool
3:56 about all their Advanced stats when
3:58 they're played together now datadriven
4:00 decision-making I I find to be the most
4:03 primary usage of analytics in the Pro
4:05 and Junior worlds specifically you're
4:09 looking at teams use and coaches using
4:12 data like line tools or data on specific
4:14 players to inform what they do if I'm
4:15 going to build a lineup do I want to
4:17 build the lineup using players that play
4:19 the best together based on the stats or
4:21 do I want to do it based on gut feelings
4:23 based on chemistry that I can see based
4:25 on friendships based on whatever data
4:27 driven decision- making has become more
4:28 and more prominent in the modern world
4:30 and this is part of of it building
4:32 lineups building rosters even that even
4:35 a management perspective furthermore you
4:36 might see players watching replays on
4:38 the bench they might be looking at their
4:40 information they might be replaying uh
4:42 past shifts players nowadays know more
4:44 and more about their analytics than ever
4:47 before some players may choose not to uh
4:48 understand or not choose to learn what
4:50 analytics are and that's totally fine
4:52 they can go about their way uh as as
4:55 well as they can uh but data driven and
4:57 data informed players uh seem to be the
4:59 ones that are taking the next step in
5:00 their game whether they know that
5:02 certain shots are working in certain
5:05 areas or certain Lanes up the ice are
5:07 becoming open against certain teams or
5:08 exploiting certain goal tenders for that
5:11 matter there are a lot of datadriven
5:13 decisions that are made within high
5:14 level NHL organizations as well as the
5:17 junior leagues that will help teams have
5:18 a competitive advantage in their
5:21 everyday hockey
5:23 games the second main benefit of data
5:26 driven hockey is proactivity I like to
5:28 categorize proactivity as preparing for
5:30 the future while being ready for the
5:33 present proactivity consists of using
5:35 data to inform us on what could be
5:37 expected out of players based on how
5:39 they're doing currently and what they
5:41 may be able to do Lucas Raymond plays
5:43 for the Detroit rings and as on the left
5:45 here you can see his uh his sort of
5:47 player card if you will this can this
5:49 shows his individual on ice and usage
5:51 metrics on the right hand side of it
5:55 where you see he's on Pace for 27 goals
5:58 and 42 assists this season uh and this
6:01 is basic from uh Advanced Tas websites
6:03 that will calculate the the paces that
6:04 you compared to everybody else in the
6:06 Inter National rocket league as you can
6:09 see by the star number on the left the
6:11 98 that's in yellow that means that he's
6:14 in the 98th percentile of assists
6:17 meaning that there are only 2% more in
6:19 the NHL that have more assists than him
6:20 which is quite
6:23 outstanding however we see that in the
6:26 projected gsva timeline on the left
6:28 there that is game score value added
6:29 it's one of those metrics that you can
6:34 use uh to add uh wins or value over time
6:36 and and compare year by year and predict
6:39 into the future uh this gsva timeline
6:41 shows a bit of a plateau going into 2025
6:45 2026 and 2027 this implies that Lucas
6:48 Raymond may have a high floor because
6:52 three wins 3.5 wins is really good for a
6:54 National Hockey League player consider
6:56 zero to be basically a replacement level
6:57 uh like a fourth ler that would be
7:00 scratched so 3 five is tremendous we
7:02 could assume someone like Conor McDavid
7:04 would be extremely high like near the
7:06 six maybe even perhaps uh but Lucas
7:08 Herman is in a high percentile in the
7:10 league because of his stuff on the right
7:11 you can see there those current
7:13 statistics are looking really good he's
7:15 in very high percentiles out of all
7:17 National Hockey League players however
7:19 it looks like going into his uh next
7:22 contract which will be up soon uh he'll
7:24 need to prove himself even further
7:26 because of this expected Plateau we
7:29 could call this a high floor but also a
7:31 low ceing type of player so analytics
7:34 can help us whether it be managers front
7:36 office or ownership in general to
7:38 understand what players could be before
7:41 we Dole out contracts did Leon Dr settle
7:43 for example uh would would anyone have
7:46 expected him to overcome $8.5 million
7:49 per year as a forward and get 100 Point
7:51 Seasons back to back to back uh Maybe
7:53 not maybe so but with the sense of
7:56 productivity maybe they were sure in the
7:58 O organization that he would do well so
7:59 they signed him at a certain point in
8:01 this contract to a longterm versus a
8:03 short term all these decisions can be
8:05 influenced by data but they don't have
8:09 to be uh so as a manager coach or a
8:10 owner when you're doing out money to
8:12 hockey players and you're spending
8:15 millions of dollars on your front office
8:16 uh it's important to understand the
8:18 value of data where it can be used to
8:20 benefit you and gain an advantage that
8:23 you otherwise would not
8:26 have the third benefit of datadriven
8:28 hockey is a sense of certainty and it's
8:31 not enti L the idea of certainty where
8:33 you would know the future and you
8:35 understand how players are going to pan
8:37 out per se but a sense of certainty
8:39 meaning you have using data you have the
8:42 idea of how a player could pan out uh
8:44 and this is a really important tool for
8:45 managers for front offices for
8:48 ownerships even for agents if you're
8:50 going to vouch for your own player if
8:52 you're able to prove that based on their
8:54 comparables to other players they're
8:56 going to pan out a really good way you
8:57 can get them a lot more money than you
8:59 wouldn't have before if you didn't use
9:01 use data so a way to use this would be
9:03 to use a software uh such as hockey
9:06 prospecting which is a payable Service
9:09 uh made by Byron Bader this is a
9:11 prominent guy on Twitter uh who you
9:13 might know he is very involved with anal
9:15 front offices in the past where he's run
9:17 some of their drafts and part of that is
9:20 using this hockey prospecting model
9:23 basically it takes the NHL point value
9:24 and it takes a percentage of that and
9:27 says how many OHL points would be one
9:29 NHL point for
9:31 example if as you can see there in the
9:34 second year of Andrew mapani on the left
9:38 you see he's got 18 in that OHL d0 which
9:41 is your draft year D1 is the year after
9:43 your draft otherwise known as draft plus
9:45 one draft plus two draft plus three you
9:47 can see there and the D minus one is the
9:50 one before your draft year uh so as you
9:53 can see Andrew mapani put 18 and then 38
9:56 and 43 this means that in the prime of
9:59 Andrew mapi's NHL career he's expected
10:01 to put up that many points compared to
10:04 that exact season in the NHL it takes
10:07 OHL points for example if puts up 100
10:10 OHL points that might put him at 43 but
10:12 it all depends on things like age
10:13 depends on a lot of other factors that
10:15 go into this model to make it as
10:17 accurate as it can be in a prediction
10:19 type of scenario as you can see on the
10:23 right I've chosen Yuri kuluk as a decent
10:25 uh comparable as you can see with the
10:26 trend that they're both going they're
10:29 almost identical in their same age years
10:32 their D minus1 their d0 and their d+1
10:34 and their D plus2 are almost identical
10:36 in points however you can see Yuri Kulik
10:39 is already in the AHL now the AHL and
10:40 the OHL being different leagues but the
10:42 same points still kind of means the same
10:44 thing where they're both on Pace to have
10:48 in their Prime in the NHL a 45ish point
10:50 uh total regardless of what league
10:52 they're playing in in that year but this
10:53 is interesting to look at compared to
10:55 what other prospects are in the model if
10:56 you have a young up and cominging
10:58 Prospect in your organization and you're
11:00 not sure how to value him you can look
11:02 at something like this and say wow okay
11:04 compared to other players uh in their
11:06 age times when they were 18 when they
11:08 were 17 they're they did this well and
11:10 he's doing the same as they were or even
11:11 better that means he's going to be
11:13 better than them possibly it's not
11:17 exactly certain but it's a sense of
11:19 certainty analytics are not inherently
11:22 wrong no one can predict the exact
11:24 future like all sports hockey will
11:26 always be a game where legally every
11:28 game of hockey is random and there's an
11:29 opportunity for both teams to win the
11:32 game uh scandals and everything
11:34 aside former New Jersey Devil's Head of
11:36 analytics Sunny MAA said a quote that
11:38 really struck out to me it says never
11:40 underestimate the randomness of hockey
11:42 it's not impossible that we exceed
11:44 expectations it's not impossible that
11:46 players play better than what the stats
11:47 are expecting them to do it's also not
11:49 impossible that they play worse for that
11:51 matter the game of hockey is a game it's
11:53 a sport we love to watch it because
11:54 we're not sure what's going to happen so
11:55 that's a really key Point here is that
11:57 analytics can inform us on what may
11:59 happen and what could happen based on
12:01 informed data but it's not going to
12:03 predict the future
12:05 exactly now compared to other sports
12:06 like baseball as you can see in the
12:08 background there we've got Jose Brios
12:10 giving the ball uh to manager John
12:12 Schneider this was a pivotal moment in
12:14 the playoffs uh last season for the Blue
12:17 Jays where Brios was pulled early in the
12:19 game based on analytical decisions that
12:21 were made before the game had even
12:23 happened and assumptions were made that
12:25 this would be better for the bles to
12:27 pull him now and put somebody else in to
12:29 finish the job and do better
12:30 analytically of course this did not
12:32 happen and the BL just ended up losing
12:34 the series which is unfortunately sort
12:36 of plag the analytics Community being
12:37 that everyone sort of is hating on
12:39 analytics and saying that stats are not
12:41 the way to go you got to understand the
12:43 game moment and balance that with the
12:46 present moments so I I like to say that
12:47 hockey is more random than baseball
12:50 because of team play 1 V one in baseball
12:51 is super important it's a pitcher versus
12:54 a batter hockey in the sense is is very
12:57 fluid I call it the fluidity of hockey
12:58 is the reason why it's so special is
13:01 because it's 5v5 everyone has different
13:02 variables and everyone's part of the
13:04 situation at the same time you also
13:06 can't score four goals in the same play
13:09 as you could with a grand slam um so
13:11 hockey becomes you know harder to
13:14 predict in the where you can't really
13:16 understand uh how fast things are going
13:17 to go how slow things are going to go it
13:19 could be 0 it could be
13:22 76 um whereas baseball can be opened up
13:24 uh tremendously in one play via the
13:26 one-on-one scenario it wasn't the
13:28 analytics fault per se that Jos Brio
13:30 situation didn't work out it's the
13:32 people who were using analytics who made
13:33 the decisions that resulted in the
13:36 negative consequences for the Blue Jays
13:37 so this can happen in any sport this is
13:39 not just something that has never
13:41 happened before somebody has has run
13:42 every data thing they can possibly think
13:44 of and understood the situation to the
13:45 best of their ability and said I want to
13:48 do this because of this a b and c this
13:50 could be analytics driven this could be
13:52 Personnel driven they could have this
13:53 they could have won this game and then
13:54 everyone would be applauding analytics
13:56 in the sense so this one game sort of
14:00 defined the idea of you know strongly
14:02 abiding by the analytics gospel which is
14:04 not really what I'm hoping to convey
14:05 today I'm hoping to sh share that
14:07 analytics can be used and should be used
14:09 for that matter because of its ability
14:11 to help us understand the game further
14:14 than we could at face value however
14:16 sometimes things can happen you can't
14:19 predict the exact future balancing past
14:20 and present methods of evaluating
14:22 athletes involves being ready for
14:24 Randomness you have to sort of hope for
14:26 the best and prepare for the worst in
14:27 that sense prepare for injuries prepare
14:30 for what you call and hockey puck luck
14:32 prepare for any other variables that may
14:34 influence the game that what makes it
14:35 fun to watch as fans because it's so
14:37 Random you can't predict every single
14:39 game that's why betting is not 100%
14:41 perfect in the modern
14:45 schemes to conclude simply put scouting
14:47 information is context analytics is
14:51 context personal opinions are biases
14:53 this means that scouting and analytics
14:55 are both useful in the modern Sports
14:58 World analytics have become increasingly
14:59 more popular
15:01 but both scouting and analytics are
15:03 super important in your decision- making
15:04 and they can both influence decision-
15:07 making to radical or marginal degrees so
15:09 be aware of that and be aware of the
15:11 personal opinions that can be biases
15:14 when you're evaluating players and teams in
15:14 in
15:17 sports thank you for watching and I'll
15:19 see you in the next video
