Canucks: Ranking Elias Pettersson among NHL’s young stars

The stats of Vancouver Canucks forward Elias Pettersson compare very favourably to the stats of the other young star forwards’ first two seasons.

When Elias Pettersson launched his NHL career with ten goals in his first ten games, Vancouver Canucks fans were put on notice that they had something special here. And his play throughout his first two seasons has cemented his status as one of the top young forwards in the game today.

But where exactly would he be slotted among the NHL’s other young stars? How does he measure up against the likes of Connor McDavid and Auston Matthews and Jack Eichel, just to name a few? We shall conduct a comparative analysis to help us answer this question.

We start by setting out the parameters that will determine which players qualify for this study, and which do not. First, for the purposes of determining who qualifies as a young player, this study will set five years in the NHL as a maximum.

Further, we will also establish a maximum age of 25 years, to rule out mid-career transfers, such as Artemi Panarin. And finally, for the purposes of this assessment of “top young forwards,” we will focus only on the players’ offensive abilities.

Comparing Pettersson’s performance over his two years with those of other top young forwards is complicated by the fact that almost all of them have tenures in the NHL that exceed two years, and it would be unfair to compare Pettersson’s output at two years into his development as an NHL player with other players’ outputs at three, four, or five years into theirs.

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Therefore, our comparative analysis will be limited to each player’s performance during his rookie and second-year seasons (and third-year season for those who played 25 games or less in their first year, and thus carried their rookie status over into their second year; these players are starred in the charts below).

The first criterion to be considered is simply total points in rookie and second-year seasons, admittedly a rather blunt tool for the purposes of conducting player comparisons, but a tool that provides us with at least a starting point for our analysis.

Following is a list of the NHL’s top offensive forwards who have entered the NHL in the past five years, and who are 25 years old or younger, arranged in order of total points over their rookie and second-year seasons. And we see that Pettersson comes in at No. 5, though in a group of seven players bunched tightly between 128 and 135 points:

1)   148      Connor McDavid
2)   147   * Mathew Barzal
3)   135   * William Nylander
4)   134      Patrik Laine
5)   132      Elias Pettersson
6)   131      Auston Matthews
7)   130      Mitch Marner
8)   128      Alex DeBrincat
9)   128   * Kyle Connor
10) 122   * Mikko Rantanen
11) 116   * Brock Boeser
12) 114      Sebastian Aho
13) 113      Jack Eichel
14) 106      Brayden Point
15) 102      Nikolaj Ehlers
16)   98       Andrei Svechnikov
17)   97       Matthew Tkachuk

McDavid is, as expected, leading the way. However, given the degree of dominance he has exhibited ever since entering the league, it may be a bit surprising to see him not having a significant lead over the second-place Barzal.

What we see demonstrated here is a weakness in using the category of “total points” as a criterion for comparing player performance. This chart shows McDavid’s and Barzal’s points totals as virtually even, but it provides no sense of the contexts of those two points totals, that is, no sense of how many games it took for these players to compile their respective points.

As it turns out, it took Barzal 164 games to get his 147 points, while it took McDavid only 127 games to get his 148 (his being sidelined for nearly half his first season with a broken collarbone), making his performance far superior to that of Barzal.

Nylander is another player whose placement on the list is affected when number of games played is factored in. His 135 points listed in the chart were earned in the 22 games he played in his first year plus the 82 games he played in the following season plus the 81 games he played in the season after that, a total of 185 games, in comparison to the maximum of 164 games played by all the non-starred players. When his number of games is factored in, he falls from third on the list to eleventh.

Following are the performances of our 17 players when measured by point-per-game (PPG), with each player’s ranking in the earlier “total points” chart in parentheses beside his name:

1) 1.165      McDavid (1)
2)    .950      Pettersson (5)
3)    .910      Matthews (6)
4)    .886   * Barzal (2)
5)    .865      Laine (4)
6)    .829   * Boeser (11)
7)    .818      Marner (7)
8)    .796      Eichel (13)
9)    .780      DeBrincat (8)
10)  .739   * Rantanen (10)
11)  .730   * Nylander (3)
12)  .719   * Connor (9)
13)  .713      Aho (12)
14)  .707      Point (14)
15)  .674      Tkachuk (17)
16)  .662      Ehlers (15)
17)  .653      Svechnikov (16)

One of the winners with this shift to points-per-game is Pettersson, who moves past Laine, Nylander, and Barzal into second place, trailing only McDavid. Further, this more refined analysis succeeds in capturing McDavid’s dominance in a way that the “total points” chart did not; his 1.165 PPG represents a staggering 22.6 percent lead over Pettersson’s .950 PPG.

Points-per-game is clearly a criterion that provides a sharper assessment of player performance than does raw points data alone. However,  it does possess one glaring shortcoming: it does not take into account ice time as a variable in the consideration of scoring efficiency.

For example, you could have two players who each play 82 games, and each earn 82 points in those games, giving them both a points-per-game rating of 1.0, which suggests they are performing at the same level. But what if player A was getting twenty minutes of ice time per game, and player B was getting only ten minutes? Despite the fact their points-per-game ratings suggest they are performing at the same level, player B is actually scoring at a rate twice that of player A.

Obviously, our assessment needs to be refined still further, and turning to the world of advanced statistics will allow us to do that, though not until Part 2 of this analysis. So, be watching for it.