Forward Usage and Stats by TOI Rank on Team

The other day I posted an article on evaluating defensemen by their ranking on their team. In this article I am going to do the same but for forwards and focus on the offensive side of the game.

I grouped forwards in a similar way to how I grouped defensemen. Specifically, this is my methodology:

  • I used 5v5close data to eliminate score effects
  • I used data from the first two thirds of the season (first 820 games of the season) because few trades occurred before that point in the season. Trades can mess up rankings of players and how do you rank a player that played on two different teams.
  • I only accounted for players who played in at least 20 games.
  • I then ranked players by their %ofTeam TOI stat which you can find on puckalytics.com.
  • Forwards were ranked as F1, F2, …, F12 and F13 (or depth forward). F1-12 has one player from every team, F13/depth is every other forward that played at least 20 games but not among the teams top 12 forwards.
  • I looked at 2011-12, 2013-14 and 2014-15 seasons individually as well as combining the individual seasons. The F1 group for the 3 seasons combined are F1 from 2011-12 plus F1 from 2013-14 plus F1 from 2014-15.

Let’s start by looking at CF% and GF% by forward ranking.

ForwardRank_3yrCFPctGFPct

As you move down a teams depth chart at forward both CF% and GF% drop off as one might expect. It is important to note though that the slope of the GF% line is much more significant than that of the CF% line. The result is that typically top four forwards on a team will out perform their CF%, forwards 5-7 will perform on par with their CF% and beyond that players will typically under perform their CF%. This means that while CF% may tell you which players are better than others, it will fail to fully identify the relative strength of good players over poor players.

Taking a look at CF/60 and CA/60 we see the two behave quite differently.

ForwardRank_3yrCF60CA60

From top to bottom, CA60 is relatively stable but CF60 drops off as you move down the line up.

We see the same thing with the percentages.

ForwardRank_3yrShFPctSvPct

Save percentages fluctuate a bit but are mostly stable moving down the line up. Shooting percentages on the other hand generally drop off as you move down the line up.

These observations have an expected result on GF/60 and GA/60.

ForwardRank_3yrGF60GA60

GA/60 is relatively stable while GF/60 varies quite a lot. The conclusion is that good offensive forwards get awarded ice time and defensive play generally has no impact. This differs slightly from defensemen where we saw defensive stats have more variability as you move down the depth charts though it isn’t always linear.

There is an important observation to be made with respect to PDO though.

ForwardRank_3yrPDO

Not everyone can be expected to regress towards 100. Depending on individual talent, position in lineup, and defense/goaltender ability your expected PDO can vary (and to a larger degree than this chart would indicate). I would not consider it abnormal for a players expected PDO to range anywhere from 97 to 103 depending on talent, usage, quality of line mates, and quality of goaltending.

Quality of Competition

I want to throw something in here on quality of competition because one claim many people make is good players play against good players and poor players play against poor players which has an impact on their statistics.

Here is a QoC chart for forwards by their rank on their team.

ForwardRank_3yrQoC

Hey, look at that. Top forwards play against good offensive players. Now, before you get too excited and run off telling everyone that QoC matters have a look at the scale. It’s tiny. It’s from 2.1 goals/60 to 2.4 goals/60. Let’s plot this with GF/60 and GA/60 on a more ordinary scale.

ForwardRank_3yrQoC_vs_goalrates

Do you still think QoC matters? QoC exists but over larger sample sizes (certainly at a seasons worth of data) it really isn’t likely to have much of an impact. Talent and quality of teammates will he a much larger impact.

Zone Starts

ForwardRank_3yr_ZoneStarts

The above chart shows the offensive/defensive zone face off percentages by forward rank (not shown are neutral zone face offs). The higher you are in the depth chart the more offensive talent you have and the more offensive zone starts you will get. At the bottom of the line up you are getting significantly more defensive zone starts than offensive ones.

(Note: I didn’t show a face off chart for defensemen but it is pretty similar to the one above.)

Unlike defensemen there doesn’t seem to be any trends associated with save percentage or much difference between high and low Corsi teams aside from what you would expect. In reality there isn’t all that much interesting in this sort of analysis for forwards since. Though there may be exceptions, for the most part forwards get ice time depending largely on their offensive ability (shot generation and shot quality/shooting percentage) and less so on their defensive ability.

The key takeaways from this analysis are:

  • Better offensive forwards get more ice time
  • Being better offensively is a combination of generating more shots and higher quality shots.
  • Because shot quality is a talent and varies throughout the line up, a Corsi evaluation of forwards will underestimate the value of the top forwards and over estimate the value of weaker forwards though it will typically identify which are the best forwards and which are the worst.
  • PDO doesn’t regress to 100 for all players, particularly forwards and rather than suggesting regression to 100 one should view a range of 97-103 as being a typical range for forwards over large sample sizes (a season or more).