Unconventional Wisdom - Who is carrying whom?

There are a lot of different ways to attempt to look at how a player does in comparison to his teammates, notably by comparing on-ice and off-ice production. But what about when they are on the ice? Who is responsible for point production and who is just dragging his teammates down?

The Pittsburgh Penguins and their depth, or lack thereof, has been discussed at length this season. There have been complaints about lack of production, but very little emphasis on how difficult it is to do so when playing with the calibre of teammates they have had to endure in this injury riddled season. So I wanted to take a look at the on-ice production and try to determine who is carrying whom this season.

So in order to do so we are going to take a look at Individual Points Percent, which is the Points the player scored this season divided by the Goals For while the player is on the ice. Its not a perfect measure, as there are situations in which a player can directly contribute to a Goal being scored without actually getting a Point for it, or on even rarer occasions get an Assist despite no longer being on the ice. But in general it is a good way of showing who on the ice is directly contributing to point production and who is just a passenger. And while scoring is the ultimate goal, I know there will be people who are interested in possession numbers as well, so I went ahead and calculated IFP and ICP as well.

I ran the numbers for all 36 skaters that have played for the Penguins this year, but knowing there were bound to be serious sample size issues I did make a judgement call on the cutoffs for my bubble chart. Ideally from a sample size perspective we want players with 750+ minutes, although those with 500+ minutes have played enough to make reasonable assumptions. However, if I used that cutoff we would miss out on a couple of others that should be included in our lists, so I instead chose to look at those that have played 21+ games, which at this point in the season is the equivalent of breaching the 25 games played cutoff the NHL uses for awards and rookies. And despite not reaching that benchmark I did feel compelled to include Bennett, Stempniak, and Goc.

So cutting out Conner, Sill, Zolnierczyk, Jeffrey, D`Agostini, Ebbett, Drazenovic, Letourneau-Leblond, Dumoulin, and Samuelsson leaves us with 26 skaters, which I separated into 17 forwards and 9 D. I did that because in recently looking over it in response to a comment we learned that there is a pretty big gap in how much control D have over where the puck goes, so the numbers would make them look much worse if we compared them to the forwards instead of just looking to see how they do amongst themselves.

Forwards - Individual Points Percent

Here we have an X-axis representing on-ice Goals For per 60, so the further right you go the more goals are scored when the player is on the ice, with the axis split at the average value of 2.56 calculated from the 10 forwards with 500+ minutes. The Y-Axis is IPP, with those higher up earning Points on a larger proportion of the goals that are scored while they are on the ice, with the axis split at the average value of 71.6%. The size of the bubble is based on GF%, although I subtracted the average of 51.6% to show which players are above or below, with white bubbles indicating negative below average results.

Player's whose names are in red are those that have played fewer than 500 minutes of 5-on-5 and as such could possibly have sample size issues in their results. If we look at the different quadrants, those in the top right are scoring well and doing most of the work for their lines. Conversely, those in the bottom right are playing on high scoring lines while contributing less than their peers and are as such mostly passengers on the line. Then our top left are those who are stuck playing with low scoring teammates but are doing their best by capitalizing on scoring chances despite a lackluster performance by those around them. And lastly the bottom left then are those that are on a low scoring line but are largely at fault for dragging down their linemates' production.

Forwards - Individual Fenwick Percent

Our X-axis is based on Fenwick For per 60, the further right one goes the more offensive shot production that player sees, with the axis split at the average of 40.0. The Y-Axis then is IFP, the higher up one goes the more of the shot attempts are coming from that player's own stick, with the axis split at the average of 24.2%. The bubble size is based on FF%, but with the average value of 49.7% subtracted to allow us to see who is performing above or below average, with white bubbles representing negative below average results.

Once again players in red are those with smaller sample sizes of below 500 5-on-5 minutes. Players in the top right are good possession players that are largely responsible for their line's shot attempts, while those in the bottom right have good possession numbers but are more akin to passengers as far as possession is concerned. Those in the top left are struggling to contribute to possession despite being with less than ideal linemates while those in the bottom left are likely responsible for the poor possession numbers.

Forwards - Individual Corsi Percent

There is very little difference between Fewnick and Corsi other than the raw numbers. The X-axis splits on the average of 52.8 CF60. The Y-axis splits on the average of 23.4% ICP. And the bubble size is based around how far above or below the average of 48.5 CF%. Otherwise, everything that was said about Fenwick applies.

Defensemen - Individual Points Percent

The same explanations for Forwards can be applied to the D, only the raw numbers have changed. The X-axis is split at the average 2.35 GF60 calculated from the 8 D with 500+ 5-on-5 minutes. The Y-axis is split at the average 30.6% IPP. The bubble size is based on being above or below the average 49.2 GF%.

Defensemen - Individual Fenwick Percent

Once more we can look at the descriptions in the Forwards, the only difference being the raw numbers. The X-axis splits at the average 38.3 FF60. The Y-axis splits at the average 14.4% IFP. And lastly the bubble size is based around the average 48.6 FF%.

Defensemen - Individual Corsi Percent

And one final time, the same descriptions about possession listed with Forwards above but with different raw numbers. The X-axis splits at the average 50.3 CF60. The Y-axis splits at the average 15.5% ICP. And the bubble size is based around the average 47.4 CF%.

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