Roles and Stats Part II: Roles and Offensive Stats – Forwards

This is part two of my series on how roles impact statistics. In Part I I looked at a brief history of the debate and also described the methods used in this research. The history section is optional but I recommend reading the methods portion as it describes the statistics and methods used which will not be repeated in great detail here.

This second post in the series will look at the relationship between roles and offensive statistics (GF60 Rel, CF60 Rel, and Sh% Rel) for forwards.

Leading and Trailing TOI%

Lead_vs_Trail_vs_GF60

The chart above looks at the relationship between leading and trailing ice time with goals for rates. As one would expect, those that get more offensive roles (get more ice time when team is trailing) generally produce offense at a higher rate in 5v5 close situations. We can see this by moving from bottom to top in any particular column generally results in moving up the scale (darker red up to darker blue). Similarly, when moving from left to right in any row generally moves down the scale into lighter blues or deeper reds though the change is less significant than moving up/down the columns.

The next thing I want to look at is whether this greater offense is a result of more shots or a result of a higher shooting percentage or both.

Lead_vs_Trail_vs_CF60

The above table for CF60 Rel clearly shows that more shots is a factor in the greater offense as the trends in the GF60 Rel chart are matched here. Players teams rely on for more offense produce more shots and those they rely on for less offense produce fewer.

Lead_vs_Trail_vs_ShPct

The same is largely true for for shooting percentage as well. Both outshooting and higher shooting percentages play roles in producing offense.

Defensive and Offensive Face Offs

The second method for identifying roles in defensive and offensive zone face offs.

DZFO_vs_OZFO_vs_GF60

As we move up columns from bottom to top we move up the scale from less offense to more offense just like we did when looking at leading vs trailing TOI%. However as we move from left to right across rows doesn’t move down the scale as much as we saw with leading vs trailing TOI% and for a couple of rows actually moves up the scale.

DZFO_vs_OZFO_vs_CF60

We see pretty much the same trends in the CF60 Rel chart.

DZFO_vs_OZFO_vs_ShPct

Ditto for the Sh% Rel chart.

Good offensive players get good offensive zone starts.

Penalty Kill and Power Play

The final role identifiers I want to look at are penalty kill and power play ice time. This first chart was previewed in Part I of this series of posts.

PK_vs_PP_vs_GF60

No surprise here. Players that get more powerplay ice time, espescially those that also get little short handed ice time, have the highest 5v5close goal production rates. Conversely those who get little power play ice time produce the worst in 5v5close situations.

PK_vs_PP_vs_CF60c

The players who get the most power play ice time lot of shots in 5v5 play while those that don’t get power play ice time don’t generate many shots.

PK_vs_PP_vs_ShPct

Those that get a lot of ice time shoot for higher shooting percentages too though the leading/trailing and offensive/defensive zone face off charts indicate a stronger relationship between role.

Summary

Players who get more ice time when the team is trailing and on the power play or get more offensive zone face offs are also the same players that produce the most offense (generally speaking). They also do this through a combination of generating more shots and converting those shots into goals at a higher rate. This is no surprise but this is a good visual representation of what is happening. The fact that this methodology is working as expected is also important as we move forward into more controversial territory (aka save percentages).

 

This article has 1 Comment

  1. What I found interesting is that the brightest red spot on the special teams charts for GF and Sh% were those who got very little usage on either the PP or PK. Its as if there is a certain threshold of player that just isn’t trusted to play in any situation.

    We see something similar at that end in the leading vs trailing charts, the worst overall offensive players aren’t spending a lot of time on the ice in any unbalanced score situation. Conversely there seems to be a group of talented scorers that get called upon to play big minutes in both situations. There is still an obvious trend in offensive players getting used more in offensive roles than defensive ones, I just thought it was interesting how the two-way guys stand out.

    To a lesser extent we also see something like that in the zone start charts, although that seems to be mostly influenced by OZ% with DZ% being significantly less important (presumably this will change when we get to see the defensive charts). We do get some indication of the talent of the two-way guys, as those who get used at both ends of the ice do well (although still not as well as the pure offensive specialists) and those who are given defensive roles or not overly trusted in any situation are less effective offensively.

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