Roles and Stats Part III: Roles and Defensive Stats – Forwards

This is part three 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 discussed in great detail here.

The second post in the series looked at the relationship between roles and offensive statistics (GF60 Rel, CF60 Rel, and Sh% Rel) for forwards. In this third post I will look at the defensive statistics (GA60 Rel, CA60 Rel and Sv% Rel) which ought to be interesting as defensive statistics have historically been difficult to get a handle on (compared to offensive statistics).

Leading and Trailing TOI%

The first role indicators that I am looking at is leading and trailing TOI%. Here is how they compare to 5v5close GA60 rates.

Lead_vs_Trail_vs_GA60

Goals against are bad so increasing them (blue) indicates player is worse off. Here we see that players that are given more ice time trailing also give up more goals against in close situations while those that get less ice time trailing give up fewer goals. Getting more defensive ice time protecting a lead does not necessarily mean giving up fewer goals though (moving left to right along a row does not necessarily mean lower GA60).

Another thing you may notices is that the scale in this chart is approximately half that of the GF60 Rel chart in Part II of this series of posts. While there is a clear relationship between Trailing TOI% and GA60 Rel, the relationship is not as strong as the relationship with GF60.

Lead_vs_Trail_vs_CA60

This is where things get a little interesting. There is not a significant pattern here, certainly not like the GA60 Rel chart which is quite interesting. Players that get more ice time playing catch-up hockey give up more goals, but don’t give up more shots. That can only mean one thing…

Lead_vs_Trail_vs_SvPct

Yes, those players that get more ice time when trailing give up more goals in 5v5close situations because they have a negative impact on their own teams save percentage.

Again it is worth noting that the CA60 Rel and Sv%Rel charts have scales about half that of CF60 Rel and Sh%Rel charts from the last post so players do have a greater ability to impact offense than defense.

Defensive and Offensive Face Offs

DZFO_vs_OZFO_vs_GA60

Players that get the most offensvie zone face offs also give up the most goals against. Clearly starting in the offensive zone is not helping them in the goals against department. This is just further evidence that face off zone starts don’t really have significant impacts on results.

DZFO_vs_OZFO_vs_CA60

Not much of a pattern here either. Face off zone starts don’t seem to correlate with shots against.

DZFO_vs_OZFO_vs_SvPct

There is somewhat of a pattern here. Those that get more offensive zone face offs do generally have a negative impact on save percentages. Getting more defensive zone face offs does not seem to have any correlation with save percentages.

Penalty Kill and Power Play

PK_vs_PP_vs_GA60

You probably guessed what you might see here but those that get the most powerplay ice time generally have a negative impact on goals against rates. Those players that get little to no powerplay ice time generally reduce goals against in 5v5close situations. In the middle there is quite a mix though generally moving down the chart brings in more red grid cells.

PK_vs_PP_vs_CA60

Again, not much of a connection between role and shots against. Some players getting significant defensive time end up giving up a lot of shots against.

PK_vs_PP_vs_SvPct

And this chart confirms once again that offensive players are generally have worse save percentages relative to their teammates while those that get little or no power play ice time generally boost their teams save percentage in 5v5close situations.

Summary

In the last post we saw that players that get the offensive assignments also happen to produce the most offense. However in this post we see that these same players also give up the most goals against by having a negative impact on their goalies save percentage.

A few weeks ago I tweeted a poll about whether the best defense is a good offense and 52% of respondents agreed (22% thought it was most true and another 30% thought it was equally true) however from these charts this is not correct. The best defense is typically not a good offense. In fact a good offense often leads to a worse defense.

The positive for offensive players is that on average the positive impact to their teams offense is greater than their negative impact to their team defense so overall they are doing better. However it is clear that the best defense is not necessarily a good offense (52% of you got that wrong).

What we don’t see though is offensive players giving up more shots against so their negative influence on goals against is typically through negative impacts to their goalies save percentages. This doesn’t mean that forwards can’t impact shots against, it just can’t be generalized according to offensive and defensive roles as defined here. The clear takeaway from this though is that forwards can and do influence save percentage. In a future post in this series I’ll look at persistence and predictability of Sv%Rel so stay tuned but I’ll turn my attention to defensemen first.

 

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  1. This kind of highlights one of the main reasons I am not a big fan of the type of people who use Corsi as their primary source of player evaluation. Looking at these charts (and various attempts to research the topic myself over the years) my postulation is that one of the issues with looking at CA as a defensive metric is that it presumes that blocked shots are a bad thing. Players who play defensive roles also tend to be the types of guys that block shots, and while that may not necessarily improve puck possession we can safely say that 100% of the shots that are blocked are not going into the back of the net. So when we see some of the extreme defensive players on these charts giving up more shot attempts but also coming away with solid Sv% it makes sense to me. Of course blocking shots isn’t the only reason they have better Sv%, I’m sure there is also an ability to influence shot quality by making opponents take bad angle shots and boxing them out of the slot.

    It is pretty easy to evaluate offense, a player is scoring, that is great. The team scores more when he is out there, also good. More shots often means more goals. While some supplementary roleplayers may get undervalued (like the power forward who crashes the net and screens/opens up holes for other players to score, he may not get many points of his own but is invaluable to the team’s scoring success), by and large offense is easy to see. Defense, on the other hand, is quite tricky. Did they get scored on due to fault of their own, did a teammate (maybe even the goalie) make a mistake, or did they do everything right and just get beat by a really good scorer (or a lucky bounce)? When defensive players are doing well we often don’t even notice them, they usually don’t stand out unless they do screw up. And unfortunately there doesn’t seem to be a particularly good way to measure defensive talent, although what little we do have to work with suggests that there is something there.

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