Jul 022014
 

The other day I looked at the effect that Mike Weaver and Bryce Salvador had on their teams save percentage (if you haven’t read it, definitely go give it a read) when they were on the ice versus when they weren’t on the ice. Today I am going to take a look at the Maple Leaf defensemen to see if there are any interesting trends to spot. We’ll start with the new acquisitions.

Stephane Robidas

RobidasOnOffSavePct

(Blue line above orange is good in these charts, opposite is not good)

Aside from 2008-09 he has had a negative impact on his team save percentage. In 2007-08, 2009-10 and 2010-11 his main defense partner was Nicklas Grossman but in 2008-09 his main defense partner was Trevor Daley. Did this have anything to do with his poor effect on save percentage in 2008-09? Well, aside from last season Daley’s on-ice save percentage has been at or better than the team save percentage so there might be something to that.

Roman Polak

PolakOnOffSavePct

Not really a lot happening there except in 2011-12 when he was worse than the team (and the team had significantly better goaltending). Rembember though, the Blues have a pretty good defense so it is quite possible that not being worse than the rest of them is a good thing. Will be interesting to see how he does in a Leaf jersey this season.

Dion Phaneuf

PhaneufOnOffSavePct

Aside from 2008-09 there has been a slight positive impact on save percentage when he is on the ice. In 2008-09 he didn’t have a regular defense partner. At 5v5 he played a total of 1348:08 in ice time and his main defense partners were Giordano (364:56), Vandermeer (342:47), Pardy (304:27), Leopold (163:47), Regehr (85:08) and Sarich (77:41). That variety in defense partners can’t be a good thing. But, maybe Phaneuf has a slight positive impact on save percentage.

Cody Franson

FransonOnOffSavePct

So, he was good for a few years and then he was bad. What happened? Well, he was traded to the Leafs. For the 2009-10 and 2010-11 seasons his main defense partner was Shane O’Brien and he also spent significant time with Hamhuis. This could be a case of him playing “protected” minutes as he had really easy offensive QoC but I generally don’t think QoC has anything near as significant an impact as other factors so I am not sure what is going on. He has had pretty weak QoC the last couple seasons too so who knows.

Jake Gardiner

GardinerOnOffSavePct

It is only 3 seasons of data but so far so good for Gardiner. He has been a boost to the teams save percentage and that is on top of his good possession numbers. In my opinion, Gardiner is quite likely the best defenseman. I’ll drop the “quite likely” from that statement when he repeats his success but against tougher QoC as that will remove any doubt.

Now, let’s take a look at a couple of departing Leaf defensemen.

Carl Gunnarsson

GunnarssonOnOffSavePct

Save for 2010-11 Leaf save percentage has been better whith Gunnarsson on the ice. His two main defense partners that year were Luke Schenn and Mike Komisarek so maybe we can forgive him. In 2009-10 his defense partner was mainly Beauchemin or Kaberle and starting in 1011-12 it has mainly been Phaneuf.

Tim Gleason

GleasonOnOffSavePct

Tim Gleason gets a lot of criticism from Leaf fans, the analytics community, and maybe pretty much everyone but his teams have generally had a positive boost in save % when he is on the ice and in some cases a significant boost.

Based on the loss of Gunnarsson and Gleason, two defenseman who seem to be able to boost on-ice save percentage, and the addition of Robidas who has a negative impact and Polak who has more neutral impact it is quite possible the Leafs suffer a drop off in save percentage this season.

That said, I am not certain what to make of the impact we see and why they occur. Of the 9 defenseman I have presented charts for the past few days (the 7 above as well as Weaver and Salvador in my previous post) it seems that the majority of them have all but one or two of their seasons consistently boosting or inhibiting their teams save percentage. More investigation is needed as to why but I am becoming fairly confident that this is a repeatable talent. There is just too much consistency to consider it purely random.

 

Jan 232013
 

One of the challenges in hockey analytics, or any type of data analysis, is how to best visualize data in a way that is exceptionally informative and yet really simple to understand. I have been working on a few things can came up with something that I think might be a useful tool to understand how a player gets utilized by his coach.

Let’s start with some background. We can get an idea of how a player is utilized by looking at when the player gets used and how frequently he gets used.  Offensive players get more ice time on the power play and more ice time when their team is trailing and needs a goal. Defensive players get more ice time on the PK and when they are protecting a lead. This all makes sense, but the issue is some teams spend more time on the PP or PK than others while bad teams end up trailing more than good teams and leading less. This means doing a straight time on ice comparison between players on different teams doesn’t always accurately depict the usage of the player. If a player on the Red Wings plays the same number of minutes with the lead as a player on the Blue Jackets it doesn’t mean the players are used int he same way.  The Blue Jackets will lead a game significantly less than the Red Wings thus in the hypothetical example above the Blue Jackets are depending on their player a higher percent of the time with a lead than the Red Wings are their player.

To get around this I looked at percentages. If Player A played 500 minutes with a lead and his team played a total of 2000 minutes with a lead during games which Player A played, then Players A’s ice time with a lead percentage would be 25%. In games in which Player A played he was used in 25% of the teams time leading. I can calculated these percentages for any situation from 5v5 to 4v5 or 5v4 special teams to leading and trailing situations. The challenge is to visualize the data in a clear and understandable way. To do this I use radar charts. Lets look at a couple examples so you get an idea and we’ll use players that have extreme and opposite usages: Daniel Sedin and Manny Malhotra.

For those not up to speed on my terminology f10 is zone start adjusted ice time which ignores the 10 seconds after a face off in either the offensive or defensive zone.

The charts above are largely driven by PP and PK ice time but players that are used more often in offensive roles will have their charts bulge to the top and top right while those in more defensive roles will have their charts bulge more to the bottom and bottom left. Also, the larger the ‘polygon’ the more ice time and more relied on the player is. In the examples above, Sedin is clearly used more often in offensive situations and clearly gets more ice time.

Let’s now look at a player who is used in a more balanced way, Zdeno Chara.

That is a chart that is representative of a big ice time player who plays in all situations. We can then take it a step further and compare players such as the following.

In normal 5v5 situations Gardiner was depended on about as much as Phaneuf, but Phaneuf was relied on a lot more on special teams and a bit more when protecting a lead. Of course, you can also compare across teams with these charts:

Phaneuf and Chara were depended on almost equally in all situations except on the PP where Phaneuf was used far more frequently.

I am not sure where I will go with these charts but I think I’ll look at them from time to time as I am sure they will be of use in certain situations and I have a few ideas as to how to expand on them to make them even more interesting/useful.