Feb 112013
 

When I updated stats.hockeyanalysis.com this season I added new metrics for Quality of Teammates (QoT) and Quality of Competition (Q0C). The QoC metrics are essentially the average Hockey Analysis Rating (HARO for offense, HARD for defense and HART for overall) of the opponents that the player plays against. What is interesting about these ratings, as compared to those found elsewhere, is that I split the QoC rating up into offensive and defensive metrics. Thus, there is a QoC HARO rating for measuring the offensive quality of competition, a QoC HARD for measuring the defensive quality of competition, and a QoC HART for overall quality of compentition (basically the average of QoC HARO + QoC HARD). The resulting metrics give a result that is above 1.00 for above average competition and below 1.00 for below average competition and 1.00 would be average competition.

Let’s take a look at defensemen first and take a look at the defensemen who have the highest QoC HARO during 5v5close situations over the previous 2 seasons. This should identify the defensemen who have face the best offensive players and her are the top 15.

Player Name HARO QOC
GIRARDI, DAN 1.036
CHARA, ZDENO 1.036
GARRISON, JASON 1.035
MCDONAGH, RYAN 1.034
WEAVER, MIKE 1.033
GORGES, JOSH 1.031
ALZNER, KARL 1.029
GLEASON, TIM 1.026
SEABROOK, BRENT 1.025
BOYCHUK, JOHNNY 1.025
SUBBAN, P.K. 1.025
PHANEUF, DION 1.025
CARLSON, JOHN 1.022
HAMONIC, TRAVIS 1.021
LIDSTROM, NICKLAS 1.021

That’s actually a pretty decent representation of defensive defensemen though there is a bias towards the eastern conference in large part because the eastern conference has more offense (the top 4 teams in goals for last year were eastern conference teams while 9 of the 11 lowest scoring teams were from the western conference).

Now, lets take a look at the forwards with the toughest offensive competition.

Player Name HARO QOC
SUTTER, BRANDON 1.032
PERRON, DAVID 1.032
CALLAHAN, RYAN 1.031
FISHER, MIKE 1.03
SYKORA, PETR 1.029
BOLLAND, DAVE 1.028
ZAJAC, TRAVIS 1.028
ELIAS, PATRIK 1.028
BERGERON, PATRICE 1.027
HAGELIN, CARL 1.027
ZUBRUS, DAINIUS 1.027
PLEKANEC, TOMAS 1.027
WEISS, STEPHEN 1.026
RECCHI, MARK 1.026
ERAT, MARTIN 1.025

Not a lot of surprises there.  They are mostly third line defense first players (IMO Brandon Suter is the best defensive center in the NHL and this is just more evidence of why) or quality 2-way players though as you go further down the list you start to see more offensive players showing up like Alfredsson and Spezza which is probably evidence of a coach wanting to line match top line against top line instead of a checking line against top line.

Where things get interesting is looking at who is 300th on the list of forwards in HARO QoC. It’s none other than Manny Malhotra of massive defensive zone start bias fame. Malhotra’s HARO QoC is just 0.980 while the Canucks center who is assigned mostly offensive zone starts, Henrick Sedin, has a HARO QoC 0.994, which isn’t real difficult but is somewhat higher than Malhotra’s. So, despite all those defensive zone starts by Malhotra (presumably because he is considered a better defensive player), Henrik Sedin plays against tougher offensive opponents. How can this be? Despite Malhotra’s significant defensive zone start bias his five most frequent 5v5close opponent forwards over the previous 2 seasons are David Jones, Matt Stajan, Tim Jackman, Joran Eberle, Matt Cullen. Aside from Eberle those guys don’t really scare you much. It seems Malhotra was facing Edmonton’s top line but not Calgary’s, Minnesota’s or Colorado’s. Henrik Sedin’s top 5 opposition forwards are Dave Bolland, Dany Heatley, Curtis Glencross, Olli Jokinen and Jarome Iginla. Beyond that you have Backes, O’Reilly, Bickell, Thornton, Zetterberg, and Getzlaf. Despite the massive offensive zone start bias, it seems the majority of teams are still line matching power vs power with the Sedins. The conclusion is defensive zone starts does not immediately imply playing against quality offensive players. It can be argued that despite the defensive zone starts Manny Malhotra plays relatively easy minutes.

Using a rigid zone start system like the Vancouver Canucks do actually makes it easier for opposing teams to line match on the road as they know who you are likely to be putting on the ice depending on where the face off is. If the San Jose Sharks want to avoid a Thornton against Malhotra matchup, just don’t start Thornton in the offensive zone. Here are all the forwards with >750 5v5close minutes and at least 40% of the face offs they were on the ice for being in the defensive zone along with their HARO QoC.

Player Name HARO QOC
Manny Malhotra 0.980
Jerred Smithson 0.977
Max Lapierre 0.970
Adam Burish 0.982
Steve Ott 0.993
Jay McClement 0.983
Sammy Pahlsson 1.014
Brian Boyle 1.010
Dave Bolland 1.028
Kyle Brodziak 1.002
Matt Cullen 0.998
Paul Gaustad 0.993

Only 4 of the 12 heavy defensive zone start forwards faced opposition that was above average in terms of quality while the majority of them rank quite poorly.

It is also interesting to see who plays against the best defensive forwards.  One might assume it is elite offensive first line players but as we saw above, teams seemed to want to avoid matching up top offensive players against Manny Malhotra. So, let’s take a look.

Player Name HARD QOC
FRASER, COLIN 1.044
BOLL, JARED 1.043
MAYERS, JAMAL 1.037
JACKMAN, TIM 1.035
MACKENZIE, DEREK 1.032
ABDELKADER, JUSTIN 1.031
CLIFFORD, KYLE 1.031
EAGER, BEN 1.029
BELESKEY, MATT 1.028
MILLER, DREW 1.028
KOSTOPOULOS, TOM 1.027
MCLEOD, CODY 1.025
NICHOL, SCOTT 1.024
WINCHESTER, BRAD 1.023
PAILLE, DANIEL 1.021

Pretty much only tough guys and 3rd/4th liners on that list. Teams are deliberately using the above players in situations that avoid them facing top offensive players and as a result are facing other teams third and fourth lines and thus are facing more defensive type players.

The one conclusion we can draw from this analysis is that quality of competition is driven by line matching techniques more so than zone starts.

 

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.