Apr 012013
 

I have been on a bit of a mission recently to push the idea that quality of competition (and zone starts) is not a huge factor in ones statistics and that most people in general over value its importance. I don’t know how often I hear arguments like “but he plays all the tough minutes” as an excuse as to why a player has poor statistics and pretty much every time I do I cringe because almost certainly the person making the argument has no clue how much those tough minutes impact a players statistics.

While thinking of how to do this study, and which players to look at, I was listening to a pod cast and the name Pavel Datsyuk was brought up so I decided I would take a look at him because in addition to being mentioned in a pod cast he is a really good 2-way player who plays against pretty tough quality of competition. For this study I looked at 2010-12 two year data and Datsyuk has the 10th highest HART QoC during that time in 5v5 zone start adjusted situations.

The next step was to look how Datsyuk performed against various types of opposition. To do this I took all of Datsyuk’s opponent forwards who had he played at least 10 minutes of 5v5 ZS adjusted ice time against (you can find these players here) and grouped them according to their HARO, HARD, CorHARO and CorHARD ratings and looked at how Datsyuk’s on-ice stats looked against each group.

OppHARO TOI% GA20
>1.1 46.84% 0.918
0.9-1.1 34.37% 0.626
<0.9 18.79% 0.391

Lets go through a quick explanation of the above table. I have grouped Datsyuk’s opponents by their HARO ratings into three groups, those with a HARO >1.1, those with a HARO between 0.9 and 1.1 and those with a HARO rating below 0.9. These groups represent strong offensive players, average offensive players and weak offensive players. Datsyuk played 46.84% of his ice time against the strong offensive player group, 34.37% against the average offensive player group and 18.79% against the weak offensive player group. The GA20 column is Datsyuk’s goals against rate, or essentially the goals for rate of Datsyuk’s opponents when playing against Datsyuk. As you can see, the strong offensive players do significantly better than the average offensive players who in turn do significantly better than the weak offensive players.

Now, let’s look at how Datsyuk does offensively based on the defensive ability of his opponents.

OppHARD TOI% GF20
>1.1 35.39% 1.171
0.9-1.1 35.36% 0.994
<0.9 29.25% 1.004

Interestingly, the defensive quality of Datsyuk’s opponents did not have a significant impact on Datsyuk’s ability to generate offense which is kind of an odd result.

Here are the same tables but for corsi stats.

OppCorHARO TOI% CA20
>1.1 15.59% 15.44
0.9-1.1 77.79% 13.78
<0.9 6.63% 10.84

 

OppCorHARD TOI% CF20
>1.1 18.39% 15.89
0.9-1.1 68.81% 18.49
<0.9 12.80% 22.69

I realize that I should have tightened up the ratings splits to get a more even distribution in TOI% but I think we see the effect of QoC fine. When looking at corsi we do see that CF20 varies across defensive quality of opponent which we didn’t see with GF20.

From the tables above, we do see that quality of opponent can have a significant impact on a players statistics. When you are playing against good offensive opponents you are bound to give up a lot more goals than you will against weaker offensive opponents. The question remains is whether players can and do play a significantly greater amount of time against good opponents compared to other players. To take a look at this I looked at the same tables above but for Valtteri Filppula, a player who rarely gets to play with Datsyuk so in theory could have a significantly different set of opponents to Datsyuk. Here are the same tables above for Filppula.

OppHARO TOI% GA20
>1.1 42.52% 1.096
0.9-1.1 35.35% 0.716
<0.9 22.12% 0.838

 

OppHARD TOI% GF20
>1.1 32.79% 0.841
0.9-1.1 35.53% 1.197
<0.9 31.68% 1.370

 

OppCorHARO TOI% GA20
>1.1 12.88% 19.03
0.9-1.1 78.20% 16.16
<0.9 8.92% 14.40

 

OppCorHARD TOI% GF20
>1.1 20.89% 15.48
0.9-1.1 64.94% 17.16
<0.9 14.17% 19.09

Nothing too exciting or unexpected in those tables. What is more important is how the ice times differ from Datsyuk’s across groups and how those differences might affect Filppula’s statistics.

We see that Datsyuk plays a little bit more against good offensive players and a little bit less against weak offensive players and he also plays a little bit more against good defensive players and a little bit less against weak defensive players. If we assume that Filppula played Datsyuk’s and that Datsyuk’s within group QoC ratings was the same as Filppula’s we can calculate what Filppula’s stats will be against similar QoC.

Actual w/ DatsyukTOI
GF20 1.135 1.122
GA20 0.905 0.917
GF% 55.65% 55.02%
CF20 17.08 17.09
CA20 16.37 16.49
CF% 51.05% 50.90%

As you can see, that is not a huge difference. If we gave Filppula the same QoC as Datsyuk instead of being a 55.65% GF% player he’d be a 55.02% GF% player. That is hardly enough to worry about and the difference in CF% is even less.

From this an any other study I have looked at I have found very little evidence that QoC has a significant impact on a players statistics. The argument that a player can have bad stats because he plays the ‘tough minutes’ is, in my opinion, a bogus argument. Player usage can have a small impact on a players statistics but it is not anything to be concerned with for the vast majority of players and it will never make a good player have bad statistics or a bad player have good statistics. Player usage charts (such as those found here or those found here) are interesting and pretty neat and do give you an idea of how a coach uses his players but as a tool for justifying a players good, or poor, performance they are not. The notion of ‘tough minutes’ exists, but are not all that important over the long haul.

 

 

Oct 302012
 

Offensive players generally get all of the attention but defensive players are often just as valuable to a team.  Ask any NHL fan who the top offensive centers in the league are and they will quickly ramble off a few names from Crosby to Stamkos to Getzlaf to Malkin, etc.  Ask a fan to list the top defensive centers and the task becomes a little more difficult.  So, I decided to look into defensive centers a little further.

What makes a valuable defensive center?  Well, they should play against tough competition, they should give up fewer goals than expected, and they should be trusted to play a lot on the penalty kill.  So, with that in mind, I decided to set the following parameters in my defensive center search.

1.  I limited myself to players who have played >2000 minutes of 5v5 zone start adjusted ice time over the past three seasons.

2.  I only considered players who had an average opposition goals for per 20 minutes of ice time above 0.800 (i.e. only consider players who played against tough offensive opponents, must have OppGF20>0.800).

3. I then eliminated all forwards with a goals against per 20 minutes of ice time >0.800 (i.e. eliminate players who didn’t get good defensive results, must have GA20<0.800).

4.  I then took each players on ice goals against rate and divided it by his line mates goals against rate to ensure that they are performing better than their line mates and make their line mates better defensively (GA20/TMGA20 < 1.00).

5.  I then eliminated any players who didn’t have >300 minutes of 4v5 PK ice time over the past 3 seasons.

After doing this I got the following list of players sorted by GA20/TMGA20, or in English  sorted by how much better defensively they were than their line mates.

  1. Brandon Sutter
  2. Samuel Pahlsson
  3. Mikko Koivu
  4. Frans Nielsen
  5. Travis Zajac
  6. Martin Hanzal
  7. Mike Richards
  8. Brooks Laich
  9. Jordan Staal
  10. Joe Pavelski

Honorable Mentions:  Logan Couture, Pavel Datsyuk, Mikhail Grabovski and Alexander Steen missed the cut due to not having enough PK minutes.  Couture would have been slotted second behind Sutter, Datsyuk between Pahlsson and Koivu, and Grabovski and Steen immediately after Hanzal.  Plekanec, Kopitar, Bergeron and Legwand met the PK ice time criteria and would come in after Pavelski except that their line mates had a better GA20 when not playing with them so they were cut from the list.

All in all I am pretty happy with the defensive forward list above.  They all make sense and the only real surprise on the list might be Frans Nielsen but that is mostly because I don’t pay attention to he Islanders (who does really?) and this haven’t really paid much attention to him.  For a player on the lowly Islanders to meet these criteria it probably means he is a pretty good defensive player.

It is interesting to see Sutter and Jordan Staal both make this list as they were traded for each other this past summer.  When I compared these two players after the trade when down I suggested that Sutter is one of the best defensive forwards in the NHL and this certainly backs that up.

What do you think?  Am I missing someone from this list of elite defensive centers?