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.

 

 

  4 Responses to “The effect of QoC on Stats:Pavel Datsyuk vs Valtteri Filppula”

  1.  

    I have a question. How will the loss of Sidney Crosby impact the Penguins offense and defense? Sidney has been playing at a GOAT (Greatest of All-Time) level, and some of his teammates have benefited from that.

  2.  

    I have another question (a general one). What is the effect of a 1st or 2nd line Defenseman going down? What about the effect of a 1st or 2nd line Forward? With the Forward, my thought is that the some of the 3rd (and maybe 4th) lines will have to be propelled to the 1st or 2nd lines; these lines are more known for their defense than offense, and because of this, they will possess the puck less, meaning less scoring chances.

    With the Defenseman, the impact would probably be a little more severe (there are less Defensemen than forwards, so it is a lot harder to replace a Defenseman than a Forward). The backup (3rd or 4th line) Defenseman probably isn’t as good defensively as the 1st or 2nd line Defenseman, which means more scoring opportunities for the other team; I would think their offense would decrease as well, because as a Defenseman, it is important to disengage the puck from the opponent and pass it on to the Forwards, which the 3rd or 4th liner might not have as much skill in.

    •  

      Tough question to answer because it depends so much on the team that is losing the player and how deep they are at each position. The majority of teams probably have more depth up front than on defense so losing a defenseman is probably worse in general, but there would be exceptions to that.

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