Apr 122013
 

Now that I have added home and road stats to stats.hockeyanalysis.com I can take a look at how quality of competition differs when the team is at home vs when they are on the road. In theory because the home team has last change they should be able to dictate the match ups better and thus should be able to drive QoC a bit better. Let’s take a look at the top 10 defensemen in HARO QoC last season at home and on the road (defensemen with 400 5v5 home/road minutes were considered).

Player Name Home HARO QOC Player Name Road HARO QOC
GIRARDI, DAN 8.81 MCDONAGH, RYAN 6.73
MCDONAGH, RYAN 8.49 GORGES, JOSH 6.48
PHANEUF, DION 8.46 GIRARDI, DAN 6.03
GARRISON, JASON 8.27 SUBBAN, P.K. 5.95
GORGES, JOSH 8.25 PHANEUF, DION 5.94
GLEASON, TIM 8.21 GUNNARSSON, CARL 5.48
SUBBAN, P.K. 8.19 ALZNER, KARL 5.35
WEAVER, MIKE 7.92 STAIOS, STEVE 5.15
ALZNER, KARL 7.74 TIMONEN, KIMMO 4.95
REGEHR, ROBYN 7.72 WEAVER, MIKE 4.67

There is definitely a lot of common names in each list but we do notice that the HARO QoC is greater at home than on the road for these defensemen. Next I took a look at the standard deviation of all the defensemen with 400 5v5 home/road minutes last season which should give us an indication of how much QoC varies from player to player.

StdDev
Home 3.29
Road 2.45

The standard deviation is 34% higher at home than on the road which again confirms that variation in QoC are greater at home than on the road.  All of this makes perfect sense but it is nice to see it backed up in actual numbers.

 

 

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.

 

 

Mar 202013
 

I generally think that the majority of people give too much importance to quality of competition (QoC) and its impact on a players statistics but if we are going to use QoC metrics let’s at least try and use the best ones available. In this post I will take a look at some QoC metrics that are available on stats.hockeyanalysis.com and explain why they might be better than those typically in use.

OppGF20, OppGA20, OppGF%

These three stats are the average GF20 (on ice goals for per 20 minutes), OppGA20 (on ice goals against per 20 minutes) and GF% (on ice GF / [on ice GF + on ice GA]) of all the opposition players that a player lined up against weighted by ice time against. In fact, these stats go a bit further in that they remove the ice time the opponent players played against the player so that a player won’t influence his own QoC (not nearly as important as QoT but still a good thing to do). So, essentially these three stats are the goal scoring ability of the opposition players, the goal defending ability of the opposition players, and the overall value of the opposition players. Note that opposition goalies are not included in the calculation of OppGF20 as it is assume the goalies have no influence on scoring goals.

The benefits of using these stats are they are easy to understand and are in a unit (goals per 20 minutes of ice time) that is easily understood. GF20 is essentially how many goals we expect the players opponents would score on average per 20 minutes of ice time. The drawback from this stat is that if good players play against good players and bad players play against bad players a good player and a bad player may have similar statistics but the good players is a better player because he did it against better quality opponents. There is no consideration for the context of the opponents statistics and that may matter.

Let’s take a look at the top 10 forwards in OppGF20 last season.

Player Team OppGF20
Patrick Dwyer Carolina 0.811
Brandon Sutter Carolina 0.811
Travis Moen Montreal 0.811
Carl Hagelin NY Rangers 0.806
Marcel Goc Florida 0.804
Tomas Plekanec Montreal 0.804
Brooks Laich Washington 0.800
Ryan Callahan NY Rangers 0.799
Patrik Elias New Jersey 0.798
Alexei Ponikarovsky New Jersey 0.795

You will notice that every single player is from the eastern conference. The reason for this is that the eastern conference is a more offensive conference. Taking a look at the top 10 players in OppGA20 will show the opposite.

Player Team OppGF20
Marcus Kruger Chicago 0.719
Jamal Mayers Chicago 0.720
Mark Letestu Columbus 0.721
Andrew Brunette Chicago 0.723
Andrew Cogliano Anaheim 0.723
Viktor Stalberg Chicago 0.724
Matt Halischuk Nashville 0.724
Kyle Chipchura Phoenix 0.724
Matt Belesky Anaheim 0.724
Cory Emmerton Detroit 0.724

Now, what happens when we look at OppGF%?

Player Team OppGF%
Mike Fisher Nashville 51.6%
Martin Havlat San Jose 51.4%
Vaclav Prospal Columbus 51.3%
Mike Cammalleri Calgary 51.3%
Martin Erat Nashville 51.3%
Sergei Kostitsyn Nashville 51.3%
Dave Bolland Chicago 51.2%
Rick Nash Columbus 51.2%
Travis Moen Montreal 51.0%
Patrick Marleau San Jose 51.0%

There are predominantly western conference teams with a couple of eastern conference players mixed in. The reason for this western conference bias is that the western conference was the better conference and thus it makes sense that the QoC would be tougher for western conference players.

OppFF20, OppFA20, OppFF%

These are exactly the same stats as the goal based stats above but instead of using goals for/against/percentage they use fenwick for/against/percentage (fenwick is shots + shots that missed the net). I won’t go into details but you can find the top players in OppFF20 here, in OppFA20 here, and OppFF% here. You will find a a lot of similarities to the OppGF20, OppGA20 and OppGF% lists but if you ask me which I think is a better QoC metric I’d lean towards the goal based ones. The reason for this is that the smaller sample size issues we see with goal statistics is not going to be nearly as significant in the QoC metrics because over all opponents luck will average out (for every unlucky opponent you are likely to have a lucky one t cancel out the effects). That said, if you are doing a fenwick based analysis it probably makes more sense to use a fenwick based QoC metric.

HARO QoC, HARD QoC, HART QoC

As stated above, one of the flaws of the above QoC metrics is that there is no consideration for the context of the opponents statistics. One of the ways around this is to use the HockeyAnalysis.com HARO (offense), HARD (defense) and HART (Total/Overall) ratings in calculating QoC. These are player ratings that take into account both quality of teammates and quality of competition (here is a brief explanation of what these ratings are).The HARO QoC, HARD QoC and HART QoC metrics are simply the average HARO, HARD and HART ratings of players opponents.

Here are the top 10 forwards in HARO QoC last year:

Player Team HARO QoC
Patrick Dwyer Carolina 6.0
Brandon Sutter Carolina 5.9
Travis Moen Montreal 5.8
Tomas Plekanec Montreal 5.8
Marcel Goc Florida 5.6
Carl Hagelin NY Rangers 5.5
Ryan Callahan NY Rangers 5.3
Brooks Laich Washington 5.3
Michael Grabner NY Islanders 5.2
Patrik Elias New Jersey 5.2

There are a lot of similarities to the OppGF20 list with the eastern conference dominating. There are a few changes, but not too many, which really is not that big of a surprise to me knowing that there is very little evidence that QoC has a significant impact on a players statistics and thus considering the opponents QoC will not have a significant impact on the opponents stats and thus not a significant impact on a players QoC. That said, I believe these should produce slightly better QoC ratings. Also note that a 6.0 HARO QoC indicates that the opponent players are expected to produce a 6.0% boost on the league average GF20.

Here are the top 10 forwards in HARD QoC last year:

Player Team HARD QoC
Jamal Mayers Chicago 6.0
Marcus Kruger Chicago 5.9
Mark Letestu Columbus 5.8
Tim Jackman Calgary 5.3
Colin Fraser Los Angeles 5.2
Cory Emmerton Detroit 5.2
Matt Belesky Anaheim 5.2
Kyle Chipchura Phoenix 5.1
Andrew Brunette Chicago 5.1
Colton Gilles Columbus 5.0

And now the top 10 forwards in HART QoC last year:

Player Team HART QoC
Dave Bolland Chicago 3.2
Martin Havlat San Jose 3.0
Mark Letestu Columbus 2.5
Jeff Carter Los Angeles 2.5
Derick Brassard Columbus 2.5
Rick Nash Columbus 2.4
Mike Fisher Nashville 2.4
Vaclav Prospal Columbus 2.2
Ryan Getzlaf Anaheim 2.2
Viktor Stalberg Chicago 2.1

Shots and Corsi based QoC

You can also find similar QoC stats using shots as the base stat or using corsi (shots + shots that missed the net + shots that were blocked) on stats.hockeyanalysis.com but they are all the same as above so I’ll not go into them in any detail.

CorsiRel QoC

The most common currently used QoC metric seems to be CorsiRel QoC (found on behindthenet.ca) but in my opinion this is not so much a QoC metric but a ‘usage’ metric. CorsiRel is a statistic that compares the teams corsi differential when the player is on the ice to the teams corsi differential when they player is not on the ice.  CorsiRel QoC is the average CorsiRel of all the players opponents.

The problem with CorsiRel is that good players on a bad team with little depth can put up really high CorsiRel stats compared to similarly good players on a good team with good depth because essentially it is comparing a player relative to his teammates. The more good teammates you have, the more difficult it is to put up a good CorsiRel. So, on any given team the players with a good CorsiRel are the best players on team team but you can’t compare CorsiRel on players on different teams because the quality of the teams could be different.

CorsiRel QoC is essentially the average CorsiRel of all the players opponents but because CorsiRel is flawed, CorsiRel QoC ends up being flawed too. For players on the same team, the player with the highest CorsiRel QoC plays against the toughest competition so in this sense it tells us who is getting the toughest minutes on the team, but again CorsiRel QoC is not really that useful when comparing players across teams.  For these reasons I consider CorsiRel QoC more of a tool to see the usage of a player compared to his teammates, but is not in my opinion a true QoC metric.

I may be biased, but in my opinion there is no reason to use CorsiRel QoC anymore. Whether you use GF20, GA20, GF%, HARO QoC, HARD QoC, and HART QoC, or any of their shot/fenwick/corsi variants they should all produce better QoC measures that are comparable across teams (which is the major draw back of CorsiRel QoC.