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.

 

Feb 182013
 

I have some new and exciting enhancements to stats.hockeyanalysis.com for you all today. Charts, Charts, and more Charts.

Before we get to the charts though, let me also mention that I have made some modifications to my HARO, HARD and HART ratings. Most of the change is to the scale and presentation and not so much to the actual formula (though there were some tweaks there too). Instead of 1.00 being an average hockey player, 0 is and the scale has been multiplied by 100 to represent % as opposed to a ratio. So now one should interpret [Shot,Fenwick,Corsi]HARO offensive ratings to mean that when the player was on the ice his team had x% (where x is his rating) more goals [shots, fenwick, corsi] for than expected (as determined by his quality of team mates and quality of competition). This means that a positive value means more goals were scored than expected and a negative value means less goals were expected. A positive value indicates the player boosted his teams offensive performance while a negative value means he was a drag to his teams offense.

For defensive [Shot,Fenwick,Corsi]HARD ratings the effect is opposite. One should interpret the HARD ratings to mean that when the player is on the ice his team gave up x% (where his rating is x) fewer goals [shots, fenwick, corsi] than expected (as determined by quality of teammates and opposition).  So, a 10 HARO rating indicates the player boosted his teams expected goal scoring rate by 10% and a 10 HARD rating indicates the player reduced his teams expected goals against rate by 10%.  The [Shot,Fenwick,Corsi]HART ratings are simply the average of the HARO and HARD ratings.

Now on to the more exciting news, the charts. We all love charts so I have added a bunch for you all to enjoy. When you go to a player page now (i.e. Zdeno Chara) you will find a link named Visualize performance over time. Clicking this link will give you a visual representation of the players performance over the past several seasons starting in 2007-08 if their careers were active then. For example, here is Zdeno Chara’s performance charts. For forwards and defensemen there are 5 charts.

  1. Point production (G/60, A/60, First A/60 and Points/60)
  2. Individual shot, fenwick and corsi rates (shot/60, ifenwick/60, icorsi/60)
  3. HARO, HARD, FenHARO and FenHARD ratings
  4. GoalsFor%, ShotsFor%, FenwickFor% and CorsiFor%
  5. Zone Start %

This should give you a quick visualization of each players performance and how it has changed over time.

For goalies (i.e. Roberto Luongo) the only chart I have right now are 5v5 Zone Start Adjusted Save percentages.

Maybe the charts that will generate the most interest though are the new WOWY charts (sure to make you scream “WOWY!!!”). To access the WOWY charts you simply need to go to a WOWY data page and click on the “Visualize This Table” link at the top of the WOWY table (only for ‘with you’ WOWY, not ‘against you’). This will give you two WOWY bubble charts.  The first one plots teammate ‘with you’ GF% across the horizontal axis and teammate ‘without you’ GF% across the vertical access. The second chart is the same but plots CF% instead. The size of the bubbles are relative to the total TOI With.

In these plots good players will have the majority of their teammates bubbles show up below or to the right of the diagonal line from the bottom left corner to the top right corner and bad players will have the majority of their teammates above or to the left of that line. Players with a lot of teammates in the bottom right quadrant are really good because they are taking sub par players and making them look good. Players with a lot of teammates in the upper left quadrant are  bad because they make good players look bad.

For a look at two polar opposite players, take a look at Zdeno Chara’s WOWY charts compared to Jack Johnson’s WOWY charts (I have linked to the 3 year 5v5 ZS adjusted WOWY charts). Also, on Saturday I wrote a post about how bad Tyler Bozak is and if you want more evidence of that have a look at his 2 year WOWY charts. I am slowly becoming a big believer that WOWY’s are where it is at in evaluating players (though I guess I have always been a believer as this is the core of my HARO, HARD, and HART ratings). The great players are the ones who consistently make their team mates better. The good players are the ones who can really capitalize playing with great players and don’t hold them back. The bad players are those who act as drags on their team mates. These WOWY charts are a quick and easy way of visualizing the different types of players. For the Leafs, Grabovski fits into the ‘great’ category, Kessel into the ‘good’ category and Bozak into the bad.

I have a few more ideas of some charts and tables to add (I’d got some ideas for some more ‘usage’ type charts) but I think this will be the last major update for a while. That said, if you have any ideas of what you would like to see added definitely let me know and I’ll see what I can do. As for updating of the 2012-13 stats, it should be noted that they aren’t updated daily.  I have been trying (fairly successfully so far) to update them every Monday, Wednesday and Friday mornings and I hope to continue that but no guarantees.

Update: I know I said I wouldn’t do any more updates but I have made the WOWY charts better by adding WOWY charts for GF20, GA20, CF20 and CA20. Now we can easily see where a players strengths and weaknesses are (i.e. offense vs defense).

 

Dec 152010
 

I have been pretty quiet here recently not because of a lack of things I want to write about but because I needed to get my stats site up and running first so I can reference it in my writings.  Plus, getting my stats site up has been on my todo list for a real long time.  There will be a lot more stats to come including my with/against on ice pairing stats which I had up a season or two ago and many of you found interesting as well as team stats but for now let me explain what is there.

What you will find there now is my player rating system which produces the following ratings:

HARD – Hockey Analysis Rating – Defense

HARO – Hockey Analysis Rating – Offense

HART – Hockey Analysis Rating – Total

HARD+ – Hockey Analysis Rating – Defense

HARO+ – Hockey Analysis Rating – Offense

HART+ – Hockey Analysis Rating – Total

HARD is the defensive rating and is calculated by taking expected goals against while on the ice and dividing it by actual goals against while on the ice.  The expected goals against is calculated by taking the average of a players team mates goals against per 20 minutes (TMGA20) and averaging it with the players opposition goals for per 20 minutes (OppGF20).  Similarly HARO is calculated by taking a players actual goals for while on the ice and dividing it by the expected goals against while on the ice.  For both, a rating above 1.00 means that the player helped the team perform better than expected when he was on the ice where as a rating below 1.00 means the player hurt the teams performance when he was on the ice.  HART is just an average of HARD and HARO.

HARD+, HARO+ and HART+ are enhanced ratings which result from an iterative process that iteratively feeds HARD and HARO ratings into an algorithm to refine the ratings.  For the most part this iterative process produced a nice stable state but sometimes the algorithm goes haywire and things fail (i.e. for a particular season or seasons).  For this reason I am calling the + ratings experimental but if you don’t see anything wacky (i.e. large differences in every players ratings) they should be considered reliable and probably better ratings than the straight HARD, HARO and HART ratings.  Anything better than 1.00 should be considered better than the average player and anything less than 1.00 should be considered below average.

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