Mar 142013

I often see people using zone starts and/or quality of competition as a way to justify any players unexpectedly poor or unexpectedly good play. Player X has a bad goal or corsi ratio because he plays all the tough minutes (i.e. the defensive zone starts and against the oppositions best lines). I am pretty certain that quality of competition is vastly over emphasized (everyone plays against everyone to some extent) and is vastly overshadowed by individual skill and quality of teammates, and I think zone starts do as well.

Eric Tulsky at NHL posted a good review of the research into the zone start effects on corsi statistics and I recommend people give that a read. I want to look into the issue a little further though. Most of the attempts to identify the impact of zone starts on a players stats have been inferred by looking at the league-wide correlations or by actual counting of how many shots are taken after a zone face off. Both of these have their faults. As Eric Tulsky pointed out, taking a correlation of every players corsi with their zone start stats doesn’t take into account that it is the top line players that usually get the offensive zone starts and thus this likely over estimates the impact as these players do take more shots regardless of their zone start. Eric Tulsky also took the time to count the number of fenwick events that occur between an offensive zone face off and the time the puck leaves the offensive zone and estimated that to be 0.31. This would imply that every extra offensive zone start a player takes is worth 0.31 fenwick events. Of course, this doesn’t take into account that the best offensive players in the league typical get more  offensive zone starts but it also doesn’t consider what happens after the puck leaves the zone. If the puck leaves the zone under the opposing teams control there is probably a negative fenwick effect for the next several seconds of play reducing the 0.31 number further.

I want to get beyond these issues by taking a look at how zone starts affect individual players. I have previously argued that after 10 seconds of an offensive/defensive zone face off the majority of the benefit (or penalty) of an offensive (or defensive zone) face off has worn off. I wanted to take it a bit further to be sure that there is no residual effect and chose to conduct this analysis using a 45 second cut off. So, any time within 45 seconds of an offensive or defensive zone face off with no other stoppages in play will be eliminated in my face off adjusted data. This should eliminate pretty much every second of every shift that started with an offensive or defensive zone face off leaving just the play that occurred after a neutral zone face off or on the fly changes. I am going to call this ice time F45 ice time and it will represent ice time that is not in any way affected by zone starts. With this in mind, I will take a look at the differences between straight 5v5 stats and the F45 stats and the differences will give me an indication of how significant zone starts impact a players stats.

To do this I will look at both corsi for and corsi against stats on a per 20 minutes of ice time basis. It should be noted that corsi rates are about 7.5% higher during the f45 play (goal rates are ~15% higher!) so I will reduce the f45 corsi rates by 7.5% to account for this and conduct a fair comparison (previous zone start studies may have been impacted by this as well). Now, let’s take a look at eight players (Manny Malhotra, Dave Bolland, Brian Boyle, Jay McClement, Tanner Glass, Brandon Sutter, Adam Hall, and Taylor Pyatt) with an excess of defensive zone starts.

OZ% DZ% OZ%-DZ% FF20 FA20 FF%
Malhotra 12.2 54.6 -42.4 -3.09% 1.09% -1.0%
Bolland 19.8 40.5 -20.7 8.94% -5.25% 3.5%
B. Boyle 21.0 40.2 -19.2 2.87% 8.74% 0.3%
McClement 24.8 41.9 -17.1 -0.31% 1.34% -0.4%
Glass 20.5 37.1 -16.6 4.39% -6.00% 2.6%
Sutter 23.1 36.6 -13.5 -2.67% 2.32% -1.2%
Hall 20.7 33.9 -13.2 -4.06% 4.59% -2.2%
Pyatt 24.0 36.4 -12.4 0.38% -0.25% 0.2%
Average 20.8 40.2 -19.4 0.81% 0.82% 0.23%

The FF20 and FA20 columns show the % change in from 5v5 play to F45 play and the FF% column shows the 5v5 FF% – F45 FF%. The averages are a straight average, not weighted for ice time or zone starts. For players that have a significant defensive zone bias we would expect their F45 play to exhibit an increase in FF20 and a decrease in FA20 resulting in an increase in FF%. In bold are the circumstances where this in fact did happen. As you can see, this isn’t the majority of the time. It is actually kind of surprising that these heavily defensive zone start biased players didn’t see a significant and systematic improvement in their fenwick rates.

Now, let’s take a look at eight players (Henrik Sedin, Patrick Kane, Maian Gaborik, Justin Abdelkader, Kyle Wellwood, Tomas Vanek, John Tavares, Jason Arnott) who had a heavy offensive zone start bias.

OZ% DZ% OZ%-DZ% FF20 FA20 FF%
H. Sedin 49.3 16.2 33.1 -3.72% 1.81% -1.4%
P. Kane 41.4 20.3 21.1 5.94% 4.66% 0.3%
Gaborik 39.0 22.8 16.2 0.60% 2.32% -0.4%
Abdelkader 37.5 26.0 11.5 3.93% 3.49% 0.1%
K. Wellwood 36.9 27.6 9.3 4.54% -2.32% 1.7%
Vanek 36.2 27.2 9.0 -3.39% 1.06% -1.1%
Tavares 35.8 27.2 8.6 -2.39% 1.83% -1.0%
Arnott 36.4 28.0 8.4 -3.41% 1.81% -1.3%
Average 39.1 24.4 14.7 0.26% 1.83% -0.39%

For offensive zone start biased players we would expect to see their FF20 decrease, FA20 increase and FF% decrease when we remove their zone start bias. This is mostly true for FA10 (only Wellwood deviated from expectations) but less true for FF20 and FF% and overall the adjustments were relatively minor. Henrik Sedin had the greatest negative impact to his FF% but it only took him from a 55.2% fenwick player to a 53.8% fenwick player which is still pretty good. This could very well be an upper bound on the benefit of excessive offensive zone starts.

Eric Tulsky also presented a paper at the recent Sloan Sports Analytics Conference in which he suggested that a successful zone entry via carrying the puck in is worth upwards of 0.60 fenwick and upwards of 0.28 fenwick on a dump in. As pointed out earlier, Eric Tulsky counted o.31 fenwick between an offensive zone face off and the puck clearing the zone so and if the other team is clearing the zone with control of the puck, it is certainly possible that they will generate almost as many shots on their subsequent counter-rush essentially negating much of the benefit of the offensive zone start. Without studying zone exits and how frequently zone exists result in successful zone entries into opposing teams end we won’t know for sure, but the data shown above indicates that this might be the case.

The next question that might be worth exploring is, if there is no significant benefit to starting your offensive players in the offensive zone, is there a penalty? For example, might it be better for the Canucks to start the Sedin’s solely in the defensive and neutral zones on the theory that their talent with the puck will allow them to more frequently carry the puck into the offensive zone which, as Eric Tulsky showed, more frequently results in shots and goals. I am not certain of that but might be worthy of further investigation.  I suspect again any benefit/penalty of any zone start deployment will largely be overshadowed by the players individual ability and the quality of their line mates. The ability to win puck battles, control the puck and move it up the ice is the real driver of stats, not usage of the player.

All of this is to say that coaching strategy (at least player usage strategy) is probably not a significant factor in the statistical performance of the players or the outcomes of games and I suspect, as I previously found, the majority of the benefit of an offensive zone start is those situations where you win a face off, take a shot resulting in a goal or the goalie catching it or covering it for another face off.  If the play goes beyond that individual talent (puck retrieval for example) takes over and the opposition will get an opportunity to counter attack. This is why, as I previously determined, eliminating the first 10 seconds after a face off is sufficient for eliminating the majority of the effects of a zone start and even then, the effects are probably not as significant as we think they should be.


Mar 082013

Cam Charron has an interesting post on the state of Hockey Analytics over at The Score and how hockey executives are a step behind the hockey analytics bloggers but I have to disagree with one statement that Charron made.

There’s a reference in the Friedman piece to Craig MacTavish walking around looking for the “Aha!” moment when it comes to hockey analytics. I don’t think MacTavish has realized that half the hockey world is a step ahead of him in that regard. The “Aha!” moment comes when you realize that shots are a hell of a lot more predictive than goals for determining future events. As soon as you realize that hockey is a game between two teams trying to take shots on goal, I think the rest of it falls into place.

The problem with that thinking is that the minute we think hockey is all about shots and not goals the whole system could fall apart.  We know that shot quality exists. It’s a fact of life. A 45′ shot is generally not nearly as tough as a 10′ shot. A shot from 20′ after a cross ice pass is more difficult than a shot from 20′ on a two on two rush before the guys turn back for a line change. A screened shot from the point is more difficult than an unscreened shot from the point. Shot quality in that sense exists and is undisputed. The only reason shot analytics work is if over a large enough sample the quality of shots averages out such that the average quality of shot for one team is more or less equal to the average quality of shot for another team. I differ with some the extent that this is the case, but for this discussion I’ll go along with that premise. Now the problem is, when hockey starts to incentivize shots rather than goals I am not certain that that premise will hold up. There are lots of time a player could shoot the puck, but chooses not to because it is not a good scoring chance. If we start rewarding players on the basis of shot totals and that player starts shooting in those bad scoring chance situations the premise by which shot analytics is based on falls apart. Hockey at its core is, and always will be, about scoring goals. The fact that shot differentials correlate highly with winning is an interesting observation, and maybe even a useful one, but to change the focus of the game to shot differentials from goals differentials is not likely a strategy that will work in the long run.

Positive shot differentials is a result of good play and not because a team chose shot differentials as their goal and achieved it. The reality is, to generate positive shot differential you need to:

  1. When you have control of the puck you generate an offensive opportunity from that puck possession more frequently and you give up control of the puck less frequently.
  2. When you do not have control of the puck you force the opposing team to give up the puck more frequently and generate an offensive opportunity less frequently.
  3. You gain possession more frequently than the opposition be that through winning face offs or winning the puck battles after shot attempts.

If you can win the puck battles, give away the puck less frequently and force the opposition to turnover the puck more you should win the shot differential contest. I suspect shot differential is highly correlated with winning because good teams do those three things better than their opposition and not choose to shoot more often than their opposition. We really need metrics to measure those three things but unfortunately we don’t have them. The work being done on carry the puck into the offensive zone vs dumping the puck in is valuable because it hits at the heart of those good attributes (i.e. what is the best way to generate an offensive opportunity when we have possession of the puck).

This isn’t to suggest that looking at shot totals is a bad thing. So long as we live in a world where driving shots is not the primary goal, shots totals can act as a proxy for identifying players who might have some of those other good attributes and since we have no good metric for measuring them. We just have to be careful that we aren’t identifying systems that result in more shots but not more good shots. Again, shots is not the goal, goals are.

Furthermore, it is quite possible that shot differential analytics can result in a value proposition for GMs. In my post last week about the declining predictive value of corsi/fenwick I showed that as sample sizes increase corsi/fenwick does a poorer job of predicting future events at the team level than with smaller sample sizes where as the percentages and goal metrics maintain or improve their predictive value. In that post I deliberately was careful about drawing any conclusions about what it meant because, to be honest, I am not completely sure what it means though I do have a couple of theories. One is that it could mean that corsi/fenwick is largely driven by the depth of the team and for many teams the second and third lines have a fair bit of turnover over the course of 2-3 years (where as elevated shooting percentage or save percentage is largely driven by the elite players who don’t change teams nearly as often). If GMs aren’t evaluating second-tier players using shot differential metrics they may not be replacing the players with similarly talented (shot differential-wise) players. If this were true, it could mean that this is a flaw in current thinking and that a smart GM could exploit this flaw but again by filling his second and third lines with positive shot differential players. This could give his team the depth it needs to win. It is just a theory but one worth exploring more.

In the end though, hockey is all about out scoring the opponent, not out shooting them. Always has been, always will be, and that is the way it should be. Realizing that that shot differentials is highly correlated with winning is not the ‘aha’ moment in the sense that all of hockey should change focus to out shooting over out scoring at the cost of shot quality because that won’t work. The focus always has to be how to generate more shots from good scoring plays, not just generating more shots.


Mar 062013

One of the surprise player performances so far this season is that of Jakub Voracek. Voracek currently sits tied for 7th in points with 10 goals and 27 points in 24 games.  That puts him on pace to score 54 points in this lock-out shortened 48 game season which is 4 points more than he has scored in any 82 game season (career best was  50 points in 2009-10 in 81 games).

Last season when Rick Nash was on the trade block I wrote an article about Nash and in it I had a few comments about Jakub Voracek as part of a WOWY analysis. Here is what I wrote:

Nash played best when he was paired up with Voracek and Brassard and only Voracek, Brassard and Huselius made Nash a better offensive player when playing with him.  Vermette, Umberger and Malhotra were drags on his offensive numbers.  When playing apart, Voracek’s numbers are better than Nash’s.  Same for Brassard’s (who is doing it again this year, 0.782 GF20 vs Nash’s 0.613 when apart).  As an aside, the numbers suggest that Voracek is a very good offensive player  and it was probably a big mistake to trade him.  It also suggest that the Flyers aren’t getting full value from him by playing him primarily with Maxime Talbot.  If someone acquired Voracek and put him in the right situations, he could be the next Joffrey Lupul.

Voracek wasn’t traded but the departure of James van Riemsdyk and Jaromir Jagr opened up some spots on the top two lines and Voracek got a promotion from playing mostly with Talbot to playing with Claude Giroux and getting lots of powerplay time.  The results of that move are, as I predicted, very Joffrey Lupul like. Lupul put up solid but unspectacular numbers while mostly been given second line minutes and secondary power play minutes for the majority of his career. Lupul’s numbers looked unspectacular but were actually quite good considering his usage as a secondary offensive player and the quality of line mates he played with. When Lupul came to Toronto and was put on a line with another elite offensive player, given first line minutes, and first power play unit minutes, he started putting up high end offensive numbers. It wasn’t so much that Lupul had a break out season or that he had a career year, its more than he was finally given an opportunity to play with top end talent and given first line minutes.  The exact same thing happened with Voracek.  He put up solid numbers while given secondary minutes in secondary offensive roles and just needed to be given a chance to prove his worth as a first line player with quality line mates. Now he has been given that chance and the results are clear. He is a high end offensive talent.


Feb 272013

The last several days I have been playing around a fair bit with team data and analyzing various metrics for their usefulness in predicting future outcomes and I have come across some interesting observations. Specifically, with more years of data, fenwick becomes significantly less important/valuable while goals and the percentages become more important/valuable. Let me explain.

Let’s first look at the year over year correlations in the various stats themselves.

Y1 vs Y2 Y12 vs Y34 Y123 vs Y45
FF% 0.3334 0.2447 0.1937
FF60 0.2414 0.1635 0.0976
FA60 0.3714 0.2743 0.3224
GF% 0.1891 0.2494 0.3514
GF60 0.0409 0.1468 0.1854
GA60 0.1953 0.3669 0.4476
Sh% 0.0002 0.0117 0.0047
Sv% 0.1278 0.2954 0.3350
PDO 0.0551 0.0564 0.1127
RegPts 0.2664 0.3890 0.3744

The above table shows the r^2 between past events and future events.  The Y1 vs Y2 column is the r^2 between subsequent years (i.e. 0708 vs 0809, 0809 vs 0910, 0910 vs 1011, 1011 vs 1112).  The Y12 vs Y23 is a 2 year vs 2 year r^2 (i.e. 07-09 vs 09-11 and 08-10 vs 10-12) and the Y123 vs Y45 is the 3 year vs 2 year comparison (i.e. 07-10 vs 10-12). RegPts is points earned during regulation play (using win-loss-tie point system).

As you can see, with increased sample size, the fenwick stats abilitity to predict future fenwick stats diminishes, particularly for fenwick for and fenwick %. All the other stats generally get better with increased sample size, except for shooting percentage which has no predictive power of future shooting percentage.

The increased predictive nature of the goal and percentage stats with increased sample size makes perfect sense as the increased sample size will decrease the random variability of these stats but I have no definitive explanation as to why the fenwick stats can’t maintain their predictive ability with increased sample sizes.

Let’s take a look at how well each statistic correlates with regulation points using various sample sizes.

1 year 2 year 3 year 4 year 5 year
FF% 0.3030 0.4360 0.5383 0.5541 0.5461
GF% 0.7022 0.7919 0.8354 0.8525 0.8685
Sh% 0.0672 0.0662 0.0477 0.0435 0.0529
Sv% 0.2179 0.2482 0.2515 0.2958 0.3221
PDO 0.2956 0.2913 0.2948 0.3393 0.3937
GF60 0.2505 0.3411 0.3404 0.3302 0.3226
GA60 0.4575 0.5831 0.6418 0.6721 0.6794
FF60 0.1954 0.3058 0.3655 0.4026 0.3951
FA60 0.1788 0.2638 0.3531 0.3480 0.3357

Again, the values are r^2 with regulation points.  Nothing too surprising there except maybe that team shooting percentage is so poorly correlated with winning because at the individual level it is clear that shooting percentages are highly correlated with goal scoring. It seems apparent from the table above that team save percentage is a significant factor in winning (or as my fellow Leaf fans can attest to, lack of save percentage is a significant factor in losing).

The final table I want to look at is how well a few of the stats are at predicting future regulation time point totals.

Y1 vs Y2 Y12 vs Y34 Y123 vs Y45
FF% 0.2500 0.2257 0.1622
GF% 0.2214 0.3187 0.3429
PDO 0.0256 0.0534 0.1212
RegPts 0.2664 0.3890 0.3744

The values are r^2 with future regulation point totals. Regardless of time frame used, past regulation time point totals are the best predictor of future regulation time point totals. Single season FF% is slightly better at predicting following season regulation point totals but with 2 or more years of data GF% becomes a significantly better predictor as the predictive ability of GF% improves and FF% declines. This makes sense as we earlier observed that increasing sample size improves GF% predictability of future GF% while FF% gets worse and that GF% is more highly correlated with regulation point totals than FF%.

One thing that is clear from the above tables is that defense has been far more important to winning than offense. Regardless of whether we look at GF60, FF60, or Sh% their level of importance trails their defensive counterpart (GA60, FA60 and Sv%), usually significantly. The defensive stats more highly correlate with winning and are more consistent from year to year. Defense and goaltending wins in the NHL.

What is interesting though is that this largely differs from what we see at the individual level. At the individual level there is much more variation in the offensive stats indicating individual players have more control over the offensive side of the game. This might suggest that team philosophies drive the defensive side of the game (i.e. how defensive minded the team is, the playing style, etc.) but the offensive side of the game is dominated more by the offensive skill level of the individual players. At the very least it is something worth of further investigation.

The last takeaway from this analysis is the declining predictive value of fenwick/corsi with increased sample size. I am not quite sure what to make of this. If anyone has any theories I’d be interested in hearing them. One theory I have is that fenwick rates are not a part of the average GMs player personal decisions and thus over time as players come and go any fenwick rates will begin to vary. If this is the case, then this may represent an area of value that a GM could exploit.


Feb 182013

I have some new and exciting enhancements to 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).


Feb 162013

Ok, let me justify that headline a little before people get all over me.  He isn’t completely terrible as in he shouldn’t be in the league terrible.  He’s just a terrible first line center, and probably not a very good second or third line center either (at least not until he improves defensively). He’d be an OK 4th liner and injury fill in depth player at close to minimum salary. Let me explain.

The last 2 seasons Bozak has mostly played with Phil Kessel and Joffrey Lupul became his second winger when he joined the Leafs. Those are two pretty solid wingers to play with so lets look at Bozak’s production with those two solid players.

I want to compare Bozak to other top 9 players and conveniently if we look at all forwards with 1250 minutes of 5v5 zone start adjusted ice time over the past 2 seasons we come up with 270 players which is precisely an average of 9 per team, or 3 lines per team. So, how does Bozak rank among these players?

So, despite playing predominately with first line players his individual offensive stats are at a 3rd line level.

So, what about PP situations?  There are 169 forwards with 250 5v4 PP minutes over the previous two seasons while Bozak has played 417:28 which puts him among the top 65 forwards in the league. How has Bozak fared?

Think about that for a minute.  Of 169 forwards with >250 5v4 PP minutes over the past 2 seasons he ranks 5th last in shots/60 and has the 30th worst first assists/60 rankings. That means he is playing on the PP but isn’t shooting much and isn’t a primary set up man for the shooters either.

The only redeeming factors for Bozak is that he seems to be developing into a really good face off guy and he seems to be able to play with an elevated shooting percentage. His 5v5 ZS adjusted shooting percentage ranks 30th of 270 over the past 2 seasons while his 5v4 PP shooting percentage ranks 14th of 169. If you look at Bozak’s shot locations for last season you will see that the majority of Bozak’s shots and goals come from close in and 5 of his 11 5v5 goals last season came on rebounds.

So, to summarize, Tyler Bozak doesn’t shoot much, isn’t a great playmaker, isn’t good defensively (explained elsewhere) and yet coaches seem to insist on using him as a first line center. His main contribution to a team is winning face offs and going to the opposing teams net waiting for the puck to come to him so he can pot an easy close in goal. It is not completely unreasonable to believe that a guy like David Steckel could give you as good or better performance on face offs and similar lackluster offensive results with better defensive play if given the same opportunities to play with top end players that Tyler Bozak has had. That isn’t to say I want Steckel to be the Leafs new first line center, I was just trying to put Bozak’s usefulness (or lack of) into perspective.


Feb 152013

I have just added individual stats to You can access this data by conducting the normal player search but instead of selecting goals, shots, fenwick or corsi under ‘Ratings Type’ select individual stats. The individual stats included are goals, assists, first assists, points, shots, individual fenwick, individual corsi, the per 60 minutes of ice time rates for each of those stats and shooting percentage.

With this you can now find out things such as the forwards with the highest first assists per 60 minutes of 5v5 ice time over the past 5 seasons.  For the record, the top 10 are Crosby, H. Sedin, Malkin, Ribeiro, Thornton, Savard, D. Sedin, J.P. Dumont, Giroux and Spezza.  Yeah, quite the group of players for J.P. Dumont to be included in.

Individual stats are also included on the individual player pages so you can see how a players stats have changed over the years.

As usual, if you find and problems with the new features (or existing ones) be sure to let me know.

Update: I had a question about what individual fenwick and individual corsi were. Individual fenwick is shots taken + shots taken that missed the net while individual corsi is shots taken + shots taken that missed the net or were blocked.

Feb 112013

When I updated 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
SUBBAN, P.K. 1.025

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

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
EAGER, BEN 1.029

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.


Feb 012013

Last week I introduced player TOI usage charts and one use I thought they had was to look at how a players usage changed during the downside of their careers. Today I will do just that by looking at Nicklas Lidstrom’s TOI charts over the last 5 seasons. Consider this an extension to my earlier article where I took a look at Lidstrom’s last few seasons of his career. Let’s get right at it with his 5v5 chart.



Lidstrom’s last big season was clearly 2007-08 and every year since he has been below his 2007-08 levels in terms of 5v5 ice time. What is interesting to note is how little (relatively) ice time he had during the 2010-11 season, the year he won the Norris Trophy. I think it was a big mistake that he was awarded the trophy that season and this is just a little more evidence of that. In fact, Lidstrom was 4th on the Red Wings in ESTOI/Game by defensemen which is why his TOI% in the chart above were so low that year. Rafalski retired in the summer of 2011 which meant Lidstrom would get a boost to his ice time in 2011-12.

So, what about his special teams play?


On the powerplay, Lidstrom maintained his level of playing ~60% of his teams 5v4 power play minutes but his penalty kill ice time dropped significantly over the final 2 seasons of his career.

Based on the above charts, the last year I think you could consider Lidstrom a true heavy work load stud of a defenseman was in 2007-08. He was still awfully good for a couple more years and quite good until he retired but his slow decline in ice time had begun.


Jan 302013

For those familiar with my history, I have been a big proponent that there is more to the game of hockey than corsi and that players can certainly drive on-ice shooting percentage. I have not done much work at the team level, but now that I have team stats up at I figured I’d take a look.

Since shooting percentages can vary significantly over small sample sizes, my goal was to use the largest sample size possible.  As such, I used 5 years of team data (2007-08 through 2011-12) and looked at each teams shooting and save percentages over that time. During those 5 years Vancouver led all teams in 5v5 ZS adjusted save percentage shooting at 10.69% while Columbus trailed all teams with a 8.61% shooting percentage. What’s interesting to note is the top 6 teams are Vancouver, Washington, Chicago, Philadelphia, Boston and Pittsburgh, all what we would consider the teams with the best offensive talent in the league. Meanwhile, the bottom 5 teams are Columbus, Los Angeles, Phoenix, Carolina, and Minnesota, all teams (except maybe Carolina) more associated with defensive play and a defense-first system.

As far as save percentage goes, Phoenix led the league with a 91.83% save percentage while the NY Islanders trailed with an 89.04% save percentage. The top 5 teams were Phoenix, Boston, Anaheim, Nashville, and Montreal.  The bottom 5 teams were NY Islanders, Tampa, Toronto, Chicago and Ottawa. Not surprises there.

As far as sample size goes, teams on average had 7,627 shots for (or against) over the course of the 5 years which gives us a reasonable large sample size to work with.

Now, in order to not use an extreme situation, I decided to compare the 5th best team to the 5th worst team in each category and then determine the probability that their deviations from each other are solely due to randomness.  This meant I was comparing Boston to Minnesota for shooting percentage and Montreal to Ottawa for save percentage.


As you can see, there isn’t a lot of overlap, meaning there isn’t a large probability that luck is the reason for the difference between these two teams 5 year save percentages.  In fact, the intersecting area under the two curves amounts to just a 6.2% chance that the differences are luck driven.  That’s pretty small and the differences between the teams above Boston and below Minnesota would be greater. I think we can be fairly certain that there are statistically significant differences between teams 5 year shooting percentages and considering how much player movement and coaching changes there are over the span of 5 years it makes it that much more impressive. Single seasons differences could in theory (and probably likely are) more significant.


The save percentage chart provides even stronger evidence that there are non-luck factors at play.  The intersecting area under the curves equates to a 2.15% chance that the differences are due to luck alone. There is easily a statistically significant differences between Ottawa and Montreal’s 5 year save percentages. Long-term team save percentages are not luck driven!

So, the next question is, how much does it matter?  Well, the average team takes approximately 1500 5v5 ZS adjusted shots each season. The differences in shooting percentage between the 5th best team and the 5th worst team is 1.27% so that would equate to a difference of 19 goals per year during 5v5 ZS adjusted situations. The difference between the 5th best and 5th worst team in save percentage is 1.5% which equates to a 22.5 goal difference. These are not insignificant goal totals and they are likely driven solely by the percentages.

Now, how does this equate to differences in shot rates? If we take the team with the 5th highest shot rate and apply a league average shooting percentage and then compare it to the team with the 5th lowest shot rate we would find a difference of 17.5 goals over the course of a single season. This is slightly lower than what we saw for shooting and save percentages.

What is interesting is this (the percentages being more important than the shot rates) is not inconsistent with what we have seen at the individual level. In Tom Awad’s “What makes Good Players Good, Part I” post he identified 3 skills that good players differed from bad players. He identified the variation in +/- due to finishing as being 0.42 for finishing (shooting percentage), 0.08 for shot quality (shot location) and 0.30 for out shooting which would equate to out shooting being just 37.5% of the overall difference. I also showed that fenwick shooting percentage is more important than fenwick rates by a fairly significant margin.

Any player or team evaluation that doesn’t take into account the percentages or assumes the percentages are all luck driven is an evaluation that is not telling you the complete story.