May 152013
 

After last weeks untimely pinch by Dion Phaneuf in game 4 that led to an overtime goal and the Bruins taking a 3-1 lead in the first round series there was a lot of evaluation of Phaneuf as a defenseman both good and bad. I was intending to write an article to discuss the relative merits of Dion Phaneuf and attempt to get an idea of where he stands among NHL defensemen but in the process of researching that I came across some interesting Phaneuf stats that I think deserve their own post so here it is.

My observation was with respect to Phaneuf’s usage and performance when the Leafs are leading and when they are trailing over the previous 3 seasons. Let’s start of by looking at Phaneuf’s situational statistics over the past 3 seasons.

5v5 5v5close 5v5tied Leading Trailing
G/60 0.222 0.175 0.101 0.156 0.408
Pts/60 0.700 0.670 0.660 0.420 1.020
IPP 30.1% 31.1% 34.2% 20.0% 34.5%
GF20 0.773 0.721 0.640 0.692 0.986
GA20 0.841 0.760 0.943 0.865 0.714
GF% 47.9% 48.7% 40.4% 44.4% 58.0%
CF20 18.316 18.113 18.159 15.195 21.542
CA20 20.686 21.418 21.880 22.982 17.223
CF% 47.0% 45.8% 45.4% 39.8% 55.6%
OZ% 28.0% 26.7% 25.2% 24.2% 34.5%
DZ% 31.8% 30.3% 29.7% 37.5% 28.5%
NZ% 40.3% 43.0% 45.0% 38.3% 37.0%
DZBias 103.9 103.6 104.4 113.3 94.0
TeamDZBias 108.9 109 107 115.2 100.8
DZBiasDiff -5 -5.4 -2.6 -1.9 -6.8

Most of the stats above the regular readers should be familiar with but if you are not you can reference my glossary here. The one stat that I have not used before is DZBias. DZBias is defined as 2*DZ% + NZ% and thus anything over 100 indicates the player has a bias towards starting shifts in the defensive zone and anything under 100 the player has a bias towards starting in the offensive zone. I prefer this to OZone% which is OZStarts/(OZStarts+DZStarts) because it takes into account neutral zone starts as well. TeamDZBias is the zone start bias of the Leafs over the past 3 seasons and DZBiasDiff is Phaneuf’s DZBias minus the teams DZBias and provides a zone start bias relative to the team. Anything less than 0 indicates usage is more in the offensive zone relative to his teammates.

So, what does this tell us about Phaneuf.  Well, there isn’t a huge variation in either the zone start usage or the results during 5v5, 5v5close and 5v5tied situations so the focus should be on the differences between 5v5leading and 5v5trailing which are significant.

Typical score effects are when leading a team gives up more shots but of lower quality (defensive shells protect the danger zone in front of the net but allow more shots from the perimeter) and takes fewer shots but of higher quality (probably a result of more odd-man rushes due to pinching defensemen of the trailing team).  Phaneuf seems to take this concept to the extreme but more importantly Phaneuf seems to excel best in an offensive role and struggles in a defensive role. When the Leafs are trailing Phaneuf has  0.408G/60 (10th of 180 defensemen) and 1.02 points/60 (36th of 180 defensemen) but when leading Phaneuf falls to 0.156 G/50 (64th of 177 defensemen) and 0.42 points/60 (137th of 177 defensemen). Furthermore, Phaneuf’s involvement in the offensive zone drops off significantly when leading (IPP drops from 34.5% when trailing to 20.0% when leading).

In terms of on-ice stats, Phaneuf’s CF% drops from 55.6% when trailing (79th of 180 defensemen) to a very poor 39.8% when leading (164th of 177 defensemen).  Some may be thinking this is due to zone starts but Phaneuf is getting above average offensive zone starts both when trailing (ranks 100th of 180 defensemen) and when leading (ranks 154th of 177) and using even the most aggressive zone start adjustments in no way will account for the difference. Similar observations can be made with on-ice goal stats as well. Let’s look at how Phaneuf ranks among defensemen over the past 3 seasons.

Leading (of177) Trailing ( of 180)
GF20 109 25
GA20 125 71
GF% 126 36
CF20 128 31
CA20 174 154
CF% 164 79

That is a pretty significant improvement in rankings when trailing over when leading, especially in the offensive statistics (GF20, CF20). If zone starts aren’t a factor, might line mates be? He are Phaneuf’s most frequent defense partners:

Trailing:  Gunnarsson (364:33, 31.0%), Beauchemin(212:07, 18,0%), Aulie(162:09, 13.8%)

Leading: Gunnarsson (376:16, 32.5%), Aulie(234:17, 20.3%), Beauchemin(166:30, 14.4%)

Playing more with Beauchemin and less with Aulie when trailing ought to help, particularly ones offensive stats, but I doubt that is going to account for that much of a difference. Also, when leading Phaneuf has a 41.2CF% with Gunnarsson and when trailing that spikes to 54.6%. When leading Phaneuf and Beauchemin have a CF% of 37.3% and when trailing that spikes to 57.7%. With Aulie the difference is 36.6% vs 49.3%. Regardless of which defense partner Phaneuf is with, their stats dramatically improve when playing in catch up situation than when in trailing situations.

The same is true for forwards. When protecting a lead Phaneuf plays more with Grabovski and Kulemin but when playing catch up he plays a bit more with Kessel and Bozak but for all of those forwards Phaneuf’s numbers with them are hugely better when playing catch up than when protecting a lead and playing with Grabovski and Kulemin more when playing with a lead should only help his statistics as they are generally considered the Leafs better corsi players.

Let’s take a look at a chart of Phaneuf’s corsi WOWY’s when leading and when trailing.

Leading:

PhaneufLeadingCorsiWOWY201013

As you can see, when leading the majority of Phaneuf’s team mates are to the left of the diagonal line which means they have a better corsi% without Phaneuf than with.

Trailing:

PhaneufTrailingCorsiWOWY201013

When trailing the majority of Phaneuf’s team mates are near or to the right of the diagonal line which means they generally have better corsi% statistics when with Phaneuf than when apart.

So the question arises, why is this? It doesn’t seem to be zone starts. It doesn’t seem to be changes in line mates and it isn’t that the team as a whole automatically becomes a great corsi% team when trailing which Phaneuf could benefit from. When leading Phaneuf’s corsi% is 39.8% which is worse than the teams 41.2% and when trailing Phaneuf’s corsi% is 55.6% which is better than the teams 54.4%. It seems to me that the conclusion we must draw from this is that Phaneuf has been poor at protecting a lead relative to his team mates and we know his team mates have been poor at protecting a lead. Where Phaneuf excels is when he is asked to engage offensively be that when playing catch up hockey or when playing on the PP (Phaneuf’s PP statistics are pretty solid). From the first chart we know that Phaneuf has a slight bias towards more offensive zone starts (relative to his team mates) and when we dig into the numbers further it probably shows that he should be given even more offensive opportunities and given fewer defensive ones because he seems like a much better player when asked to be engaged offensively than when he is asked to be a shut down defenseman.

Acquiring a quality shut down defenseman (ideally two) this off season must be the #1 priority of Maple Leaf management and Phaneuf’s usage must shift further away from multi-purpose heavy work load defenseman to primarily an offensive usage defenseman.

 

May 012013
 

I brought this issue up on twitter today because it got me thinking. Many hockey analytics dismiss face off winning % as a skill that has much value but many of the same people also claim that zone starts can have a significant impact on a players statistics. I haven’t really delved into the statistics to investigate this, but here is what I am wondering.  Consider the following two players:

Player 1: Team wins 50% of face offs when he is on the ice and he starts in the offensive zone 55% of the time.

Player 2: Team wins 55% of face offs when he is on the ice but he has neutral zone starts.

Given 1000 zone face offs the following will occur:

Player 1 Player 2
Win Faceoff in OZone 275 275
Lose Faceoff in Ozone 275 225
Win Faceoff in DZone 225 275
Lose Faceoff in Dzone 225 225

Both of these players will win the same number of offensive zone face offs and lose the same number of defensive zone face offs which are the situations that intuitively should have the greatest impacts on a players statistcs. So, if Player 1 is going to be more significantly impacted by his zone starts than player 2 is impacted by his face off win % losing face offs in the offensive zone must still have a significant positive impact on the players statistics and winning face offs in the defensive zone must must still have a significant negative impact on the players statistics. If this is not the case then being able to win face offs should be more or less equivalent in importance to zone starts (and this is without considering any benefit of winning neutral zone face offs).

Now, I realize that there is a greater variance in zone start deployment than face off winning percentage, but if a 55% face off percentage is roughly equal to a 55% offensive zone start deployment and a 55% face off win% has a relatively little impact on a players statistics then a 70% zone start deployment would have a relatively little impact on the players statistics times four which is still probably relatively little.

I hope to be able to investigate this further but on the surface it seems that if face off win% is of relatively little importance it is supporting of my claim that zone starts have relatively little impact on a players statistics.

 

Apr 192013
 

Tyler Dellow has a post at mc79hockey.com looking at zone starts and defensemen and if you read it the clear conclusion is that zone starts seem to matter quite a bit. In the third chart you can see that defensemen who get the most extreme defensive zone starts have an average corsi% of 44.7% while the average corsi% for defensemen with the most extreme offensive zone starts is 53.3%. This would seem to indicate that for defensemen zone starts can impact your corsi% anywhere from -5.3% to +3.3%. This is far more significant than I have estimated myself using a different methodology so I pondered that part of the reason for this is that when you start in the defensive zone you are playing with weaker quality of teammates than when you start in the offensive zone. My reasoning is that players that get used primarily in the defensive zone are often weak offensive players as if you are a good offensive player you will be given offensive opportunities. I wanted to explore this concept further and that is what I present to you here.

Unlike Tyler Dellow I used forwards in my analysis but it is unlikely that this will have a major impact in the analysis as forwards and defensemen are always on the ice together. One difference between my analysis and Tyler Dellow’s is I used data from stats.hockeyanalysis.com where as Tyler used stats from behindthenet.ca. Behindthenet.ca includes goalie pulled situations in their data and this has the potential to greatly emphasize the impact of zone starts. I feel it is important to eliminate this factor so I have it removed from the data. I also only used 2011-12 data but that shouldn’t have a major impact on the results.

So, my theory is that players who start in the defensive zone are weaker players overall. The challenge to this is that players who start with players that start frequently in the defensive zone likely start frequently in the defensive zone themselves and thus their stats are subject to zone start effects so if they have weak stats we don’t know whether they are due to the zone starts or because they are weak players. My solution was to look at the players zone start adjusted stats that I have on stats.hockeyanalysis.com. These stats ignore the first 10 seconds after a zone face off as it has been shown that the majority of the benefit/penalty of a zone face off has largely dissipated after 10 seconds. I understand that it may seem weird to use zone start adjusted data in a study that attempts to estimate the impact of zone starts but I don’t know what else to do.

I want to also point out that I will be using ZS adjusted FF% team mates when the team mates are not on the ice with the player and this may also mitigate the ZS impact on the teammates stats. My reasoning is, if a player has an extensive number of defensvie zone starts, it is quite possible that when his team mates are not playing with him their zone starts are more neutral or maybe even offensive zone biased. It if there ever was a way to get a non-zone start impacted FF% to use as a QoT metric this is probably the best we can do.

Ok, so what I did was compare a players 5v5 FF% (fenwick %) and zone start adjusted 5v5 TMFF% (zone start adjusted FF% of teammates when team mates are not playing with him) and came up with the following:

FFPct_vs_TMFFPct_by_ZS

As you can see, TMFF% does seem to vary across zone start profiles as I had hypothesized though to a lesser extent than the players zone start influenced FF% which is to be expected. So, if we subtract TMFF% from FF% we get the following chart:

FFPct-TMFFPct_by_ZS

This chart indicates that the zone start impact on forwards once adjusted for quality of teammates (as best we can) ranges from -2.5% to +2.15% which is significantly lower than the -5.3% to +3.3% estimate that Tyler Dellow came up with for defensemen without adjusting for quality of teammates and using goalie pulled situations included in the data. That said, this is still more significant than my own estimates when I compared 5v5 data to 5v5 data with the first 10 seconds after a zone start ignored. When I did that I calculated the impact on H. Sedin’s FF% due to his heavy offensive zone starts to be +1.4% to his FF% and considered this an upper bound. To investigate this further I plotted the average difference between 5v5 FF% and my 5v5 zone start adjusted FF% and I get the following:

FFPct-ZSAdjFFPct_by_ZS

The above is an estimate of the average impact of zone starts using my zone start adjustment methodology which ignores the first 10 seconds after a zone face off. This is significantly lower than either of the previous 2 estimates as we can see in this summary table:

Methodology ZS Impact Estimate
T. Dellow’s estimate for defensemen -5.3% to +3.3%
My TM Adjusted estimate for forwards -2.5% to +2.15%
My 10 second after Zone FO adjustment for forwards -0.5% to +0.41%

I am pretty sure none of what I have said above will put an end to the impact of zone starts on a players statistics debate but at the very least I hope it sheds some light on some of the issues involved. For me personally, I have the most confidence in my zone start adjustment method which removes the 10 seconds after a zone face off. My reasoning is studies have shown that the effect of a zone face off is largely eliminated within the first 10 seconds (see here or here) and also because it is the only methodology that compares a player to himself under similar playing conditions (i.e. same season, almost identical QoT, QoC and situation profiles) eliminating most of the opportunity for confounding factors to influence the results. If this is the case, the impact of zone starts on a players stats is fairly small to the point of being almost negligible for the majority of players.

 

Apr 172013
 

Even though I am a proponent of shot quality and the idea that the percentages matter (shooting and save percentage) puck control and possession are still an important part of the game and the Maple Leafs are dreadful at it. One of the better easily available metrics for measuring possession is fenwick percentage (FF%) which is a measure of the percentage shot attempts (shots + shots that missed the net) that your team took. So a FF% of 52% would mean your team took 52% of the shots while the opposing team took 48% of the shots. During 5v5 situations this season the Maple Leafs have a FF% of 44.4% which is dead last in the NHL. So, who are the biggest culprits in dragging down the Maple Leafs possession game? Let’s take a look.

Forwards

Player Name FF% TMFF% OppFF% FF% – TMFF% FF%-TMFF%+OppFF%-0.5
MACARTHUR, CLARKE 0.485 0.44 0.507 0.045 0.052
KESSEL, PHIL 0.448 0.404 0.507 0.044 0.051
KOMAROV, LEO 0.475 0.439 0.508 0.036 0.044
KADRI, NAZEM 0.478 0.444 0.507 0.034 0.041
GRABOVSKI, MIKHAIL 0.45 0.424 0.508 0.026 0.034
VAN_RIEMSDYK, JAMES 0.456 0.433 0.508 0.023 0.031
FRATTIN, MATT 0.475 0.448 0.504 0.027 0.031
LUPUL, JOFFREY 0.465 0.445 0.502 0.02 0.022
BOZAK, TYLER 0.437 0.453 0.508 -0.016 -0.008
KULEMIN, NIKOLAI 0.421 0.454 0.51 -0.033 -0.023
ORR, COLTON 0.401 0.454 0.5 -0.053 -0.053
MCLAREN, FRAZER 0.388 0.443 0.501 -0.055 -0.054
MCCLEMENT, JAY 0.368 0.459 0.506 -0.091 -0.085

FF% is the players FF% when he is on the ice expressed in decimal form. TMFF% is an average of the players team mates FF% when they are not playing with the player in question (i.e. what his team mates do when they are separated from them, or a quality of teammate metric). OppFF% is an average of the players opponents FF% (i.e. a quality of competition metric). From those base stats I took FF% – TMFF% which will tell us which players perform better than their teammates do when they aren’t playing with him (the higher the better). Finally I factored in OppFF% by adding in how much above 50% their opposition is on average. This will get us an all encompassing stat to indicate who are the drags on the Leafs possession game.

Jay McClement is the Leafs greatest drag on possession. A few weeks ago I posted an article visually showing how much of a drag on possession McClement has been this year and in previous years. McClement’s 5v5 FF% over the past 6 seasons are 46.2%, 46.8%, 45.3%, 47.5%, 46,2% and 36.8% this season.

Next up are the goons, Orr and McLaren which is probably no surprise. They are more interested in looking for the next hit/fight than they are the puck. In general they are low minute players so their negative impact is somewhat mitigated but they are definite drags on possession.

Kulemin is the next biggest drag on possession which might come as a bit of a surprise considering that he has generally been fairly decent in the past. Looking at the second WOWY chart here you can see that nearly every player has a worse CF% (same as FF% but includes shots that have been blocked) with Kulemin than without except for McClement and to a much smaller extent Liles. This is dramatically different than previous seasons  (see second chart again) when the majority of players did equally well or better with Kulemin save for Grabovski. Is Kulemin having an off year? It may seem so.

Next up is my favourite whipping boy Tyler Bozak. Bozak is and has always been a drag on possession. Bozak ranks 293 of 312 forwards in FF% this season (McClement is dead last!) and in the previous 2 seasons he ranked 296th of 323 players.

Among forwards, McClement, McLaren, Orr, Kulemin and Bozak appear to be the biggest drags on the Maple Leafs possession game this season.

Defense

Player Name FF% TMFF% OppFF% FF% – TMFF% FF%-TMFF%+OppFF%-0.5
FRANSON, CODY 0.469 0.437 0.506 0.032 0.038
GARDINER, JAKE 0.463 0.44 0.506 0.023 0.029
KOSTKA, MICHAEL 0.459 0.435 0.504 0.024 0.028
GUNNARSSON, CARL 0.455 0.437 0.506 0.018 0.024
FRASER, MARK 0.461 0.445 0.506 0.016 0.022
LILES, JOHN-MICHAEL 0.445 0.443 0.503 0.002 0.005
PHANEUF, DION 0.422 0.455 0.509 -0.033 -0.024
HOLZER, KORBINIAN 0.399 0.452 0.504 -0.053 -0.049
O_BYRNE, RYAN 0.432 0.505 0.499 -0.073 -0.074

O’Byrne is a recent addition to the Leafs defense so you can’t blame the Leafs possession woes on him, but in Colorado he was a dreadful possession player so he won’t be the answer to the Leafs possession woes either.

Korbinian Holzer was dreadful in a Leaf uniform this year and we all know that so no surprise there but next up is Dion Phaneuf, the Leafs top paid and presumably best defenseman. In FF%-TMFF%+OppFF%-0.5 Phaneuf ranked a little better the previous 2 seasons (0.023 and 0.003) so it is possible that he is having an off year or had his stats dragged down a bit by Holzer but regardless, he isn’t having a great season possession wise.

 

 

Apr 162013
 

If you follow me on twitter you know I am not a fan of Tyler Bozak and I have written about him in the past. As a Leaf fan I want to keep writing about his poor play because I really do not want to see him re-signed in Toronto. He isn’t a good player and simple does not deserve it, especially if he is going to be making upwards of $4M/yr on a 4+ year long contract.  Let’s take a look at how he ranks in a variety of categories over the previous 3 seasons combined as well as this season.

Statistic 3yr 2012-13
5v5 G/60 219/324 130/310
5v5 A/60 168/324 144/310
5v5 Pts/60 199/324 139/310
5v5 IGP 265/324 195/310
5v5 IAP 202/324 221/310
5v5 IPP 288/324 268/310
5v5 FF20 155/324 173/310
5v5 FA20 319/324 309/310
5v5 FF% 275/324 291/310
5v4 G/60 116/155 57/147
5v4 A/60 144/155 98/147
5v4 Pts/60 150/155 89/147
5v4 IGP 76/155 66/147
5v4 IAP 131/155 110/147
5v4 IPP 139/155 114/147

The above are his rankings among other forwards (i.e. 219/324 means 219th among 324 forwards with >1500 5v5 3yr minutes, >300 5v5 2012-13 minutes, >400 5v4 3yr minutes and >75 5v4 2012-13 minutes.  2012-13 stats for games up to but not including last nights).  For 5v5 ice time we are essentially talking the top 10-11 forwards on each team, or their regulars and on the power play we are talking the top 5 forwards in PP ice time per team.

In 3-year 5v5 goals, assists and points per 60 minutes of play Tyler Bozak is ranking approximately the equivalent of a good 3rd line player. The thing is, he is doing that while playing on the first line but his terrible IGP, IAP, and IPP numbers indicate he is doing a terrible job keeping pace with his fellow first line mates.  If you look at his 3 year fenwick numbers (FF20, FA20 and FF%) which are on-ice stats you see when Tyler Bozak has been on the ice the Leafs have been mediocre at shot generation and terrible at shot prevention. Only a handful (literally, just 5 players) have a worse shot prevention record when they are on the ice.

On the power play things aren’t much better. He is second powerplay unit material at best but he is near the bottom of the pack in every assist and point generation and only a bit better in goal production.

Overall his numbers look a little better in 2012-13 but they certainly aren’t much to write home about, especially his IGP, IAP and IPP. He still looks to be a 3rd line offensive player with terrible defensive ability.

Another thing we can look at is his WOWY numbers with his most frequent line mate Phil Kessel.

Bozak w/Kessel Bozak wo/ Kessel
3yr GF20 0.874 0.648
3yr GA20 0.995 1.297
3yr GF% 46.8% 33.3%
3yr CF20 19.60 17.43
3yr CA20 20.89 20.82
3yr CF% 48.4% 45.6%
2012-13 GF20 0.956 0.000
2012-13 GA20 0.918 0.419
2012-13 GF% 51.0% 0.0%
2012-13 CF20 19.50 8.38
2012-13 CA20 21.53 25.55
2012-13 CF% 47.5% 24.7%

When Phil Kessel and Tyler Bozak are on the ice together they are not even breaking even. When Tyler Bozak is on the ice without Kessel they are significantly worse. Individually, Tyler Bozak has scored just 3 of his 26 5v5 goals (11.5%) and 8 of his 68 points (11.8%) over the previous 3 seasons when separated from Kessel despite playing nearly 20% of his ice time apart from Kessel. When not with Kessel his goal and point production drops significantly and as we know from above it wasn’t all that impressive to start with.

Not shown are Phil Kessel’s numbers when he isn’t playing with Tyler Bozak but they are generally better than when they are together. Phil Kessel when not playing with Tyler Bozak has a GF% of 50.4% and a CF% of 51.5% over the previous 3 seasons. Tyler Bozak appears to be a drag on Kessel’s offense.

The only argument you can for keeping Bozak is that the Kessel-Bozak-Lupul/JVR line has been productive and is working so why break them up. To me that argument only works when Bozak is making $1.5M and is not a significant drag on the salary cap but you can’t be paying a player $3.5-4M to essentially be a place holder between Kessel and Lupul/JVR.

Related News Article: James Mirtle wrote an article on the tough decision Leaf management has regarding the re-signing of Tyler Bozak.

(I am going to try and include a glossary in my posts for advanced statistics mentioned in the post so those not familiar with advanced stats can find out what they mean but a full glossary can also be found here).

Glossary

  • G/60 – Goals scored per 60 minutes of play
  • A/60 – Assists per 60 minutes of play
  • Pts/60 – Points per 60 minutes of play
  • IGP – Percentage of teams goals while player was on ice that were scored by the player
  • IAP – Percentage of teams goals while player was on the ice that the player had an assist on
  • IPP – Percentage of teams goals while player was on the ice that player scored or had an assist on
  • FF20 – Fenwick (shots + missed shots) by team per 20 minutes of ice time
  • FA20 – Fenwick (shots + missed shots) against team per 20 minutes of ice time
  • FF% – % of all shot attempts (shots + missed shots) while on ice that the players team took – FF/(FF+FA)
  • GF20, GA20, GF% – same as FF20, FA20, FF% except for goals
  • CF20, CA20, CF% – same as FF20, FA20, FF% but also includes shot attempts that were blocked (corsi)

 

Apr 122013
 

Even though I think the idea of ‘usage’ and ‘tough minutes’ is a vastly over stated factor in an individual players statistics they are interesting to look at as it gives us an indication of how a coach views the player. So for all the usage fans, here is another usage statistic which I will call the Leading-Trailing Index, or LT Index for short.

LT Index = TOI% when leading / TOI% when trailing

where TOI% is the percentage of the teams overall ice time (in games that the player played in) that the player is on the ice (so a 5v5 TOI% of 20% means the player was on the ice for 20% of the time that the team was at 5v5). Thus, the LT index is a ratio of the players ice time when his team is leading to his ice time when his team is trailing adjusted for the overall ice time that the team is leading/trailing. Any number greater than 1.00 indicates the player gets a greater share of ice time when the team is leading and anything under 1.00 indicates the player gets a greater share of ice time when the team is trailing.  So, any players with an LT index greater than one is used more as a defensive player than an offensive one and anything less than one they are used more as an offensive player than a defensive one. Any player around 1.00 is a well balanced player. So, looking at this seasons data we have the following player usage:

Defensive Usage

Defenseman LT Index Forward LT Index
MICHAEL STONE 1.21 BJ CROMBEEN 1.66
KEITH AULIE 1.20 MATHIEU PERREAULT 1.45
RYAN MCDONAGH 1.19 CRAIG ADAMS 1.35
PAUL MARTIN 1.16 TRAVIS MOEN 1.33
BRYCE SALVADOR 1.15 BOYD GORDON 1.26
BRENDAN SMITH 1.14 JAMES WRIGHT 1.26
SCOTT HANNAN 1.14 MICHAEL FROLIK 1.23
ANDREJ SEKERA 1.13 BRIAN BOYLE 1.22
MIKE WEBER 1.13 MATT CALVERT 1.22
JUSTIN BRAUN 1.12 TANNER GLASS 1.20
BARRET JACKMAN 1.12 MATT MARTIN 1.19
ROBYN REGEHR 1.12 RUSLAN FEDOTENKO 1.19
CLAYTON STONER 1.12 STEPHEN GIONTA 1.19
ANTON VOLCHENKOV 1.11 CASEY CIZIKAS 1.19
RON HAINSEY 1.11 JEFF HALPERN 1.18
TIM GLEASON 1.11 DAVID JONES 1.17
ROSTISLAV KLESLA 1.11 NIKOLAI KULEMIN 1.17
ROB SCUDERI 1.10 ZACK KASSIAN 1.17
NIKLAS HJALMARSSON 1.10 RYAN CARTER 1.16
NICKLAS GROSSMANN 1.10 TORREY MITCHELL 1.16

Offensive Usage

Defenseman LT Index Forward LT Index
RYAN ELLIS 0.78 DEREK DORSETT 0.77
KRIS LETANG 0.84 RAFFI TORRES 0.77
MARK STREIT 0.86 TAYLOR HALL 0.79
KYLE QUINCEY 0.87 CORY CONACHER 0.79
MATT NISKANEN 0.87 JORDAN EBERLE 0.80
JUSTIN SCHULTZ 0.87 NAIL YAKUPOV 0.82
DOUGIE HAMILTON 0.88 RYAN NUGENT-HOPKINS 0.82
VICTOR HEDMAN 0.88 RICH CLUNE 0.82
DAN BOYLE 0.89 BLAKE COMEAU 0.82
KEVIN SHATTENKIRK 0.89 KYLE PALMIERI 0.84
ALEX PIETRANGELO 0.89 BRENDAN GALLAGHER 0.84
JOHN-MICHAEL LILES 0.90 CLAUDE GIROUX 0.86
JOHN CARLSON 0.90 VINCENT LECAVALIER 0.86
P.K. SUBBAN 0.90 DREW SHORE 0.86
LUBOMIR VISNOVSKY 0.91 TJ OSHIE 0.87
CODY FRANSON 0.91 ALEX OVECHKIN 0.87
JAMIE MCBAIN 0.91 JONATHAN HUBERDEAU 0.87
ROMAN JOSI 0.92 NICKLAS BACKSTROM 0.87
JARED SPURGEON 0.93 SCOTT HARTNELL 0.87
CHRISTIAN EHRHOFF 0.93 MARIAN HOSSA 0.87

Balanced Usage

Defenseman LT Index Forward LT Index
MICHAEL DEL_ZOTTO 0.99 BRYAN LITTLE 0.99
ERIC BREWER 0.99 MIKE FISHER 0.99
JAKUB KINDL 0.99 MIKKEL BOEDKER 0.99
ADRIAN AUCOIN 0.99 ALEXEI PONIKAROVSKY 0.99
ALEX GOLIGOSKI 0.99 JASON POMINVILLE 0.99
ERIK GUDBRANSON 1.00 CHRIS STEWART 0.99
DREW DOUGHTY 1.00 DANIEL BRIERE 1.00
THOMAS HICKEY 1.00 RADIM VRBATA 1.00
JOHNNY ODUYA 1.00 ALEX TANGUAY 1.00
SLAVA VOYNOV 1.00 GABRIEL LANDESKOG 1.00
MATT IRWIN 1.00 JIRI TLUSTY 1.00
FRANCIS BOUILLON 1.01 COLIN WILSON 1.00
JONAS BRODIN 1.01 PATRICK DWYER 1.00
BRENT SEABROOK 1.01 JADEN SCHWARTZ 1.01
JOSH GORGES 1.01 BRANDON SAAD 1.01
DUSTIN BYFUGLIEN 1.01 LEO KOMAROV 1.01
BRENDEN DILLON 1.01 DREW MILLER 1.01
GREG ZANON 1.01 DAVID PERRON 1.01
KRIS RUSSELL 1.02 TOM PYATT 1.01

It’s amazing how much more BJ Crombeem gets used protecting a lead than when trailing. You’d have to think that score effects could have a significant impact on his stats because of this. Not really a lot of surprises there though though in the case of a guy like Derek Dorsett him being in the ‘offensive usage’ category has more with the coach not wanting to use him defending a lead than hoping he will score a goal to get the team back in the game.

 

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 112013
 

Stats.hockeyanalysis.com has just gotten even better! Several people have asked why I have zone start adjusted stats for team stats and it is a good question. The answer to that is that it was just easier from a programming point of view to have the same ‘situations’ for both the player level and the team level and since I was already calculating, for example, 5v5close zone start adjusted data for players it was east to add 5v5close zone start adjusted data for teams. Since it makes sense to have non-zone start adjusted data for teams it was on my todo list to get it implemented. So, now it is done, and so much more. The situations that you can access data for at both the player and team level are:

  • 5v5
  • 5v5 home
  • 5v5 road
  • 5v5 close
  • 5v5 tied
  • 5v5 leading
  • 5v5 trailing
  • 5v5 up 1 goal
  • 5v5 up 2+ goals
  • 5v5 down 1 goal
  • 5v5 down 2+ goals
  • 5v4 PP
  • 4v5 PK

In addition to all of the above, all of the above are also available in their Zone Adjusted forms except for the 5v4 PP and 4v5 PK situations. In total, there are now 24 different situations you can search for stats on.  Have at it and don’t blame me for any lost weekends (or lost productivity at work).

(As usual, if you find any issues with the new data please let me know. The stats should be correct but while I have done some testing on the new code to display the stats but it isn’t completely tested.)

 

Apr 052013
 

I often get asked questions about hockey analytics, hockey fancy stats, how to use them, what they mean, etc. and there are plenty of good places to find definitions of various hockey stats but sometimes what is more important than a definition is some guidelines on how to use them. So, with that said, here are several tips that I have for people using advanced hockey stats.

Don’t over value Quality of Competition

I don’t know how often I’ll point out one players poor stats or another players good stats and immediately get the response “Yeah, but he always plays against the opponents best players” or “Yeah, but he doesn’t play against the oppositions best players” but most people that say that kind of thing have no real idea how much quality of opponent will affect the players statistics. The truth is it is not nearly as much as you might think.  Despite some coaches desperately trying to employ line matching techniques the variation in quality of competition metric is dwarfed by variation in quality of teammates, individual talent, and on-ice results. An analysis of Pavel Datsyuk and Valterri Filppula showed that if Filppula had Datsyuk’s quality of competition his CorsiFor% would drop from 51.05% to 50.90% and his GoalsFor% would drop from 55.65% to 55.02%. In the grand scheme of things, this are relatively minor factors.

Don’t over value Zone Stats either

Like quality of competition, many people will use zone starts to justify a players good/poor statistics. The truth is zone starts are not a significant factor either. I have found that the effect of zone starts is largely eliminated after about 10 seconds after a face off and this has been found true by others as well. I account for zone starts in statistics by eliminating the 10 seconds after an offensive or defensive zone face off and I have found doing this has relatively little effect on a players stats. Henrik Sedin is maybe the most extreme case of a player getting primarily offensive zone starts and all those zone starts took him from a 55.2 fenwick% player to a 53.8% fenwick% player when zone starts are factored out. In the most extreme case there is only a 1.5% impact on a players fenwick% and the majority of players are no where close to the zone start bias of Henrik Sedin. For the majority of players you are probably talking something under 0.5% impact on their fenwick%. As for individual stats over the last 3 seasons H. Sedin had 34 goals and 172 points in 5v5 situations and just 2 goals and 14 points came within 10 seconds of a zone face off, or about 5 points a year. If instead of 70% offensive zone face off deployment he had 50% offensive zone face off deployment instead of having 14 points during the 10 second zone face off time he may have had 10.  That’s a 4 point differential over 3 years for a guy who scored 172 points. In simple terms, about 2.3% of H. Sedin’s 5v5 points can be attributed to his offensive zone start bias.

A derivative of this is that if zone starts don’t matter much, a players face off winning percentage probably doesn’t matter much either which is consistent with other studies. It’s a nice skill to have, but not worth a lot either.

Do not ignore Quality of Teammates

I have just told you to pretty much ignore quality of competition and zone starts, what about quality of teammates? Well, to put it simply, do not ignore them. Quality of teammates matters and matters a lot. Sticking with the Vancouver Canucks, lets use Alex Burrows as an example. Burrows mostly plays with the Sedin twins but has played on Kesler’s line a bit too. Over the past 3 seasons he has played about 77.9% of his ice time with H. Sedin and about 12.3% of his ice time with Ryan Kesler and the reminder with Malhotra and others. Burrow’s offensive production is significantly better when playing with H. Sedin as 88.7% of his goals and 87.2% of his points came during the 77.9% ice time he played with H. Sedin. If Burrows played 100% of his ice time with H. Sedin and produced at the same rate he would have scored 6 (9.7%) more goals and 13 (11%) more 5v5 points over the past 3 seasons. This is far more significant than the 2.3% boost H. Sedin saw from all his offensive zone starts and I am not certain my Burrows example is the most extreme example in the NHL. How many more points would an average 3rd line get if they played mostly with H. Sedin instead of the average 3rd liner. Who you play with matters a lot. You can’t look at Tyler Bozak’s decent point totals and conclude he is a decent player without considering he plays a lot with Kessel and Lupul, two very good offensive players.

Opportunity is not talent

Kind of along the same lines as the Quality of Teammates discussion, we must be careful not to confuse opportunity and results. Over the past 2 seasons Corey Perry has the second most goals of any forward in the NHL trailing only Steven Stamkos. That might seem impressive but it is a little less so when you consider Perry also had the 4th most 5v5 minutes during that time and the 11th most 5v4 minutes.  Perry is a good goal scorer but a lot of his goals come from opportunity (ice time) as much as individual talent. Among forwards with at least 1500 minutes of 5v5 ice time the past 2 seasons, Perry ranks just 30th in goals per 60 minutes of ice time. That’s still good, but far less impressive than second only to Steven Stamkos and he is actually well behind teammate Bobby Ryan (6th) in this metric. Perry is a very good player but he benefits more than others by getting a lot of ice time  and PP ice time. Perry’s goal production is a large part talent, but also somewhat opportunity driven and we need to keep this in perspective.

Don’t ignore the percentages (shooting and save)

The percentages matter, particularly shooting percentages. I have shown that players can sustain elevated on-ice shooting percentages and I have shown that players can have an impact on their line mates shooting percentages and Tom Awad has shown that a significant portion of the difference between good players and bad players is finishing ability (shooting percentage).  There is even evidence that goal based metrics (which incorporate the percentages) are a better predictor of post season success than fenwick based metric. What corsi/fenwick metrics have going for them is more reliability over small sample sizes but once you approach a full seasons worth of data that benefit is largely gone and you get more benefit from having the percentages factored into the equation. If you want to get a better understanding of what considering the percentages can do for you, try to do a Malkin vs Gomez comparison or a Crosby vs Tyler Kennedy comparison over the past several years. Gomez and Kennedy actually look like relatively decent comparisons if you just consider shot based metrics, but both are terrible percentage players while Malkin and Crosby are excellent percentage players and it is the percentages that make Malkin and Crosby so special. This is an extreme example but the percentages should not be ignored if you want a true representation of a players abilities.

More is definitely better

One of the reason many people have jumped on the shot attempt/corsi/fenwick band wagon is because they are more frequent events than goals and thus give you more reliable metrics. This is true over small sample sizes but as explained above, the percentages matter too and should not be ignored. Luckily, for most players we have ample data to get past the sample size issues. There is no reason to evaluate a player based on half a seasons data if that player has been in the league for several years. Look at 2, 3, 4 years of data.  Look for trends. Is the player consistently a higher corsi player? Is the player consistently a high shooting percentage player? Is the player improving? Declining? I have shown on numerous occassions that goals are a better predictor of future goal rates than corsi/fenwick starting at about one year of data but multiple years are definitely better. Any conclusion about a players talent level using a single season of data or less (regardless of whether it is corsi or goal based) is subject to a significant level of uncertainty. We have multiple years of data for the majority of players so use it. I even aggregate multiple years into one data set for you on stats.hockeyanalysis.com for you so it isn’t even time consuming. The data is there, use it. More is definitely better.

WOWY’s are where it is at

In my mind WOWY’s are the best tool for advanced player evaluation. WOWY stands for with or without you and looks at how a player performs while on the ice with a team mate and while on the ice without a team mate. What WOWY’s can tell you is whether a particular player is a core player driving team success or a player along for the ride. Players that consistently make their team mates statistics better when they are on the ice with them are the players you want on your team. Anze Kopitar is an example of a player who consistently makes his teammates better. Jack Johnson is an example of a player that does not, particularly when looking at goal based metrics.   Then there are a large number of players that are good players that neither drive your teams success nor hold it back, or as I like to say, complementary players. Ideally you build your team around a core of players like Kopitar that will drive success and fill it in with a group of complementary players and quickly rid yourself of players like Jack Johnson that act as drags on the team.

 

Apr 052013
 

Yesterday HabsEyesOnThePrize.com had a post on the importance of fenwick come playoff time over the past 5 seasons. It is definitely worth a look so go check it out. In the post they look at FF% in 5v5close situations and see how well it translates into post season success. I wanted to take this a step further and take a look at PDO and GF% in 5v5close situations to see of they translate into post season success as well.  Here is what I found:

Group N Avg Playoff Avg Cup Winners Lost Cup Finals Lost Third Round Lost Second Round Lost First Round Missed Playoffs
GF% > 55 19 2.68 2.83 5 1 2 6 4 1
GF% 50-55 59 1.22 1.64 0 2 6 10 26 15
GF% 45-50 52 0.62 1.78 0 2 2 4 10 34
GF% <45 20 0.00 - 0 0 0 0 0 20
FF% > 53 23 2.35 2.35 3 2 4 5 9 0
FF% 50-53 55 1.15 1.70 2 2 1 10 22 18
FF% 47-50 46 0.52 1.85 0 0 4 3 6 33
FF% <47 26 0.54 2.00 0 1 1 2 3 19
PDO >1010 27 1.63 2.20 2 2 2 6 8 7
PDO 1000-1010 42 1.17 1.75 1 0 5 7 15 14
PDO 990-1000 47 0.91 1.95 2 1 3 4 12 25
PDO <990 34 0.56 1.90 0 2 0 3 5 24

I have grouped GF%, FF% and PDO into four categories each, the very good, the good, the mediocre and the bad and I have looked at how many teams made it to each round of the playoffs from each group. If we say that winning the cup is worth 5 points, getting to the finals is worth 4, getting to the 3rd round is worth 3, getting to the second round is worth 2, and making the playoffs is worth 1, then the Avg column is the average point total for the teams in that grouping.  The Playoff Avg is the average point total for teams that made the playoffs.

As HabsEyesOnThePrize.com found, 5v5close FF% is definitely an important factor in making the playoffs and enjoying success in the playoffs. That said, GF% seems to be slightly more significant. All 5 Stanley Cup winners came from the GF%>55 group while only 3 cup winners came from the FF%>53 group and both Avg and PlayoffAvg are higher in the GF%>55 group than the FF%>53 group. PDO only seems marginally important, though teams that have a very good PDO do have a slightly better chance to go deeper into the playoffs. Generally speaking though, if you are trying to predict a Stanley Cup winner, looking at 5v5close GF% is probably a better metric than looking at 5v5close FF% and certainly better than PDO. Now, considering this is a significantly shorter season than usual, this may not be the case as luck may be a bit more of a factor in GF% than usual but historically this has been the case.

So, who should we look at for playoff success this season?  Well, there are currently 9 teams with a 5v5close GF% > 55.  Those are Anaheim, Boston, Pittsburgh, Los Angeles, Montreal, Chicago, San Jose, Toronto and Vancouver. No other teams are above 52.3% so that is a list unlikely to get any new additions to it before seasons end though some could certainly fall out of the above 55% list. Now if we also only consider teams that have a 5v5close FF% >50% then Toronto and Anaheim drop off the list leaving you with Boston, Pittsburgh, Los Angeles, Montreal, Chicago, San Jose and Vancouver as your Stanley Cup favourites, but we all pretty much knew that already didn’t we?