Jun 112013
 

Nathan Horton has been one of the stars of these NHL playoffs as will be an integral component of the Stanley Cup finals if the Bruins are going to beat the Chicago Blackhawks. Nathan Horton is also set to become an unrestricted free agent this summer so his good playoff performance is good timing. One of the things I have noticed about Horton while looking through the statistics is that he has one of the highest on-ice 5v5 shooting percentages over the past 6 seasons of any NHL forward (ranks 16th among forwards with >300 minutes of ice time).

Part of the reason for this is that he is a fairly good shooter himself (ranks 30th with a 5v5 shooting percentage of 12.25%) but this in no way is the main reason.  Let’s take a look at how Horton’s line mates shooting percentage have been over the past 6 seasons when playing with Horton and when not playing with Horton.

Sh% w/o Horton Sh% w/ Horton Difference
Weiss 11.28% 12.84% 1.56%
Lucic 13.03% 16.98% 3.95%
Krejci 11.41% 12.10% 0.68%
Booth 8.44% 11.26% 2.82%
Frolik 6.58% 10.84% 4.26%
Stillman 10.03% 15.38% 5.35%
Zednik 8.81% 13.56% 4.75%
Average 9.94% 13.28% 3.34%

Included are all forwards Horton has played at least 400 minutes of 5v5 ice time with over the past 6 seasons along with their individual shooting percentage when with Horton and when not with Horton. Every single one of them has an individual shooting percentage higher with Horton than when not with Horton and generally speaking significantly higher.  I have previously looked at how much players can influence their line mates shooting percentages and found that Horton was among the league leaders so the above table agrees with that assessment.

It is still possible that Horton is just really lucky but that argument starts to lose steam when it seems he is getting lucky each and every year over the past 6 years (he has never had a 5v5 on-ice shooting percentage at or below league average). Whatever Horton is doing while on the ice seems to be allowing his line mates to boost their own individual shooting percentages and the result of this is that he has the 9th highest on-ice goals for rate over the past 6 seasons. He is a massively under rated player and is this summers Alexander Semin of the UFA market.

 

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.

 

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 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.

 

Mar 142013
 

Mikhail Grabovski is starting to get a little heat in Toronto. The other night against Winnipeg he benched for a good chunk of the game and people are starting to question what is wrong with Grabovski this season. Truth is, there is probably nothing wrong with Grabovski except for his line mate Jay McClement.

When one looks at Grabovski’s stats this season you will actually see that his 5v5 Goals/60 is actually up this year to 0.946 goals per 60 minutes of play from 0.895 last year and 0.924 the year before so his 5v5 goal production is certainly there. It is his assist totals that are down dramatically. The problem is his most frequent line mates are Nikolai Kulemin, Jay McClement and Leo Komarov, none of which are dynamic offensive players. McClement has never scored more than 12 goals in any season in his career and Kulemen had a 30 goal season in 2010-11 but never more than 16 otherwise and has just 9 goals in his last 97 games and Komarov is a rookie not known for his offensive ability. You can’t expect Grabovski, who probably isn’t a dynamic playmaking center to start with, to rack up a lot of assists with a pair of third line players on his wing.

On top of that, Jay McClement is actually a pretty bad hockey player. When the Leafs signed McClement in the summer I questioned the signing because he had terrible numbers in Colorado the previous 2 seasons.  In fact, over the past 2 seasons in Colorado and St. Louis he was 4th last in the league in 5v5 ZS Adjusted goals against per 20 minutes (sadly ahead of only Kessel, Bozak and Lupul). He also ranked 230th of 258 in terms of fenwick % over those 2 years. This season he is last on the Leafs in zone start adjusted fenwick % at a terrible 41.1%.

On top of McClement being pretty bad, the player McClement replaced on that line, Clarke MacArthur, is pretty good. MacArthur has the best fenwick % on the Leafs this season and in the 58:11 of 5v5 ice time he and Grabovski played together this year they had a corsi % of 57.1% while Graobovski has been at 41.7% when separated from MacArthur. Last season when Grabovski and MacArthur played together they were at 56.0% and when Grabovski was without MacArthur he was at 50.9%. In 2010-11 Grabovski’s corsi% was 55.3% with MacArthur and 47.0% without.

In summary, there is nothing wrong with Grabovski. It is the coach that took a good player who had very good ‘chemistry’ with Grabovski off Grabovski’s line replacing him with at best a mediocre 3rd liner to go with the other 3rd liner on his other wing. Maybe when Lupul comes back Carlyle will be forced to put a real top 6 winger on the Grabovski line and then people will stop asking “What is wrong with Grabovski?” but until then, blame Jay McClement (with a primary assist to Randy Carlyle).

 

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.

LidstromTOIChart

 

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?

LidstromPPPKTOIChart

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.

 

Jun 262012
 

I have had a lot of battles with the pro-corsi crowd with regards to the merits of using Corsi as a player evaluation tool.  I still get people dismissing my goal based analysis (which seems really strange since goals are what matters in hockey) so I figured I should summarize my position in one easy to understand post.  So, with that, here are 10 significant reasons why I don’t like to use a corsi based player analysis.

1.  Look at the list of players with the top on-ice shooting percentage over the past 5 seasons and compare it to the list of players with the top corsi for per 20 minutes of ice time and you’ll find that the shooting percentage list is far more representative of top offensive players than the top corsi for list.

2.  Shooting percentage is a talent and is sustainable and three year shooting percentage is as good a predictor of the following 2 seasons goal scoring rates as 3 year fenwick rates and 3 year goal rates are a far better predictor.

r^2
2007-10 FF20 vs 2010-12 GF20 0.253
2007-10 SH% vs 2010-12 GF20 0.244
2007-10 GF20 vs 2010-12 GF20 0.363

3.  I have even shown that one year GF20 is on average as good a predictor of  the following seasons GF20 as FF20 is as a predictor of the following seasons FF20 so with even just one full season of data goal rates are as good a metric of offensive talent as fenwick rate is.  Only when the sample size is less than one season (and for almost all NHL regulars we have at least a seasons worth of data) is fenwick rate a better metric for evaluating offensive talent.

4.  Although difficult to identify, I believe I have shown players can suppress opposition shooting percentage.

5.  Zone starts affect shots/corsi/fenwick stats significantly more than they affect goal stats thus the non-adjusted shot/corsi/fenwick data are less useful than the non-adjusted goal data.

6.  Although not specifically a beef with Corsi, much of the corsi analysis currently being done does not split out offensive corsi and defensive corsi but rather looks at them as a percentage or as a +/- differential.  I believe this is a poor way of doing analysis because it really is useful to know whether a player is good because he produces a lot of offense or whether the player is good because he is great defensively.  Plus, when evaluating a player offensively we need to consider the offensive capability of his team mates and the defensive capability of his opposition, not the overall ability of those players.

7.  I have a really hard time believing that 8 of the top 9 corsi % players over the past 5 seasons are Red Wing players because they are all really talented and had nothing to do with the system they play or some other non-individual talent factor.

8.  Try doing a Malkin vs Gomez fenwick/corsi comparison and now do the same with goals.  Gomez actually has a very good and very comparable fenwick rating to Malkin, but Malkin is a far better player at producing goals thanks to his far superior on-ice shooting percentage (FSh% = fenwick shooting percentage = goals / fenwick for).  Gomez every single season has a much poorer on-ice shooting percentage than Malkin and this is why Malkin is the far better player.  Fenwick/Corsi doesn’t account for this.

Malkin Gomez Malkin Gomez Malkin Gomez
Season(s) FF20 FF20 GF20 GF20 FSh% FSh%
2011-12 16.5 14.0 1.301 0.660 7.9% 4.7%
2010-11 16.1 16.4 0.949 0.534 5.9% 3.3%
2009-10 15.3 14.2 1.112 0.837 7.3% 5.9%
2008-09 12.4 16.8 1.163 0.757 9.4% 4.5%
2007-08 14.1 15.9 1.206 0.792 8.5% 5.0%
2007-11 14.7 14.7 1.171 0.745 8.0% 5.1%

 

So there you have it.  Those are some of the main reasons why I don’t use corsi in player analysis.  This isn’t to say Corsi isn’t a useful metric.  It is a useful metric in identifying which players are better at controlling play. Unfortunately, controlling play is only part of the game so if you want to conduct a complete thorough evaluation of a player, goal based stats are required.

 

Jun 152012
 

One of the top NHL unrestricted free agents this summer is the Washington Capitals Alexander Semin.  Semin  has seen his goal production drop from 40 goals in 2009-10 to 28 in 2010-11 to post-lockout low of 21 this past season and as a result peoples general view of Semin’s value has dropped significantly.  The question is, what was the reason for his drop off in offensive stats.  Is it Semin alone, or is there some other underlying reason.

Let’s take a closer look at Semin’s point totals over the past 5 seasons.

Season GP G Pts PP Pts SH Pts ES Pts ES TOI ES TOI/Pt
2011-12 77 21 54 11 0 43 1097:23 25.5 min.
2010-11 65 28 64 18 1 45 904:38 20.1 min.
2009-10 73 40 84 27 2 55 1077:22 19.6 min.
2008-09 62 34 79 30 2 47 850:02 18.1 min.
2007-08 63 26 42 20 0 22 780:48 35.5 min.

When you strip out Semin’s even strength performance you begin to realize that his point total drop off is not near as significant.  The past 4 seasons he has had 47, 55, 45 and 43 even strength points.  Now his time on ice between points increased dramatically this season but a significant part of that is likely due to his line mates.  Three seasons ago Semin’s most frequent line mates were Brooks Laich, Nicklas Backstrom, Alexander Ovechkin and Tomas Fleishmann ordered by time on ice together.  Two seasons ago they were Laich, Backstrom, Ovechkin and Johansson.  This past season they were Johansson, Perreault, Chimera and Ovechkin.  No offense to Johansson, Perreault and Chimera, but they have combined for just one 40 point season in their careers, and that was Johansson this past season playing with Semin.  That certainly has a little to do with Semin’s drop off.

Another way to look at Semin is to take a look at how his team mates perform when they are on the ice with Semin and when they are on the ice without Semin.  We can do this by looking at some nice bubble charts.

The above chart has GF20 without Semin across the horizontal axis and GF20 with Semin across the vertical axis.  For those new readers, GF20 is goals for (i.e. scored by team) per 20 minutes of ice time.  The color of the circle identifies the year and the size of the circle indicates relative ice time played with Semin.  The larger the circle, the more minutes they played with Semin, the smaller the circle the fewer.  Each forward who played at least 150 minutes with Semin are shown above.

In this chart circles in the upper left indicate that Semin had the greatest impact on his team mates offensive performance as upper-left circles indicate they performed relatively poorly without Semin and relative well with Semin.  Anyone above the 1:1 diagonal line (not shown) means that they had a better GF20 with Semin than without.  As you can see, over the past 3 seasons there is significant evidence that Semin has made his line mates better.  That changed slightly this past season though.  While Chimera and Perreault had better GF20’s with Semin, Johansson and Ovechkin did not.

Now lets take a look at the same chart but for GA20 (goals against per 20 minutes of ice time.

In this table bubbles in the lower right or below the 1:1 line are good as this indicates the player had a lower GA20 with Semin than without.  Except for Ovechkin in 2010-11 the majority of the bubbles are pretty close to the  1:1 line or slightly below.  This would seem to indicate that Semin is not a defensive liability which is relatively rate for quality offensive players.  Frequently producing big offensive numbers comes at a cost of defensive performance but this does not seem to be true for Semin.

The final bubble chart I will look at is goals for percentage (GF%) which is simply goals for divided by goals for plus goals against.

GF% is like GF20, the higher the number the better, so like the GF20 bubble chart, bubbles in the upper left above the 1:1 line are better, especially if they are above 50% (i.e. more goals for than against).  Except for Ovechkin and Johansson this past season and Morrison in 2009-10, all players had a better GF% with Semin than without.  This clearly points to Semin having a significant positive impact on his teams performance.

Maybe the most impressive thing I can point out about Semin is his overall 2-way performance relative to the rest of the league.  Of 125 players with 2500 5v5 zone start adjusted minutes of ice time over the past 3 season, Semin ranks 5th in GF20 (trailing only D. Sedin, H. Sedin, Toews, and Stamkos) and he ranks 13th in GA20.  It truly is a rare combination (for example, the Sedin’s rank 28 and 38 in GA20, Toews 60th and Stamkos 105th).

All that said, it does appear that Semin had a slight drop off in 5v5 offensive performance this past season but without further evidence it would probably be fair to presume that that was a somewhat minor drop off from an otherwise exceptional 4 years and certainly wouldn’t be enough to scare me away from making a significant offer to him as an unrestricted free agent.  He’d be a worthy addition to any NHL team.

 

May 312012
 

Lidstrom has officially announced his retirement from the NHL ending what was one of the best, if not the best, NHL career by an NHL defenseman, and for that matter one of the best careers of any NHL player.  Bobby Orr may have been a better and more dynamic defenseman in his prime but Lidstrom was the dominant defenseman during his time in the NHL and had the longevity that Bobby Orr never had.  In fact, Lidstrom was still a dominant defenseman in the NHL into his 40’s.  Since I have the data readily available, let’s take a look at Lidstrom’s impact on the Red Wings the past 5 seasons, or ages 37-41.

I believe the best way to evaluate a player is that players impact on his teammates, often called WOWY (with or without you).  Here are Lidstroms teammates WOWY goals for percentage for each player who played 300 minutes of 5v5 zone start adjusted ice time with Lidstrom over the past 5 seasons.

2011-12

Player Season w/ Lidstrom wo/ Lidstrom w/ – wo/
White 2011-12 62.9% 52.5% 10.4%
Howard 2011-12 60.6% 61.1% -0.5%
Datsyuk 2011-12 67.6% 61.3% 6.3%
Franzen 2011-12 78.8% 55.8% 23.0%
Bertuzzi 2011-12 68.8% 61.1% 7.7%
Zetterberg 2011-12 58.8% 57.5% 1.3%
Filppula 2011-12 54.5% 61.0% -6.5%
Average 2011-12 64.6% 58.6% 6.0%

2010-11

Player Season w/ Lidstrom wo/ Lidstrom w/ – wo/
Howard 2010-11 56.4% 48.4% 8.0%
Stuart 2010-11 53.2% 50.0% 3.2%
Zetterberg 2010-11 50.0% 48.2% 1.8%
Franzen 2010-11 56.7% 54.7% 2.0%
Bertuzzi 2010-11 50.0% 51.5% -1.5%
Datsyuk 2010-11 55.8% 58.1% -2.3%
Holmstrom 2010-11 52.0% 45.7% 6.3%
Average 2010-11 53.4% 50.9% 2.5%

2009-10

Player Season w/ Lidstrom wo/ Lidstrom w/ – wo/
Howard 2009-10 58.3% 47.2% 11.1%
Rafalski 2009-10 57.9% 53.6% 4.3%
Datsyuk 2009-10 63.8% 52.5% 11.3%
Zetterberg 2009-10 55.1% 50.0% 5.1%
Bertuzzi 2009-10 59.0% 39.6% 19.4%
Holmstrom 2009-10 63.3% 50.0% 13.3%
Osgood 2009-10 45.8% 32.5% 13.3%
Cleary 2009-10 51.6% 45.2% 6.4%
Average 2009-10 56.9% 46.3% 10.5%

2008-09

Player Season w/ Lidstrom wo/ Lidstrom w/ – wo/
Rafalski 2008-09 63.5% 50.0% 13.5%
Osgood 2008-09 62.0% 46.4% 15.6%
Datsyuk 2008-09 58.8% 73.3% -14.5%
Conklin 2008-09 61.5% 57.6% 3.9%
Zetterberg 2008-09 55.6% 48.8% 6.8%
Hossa 2008-09 68.4% 66.7% 1.7%
Cleary 2008-09 48.4% 45.0% 3.4%
Franzen 2008-09 73.7% 60.4% 13.3%
Average 2008-09 61.5% 56.0% 5.5%

2007-08

Player Season w/ Lidstrom wo/ Lidstrom w/ – wo/
Rafalski 2007-08 69.6% 50.0% 19.6%
Datsyuk 2007-08 77.6% 58.5% 19.1%
Osgood 2007-08 72.2% 48.6% 23.6%
Zetterberg 2007-08 69.8% 58.3% 11.5%
Hasek 2007-08 73.0% 58.3% 14.7%
Holmstrom 2007-08 78.1% 48.3% 29.8%
Average 2007-08 73.4% 53.7% 19.7%

As you can see, almost every player Lidstrom has played significant minutes with over the past 5 seasons had better results when Lidstrom was on the ice with them than when he wasn’t.  Of the 36 player seasons listed above, only 5 times did a player peform better without Lidstrom than with and only twice could you call the difference substantial (Filppula’s 6.5% in 2011-12 and Datsyuk’s 14.5% in 2008-09).  To me that is a sign of greatness, especially considering a lot of the players Lidstrom plays with are great players in their own right.

Regular readers of HockeyAnalysis.com are probably aware of my rating system that takes into account on-ice results, quality of teammates and quality of competition and takes into account all of the information shown in the tables above.  My ratings are HARO+ for offensive rating, HARD+ for defensive rating and HART+ for total overall rating.  Here are Lidstrom’s ratings over the past 5 seasons.

Season HARO+ HARD+ HART+
2011-12 1.060 1.097 1.079
2010-11 1.198 0.896 1.047
2009-10 1.157 1.014 1.086
2008-09 1.145 1.205 1.175
2007-08 1.397 1.474 1.435

Lidstrom’s 2007-08 season was truly a remarkable season.  Anything better than 1.00 is very good and above average but to have both offensive and defensive ratings in the 1.400 range or higher is truly remarkable and he deservedly earned the Norris Trophy for top defenseman in the NHL that year.  He hasn’t matched his 2007-08 season since but his numbers have still been quite good and remarkably consistent for an aging NHL player, save for his defensive rating in 2010-11.  Lidstrom won the Norris Trophy in 2010-11 but I did not believe he deserved it as there were more deserving defensemen (specifically, Zdeno Chara).  But regardless, Lidstrom has been and still is a top tier defenseman.

Over the past 4 seasons there have been 68 defensemen to play 4000+ minutes of zone start adjusted 5v5 ice time.  Of those defensemen Lidstrom ranks 9th in HARO+13th in HARD+ and 4th in HART+.  The only defensemen more valuable overall over the past 4 seasons are Chara (excellent offensive and defensive play), Willie Mitchell (excellent defensive play which we are clearly seeing these playoffs) and Marc-Edouard Vlasic (excellent defensive ability, good offensive play).  Of the 68 defensemen there are only 6 defensemen who have both a HARO+ and a HARD+ rating above 1.00 and Lidstrom is one of them (Chara, Matt Carle, Christian Ehrhoff, Toni Lydman, and Keith Yandle are the others).  Having both top end offensive ability and top end defensive ability is what makes Lidstrom so great, and this is just looking at his years at the end of his career.  Statistics from his prime years are probably ridiculously good.

The big question is how will the Red Wings possibly replace Lidstrom.  Everyone is pointing to Ryan Suter who is set to become an unrestricted free agent this summer and there is little doubt that the Red Wings will show some serious interest.  So how does Suter stack up against Lidstrom?  Let’s look at his ratings over the past 5 seasons.

Season HARO+ HARD+ HART+
2011-12 1.012 1.080 1.046
2010-11 1.004 1.271 1.137
2009-10 1.084 0.955 1.020
2008-09 0.860 0.914 0.887
2007-08 1.041 0.991 1.016

Suter had a poor 2008-09 season but otherwise has been very good as well with ratings quite close to Lidstrom’s but probably a small step back.  Of the 68 defensemen discussed above, Suter ranks 30th in HART+ over the past 4 seasons but that was dragged down by his poor 2008-09.  Over the past 3 seasons he ranks 20th (to Lidstrom’s 16th) in HART+ among defensemen with 3000 minutes of zone start adjusted 5v5 ice time with Lidstrom being the better offensive defenseman and Suter being the better defensive defenseman.  All said, Suter would be a quality replacement for Lidstrom but I would expect the Red Wings offense to take a bit of a hit with Lidstrom not being in the line up.

 

May 182012
 

Over at LeafNation.com, Cam Charron did a corsi-based analysis of Colby Armstrong and came up with mixed conclusions regarding his performance over the past several seasons.

So, causes? What caused a player with pretty good possession statistics in Atlanta to completely fall off the map in the last two seasons? System? Trust? Role? A flaw in advanced statistics when players move teams? Or was it just all the injuries that made it a lot tougher on Colby than we think?

I don’t know what the answers to those questions are, but instead of trying to answer then I thought I would take a look at Armstrongs underlying goal numbers look like.  Let’s first start off with a high level view by looking at his HARO+, HARD+ and HART+ ratings.

Season Team TOI HARO+ HARD+ HART+
2011-12 Toronto 235:24 0.449 0.784 0.616
2010-11 Toronto 588:54 1.274 0.823 1.048
2009-10 Atlanta 837:31 1.118 0.948 1.033
2008-09 Atlanta 900:34 1.214 1.016 1.115
2007-08 Pit/Atl 766:46 1.160 0.812 0.986

Save for this past season, where he simply didn’t play enough to get a reliable rating, his HARO+ rating is awfully consistent and remarkably good.  Defensively he had one good season in Atlanta but generally speaking has been extremely sub-par.  The end result is his HART+ numbers are fairly solid and a net positive player overall.  Now lets look at his WOWY stats for players he has played 150 minutes with during a single season.  We’ll start with GF20 data.

Player Year w/ Armstrong wo/ Armstrong Diff
Boyce 2010-11 1.561 1.100 0.461
Versteeg 2010-11 1.148 0.537 0.611
Kane 2009-10 0.931 1.266 -0.335
Slater 2009-10 1.051 0.815 0.236
Peverley 2009-10 0.980 0.826 0.154
Reasoner 2009-10 0.628 0.656 -0.028
White 2009-10 1.049 0.782 0.267
Reasoner 2008-09 0.850 0.719 0.131
Peverley 2008-09 1.469 0.663 0.806
Christensen 2008-09 0.266 1.067 -0.801
Kozlov 2008-09 1.399 0.702 0.697
Perrin 2008-09 0.914 0.648 0.266
Kovalchuk 2008-09 1.390 0.953 0.437
Crosby 2007-08 1.699 1.125 0.574
Malkin 2007-08 1.474 1.147 0.327
Perrin 2007-08 0.944 0.634 0.310
Average 1.110 0.853 0.257

Of the 16 player seasons, there were only 3 where the player had a worse GF20 with Armstrong than without.  That’s pretty good and on average the improvement was 0.256, or about 30%.  He even seemed to make elite offensive players such as Croby, Malkin and Kovalchuk better.  It makes me wonder if Armstrong is contributing in the same way that the players I identified in my “Mixing Toughness with Skill” article did.  Armstrong himself is not an elite offensive player, but the things he does on the ice (retrieving pucks, causing distractions on the ice, drawing attention to himself, etc.) allow the skilled players to do more.

Now let’s take a look at GA20.

Player Year w/ Armstrong wo/ Armstrong Diff
Boyce 2010-11 0.739 0.880 -0.141
Versteeg 2010-11 1.059 0.832 0.227
Kane 2009-10 1.008 1.266 -0.258
Slater 2009-10 0.901 0.815 0.086
Peverley 2009-10 1.224 1.071 0.153
Reasoner 2009-10 0.733 1.006 -0.273
White 2009-10 0.525 0.956 -0.431
Reasoner 2008-09 0.464 0.790 -0.326
Peverley 2008-09 0.851 0.900 -0.049
Christensen 2008-09 0.888 1.115 -0.227
Kozlov 2008-09 0.600 1.130 -0.530
Perrin 2008-09 0.686 1.105 -0.419
Kovalchuk 2008-09 1.137 1.139 -0.002
Crosby 2007-08 0.809 0.783 0.026
Malkin 2007-08 1.053 0.918 0.135
Perrin 2007-08 0.944 0.965 -0.021
Average 0.851 0.979 -0.128

For GA20, negative numbers are good as they indicate fewer goals against.  Interestingly, in 11 of the 16 players seasons the players saw their GA20 drop when playing with Armstrong, though six of them occurred during his previously identified good defensive season of 2008-09 (he didn’t have any consistent line mates that year).  As an average, Armstrong’s teammates saw an a 0.128 drop in GA20, or about 13% which isn’t too shabby.

Now lets take a look at how this pans out in GF%.

Player Year w/ Armstrong wo/ Armstrong Diff
Boyce 2010-11 67.9% 55.6% 12.3%
Versteeg 2010-11 52.0% 39.2% 12.8%
Kane 2009-10 48.0% 50.0% -2.0%
Slater 2009-10 53.8% 50.0% 3.8%
Peverley 2009-10 44.5% 43.5% 0.9%
Reasoner 2009-10 46.1% 39.5% 6.7%
White 2009-10 66.6% 45.0% 21.7%
Reasoner 2008-09 64.7% 47.6% 17.0%
Peverley 2008-09 63.3% 42.4% 20.9%
Christensen 2008-09 23.1% 48.9% -25.8%
Kozlov 2008-09 70.0% 38.3% 31.7%
Perrin 2008-09 57.1% 37.0% 20.2%
Kovalchuk 2008-09 55.0% 45.6% 9.5%
Crosby 2007-08 67.7% 59.0% 8.8%
Malkin 2007-08 58.3% 55.5% 2.8%
Perrin 2007-08 50.0% 39.6% 10.4%
Average 55.5% 46.0% 9.5%

Only 2 times did a player have a worse GF% with Armstrong than without.  Evander Kane saw a marginal drop in 2009-10 and Erik Christensen saw a significant drop in 2008-09.  Most other players saw significant improvements in their GF%, including Kovalchuk, Crosby and Malkin so it seems that Armstrong is a net positive player.

Looking at the above numbers, I think you can firmly put me in the lets not trade away Armstrong just to dump his salary camp.  It is quite possible that the proper thing to do with Armstrong is, if he can get healthy, promote him to the second line with Grabovski and MacArthur and he might make them even better.  Interesting concept.