Jul 142014
 

I have written a couple of posts (here and here) on rush shots as it relates to shooting percentages and I investigate this further at a later date. First though, I wanted to take a look at save percentages on rush and non-rush shots. Let’s start by looking at teach teams 5v5 road save percentages for the past 7 seasons combined.

RushvsNonRushSavePct_5v5road

A few observations:

  1. Whoa Tampa! That’s a dreadful save percentage on the rush, 2.5% below anyone else. More on this later.
  2. The teams with the best save percentages on the rush are Anaheim, Phoenix, New Jersey, and Boston.
  3. r^2 between rush save % and non-rush save % is just 0.23 which is below what I might have expected.
  4. There is significantly less variability in non-rush save percentage than rush save percentage. The standard deviation in rush save percentage is 1.09% while this standard deviation in non-rush save percentage is 0.43%. All but 7 teams have a non-rush save percentage between 92.2% and 93.0% (a range of 0.8%) while only 21 teams lie in the range from 89.4% to 91.4% (a range of 2.0%). This could be due to greater variation in the smaller sample size of rush shots but the difference in variability is greater than we see with shooting percentage (standard deviation of 0.91% on rush shots and 0.57% on non-rush shots). Could ability to make saves on rush shots be the larger factor in goaltending talent variability?

Which teams give up the highest percentage of “rush” shots? Well, this chart will provide you that answer.

PercentageOfShotsAgainstOnRush_5v5road

Boston and Los Angeles. Who’d have guessed that? Both are teams we would consider good defensive teams and yet a higher percentage of their shots against are of the tougher rush shot variety. Meanwhile Colorado, NY Islanders and Nashville give up the smallest percentage of shots on the rush. Certainly wouldn’t have predicted that for the Islanders and maybe not Colorado either. There doesn’t seem to be any trends that can be extracted from that chart as good teams and bad teams are spread throughout.

Earlier we saw that Tampa had a downright dreadful save percentage on the rush. I wanted to take a look and see if there has been any improvement over the years, particularly last year when they got some good goaltending for the first time since probably Khabibulin.

TampaBaySavePct_Rush_5v5road

For the first time in the past 7 seasons Tampa goaltending provided a league average save percentage on rush shots. Not sure one season is enough to definitively declare their goaltending problems over, but they seem to be on the right path after years of dreadful goaltending.

Since I am a Toronto fan I wanted to take a look at the Leafs as well to see how their goaltending has improved over the years with Reimer and now Bernier.

TorontoSavePct_Rush_5v5road

The Leafs had a good “rush shot” save percentage in 2007-08 but a poor save percentage on other shots with Toskala playing the majority of the games backed up by Raycroft. Everything was bad in 2008-09 though when Toskala once again had the majority of the starts while Curtis Joseph, Martin Gerber and Justin Pogge shared backup duties. Since then things have slowly gotten better, particularly when Reimer came on board the second half of the 2010-11 season and the Leafs have been a better than average team on both rush and non-rush shots the past couple seasons.

I’ll look at some other teams in future posts. If you have any teams that you’d like me to look at (i.e. teams that are particularly interesting due to change in goalies or whatever) let me know and I’ll take a look.

 

Jul 022014
 

The other day I looked at the effect that Mike Weaver and Bryce Salvador had on their teams save percentage (if you haven’t read it, definitely go give it a read) when they were on the ice versus when they weren’t on the ice. Today I am going to take a look at the Maple Leaf defensemen to see if there are any interesting trends to spot. We’ll start with the new acquisitions.

Stephane Robidas

RobidasOnOffSavePct

(Blue line above orange is good in these charts, opposite is not good)

Aside from 2008-09 he has had a negative impact on his team save percentage. In 2007-08, 2009-10 and 2010-11 his main defense partner was Nicklas Grossman but in 2008-09 his main defense partner was Trevor Daley. Did this have anything to do with his poor effect on save percentage in 2008-09? Well, aside from last season Daley’s on-ice save percentage has been at or better than the team save percentage so there might be something to that.

Roman Polak

PolakOnOffSavePct

Not really a lot happening there except in 2011-12 when he was worse than the team (and the team had significantly better goaltending). Rembember though, the Blues have a pretty good defense so it is quite possible that not being worse than the rest of them is a good thing. Will be interesting to see how he does in a Leaf jersey this season.

Dion Phaneuf

PhaneufOnOffSavePct

Aside from 2008-09 there has been a slight positive impact on save percentage when he is on the ice. In 2008-09 he didn’t have a regular defense partner. At 5v5 he played a total of 1348:08 in ice time and his main defense partners were Giordano (364:56), Vandermeer (342:47), Pardy (304:27), Leopold (163:47), Regehr (85:08) and Sarich (77:41). That variety in defense partners can’t be a good thing. But, maybe Phaneuf has a slight positive impact on save percentage.

Cody Franson

FransonOnOffSavePct

So, he was good for a few years and then he was bad. What happened? Well, he was traded to the Leafs. For the 2009-10 and 2010-11 seasons his main defense partner was Shane O’Brien and he also spent significant time with Hamhuis. This could be a case of him playing “protected” minutes as he had really easy offensive QoC but I generally don’t think QoC has anything near as significant an impact as other factors so I am not sure what is going on. He has had pretty weak QoC the last couple seasons too so who knows.

Jake Gardiner

GardinerOnOffSavePct

It is only 3 seasons of data but so far so good for Gardiner. He has been a boost to the teams save percentage and that is on top of his good possession numbers. In my opinion, Gardiner is quite likely the best defenseman. I’ll drop the “quite likely” from that statement when he repeats his success but against tougher QoC as that will remove any doubt.

Now, let’s take a look at a couple of departing Leaf defensemen.

Carl Gunnarsson

GunnarssonOnOffSavePct

Save for 2010-11 Leaf save percentage has been better whith Gunnarsson on the ice. His two main defense partners that year were Luke Schenn and Mike Komisarek so maybe we can forgive him. In 2009-10 his defense partner was mainly Beauchemin or Kaberle and starting in 1011-12 it has mainly been Phaneuf.

Tim Gleason

GleasonOnOffSavePct

Tim Gleason gets a lot of criticism from Leaf fans, the analytics community, and maybe pretty much everyone but his teams have generally had a positive boost in save % when he is on the ice and in some cases a significant boost.

Based on the loss of Gunnarsson and Gleason, two defenseman who seem to be able to boost on-ice save percentage, and the addition of Robidas who has a negative impact and Polak who has more neutral impact it is quite possible the Leafs suffer a drop off in save percentage this season.

That said, I am not certain what to make of the impact we see and why they occur. Of the 9 defenseman I have presented charts for the past few days (the 7 above as well as Weaver and Salvador in my previous post) it seems that the majority of them have all but one or two of their seasons consistently boosting or inhibiting their teams save percentage. More investigation is needed as to why but I am becoming fairly confident that this is a repeatable talent. There is just too much consistency to consider it purely random.

 

Jul 012014
 

The other day I commented on twitter that I would be happy if the Leafs signed defenseman Mike Weaver because I think he is a defensive defenseman that I think the Leafs could really use. I have thought of Mike Weaver as a premier defensive defenseman for quite some time now. I always seem to get a little flak over it but that’s fine, I can handle it. For example, as a response to my Weaver comment on twitter Eric Tulsky thought it would be prudent to point out a “flaw” in my thought process.

 

And of course, Tyler Dellow never passes up an opportunity to take a jab at me (or anyone who he disagrees with) took the opportunity to re-tweet it.

Now, of course I had thought of responding with a tweet to the effect of “Florida’s save percentage was probably is a bit of a factor in that regression” but I didn’t want to get into a twitter debate at that moment and I was confident I could come up with more concrete evidence. So here is that evidence.

SavePercentageWeaverOnOffIce

The above chart shows the save percentage of Weaver’s team when Weaver is on the ice vs when Weaver is not on the ice including only games in which Weaver has played in (i.e. it is better than just using team save percentage for that season and also allows us to combine his time in Florida and Montreal last season). As you can see, there has only been one season in the last 7 in which his team had a worse save percentage when he is on the ice than not. That is reasonably compelling evidence. It’s difficult to say what happened that season but his main defense partners were a young Dmitry Kulikov and Keaton Ellerby so maybe that was a factor. An investigation of Kulikov’s and Ellerby’s impact on save percentage over the years may help us identify why Weaver slipped that year. It could have been a nagging injury as well. Or, it could just be randomness associated with save percentage.

Regardless of the “reason” for the slide in 2011-12 it is pretty difficult to argue that there has been significant “regression” the past 3 seasons as Tulsky and Dellow so eagerly wanted to point out as the past 2 seasons Weaver has seemingly had a significant positive impact on his teams save percentage. Since I made that statement there has been one seasons of “regression” so to speak and two seasons in support of my claim. I guess that means it is 2-1 in my favour. It continues to appear that Weaver is a good defenseman who can suppress shot quality against.

Another defenseman I have identified as a defenseman who possibly can suppress opposition save percentage is Bryce Salvador. Here is Salvador’s on/off save percentage chart similar to Weaver’s above (2010-11 is missing as Salvador missed the season due to injury).

SavePercentageSalvadorOnOffIce

Salvador’s on-ice save percentage has been better than the teams save percentage every year since 2007-08. Regression? Doesn’t seem to be.

To summarize, there are a lot of instances where if we simply do a correlation of stats from one year to the next or  make observations of future performance relative to past performance we see the appearance of regression. In fact, the raw stats do in fact regress. That doesn’t necessarily mean the talent doesn’t exist, just that we haven’t been able to properly isolate the talent. The talent of the individual player is only a small factor in what outcomes occur when he is on the ice (a single player is just one of 12 players on the ice during typical even strength play) so it is difficult to identify without attempting to account for these other factors (quality of team mates in particular).

Possession and shot generation/suppression is important, but ignore the percentages at your peril. They can matter a lot in player evaluation.

 

Jun 122014
 

The rumour is out there that Sunny Mehta has been hired as Director of Hockey Analytics of the New Jersey Devils (if true, a big congrats to Sunny). This sparked some twitter discussion about the Devils and analytics and Devils defensemen including Bryce Salvador.

I have been a bit of a fan of Salvador, at least statistically, though clearly there are a lot of Devils fans that do not like him and I think it is because of a focus on corsi. One person tweeted me an image of Salvador’s corsi rel % suggesting it was “pretty ugly”. While maybe true the game isn’t about Corsi it is about goals. Here is what I know about Salvador. In 5v5close situations he led the Devils defensemen in on-ice save percentage last season, the season before, and the season before that. He missed 2010-11 due to injury but in 2009-10 he was second best trailing only Andy Greene, his regular defense partner. Either he is extremely lucky (every year) or he is doing something right.

Lets look at this a different way. Over the past 3 seasons Bryce Salvador has had the third best 5v5close save percentage in the league when he is on the ice despite the Devils ranking 23rd in team save percentage. The two players ahead of him play for Boston (Dougie Hamilton) and Los Angeles (Willie Mitchell) who have significantly better goaltending (3rd and 8th best 5v5close save percentages over past 3 seasons) and again, they played in front of far better goaltending.

In February 2012 I wrote an article attempting to quantify a defenders effect on save percentage and in it I identified Salvador as one of the best defensemen at boosting his teams save percentage. In the 2 seasons since he has done nothing but support that claim.

So, what does this all mean? Well, it takes a player who had a team worst 15.9 CA/20 in 5v5close situations this past season to a team best 0.49 GA/20.  Over the past 3 seasons only Dougie Hamilton (Boston), Willie Mitchell (Los Angeles) and Alec Martinez (Los Angeles) have seen goals scored against them at a lower rate than Bryce Salvador.

I know the majority of people are on the corsi bandwagon these days and some will dismiss any argument that runs counter to it but I think the evidence is clearly on Salvador’s side here. All evidence suggest he is really good as suppressing opposition shot quality and in turn suppressing the number of goals scored against the Devils. If I were the new Director of Hockey Analytics for the Devils I wouldn’t be recommending getting rid of Salvador.

 

Feb 272013
 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

Jan 302013
 

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

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

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

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

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

TeamShootingPercentageComp

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

TeamSavePercentageComp

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

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

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

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

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

 

Apr 262012
 

While doing my earlier post on Luongo’s value I noticed that Luongo’s 5v5close zone start adjusted save percentage relative to the rest of the league is much more mediocre than his 5v5 save percentage.  I decide to look into this further and realized that this is in large part due to zone start effects, and not score effects.  This got me to look into zone start effects on a goalies save percentage further.

I previously wrote an article where I described a simple and straight forward for adjusting for zone starts.  Basically you can fully account for zone start effects by ignoring the first 10 seconds after an offensive or defensive zone face off so this is what I have been doing ever since.  I hadn’t yet considered the effect on a goalies save percentage though so here it is.  In the table below you will find all goalies who played 3000 5v5 minutes over the previous 3 seasons.  There are 46 such goalies.

Goalie 5v5 Sv% ZS Adj. Sv% Diff. 10Sec Sv% 10Sec SA%
MICHAL NEUVIRTH 91.8% 90.5% 1.4% 96.0% 24.6%
JIMMY HOWARD 92.3% 90.6% 1.7% 98.2% 22.0%
ROBERTO LUONGO 93.0% 91.5% 1.5% 98.4% 21.6%
TIM THOMAS 93.1% 92.0% 1.1% 97.4% 21.0%
HENRIK LUNDQVIST 93.1% 92.0% 1.1% 97.4% 20.5%
TUUKKA RASK 93.0% 91.8% 1.2% 97.9% 20.3%
COREY CRAWFORD 92.2% 90.7% 1.4% 97.9% 20.1%
TOMAS VOKOUN 92.9% 91.7% 1.2% 97.7% 19.7%
EVGENI NABOKOV 92.6% 91.4% 1.2% 97.4% 19.5%
DWAYNE ROLOSON 91.4% 90.0% 1.4% 97.3% 19.1%
BRIAN BOUCHER 91.8% 90.4% 1.4% 97.9% 18.8%
SCOTT CLEMMENSEN 92.1% 90.7% 1.3% 97.9% 18.5%
JOSE THEODORE 92.6% 91.6% 1.0% 97.0% 18.4%
SERGEI BOBROVSKY 92.3% 91.0% 1.3% 97.8% 18.4%
SEMYON VARLAMOV 92.9% 91.9% 1.0% 97.4% 18.2%
JAMES REIMER 92.7% 91.7% 0.9% 96.9% 17.9%
RAY EMERY 91.8% 90.9% 0.9% 96.1% 17.8%
JOHAN HEDBERG 92.1% 91.1% 0.9% 96.5% 17.6%
MIIKKA KIPRUSOFF 92.5% 91.5% 1.0% 97.4% 17.5%
CRAIG ANDERSON 92.3% 91.2% 1.0% 97.2% 17.1%
JEAN-SEBASTIEN GIGUERE 91.7% 91.1% 0.6% 94.8% 17.0%
DEVAN DUBNYK 92.1% 91.0% 1.1% 97.4% 16.7%
PETER BUDAJ 92.1% 91.1% 0.9% 96.7% 16.6%
ANTTI NIEMI 92.7% 91.7% 1.0% 98.1% 16.1%
MARTY TURCO 91.8% 90.7% 1.1% 97.3% 16.0%
MARTIN BRODEUR 91.7% 90.5% 1.1% 97.7% 15.8%
JONATHAN QUICK 92.6% 91.9% 0.6% 96.1% 15.2%
BRIAN ELLIOTT 91.2% 90.3% 0.9% 96.2% 15.2%
CAREY PRICE 92.5% 91.9% 0.5% 95.6% 14.8%
JONAS GUSTAVSSON 90.9% 89.8% 1.1% 97.3% 14.7%
DAN ELLIS 91.3% 90.5% 0.8% 96.1% 14.3%
KARI LEHTONEN 92.6% 92.2% 0.5% 95.4% 14.2%
CAM WARD 92.6% 91.9% 0.7% 97.0% 13.5%
PEKKA RINNE 93.0% 92.5% 0.5% 96.1% 13.5%
CHRIS MASON 91.0% 90.7% 0.4% 93.3% 13.4%
MARC-ANDRE FLEURY 91.6% 90.9% 0.7% 96.4% 13.4%
ILYA BRYZGALOV 92.8% 92.3% 0.5% 96.2% 13.3%
NIKOLAI KHABIBULIN 91.0% 90.0% 1.0% 97.7% 13.3%
RYAN MILLER 92.7% 92.2% 0.5% 96.3% 13.2%
NIKLAS BACKSTROM 92.6% 92.2% 0.4% 95.2% 12.9%
ONDREJ PAVELEC 92.1% 91.5% 0.6% 96.0% 12.8%
STEVE MASON 91.2% 90.5% 0.8% 96.5% 12.6%
JONAS HILLER 92.6% 91.9% 0.7% 97.3% 12.5%
MIKE SMITH 92.4% 91.9% 0.6% 96.4% 12.4%
JAROSLAV HALAK 92.9% 92.4% 0.5% 96.8% 11.8%
MATHIEU GARON 91.0% 90.5% 0.5% 95.2% 11.4%
Average 92.3% 91.4% 0.9% 96.9% 16.2%

Included in the table are 5v5 save percentage, 5v5 zone start adjusted save percentage, the difference between 5v5 save percentage and zone start adjusted save percentage, the goalies save percentage on shots within 10 seconds of an offensive/defensive zone face off, and the percentage of shots that the goalie faced that were within 10 seconds of an offensive/defensive zone face off.

As you can see, the average within 10 seconds of a face off save percentage is significantly higher than the average face off adjusted save percentage (97.9% vs 91.4%) and the variation of the percentage of shots faced within 10 seconds if a face off across goalies is very significant (average 16.2%, low of 11.4%, high of 24.6%).  Furthermore, this average seems to be team driven (i.e. Rask/Thomas have quite similar/high percentages, S. Mason/Garon, Pavelec/C. Mason quite low).  This can introduce a significant bias into a goalies save percentage.  If we calculate an expected save percentage based on the number of ‘within 10 second’ and ‘during normal play’ shots and the average save percentages for those situations, the expected save percentage of Neuvirth would be 92.7% while the expected save percentage of Mathieu Garon would be 92.0%.  Now that is just a 0.7% difference which you may not think is huge, but the lowest 5v5 save percentage is 90.9% and the highest is 93.1% for a range of 2.3%.  That means a 0.7% variation due to within 10 seconds of a face off vs normal play could account for 30% of all variation in goaltender save percentage.  I am too lazy to look into it, but I wonder if variation in number of power play shots faced has as much impact on overall save percentage.  I am certain that this is a far more significant factor than score effects.

Thus, I think it is extremely important to factor out shots faced immediately after a face off when evaluating goaltender performance (and any players performance for that matter).  Furthermore, I fully stand by my previous Luongo post where I suggest he is a mere middle of the pack goalie.

 

Feb 052012
 

One of my beefs in the analysis and evaluation of hockey players is the notion that PDO (on-ice shooting percentage plus on-ice save percentage) can be used as a proxy for luck.  A perfect example of how PDO is used as a proxy for luck is this article by Neil Greenberg about the Washington Capitals.

For example, when Alex Ovechkin has been on the ice during even strength this season, the team has a shooting percentage of 8.2 percent and has saved shots at a rate of .917. So that makes his PDO value 999 (.082+.917=.999), which is almost exactly the league average. In other words, Ovechkin has seen neither very good nor very bad “puck luck” this season.

What’s useful about this metric is that it’s “unstable,” and over a large-enough sample will regress to 1000. Why 1000? Because every shot that is a goal is a shot not saved, and vice versa.

My beef with such an analysis is the notion that for all players PDO regresses to 1000 and any players with PDO above 1000 are lucky  and any players with a PDO below 1000 are unlucky.  While I do believe luck can influence PDO over small sample sizes, not all players have a natural PDO level of 1000 and there are two reasons why.

1.  Not all players play in front of perfectly average goalies which will have a major impact on the save percentage portion of PDO.

2. Players can drive shooting percentages.

To show you what I mean on point 2, I took 4 years (2007-08 to 2010-11) of 5v5 zone start adjusted data and grouped forwards based on their ice time over those 4 years and then calculated the on-ice shooting and save percentages and PDO for each group.  Here is what I found.

TOI (minutes) SH% SV% PDO
<500 7.5% 90.9% 983.5
500-999 7.9% 91.2% 991.2
1000-1499 8.0% 91.2% 992.2
1500-1999 8.2% 91.2% 993.4
2000-2499 8.6% 91.1% 997.0
2500-2999 9.0% 91.2% 1001.9
3000-3499 9.3% 91.2% 1004.4
3500-4000 9.8% 90.8% 1006.1
4000+ 10.4% 90.8% 1012.4

PDO varies from 983.5 up to 1012.4 depending on the group’s ice time.  This is largely driven by shooting percentage which varies from 7.5% to 10.4% with the players with the lowest amount of ice time having the lowest on-ice shooting percentage and the players with the most ice time having the highest shooting percentage.  Order is the enemy of luck so seeing shooting percentages ordered this nicely tells me something other than luck is happening.  Driving on-ice shooting percentage is a skill.  This means more talented players can have a natural PDO (the PDO that they should regress to) above 1000 and less talented players can have a nautral PDO below 1000.  Factor in the goaltending and a player could have a natural PDO well above or well below 1000.

Now, this is not to say that luck isn’t a factor in a players PDO, especially over small sample sizes, it’s just we can’t estimate that luck by assuming every players natural “regress to” PDO is 1000.  Daniel Sedin has a PDO of 1043 this season (through Thursday February 2nd).  Is it fair to suggest he has been luck and should see his PDO regress to 1000?  When you consider his4-year PDO is 1035 (and his 3 year PDO is 1054) probably not.  His natural, “regress to” PDO is probably not that far off his current 1043 PDO.  Now if you are talking about Todd Bertuzzi this season it’s a different story.  Through Thursday he had a a PDO of 1056 while his 4-year PDO is 994 and he hasn’t had a PDO above 1000 in any of the previous 3 seasons.  It is probably fair to presume that Bertuzzi’s natural regress to PDO is much closer to 1000, maybe even below 1000 in which case it is fair to conclude that Bertuzzi has probably been quite lucky so far this season and is unlikely to continue at this pace the remainder of the season.

When used properly PDO can be an indication of luck but to do so we need to consider the context of a players PDO, not just assume all players PDO’s will necessarily regress to 1000.

 

Mar 182011
 

The guys over at Behind the Net have initiated a ‘prove shot quality exists’ competition and in response to that Rob Vollman took a quick and dirty look at shooting percentage suppression.  As I showed the other day, Rob’s logic was a little off.

Rob started off by identifying a number of players with high on ice save percentages over the past 3 seasons.  Some of these guys included low minute players mostly playing on the fourth line against other fourth line caliber players, but there were a handful of players who played relative significant number of minutes and still put up good on ice save percentages.  Let me remind you of a few names that Rob identified:  forwards Marco Sturm, Manny Malhotra, Tyler Kennedy, Travis Moen, Taylor Pyatt, Michael Ryder, defensemen Kent Huskins, Sean O’Donnell, Mike Weaver, Mark Stuart.  I’ll get back to these guys later but I’ll claim that Rob dismissed some of them prematurely by claiming they played against weak competition.

As you may or may not know I have developed offensive and defensive ratings for every player and these can be found at http://stats.hockeyanalysis.com/ Furthermore, I have created these using goals for/against as well as shots for/against, fenwick for/against, and corsi for/against.  For clarification, fenwick is shots + missed shots while Corsi is shots + missed shots + blocked shots.  For this study I decided to use fenwick instead of shots because I had the data handy and I was too lazy to get the shot data in the right format but there shouldn’t be a significant difference (the two are very highly correlated).

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Jan 062011
 

The score of a game influences how a team plays.  When a team is trailing they play a more aggressive offensive game, when they are up a goal or more, they play a more defensive game.  The question I answer today is, how does score influence a teams save percentage.

To answer this question I looked at the past 3 seasons of 5v5 even strength save percentage data when the score is tied, when the team is up by a goal, when the team is up by 2 or more goals, when the team is down a goal and when the team is down by 2 or more goals.  For each team and score category I have a data point for 2007-08, 2008-09, 2009-10 as well as a three year average (2007-10).  For each score category I sorted from lowest to highest save percentage and then plotted them on one chart and got the following:

As you can see, when the game is tied generally produces higher save percentages than when a team is leading or trailing and when a team is trailing their save percentages are at their worst.  This is probably not surprising as a team will open up its game in hopes of creating offense but also puts them at risk defensively.  Now, what that table doesn’t tell us is if all teams experience the same score effects or, for whatever reason, do some teams actually have improved save percentages when trailing or leading.  The following chart shows each teams 3 year save percentage by score ordered from lowest 5v5 game tied save percentage.

The majority of teams have the majority of their leading or trailing save percentages below the game tied save percentages but there are a number of occassions where that doesn’t occur and they are mostly related to up2 or up2+ save percentages.  The only teams that had a down1 or down2+ save percentage above game tied save percentage were:

  1. Dallas – Down1: 92.51% vs Tied: 91.74%
  2. Detroit – Down1: 93.05% vs Tied: 92.16%
  3. Pittsburgh: Down2+: 92.87% vs Tied: 92.78%
  4. Minnesota:  Down2+: 93.21% vs Tied: 92.89%
  5. Florida: Down1: 93.92% vs Tied: 93.23%

On average, teams had their down 1 goal save percentage 1.3% lower than their game tied save percentage and their down 2+ goal save percentage 1.90% lower than their game tied save percentage.  The average team save percentage at 5v5 tied is 92.7% vs 91.4% down a goal, 90.8% down 2+ goals, 92.2% up a goal and 92.1% up 2 goals.  Tailing can have a sizable negative impact on save percentage where as leading can have a minor negative impact.

So what does this mean?  It means we need to be careful when evaluating goalies (and probably shooters to some extent) based on save percentage (special team effects) or even 5v5 even strength save percentage because the game situations a goalie has been exposed to will influence the goalies save percentage.  A goalie on a weak team will have his save percentage lowered simply because his team is going to be trailing more often and be forced to take chances to create offense and thus he will be exposed to tougher shots where as a goalie on a good team who leads the game more than they trail a lot will not face as many tough shots.

One interesting thing I noticed while doing all this was the Toronto Maple Leafs up by a single goal performance over the last 3 seasons.  While they were middle of the pack 5v5 game tied (16th in 3 year 5v5 game tied save percentage), they were downright horrific when they got up a goal.  They just couldn’t hold a lead.  The three worst single season save percentages when up a goal were the 2009-10 Leafs, 2008-09 Leafs, and the 2007-08 Leafs so they were three for three there.  Over the course of the past 3 seasons the Leafs posted an 88.4 save percentage when up a goal which was 3.44 standard deviations from the mean.  Next worse what the Ottawa Senators who were well ahead of them at 90.8, a mere 1.23 standard deviations from the mean.  The good news for Leaf fans is their 5v5 up a goal save percentage is much better this year: 95.6% (better than any team in any of the last 3 seasons), 97.2 for Gustavsson and 93.9% for Giguere so they are much better at maintaining the lead.  Unfortunately this season they can’t score well enough to get them a lead to protect.