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 102014
 

Yesterday I introduced the concept of rush shots which are basically any shot we can identify as being a shot taken subsequent to a rush up the ice which can be determined by the location of previous face off, shot, hit, giveaway or takeaway events. If you haven’t read the post from yesterday go give it a read for a more formal definition of what a rush shot is. Today I am going to take a look at how rush shots vary when teams are leading vs trailing as well as investigate home/road differences as arena biases in hits, giveaways and takeaways might have a significant impact on the results.

Leading vs Trailing

One hypothesis I had is that a team defending a lead tends to play more frequently in their own zone and thus have the potential to generate a higher percentage of shots from the rush. Here is a table of leading vs trailing rush shot statistics.

Game Situation Rush Sh% Other Sh% Overall Sh% % Shots on Rush
Leading 10.43% 8.03% 8.62% 24.3%
Trailing 9.36% 7.15% 7.63% 22.0%
Leading-Trailing 1.07% 0.89% 0.98% 2.28%

As expected, teams get a boost in the percentage of overall shots that are rush shots when leading (24.3%) compared to when trailing (2.28%). This higher percentage of shots being rush shots would factor in to the higher shooting percentages but it actually doesn’t seem to be all that significant. The more significant impact still seems to be that teams with the lead experience boosts in shooting percentage on both rush and non-rush shots. The hypothesis that teams have a higher shooting percentage when leading due to the fact that they have more shots on the rush doesn’t seem to be true. It’s just that they shoot better. Note that empty net situations are not considered and thus the shooting percentages when leading are not a result of empty net goals.

 Home vs Road

My concern with home stats is the various arena game recorders dole out hits, giveaways and takeaways at different rates. I determine what is a rush and what isn’t based in part on those events so there is the potential of significant arena biases in rush shot stats. To investigate I looked at the percentage of shots that were rush shots at home and on the road for each team. Here is what I found.

RushShotPercentage_Home_vs_Road

That is about as conclusive as you can get. The rush shot percentage at home is far more variable than on the road with higher highs and lower lows. It is possible that last change line matching usage tactics that coaches can more easily employ at home could account for some of the added variability but my guess is it is mostly due to arena scorer biases. From the chart above I suspect Buffalo, Minnesota, New Jersey  and Pittsburgh don’t hand out hits, giveaways and takeaways as frequently as other arenas. This chart takes a look at last years real time stats (the above chart is for last 7 seasons combined).

HitsGiveawaysTakeaways_Home_vs_Road

Most teams have more hits+giveaways+takeaways on home ice than on the road. The teams that have more on the road than at home are Buffalo, Minnesota, New Jersey, Pittsburgh and St. Louis. Despite comparing a 7-year chart with a 1-year chart the two charts seem to align up fairly well. There does seem to be significant arena biases in rush shot statistics so when looking at team and player stats it is certainly best to consider road stats only.

 

Jul 092014
 

I have been pondering doing this for a while and over the past few days I finally got around to it. I have had a theory for a while that an average shot resulting from a rush up the ice is more difficult than a shot than the average shot that is generated by offensive zone play. It makes sense for numerous reasons:

  1. The rush may be an odd-man rush
  2. The rush comes with speed making it more difficult for defense/goalie to defend.
  3. Shots are probably take from closer in (aside from when a team wants to do a line change rarely do they shoot from the blue line on a rush).

To test this theory I defined a shot off the rush as the following:

  • A shot within 10 seconds of a shot attempt by the other team on the other net.
  • A shot within 10 seconds of a face off at the other end or in the neutral zone.
  • A shot within 10 seconds of a hit, giveaway or takeaway in the other end or the neutral zone.

I initially looked at just the first two but the results were inconclusive because the number of rush events were simply too small so I added giveaway/takeaway and hits to the equation and this dramatically increased the sample size of rush shots. This unfortunately introduces some arena bias into the equation as it is well known that hits, giveaways and takeaways vary significantly from arena to arena. We will have to keep this in mind in future analysis of the data and possibly consider just road stats.

For now though I am going to look at all 5v5 data. Here is a chart of how each team looked in terms of rush and non-rush shooting percentages.

Rush_vs_NonRush_ShootingPct_2007-14b

So, it is nice to see that the hypothesis holds true. Every team had a significantly higher shooting percentage on “rush” shots than on shots we couldn’t conclusively define as a rush shot (note that some of these could still be rush shots but we didn’t have an event occur at the other end or neutral zone to be able to identify it as such). As a whole, the league has a rush shot shooting percentage of 9.56% over the past 7 seasons while the shooting percentage is just 7.34% on shots we cannot conclusively define as a rush shot. Over the 7 years 23.5% of all shots were identified as rush shots while 28.6% of all goals scored were on the rush.

In future posts over the course of the summer I’ll investigate rush shots further including but not limited to the following:

  • How much does the frequency of rush shots drive a teams/players overall shooting/save percentages?
  • Are score effects on shooting/save percentages largely due to increase/decrease in rush shot frequency?
  • Are there teams/players that are better at reducing number of rush shots?
  • Can rush shots be used to identify and quantify “shot quality” in any useful way?
  • How does this align with the zone entry research that is being done?

 

 

Jul 042014
 

The other day I put up a post on Mike Weaver’s and Bryce’s Salvador’s possible ability to boost their goalies save percentage and I followed it up with a post on the Maple Leafs defensemen where we saw Phaneuf, Gunnarsson, Gleason and Gardiner all seemingly able to do so as well while Robidas had the reverse effect (lowering goalie save percentage). This got some fight back from the analytics community suggesting this is not possible. My question to them is, why not?

Their answer is that if you do year over year analysis of a players on-ice save percentage or a year over year analysis of a players on-ice save percentage relative to their teams you will find almost no correlation. While this is true I claim that this is not sufficient to prove that such a talent does not exist. Here is why.

We Know Players Can and Do Impact Save %

The most compelling argument that players can and do impact save % is that we see it happening all the time and it is fully accepted among the hockey analytics community. It is known as score effects. Score effects are a well entrenched concept in hockey analytics.  It is why we often look at 5v5 “close” or 5v5 tied statistics instead of just 5v5 statistics. Generally speaking, the impact score effects have is that the trailing teams usually experiences an increase in shot rate along with a decrease in shooting percentage while the team protecting the lead experiences a decrease in shot rate but an increase in shooting percentage. The following table shows the Boston Bruins shooting and save percentages when tied, leading and trailing over the past 7 seasons combined.

. Tied Leading Trailing
Shooting% 7.27% 9.14% 7.66%
Save% 93.36% 93.86% 92.53%

The difference in the Bruins save percentage between leading and trailing is 1.33%. This is the difference between a .923 save percentage goalie and a .910 save percentage goalie which is the difference between an elite goalie and a below average goalie. That is not insignificant. Is this the goalies fault or does it have something to do with the players in front of him? The latter seems most likely. It makes sense that when protecting the lead the players take fewer risks in an attempt to generate offense and in return give up fewer good scoring chances against albeit maybe more chances in total. Conversely, the team playing catch up take more offensive risks so they end up giving up more quality scoring chances against. This is reflected in their teams save and shooting percentages when leading and trailing.

So, now if a team can play a style that boosts the team save percentage when they are protecting a lead, why is it so inconceivable that a player could see the same impact in his on-ice save percentage if that player plays that style of hockey all the time? If Mike Weaver and Bryce Salvador play the same style all the time that teams play when protecting a lead, why can they not boost on-ice save percentage? There is no reason they can’t.

It is Difficult to Detect because Individual Players Don’t Have a lot of Control of Outcomes

The average player’s individual ability to influence of what happens on the ice is actually fairly small as there are also 9 other skaters and 2 goalies on the ice with him. At best you can say the average player has a ~10% impact on outcomes while he is on the ice. That isn’t much. Last week James Mirtle tweeted a link to Connor Brown’s hockeydb.com page as evidence why +/- is a useless statistic. Over the course of three OHL season’s Brown’s +/- went from -72 to -11 to +44. I suggested to Mirtle that if this is the criteria for tossing out stats we can toss out a lot of stats including corsi% because most stats are highly team/linemate dependent. When challenged that this dramatic of reversal is not seen in corsi% I cited David Clarkson as an example.  In 2012-13 Clarkson was 4th in CF% but in 2013-14 he was 33rd (of 346) in CF%. From one year to the next he went from 4th best to 14th worst. Why is this? WEll, Clarkson essentially moved from playing with good corsi players on a good corsi team to playing with bad corsi players on a bad corsi team. No matter how much puck possession talent Clarkson has (or hasn’t) his talent doesn’t dominate over the talent level of the 4 team mates he is on the ice with.

Now think about how many players change teams from one year to the next and think about how many players get moved up and down a line up and change line mates from one season to next. It is not an insignificant number. TSN’s UFA tracker currently has 109 UFA’s getting signed starting July 1st, the majority of them changing teams. There are only ~800 NHL players (regulars and depth players) in a season so that is pretty significant turnover. Some teams turn over a quarter to half their line up while others stay largely the same. With that much roster turnover and with so little ability for a single player to drive outcomes it should be expected that the majority of statistics see relatively high “regression”. Regression doesn’t mean lack of individual talent though.

Think of this scenario. We have a player with an average ability to boost on-ice save percentage and he has been playing on a team with a number of players who are good at boosting on-ice save percentage but generally speaking he doesn’t play with those players. Under this scenario it will appear that the player is poor at boosting on-ice save percentage because he is being compared to  players who are good at it. Now that player moves to another team who isn’t very good at boosting on-ice save percentage. Now that same average player will look like he is a good player because he has a better on-ice shooting percentage than his teammates. The result is little year over year consistency but that doesn’t mean there aren’t talent differences among players.

Hockey is not like baseball which is a series of one-on-one matchups between pitcher and batter or isolated attempts to make a fielding play on a hit ball. Outcomes in hockey are completely interdependent on up to 12 other players on the ice. QoT is the largest driver of a players statistics in hockey. Only when we factor out QoT completely can we truly be able to identify every players talent level for any metric we measure. This is a kind of like the chicken and an egg problem though because to identify a players talent level we need to know the talent level of their team mates which in turn required knowledge of his own talent level. We can’t just look at year over year regression to isolate talent level.

Comments

The “team” aspect in hockey is more significant than any other sport and any particular players statistics are largely driven by the quality of his team mates. Even more than teammates, style of play can be a significant factor in a players statistics. The quality of the players that a particular player plays with is a function of both the team the plays on and the role (offensive first line vs defensive third line) he is playing on the team and this is maybe the greatest driver of a players statistics. This is why David Clarkson can be a Corsi king in New Jersey and a Corsi dud in Toronto. It also accounts for why James Neal can be a 25 goal guy playing on the first line in Dallas to a 40 goal guy in Pittsburgh (and probably back to a 25 guy guy in Nashville next year).  This also accounts for why year over year correlation in many stats is not very good despite there being measurable differences in the talent that that stat is measuring. Significant statistical regression is not sufficient, in my opinion, to conclude insignificant controllable talent if no significant attempt to completely isolate individual contribution to team results has been successfully made.

Just for fun, here is a chart of Lidstrom’s on-ice save percentage vs team save percentage. It is pretty outstanding that an offensive defenseman can do this too.

LidstromOnOffSavePct

 

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 232014
 

More often than not the first thing I look at when I want to evaluate a player is their WOWY stats to see if the player boosts the performance of their teammates or suppress it when he is on the ice. Let’s take a look at a WOWY comparision of Umberger and Hartnell starting with some links to their WOWY pages.

When on any of those pages you can click “Visualize this table” to get some charts that I find are often a quick way of getting an overview of the player in question. For example, here is a CF% WOWY chart for Hartnell from last year.

Hartnell-CF-WOWY-2013-14

In these charts it is better to have bubbles below and to the right of the one-to-one diagonal line from that runs from the lower left to the upper right. For Hartnell in 2013-14 every single teammate was the the lower right of this diagonal line which is really good. Not a lot of players have charts this nice. If you go back and look at previous years you will see that Hartnell has accomplished this relatively consistently. This is a good thing. Now let’s take a look at Umberger’s.

Umberger-CF-WOWY-2013-14

That is a much less impressive chart as the majority of Umberger’s team mates have performed better when not playing with him. This is not good and yet is is fairly typical for Umberger to have WOWY charts that look like this.

This is a table of how Umberger’s line mates performed with and without Umberger last season. Listed are all forwards who played at least 100 minutes of 5v5 ice time with Umberger.

Line mate With Umberger Without Umberger
Ryan Johansen 50.2% 50.8%
Nick Foligno 50.4% 52.0%
Artem Anisimov 40.1% 53.3%
Blake Comeau 46.1% 54.6%
Mark Letestu 42.8% 52.1%

And now for Hartnell’s line mates who played at least 100 minutes with Hartnell last year.

Line mate With Hartnell Without Hartnell
Claude Giroux 55.7% 49.5%
Jakub Voracek 57.1% 52.5%
Brayden Schenn 51.9% 46.3%
Wayne Simmonds 53.9% 46.3%

Again, you can go back to previous seasons and the general trend for the two players is pretty much the same. Players perform worse when playing with Umberger than when not and players perform better when playing with Hartnell than when not.

From a WOWY perspective, Umberger is a below average player and Hartnell is an above average player. In fact there aren’t many players that have WOWY charts that look better than Hartnell’s except for the true star players (such as Toews, or Bergeron, or Kopitar, etc.).  Hartnell in my opinion is easily a top 6 player. Umberger I am not sure I’d really want on my team in any significant role. With this trade the Blue Jackets get better in two ways. First by adding a good player in Hartnell and second by subtracting a poor player in Umberger (classic case of addition by subtraction).

 

Jun 192014
 

My intention is to add primary point totals to stats.hockeyanalysis.com sometime this summer but I have calculated them over the past 4 seasons during 5v5close play and thought I’d present a more complete leader board here (I mentioned the top 5 on twitter already).

Rank Player Name PPts/60
1 CROSBY, SIDNEY 2.87
2 STAMKOS, STEVEN 2.17
3 MALKIN, EVGENI 2.11
4 TOEWS, JONATHAN 2.04
5 KADRI, NAZEM 1.98
6 SKINNER, JEFF 1.96
7 TAVARES, JOHN 1.95
8 VANEK, THOMAS 1.95
9 PERRY, COREY 1.93
10 GIROUX, CLAUDE 1.91
11 KUNITZ, CHRIS 1.89
12 SEDIN, DANIEL 1.86
13 KESSEL, PHIL 1.85
14 RYAN, BOBBY 1.84
15 KANE, PATRICK 1.83
16 SEMIN, ALEXANDER 1.81
17 EBERLE, JORDAN 1.81
18 PACIORETTY, MAX 1.81
19 WHEELER, BLAKE 1.80
20 HALL, TAYLOR 1.78
21 KOPITAR, ANZE 1.77
22 BENN, JAMIE 1.76
23 COUTURE, LOGAN 1.75
24 SELANNE, TEEMU 1.75
25 SEGUIN, TYLER 1.73
26 POMINVILLE, JASON 1.73
27 KANE, EVANDER 1.72
28 CARTER, JEFF 1.71
29 SHARP, PATRICK 1.71
30 PERREAULT, MATHIEU 1.69
31 PAVELSKI, JOE 1.69
32 DATSYUK, PAVEL 1.69
33 FRANZEN, JOHAN 1.68
34 LADD, ANDREW 1.68
35 NASH, RICK 1.68
36 LUPUL, JOFFREY 1.68
37 TANGUAY, ALEX 1.67
38 PERRON, DAVID 1.66
39 DUPUIS, PASCAL 1.66
40 DUCHENE, MATT 1.65
41 STEEN, ALEXANDER 1.65
42 ST._LOUIS, MARTIN 1.65
43 WILSON, COLIN 1.63
44 KREJCI, DAVID 1.63
45 IGINLA, JAROME 1.62
46 JAGR, JAROMIR 1.61
47 WHITNEY, RAY 1.61
48 GRABNER, MICHAEL 1.60
49 MACARTHUR, CLARKE 1.60
50 HORTON, NATHAN 1.59

It is amazing how far ahead of everyone Crosby is. He is in a league of  his own offensively. Most of the names on here you’d expect but it is surprising to see Kadri that high as well as Perreault at #30 who the Ducks picked up pretty cheaply from the Capitals last September.

Primary points are goals and first assists (secondary assists are not included).

5v5close play is 5v5 play when teams are within 1 goal of each other in first or second period or tied in the third period.

 

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.

 

Jun 062014
 

I am in the process of planning off season upgrades to stats.hockeyanalysis.com and I am seeking your input as I know a number of you have made suggestions/requests in the past (some of which I haven’t kept track of unfortunately). Here are some of my planned upgrades.

  • Generally I’d like to add more charts and graphs to complement the stat tables that currently dominate the site, especially on the player and team summary pages. If you have any thoughts/examples on how best to visualize the data let me know.
  • I am likely to add new situations such as 4v4, 5v5close home/road splits, all situations, and maybe 5v5 by period. Any others you would like to see?
  • Clean up of the player pages adding charts and graphs and more summary statistics.
  • Addition of team pages.
  • I have some new usage statistics that I want to add such as ratio of ice time leading vs ice time trailing.
  • I may consider removing the HARO/HARD/HART ratings because they take up a lot of space, are time consuming to calculate, and not well used. May replace with something similar if I have some time to do some research. Would anyone object if I removed them completely?
  • WOWY usage statistics would also be cool to do to see if player usage changes with and apart from other players.

My ultimate goal, which will require a fairly significant overhaul of my code and database structure, is to add the ability to calculate statistics, including WOWY’s, for any specified period of time. This is non-trivial and potentially very time consuming though so no guarantees here but this is one of the more common requests that I get.

I may also look into 3-player “with-you” stats as well because I know there is  interest in seeing how a complete forward line performs together, not just pairs of players.

I also have some ideas on some research projects I want to do this summer so everything is time permitting but I really hope to have some nice upgrades for next season. If you have any other suggestions or requests please add them in the comments.