Archive for May, 2012

I can only imagine most of the Devils fans I know channeling their inner-Puddy this week. Also did you catch the Hockey Pron?

While we have some time before the Finals start on Wednesday let’s look at the Kings and Coyotes numbers in their respective Conference Finals, plus their performances put end-to-end. First the West:

LA vs PHO Fenwick (c) Hashtag Hockey

LA vs PHO Fenwick (c) Hashtag Hockey

The Kings continued to tilt the ice in their favor, never allowing the Coyotes to as much as pull even with them in the SMS numbers. Perhaps for some of the Finals games I will try to get my hands on per-period stats or break them down per-game. Gamewide stats are interesting but they don’t really tell the story of the game, so with fewer and fewer games I’ll change the way I analyze the numbers.

LA vs PHO SMS (c) Hashtag Hockey

LA vs PHO SMS (c) Hashtag Hockey

The Coyotes decided to turn up the heat in Game 5, but not until their backs were against the wall. In the first four games they served up a mediocre-but-steady diet of shots and missed in the mid-thirties, while the Kings had three games over sixty and only one game under fifty. I think about score effects often when I post these type of charts, but is at least a little remarkable how consistent the Yotes were in games 1-4.

LA vs PHO sh rate (c) Hashtag Hockey

LA vs PHO sh rate (c) Hashtag Hockey

Recently, I examined a new way of looking at Sh + MSh, and while that piece was on the player-level, since I had the data anyway I thought I’d look at teamwide Sh-Rates. If you haven’t read that article, the figures above are basically the proportion of Shots + Missed Shots that hit the net, or (Sh / Sh+MSh). Something I have noticed is that the numbers seem to vary together–in Game 2 we see a discrepancy of +.09 for the Kings, but every other game is within just a few percentage points.

NJ vs NYR fenwick (c) Hashtag Hockey

NJ vs NYR fenwick (c) Hashtag Hockey

We see a different picture in the East, with the Devils managing to outshoot the Rangers in games 2 and 3 (winning one and losing one,) but in the other games the Rangers were throwing more pucks at the Devils–by design one would think.

NJD vs NYR SMS (c) Hashtag Hockey

NJD vs NYR SMS (c) Hashtag Hockey

NJ vs NYR sh rate (c) Hashtag Hockey

NJ vs NYR sh rate (c) Hashtag Hockey

Here we see a Rangers team that did not pull the trigger much in Game 3 (just 22 Sh but only 4 MS) and was quite selective in Game 5 as well (35 Sh to 8 MS). The Devils won the last three games of the series while potting 4 goals on 16 shots in Game 4 (excluding the EN goal).

Now that we have a good sample of games played, I thought it might be cool to stack up each team’s games end to end to look at their respective runs through the tournament. Note that SMS are adjusted per 60 mins here.

LA vs OPP fenwick (c) Hashtag Hockey

LA vs OPP fenwick (c) Hashtag Hockey


LA vs OPP sms 60 (c) Hashtag Hockey

LA vs OPP sms 60 (c) Hashtag Hockey

While the Coyotes started to look like they wanted to make it a series after shutting out the Kings in Game 4 and taking a lead midway through Game 5, these numbers suggest the Kings manhandled Phoenix more consistently than the Canucks or Blues. Their Fenwick rate was consistently near-.600 throughout and they topped 60 SMS/60 twice, after not getting to that mark in any game since the start of the Playoffs.

NJ vs OPP fenwick (c) Hashtag Hockey

NJ vs OPP fenwick (c) Hashtag Hockey


NJ vs OPP sms 60 (c) Hashtag Hockey

NJ vs OPP sms 60 (c) Hashtag Hockey

The Devils have certainly played a different brand of hockey than the Kings–where Los Angeles has played 4 games out of 14 with a SMS/60 rate less than 40 (28.6%), New Jersey has played 10 of their 18 games under 40 SMS/60 (55.6%). You don’t have to read the tea leaves too much to wonder how the Devils will try to play the high-octane Kings…will they try to run with them or sit back and weather the storm?

Finally, let’s look at how the two goalies have stacked up. Their even-strength Sv% are near-identical, but Quick owns the Ov Sv% advantage, as Brodeur and the Devils have allowed 15 PPG on 77 Sh (.805) plus another 1 in 4 for Hedberg. Each netminder has notched 11 Quality Starts, but the Devils have played four more games than the Kings, which brings Brodeur’s QS% to just .611 and Quick’s to .786.

Brodeur vs Quick 2012 NHL PLayoffs (c) Hashtag Hockey

Brodeur vs Quick 2012 NHL PLayoffs (c) Hashtag Hockey

Of course, these stats are more descriptive than predictive, but hopefully they are enlightening. While I would guess that a lot of the talking heads will have the Kings, New Jersey fans can still dream of bringing Lord Stanley’s hardware back home…

I have been thinking a lot about missed shots lately, wondering what (if anything) they can tell us and how we could incorporate them into statistical hockey analysis. Of course, at the team level, Fenwick comparisons are informative to look at, and things like score effects can tell us how players perform in different game situations. Those kinds of numbers tell use how specific players impact the game when they are on the ice vs when they are off. But I have been wondering how we can look at missed shots for a player compared to his shots on goal (and also blocked shots, but more on that in a minute.) I decided to do some exploratory research–meaning I don’t have a particular hypothesis in mind, I just want to get my hands dirty and dig through some data to see if anything interesting comes out.

As seen in the above clip, missed shots are not all created equal. Ovechkin’s first slap shot sails wide and hits the boards, while his second attempt is driven hard and ricochets off the post. Both of these are counted the same on the stat sheet, meaning it’s difficult to say that missed shots indicate a particular skill or measure *one* thing. That said, not all shots on goal are created the same either. Some are laser shots that require a great save by the netminder, some are soft shots that are no trouble at all to handle, and sometimes a goalie will reach out and glove a shot that was otherwise going to go wide, essentially turning a missed shot into a shot on goal (I noticed Braden Holtby has a fondness for doing this.) Additionally, as in the clip above for Ovi’s second shot, hockey is a dynamic game where a skater in motion attempts to shoot a puck that is also in motion toward a fixed goal, with constantly changing angles and obstacles.

What we can say is that a skater cannot record a missed shot without having the puck and at least a reasonably good look at the net, so MS could function as an indicator of puck possession and inclination for pulling the trigger on a shot. Additionally, because the difference between a shot on goal and a missed shot can be a split-second timing difference or a half-inch change in shot trajectory, I tend to think that looking at shots on goal and missed shots together is more representative of a player’s behavior on the ice than just SOG. What about blocked shots? It would seem logical to look at SOG, MS, and BS together because those are the only three possible outcomes when a player launches the puck off his stick, and certainly that is what Corsi does, BS are a bit more murky because a BS can be due to another player’s skill in getting in front of pucks, or the shooter’s bad decision to shoot into the legs of the other players. From my perspective, SOG + MS indicate puck possession and opportunity where BS brings in more confounding variables. So for now, let’s just look at Sh + MSh, which I will refer to as SMS.

Literature Review: I did find a couple other articles on this topic, including a couple from Hawerchuk himself over at Arctic Ice Hockey:

My approach is admittedly simpler than his, as I am not splitting my data into home/road, or only 5v5 situations. Nevertheless, I believe that there is something to be said for just looking at aggregate numbers from a season (though it necessitates looking at historical data to add context. Again, more on this later.)

Methodology: For this study, I pulled data from the stats portal for the top-600 players ranked on goals scored for the 2011-12 season. I have mentioned that I am primarily interested in fantasy hockey, so 600 players is certainly more than the pool of fantasy-relevant players. I added their shots and missed shots together, and then looked at the proportion of a player’s SMS that were in fact SOG. So for example, a player that had 200 S and 100 MS (300 SMS) would have a shot ratio of .67 (200/300). Knowing that the nature of shots taken by forwards and defensemen are different (i.e. shots from the blue line are more likely to miss the net,) I split the data by Forward and Defense. On the one hand, I wanted to take a large sample, but on the other hand, this naturally included some outliers–see: Akim Aliu (RW-CGY) with just 3 GP, 3 Sh, 2G, and zero MS. So, I filtered out players that had less than 50 shots, thinking that while this is completely an arbitrary number, I would be hard-pressed to find a fantasy-relevant player with so few shots. The final samples included 352 Forwards and 142 Defensemen. When I charted the shot ratios of the samples, they fell rather neatly into some nice normal distributions:

Fig.1: 2011-12 Shot Rate Distribution, Forwards

I’m trying to ensure these charts are readable, still working on that, I’m afraid it’s my design template…but in any case: N = 352, M = 0.74, SD = 0.04.

Fig. 2: 2011-12 Shot Rate Distribution, Defense

N = 142, M = 0.69, SD = 0.04

The bell curve on the forward distribution is very evident, and while the defense distribution is a little choppy, the data are backed up by statistical tests of normality–after removing the low-end outliers, both distributions passed a K-S test (p = .200 for forwards and defense) and a Shapiro-Wilk test (p = .687 for forwards; p = .634 for defense.) Note that these tests are opposite of most significance tests in that small p values indicate significant difference from a normal distribution, while large p values indicate no statistical difference from normal.

So the data are normally distributed, great. What does this mean for us? One relevant way is to look at Z-scores for players, meaning how far away they are from the mean of the sample. Let’s brush up on the properties of a normal distribution quickly:

In a normal distribution, the mean value is at the highest point of the curve, while the standard deviation is a measurement of distance from the mean (either direction). As the image above shows, 68.2% of all data fall between +/- 1 SD from the mean, 95.4% are +/- 2 SD, and 99.7% are +/- 3 SD. A Z-Score is simply a reflection of how many SD the data point is from the mean, with the benefit that it is directional (Z-scores could be something like -1.2, meaning the data point is more than 1 SD lower than the mean, or +1.9, which would be almost 2 SD greater than the mean.)

Finally, let’s look at some specific player data!  I’ll have to think of a better way to present a larger list of players, perhaps as the start of next season gets closer I could give a full list in my draft prep kit…for now, if you are wondering about a specific player, just tweet me @Hashtag_Hockey or post a comment here.


Jonathan Toews: 185 S, 35 MS (220 SMS), 0.84 s-rate, Z = 2.50

Zach Parise: 293 S, 68 MS (361 SMS), 0.81 s-rate, Z = 1.75

Olli Jokinen: 223 S, 54 MS (277 SMS), 0.81 s-rate, Z = 1.75

John Tavares:286 S, 80 MS (366 SMS), 0.78 s-rate, Z = 1.00

Rick Nash: 306 S, 87 MS (393 SMS), 0.78 s-rate, Z = 1.00

Steven Stamkos: 303 S, 109 MS (412 SMS), 0.74 s-rate, Z = 0.00

Evgeni Malkin: 339 S, 117 MS (456 SMS), 0.74 s-rate, Z = 0.00

Ryan Kesler: 222 S, 95 MS (317 SMS), 0.70 s-rate, Z = -1.00

Alex Ovechkin: 303 S, 135 MS (438 SMS), 0.69 s-rate, Z = -1.25

Anze Kopitar: 230, 103 MS (333 SMS), 0.69 s-rate, Z = -1.25

Mike Richards: 171 S, 103 SMS (274 SMS), 0.62 s-rate, Z = -3.00


Kyle Quincey: 168 S, 53 MS (221 SMS), 0.76 s-rate, Z = 1.75

Mark Streit: 149 S, 49 MS (198 SMS), 0.75 s-rate, Z = 1.50

Dennis Seidenberg: 174 S, 64 MS (238 SMS), 0.73 s-rate, Z = 1.00

Dan Boyle: 252 S, 98 MS (137 SMS), 0.72 s-rate, Z = 0.75

Zdeno Chara: 224 S, 86 MS (310 SMS), 0.72 s-rate, Z = 0.75

Marek Zidlicky: 70 S, 27 MS (97 SMS), 0.72 s-rate, 0.75

Shea Weber:230 S, 105 MS (335 SMS), 0.69 s-rate, Z = 0.00

Dion Phaneuf: 2202 S, 91 MS (293 SMS), 0.69 s-rate, Z = 0.00

Dustin Byfuglien: 223 S, 128 MS (351 SMS), 0.64 s-rate, Z = -1.25

S-Rate Over Time

Of course, because the Z-score is calculated relative to the sample, the next step is to try to approximate population parameters. I just pulled one season’s worth of data for this study, so going forward I’ll have to see how many years I can put together. Additionally, s-rate seems to be pretty reliable from year to year…

Ovechkin–2011-12: 0.69; 2010-11: 0.70; 2009-10: 0.68; 2008-09: 0.71; 2007-08: 0.69

Toews–2011-12: 0.84; 2010-11: 0.80; 2009-10: 0.79; 2008-09: 0.81; 2007-08: 0.82

Mike Richards–2011-12: 0.62; 2010-11: 0.66; 2009-10: 0.71; 2008-09: 0.70; 2007-08: 0.70

Other Thoughts

I have not controlled whatsoever for score effects, QualComp, TOI, PP time, or anything else. Part of the reason for that is that I am working from what I have available from the portal, so if I can find more data to merge in (I do have some TOI data but I’m struggling with a data cleaning issue) I will continue to expand the scope. However, I think this way has a certain amount of parsimony, which I like. Einstein said, “Make things as simple as possible, but no simpler.”

Separating blocked shots from SOG and MS has a certain logic, but it also seems like using two of three is just not telling the whole story. It would be possible to include all three in some kind of metric using weights to adjust for MS and BS. Particularly for defensemen, their numbers of BS compared to MS and SOG can get really out of hand (Mark Stuart: 60 S, 40 MS, 182 BS). Maybe this kind of metric is only applicable to forwards due to the nature of where they play and from where they take their shots.

Last, I am curious if there is anything to be learned when looking at different “types” or “styles” of player–sniper, playmaker, grinder, etc., or players whom we typically classify as pass first/shoot first, or even combinations such as the ill-fated Nash/Carter combo in Columbus this year.


I am very interested to hear what anyone thinks about these data and this methodology. As I said at the start, it’s exploratory research so I was just messing around a bit, but I am optimistic about some of the tests it passed and reliability it has started to show. Could be an interesting way to look at offensive production…?

Alright, enough maths…I’m going to kill some more demons in Sanctuary…

It’s been a while since I put up more than a couple simple charts, so let’s dig in and look at the Elite Eight of 2012! Only one series has yet to be decided, so let’s start with the Caps/Rangers. This time, I have included Fenwick/60, or Sh+MSh adjusted for overtime games. This post will be long so I’ll put a table of contents at the top for easier navigation:

**If you need a refresher on the stats used here, check out the Glossary**





All Teams Fenwick/60

Goalie Stats

Washington Capitals (#7) vs New York Rangers (#1)

The teams have alternated wins in this series, leading to a Game Seven tomorrow night…they have flip-flopped so much we could call this the Romney Series (hey, you got politics in my hockey! you got hockey in my politics!!) Quoth Homer Simpson: “Okay Marge, its your child against my child. The winner will be showered with praise. The loser will be taunted and booed until my throat is sore!!” I couldn’t find a clip of that line so here’s another from the same episode

If the Rangers win, this series will probably be remembered by the Game 5 Joel Ward kerfuffle…which would be unfortunate because like any sports implosion, they usually find a scapegoat *coughBARTMAN* and just blame it on one guy instead of realizing all the events that led up to that flashpoint. After Game 1, which ended 3-1 Rangers, the teams played very evenly for four games, trading one-goal victories. Then, in game 5 and 6, New York seemed to turn on the afterburners like Maverick and Goose or Brian and Dominic, depending on what year you were born. Score effect seems to have played a huge factor in Gm 5, but Washington only mustered 24.2 SMS/60, and was outshot 68-32 in Fenwick, which shows they turtled in a big way. It is reasonable to expect that Game 7 will be more even like the early games in the series, but if Washington gets a lead, they might consider keeping their foot on the gas, as two blown leads in the last three games of a series seems like a pretty good way to get your coach fired. The League has announced that the conference finals will begin on Sunday, so don’t be surprised if the series winner drops the first game to the more rested Devils. Speak of the…Devils…(ugh,) Let’s move to the other series in the East…


New Jersey Devils vs Philadelphia Flyers

I admit I feel a bit reedemed, as I wrote about Bryz in Round 1 and how lucky he was that Fleury shit the bed the way he did…though picking on the Flyers for having bad goaltending is like picking on Justin Bieber for his haircut. Just because it’s an easy joke doesn’t mean I’m not going to make it o_O

The Flyers came out strong in Game 1 and then sputtered out, particularly in Game 2 and Game 4. The Devils blocked a moderate amount of Philadelphia’s shots, but not enough to cover for the fact that the Bullies just didn’t let rip with enough pucks, especially if we consider that they probably had a good idea “Y U Heff 2 B Mad” wasn’t likely to strap the team on his universe-contemplating shoulders and carry them through. One would think that based on the way the Rangers and Capitals are playing, the plucky Devils will be underdogs in the East Finals, but maybe Broduer the Timeless one has got some magic left in those 40-year old bones.


Los Angeles Kings vs St Louis Blues

Bill Simmons mentioned on the B.S. Report this week that Kings fans are sort of waiting for the other shoe to drop, because everyone who followed this team in the regular season remembers the ABYSMAL offense of the Kings for a good chunk of the season. I’ve got a first-hand view of things out here, and they seem to have convinced themselves that the addition of Jeff Carter was their saving grace, but as long as they keep winning, we don’t have to revisit the 30th-ranked G/Gm the team carried for about four months…but who am I kidding, I’m going to probably reach for Carter in next year’s fantasy draft. Besides, we all know Jon Quick is the real Savior-on-skates…these charts were posted earlier, but I thought I’d revisit them here. Note that since no game went to overtime, the Sh + MSh numbers are also the SMS/60 figures, so no point in being redundant redundant.

The West bracket has been a showcase of elite goaltending, and the Quick vs Smith matchup is very enticing to people like me who prefer defensive struggles to barnstorming Pennsylvania-style games. There is no love lost between these division rivals, and people seem to forget that it would not have taken much for the Kings to wind up with the #3 seed and the Yotes to get #8.


Phoenix Coyotes vs Nashville Predators

Mike Smith continues to play the part of the “Hot Goalie” and the Nashville Predators are bounced sooner than many predicted. If the Preds don’t sign Ryan Suter or Radulov decides to go back to Mother Russia, the Preds may not be in a position to come back as contenders next year. Stores of franchises turning themselves around are good for sports leagues, SEE: Lions, Detroit; Rays, Tampa, Tigers, also Detroit, but if they don’t get any hardware, they typically get lost in the history books.

We got a few very even games sandwiched between two lopsided games in Games 1 and 5. Look at the disparity in the last game. The Preds split their SOG pretty evenly across periods, 10-12-11, and unfortunately I do not have MS by period. If anyone knows where to find those numbers please let me know! I’m STILL not taking Mike Smith on my fantasy team next year, but you can’t deny he’s got a fair chance to bring that Conn Smythe trophy back to the desert.


All Teams Fenwick/60

Now that I have started adjusting for time, we can look across series and see how the teams compared in their shot output:

A couple of things surprise me–first, that six of the eight teams are basically between 35 and 40 S+MS/60, with Washington trailing the pack but just barely and Nashville leading the field by more than just a few shots. This is where adjusting for score effects would be beneficial. Per Behind the Net, this is the closest I could find, but I’ll keep looking.


Goalie Stats

Of course I saved the best for last! We knew there some top-notch goalies (and also Bryz) going into this round, but raise your hand if you predicted Rinne AND Elliott would have a lower Ov Sv% than Bryzgalov. Ok, put your hands down now, liars.

Smith and Quick, #1 vs #2. LAK vs STL was supposed to be a 10-goal series (that’s combined) but it didn’t really turn out that way. We could very well be in for 6 or 7 2-1 games, but on a per-game basis, anything can happen. That’s why they play the games! Brian Elliott–yikes.

So what do you think? Who do you think will make it out of the NYR-WAS series, and who will make it to the finals? Post a comment below and share your thoughts!


Just a quick glance at the numbers from this series…I want to do some more work now that we have two rounds in the books at least for two teams. Have been wondering lately what kind of variance there is for missed shots, both player and team level. If you have ideas or thoughts, please post them in the comments!


Also, this…

I wanted to get this up over the weekend but my monitor decided to have a stroke, which put me out of commission for a little while.


Philadelphia scored on better than 1-in-8 shots, but while everyone was busy comparing Marc-Andre Fleury to various cheeses and kitchen tools, Ilya Bryzgalov posted the lowest Ev Sv% of any East goalie, 0.882. The Flyers’ second round opponent features Marty Brodeur, who at age 67, posted the best Ev Sv% in the East at .956. It may be a few more weeks before he can go back to telling kids to get off his lawn.

From that timeless classic of our age, Basketball: “Soon it was commonplace for entire teams to change cities in search of greater profits. The Minneapolis Lakers moved to Los Angeles where there are no lakes. The Oilers moved to Tennessee where there is no oil. The Jazz moved to Salt Lake City where they don’t allow music.” Add to that, “and they moved hockey teams to places like Florida and Arizona, where there is no ice.”