December 30, 2007

Upgrades

My statistics site was getting way too many columns that I found it necessary to make a few changes. The biggest change is that I grouped my statistics into two categories: one for goals and one for shots. At this time I also changed the code that is behind the scenes as it would make future changes easier. These changes made it possible to highlight the sorted column and also add/delete columns easily as I see fit. You will note these changes in the team pages as well as the forward and defense pages.

Another feature I decided to add is a items to display per page options. I have always only shown 30 items per page; now there is the option of showing more. Please only use ALL if you really need to see all the players as it can be a bit slow. I also allow users to sort by ice time per game, second assists (in the shooting statistics section).

I also added the requested column: average shot distance (only for even strength). You can also see how many shots/game each player gets (or shots/60 min)

If there are any problems with the site, please let me know.

December 20, 2007

Everything you wanted to know about the Shoot-out

Edmonton started the season going 10-1 in the shoot-out (now they are 10-2), this event brought about an interesting discussion on shootouts. I've been holding back posting anything about shoot-outs primarily due to lack of data, but after two seasons (plus a bit). It got me thinking: what led to Edmonton doing so well? Was it all luck or was skill a big part of it.

Team winning percentage
The first thing I looked at was year over year comparisons:
Looking at this without knowing any details it would appear that the shootout results are in fact perfectly random, you can sort of see some clustering in the middle, but other than that there are too many anomalies to make sense of anything. Let me however make a few key points:
1. LA: Lost Garon (80% shootout save percentage after 2005-2006)
2. Carolina: After the playoffs dumped Gerber and Ward has 1 win in three seasons
3. Florida: Lost Luongo
4. Philadelphia: switched to Niittymaki
5. Pittsburgh: started to use Fleury more often
6. San Jose: Toskala thankfully only played 1 shoot-out game in2006-2007 (0 wins in his career)

Remove the above points and you have a data set that has so much more structure. Of course I could probably think of good reasons to exclude every point on this graph, I'm simply trying to show that there are some key points that shouldn't be included because too much changed.


Since it appeared that Goalies were a big part of the shootout results. I figured I'd best compare goalie's save percentages in the shootout. Now the graph below BIG = BAD, small = GOOD. The graph below shows player's Z-scores. A Z-score of -2 means that only 2.5% of players will do better, a Z-score of -1 means that 16% of players will do better than that player. A score of 0 means that 50% of players are better, 50% are worse. A score of +1 means that 16% of players are worse and a score of +2 means that 2.5% of players are worse. [Very simplistic explanation]. These Z-scores are necessary because each goalie sees a different number of shots and it's easier to stop 100% of the shots if you only see three shots than it is to stop 100% of the shots when you see 100 shots. The main point I want to make is that having a Z-score of 2 one year suggests that you will have a Z-score of 1 the next (this is called a regression to the mean).This regression to the mean is quite small, as it is saying that half of the average goalie's save percentage results are due to luck the other half come from skill (in a given season). [Individuals could be quite different]

In translation: Garon's 90% save percentage on the shootout, should be closer to (0.9-0.66)/2+0.66 = 78%r 78% (his career save percentage is: 46/58 = 79%).

Save percentage By Round
Now there are a few things to keep in mind:

Statistics by round:
1: 616 Shots. Shooting%: 0.3669
2: 616 Shots. Shooting%: 0.3344
3: 460 Shots. Shooting%: 0.2978
4: 83 Shots. Shooting%: 0.3494
5: 40 Shots. Shooting%: 0.3500
6: 24 Shots. Shooting%: 0.2917
7: 14 Shots. Shooting%: 0.286
8: 11 Shots. Shooting%: 0.364
9: 4 Shots. Shooting%: 0.250
10: 2 Shots. Shooting%: 0
11: 2 Shots. Shooting%: 0
12: 2 Shots. Shooting%: 0
13: 2 Shots. Shooting%: 0
14: 2 Shots. Shooting%: 1
15: 2 Shots. Shooting%: 0.5
Note Round 9+ 16 shots, 25% shooting percentage.

In general later rounds are easier to stop. So if a goalie plays until the 7th or 8th round his numbers should be better than a goalie who only sees the first two shooters.


Shooters:

On the surface it would appear that shooting percentage is all luck as there is no correlation between years. That being said, the graph below could just be the result of playing different opposition.
What is a guy like Garon worth in just the shootout. Well assuming average number of shootouts (10) he wins you an additional 1-2 points (or about $0.75M - 1.0M). [Based on this image , 35% shooting percentage and 80% save percentage]. Of course if you're Edmonton and discovered a way to get to the shootout 3x as often as expected, well then Garon is even more useful.

December 19, 2007

Brad Stuart

It's hard to miss Brad Stuart. A lot of good things have, been said about him. But he seems to have been involved in a lot of bad trades recently (Stuart being the bad part).

Traded from a losing San Jose team (to a losing) Boston team for Joe Thornton. Many people might point to Thornton as the reason San Jose made the playoffs, but it appears that the loss of a key defenseman wasn't too big of a problem. (Boston ended up with 74 points)

From Boston, Stuart (along with Primeau) was traded for a 1st round draft pick, Kobasew and a solid defender: Andrew Ference. Interestingly, after the trade Calgary struggled and just squeezed themselves into a play-off spot

Stuart is now playing for a team that, as of last year, appeared to only need a goaltender: Los Angeles. Labarbera has provided the goaltending, but now the team lacks any sort of defense. Allowing over 30 shots per hour at even strength and the shots that hit the net are almost 20% more difficult to stop.

Stuart may not be the worst on the team in terms of plus-minus, but he certainly is up there with his -10 (and another -2 vs Detroit tonight).

Looking at a short window of history, Stuart may have just signed and been traded to bad teams, his record in the previous 187 games is appalling: 42% winning percentage.

"[Brad Stuart] is prone to mistakes in pressure situations, which has led him into the coach's doghouse in the past. Doesn't use his size effectively enough." - Sportsnet.ca, what more can I really say...

West continues to dominate East

West vs. East (This season)
30 Wins
24 Losses
4 OTW or SOW
2 OTL or SOL
Winning percentage = 30/54 = 56%

185 GF
163 GA
Pythagorean percentage = 1852/(1852+1632) = 56%

What's interesting is that it has stayed so consistent over the last few years.

December 16, 2007

Buy low - Sell high

I haven't said much (if anything) about Lupul, I was curious whether he would rebound or disappear. Also, I didn't know much about Lupul so it was best I kept my mouth shut. I didn't even realize that Lupul was in fact draft 7th overall.

I decided to create a short list of players who were drafted 7th and had a bad season in their early 20's.

1990: Sydor 24 - 58 GP: 12 Points
1993: Arnot 24 - 70 GP: 33 Points
1995: Doan 22 - 79 GP: 22 Points
1996: Rasmussen 23 - 67 GP: 14 Points
1997: Mara 23 - 75 GP: 24 Points
1998: Malhotra 22 - 59 GP: 10 Points
1999: Beech - 14 GP: 4 Points in 2 years
2001: Komisarek 23 - 71 GP: 6 Points
2002: Lupul 81 GP: 28 Points (-29)


Often, if you look at past players, teams will trade these players during or right after their bad seasons. However, teams are quickly disappointed as their high draft pick succeeds in their new environment. What's interesting is that Edmonton didn't like Lupul because of his bad plus minus, so the team picked up Souray (ranked second last in plus minus) and Pitkanen (ranked fourth last in plus minus).

The point I'm trying to make is that all players have bad seasons (often early in their careers) and teams view this as a good predictor of future performance, when draft position is probably a better prediction of future performance than one season. Anaheim was smart to see a player who had done better than expected one year and got a great deal for him (sell high). However Edmonton, after one bad season, dumped this 1st round draft pick (plus their captain) for Philadelphia's trash.

In conclusion, don't give up on a player after one bad season. Also, don't get too excited by one great year either.

November 19, 2007

Blocked Shots

In regards to James Mirtle's blocked shots post I have a list of player's blocked shots with their ice times.
I have also completed a regression of blocked shots vs. ice time
Forwards: 1.2 * EV hours + 7.0 * PK hours = blocked shots
Defense: 3.8 * EV hours + 11.5 * PK hours = blocked shots
Regression was completed using 2006-2007 data.
Did the regression in such a way that the constant variable was no significant.

columns:
ev = even strength ice time in hours
sh = short handed ice time in hours
P = D: defense, F: forward


NILastnameblockedshevP
1AVolchenkov791.35.1D
2MKomisarek691.05.9D
3JSmith610.94.8D
4GZanon601.44.8D
5MEaton501.45.1D
6BClark501.05.0D
7BLukowich480.94.5D
8BWitt471.14.7D
9RBlake461.34.6D
10NHavelid461.25.2D
11RHamrlik440.95.7D
12LKukkonen440.64.6D
13CPhillips431.45.6D
14KKlee400.85.0D
15GExelby390.94.8D
16SHannan391.25.5D
17PKubina391.44.8D
18FKuba381.15.5D
19HGill381.95.4D
20AFoote381.65.6D
21KBallard371.05.2D
22DTarnstrom360.94.6D
23TLydman361.24.6D
24KTimonen361.15.1D
25PRanger361.16.5D
26PBoucher350.95.3D
27RMartinek351.04.8D
28AWard350.74.8D
29TGleason341.05.1D
30RScuderi341.05.2D
31BOrpik340.35.1D
32FTyutin340.85.4D
33BJackman331.04.8D
34MOhlund330.84.9D
35GWesley331.24.3D
36AAlberts330.95.4D
37BHedican330.75.2D
38SGonchar321.25.5D
39JLiles320.04.2D
40AGreene321.05.4D
41HTallinder321.45.0D
42JModry310.84.6D
43MRozsival311.05.7D
44AWozniewski311.04.2D
45DKeith301.46.2D
46NSchultz301.34.9D
47DGirardi300.95.4D
48BSopel291.14.3D
49RSalei281.16.4D
50BSalvador280.93.7D
51AMarkov281.05.6D
52AMiller280.94.2D
53JJohnson270.75.6D
54BMezei270.94.1D
55MVlasic271.36.0D
56BSeabrook271.04.9D
57KJohnsson271.24.9D
58ALilja271.04.4D
59WRedden270.84.9D
60IWhite270.45.4D
61KMclaren270.95.6D
62AMeszaros270.84.8D
63CPronger261.76.3D
64WMitchell261.35.2D
65JFinger260.33.8D
66BAllen260.94.7D
67RSuter260.84.9D
68BGervais260.63.8D
69RRegehr251.34.9D
70RKlesla250.95.4D
71DMorris250.94.8D
72BMccabe251.03.8D
73SO'brien250.86.1D
74TEnstrom250.84.5D
75MJurcina251.14.2D
76KRachunek250.44.3D
77LVisnovsky240.35.1D
78JBouwmeester241.56.8D
79SStaios241.16.0D
80FKaberle240.45.1D
81CRivet241.04.8D
82MCommodore231.04.1D
83JMckee230.42.3D
84CEhrhoff230.64.5D
85NLidstrom231.65.7D
86TPoti230.83.6D
87NPaetsch230.23.9D
88VVishnevski230.74.6D
89PMara230.64.1D
90ASutton230.73.6D
91DSeidenberg220.33.2D
92TGilbert220.55.7D
93KRussell220.13.4D
94ZChara220.95.4D
95SRobidas200.94.8D
96MNorstrom200.93.7D
97NKronvall200.63.7D
98DHamhuis201.35.2D
99AEriksson200.53.1D
100AZhitnik200.74.6D
101TDaley200.95.1D
102JHejda191.24.7D
103MJones190.64.3D
104TKaberle190.45.9D
105LSmid190.73.1D
106JWisniewski190.54.8D
107DMurray190.43.0D
108CSarich191.14.7D
109BCoburn190.55.2D
110RJones190.83.8D
111GDe%20vries191.04.9D
112RGetzlaf180.54.3F
113FBeauchemin181.56.6D
114BStuart181.04.9D
115GCampbell180.93.4F
116DSydor180.24.1D
117JPavelski180.43.3F
118TMitchell180.73.7F
119MSkoula181.15.2D
120SMorrisonn181.34.6D
121CDrury181.14.4F
122DWideman180.54.2D
123MVan%20ryn180.84.5D
124KHuskins170.75.4D
125AAucoin170.95.5D
126DSteckel171.42.8F
127CBackman170.44.1D
128NBoynton170.74.2D
129DGrebeshkov170.43.7D
130MCarle170.23.9D
131RJohnson171.02.5F
132LKrajicek170.22.0D
133BBurns171.15.4D
134MParrish170.33.4F
135MZidlicky170.24.8D
136SZubov171.05.5D
137DRoy170.84.2F
138NWallin170.62.9D
139PMartin171.14.7D
140JOduya170.85.1D
141MGrier171.13.8F
142BRafalski170.85.5D
143JLehtinen170.84.2F
144DPhaneuf161.15.8D
145MGreene160.72.9D
146ABurish160.83.0F
147JHalpern160.93.9F
148JWard160.83.6F
149AOvechkin160.05.2F
150BCampbell161.05.3D
151MStaal160.64.9D
152MStuart160.33.4D
153SHnidy150.64.4D
154CMurphy150.13.5D
155RHainsey150.45.1D
156MStreit150.24.2D
157RWhitney150.64.0D
158SWagner150.53.8D
159RKesler151.03.8F
160BGuite150.72.5F
161KFoster150.43.6D
162BPothier150.45.0D
163CCampoli150.23.8D
164MCullen150.34.0F
165RBrind'amour151.25.0F
166BShanahan151.04.0F
167NAntropov151.24.5F
168AFerence150.63.9D
169MMottau150.43.2D
170JCarter150.44.0F
171MMalik150.62.9D
172PBrisebois150.24.0D
173MWeaver150.52.9D
174SOzolinsh150.02.4D
175AKopitar140.74.5F
176MHandzus141.13.6F
177MLombardi141.14.0F
178EBrewer140.73.7D
179JStaal141.24.1F
180SCrosby140.25.0F
181MLundin140.34.2D
182JMadden141.14.4F
183RUmberger140.74.0F
184SKapanen140.63.0F
185MSundin140.54.8F
186SO'donnell131.54.8D
187RZednik130.04.2F
188SYelle131.22.2F
189JWilliams130.94.7F
190KSauer130.84.6D
191MTjarnqvist130.92.8F
192EJovanovski130.34.8D
193SBegin130.72.7F
194FBouillon130.63.4D
195RWarrener130.52.7D
196BRadivojevic130.24.3F
197SMccarthy130.22.6D
198JSchultz131.03.4D
199JSpacek130.52.5D
200CHiggins130.34.4F
201GMetropolit130.73.5F
202RPark130.82.4F
203DBrown120.84.2F
204JSlater120.32.5F
205JMcclement120.73.3F
206MReasoner121.13.3F
207AHall121.12.8F
208TKostopoulos120.72.9F
209KChipchura120.62.5F
210CKilger120.73.5F
211JStoll121.03.9F
212JVandermeer120.74.1D
213CKelly121.33.5F
214AHilbert120.53.2F
215ASekera120.22.4D
216CAdams120.82.7F
217ZParise120.14.7F
218DHatcher120.62.5D
219MFisher120.94.2F
220RDvorak110.94.0F
221NBackstrom110.04.1F
222BLaich110.92.8F
223ZMichalek110.53.2D
224RNiedermayer111.23.9F
225KBrodziak110.93.0F
226DHeatley110.15.1F
227SVeilleux110.73.4F
228MNilson110.92.4F
229MRichards111.04.2F
230RFedotenko110.34.0F
231PAxelsson110.93.6F
232OJokinen100.15.5F
233JSmithson100.93.4F
234JErskine100.62.3D
235TLetowski100.62.2F
236MPopovic100.02.1D
237EPerrin100.73.9F
238ARourke100.00.6D
239JPitkanen100.32.3D
240ABurrows100.92.8F
241SReinprecht100.13.6F
242MNiskanen100.24.4D
243TPyatt100.03.5F
244RHolik100.33.8F
245MBergeron100.12.5D
246JStrudwick100.22.4D
247NDawes100.13.0F
248TWhite100.94.4F
249DKrejci100.12.4F
250THunter100.63.4F
251RFitzpatrick100.22.0D
252DKalinin100.31.7D
253MMoulson90.11.2F
254PO'sullivan91.03.7F
255VPeltonen90.84.6F
256CConroy90.74.3F
257KKlein90.11.0D
258JOrtmeyer90.83.3F
259BGordon91.43.3F
260PDupuis90.73.5F
261MHanzal90.33.7F
262NHorton90.35.0F
263RTorres90.14.9F
264SHorcoff91.05.0F
265KBieksa90.63.0D
266RBourque90.73.0F
267MKoivu91.14.4F
268ASteen91.14.4F
269PDatsyuk90.64.7F
270PNummelin90.03.1D
271RWhitney90.24.3F
272BBetts91.03.0F
273SHartnell90.03.9F
274DJanik90.01.5D
275JHlavac90.05.0F
276VProspal90.05.2F
277JCorvo90.03.4D
278JHamilton90.02.6F
279PSchaefer90.44.2F
280BMorrison90.43.4F
281BDevereaux91.03.4F
282CKobasew90.74.2F
283TConnolly90.72.7F
284SBrookbank90.42.8D
285JZeiler80.12.3F
286CKunitz80.44.8F
287AMiller80.23.0F
288DMoss80.42.7F
289DLegwand80.73.9F
290MMalhotra80.84.0F
291RNash80.74.3F
292MPettinger81.13.5F
293MGreen80.04.6D
294EStaal80.15.1F
295KTkachuk80.03.6F
296RVrbata80.54.0F
297SWeiss80.44.6F
298BMclean80.82.3F
299RMalone80.64.4F
300JGorges80.21.2D
301BRitchie80.72.3F
302MZigomanis81.02.9F
303MStajan81.14.5F
304BSmolinski80.63.8F
305SBernier80.03.6F
306DFritsche80.13.3F
307RLang80.24.5F
308MNaslund80.04.0F
309MGoc80.61.9F
310JThornton80.35.2F
311PDemitra80.42.3F
312DCleary80.93.7F
313SGagner80.03.7F
314RBonk80.24.0F
315JArnott80.14.3F
316DAlfredsson81.04.7F
317AVermette81.13.1F
318WWalz80.32.2F
319JNovotny80.83.4F
320BDubinsky80.03.5F
321SSalo80.11.1D
322JLupul80.64.5F
323MKnuble80.34.2F
324MSturm80.53.9F
325BBerard80.11.3D
326SPahlsson81.23.8F
327TMarchant71.14.0F
328VKoistinen70.02.7D
329JMayers70.92.9F
330NKapanen70.13.1F
331MTalbot71.03.1F
332PStastny70.54.2F
333JLeopold70.21.7D
334APonikarovsky70.24.1F
335SKoivu70.34.3F
336MDandenault70.53.3D
337PSharp71.03.9F
338AZyuzin70.11.3D
339MMichalek70.33.7F
340DSetoguchi70.02.3F
341DWeight70.13.4F
342MOuellet70.03.8F
343ILaperriere70.53.2F
344JCheechoo70.14.3F
345PRissmiller70.72.9F
346BRolston70.94.3F
347EBelanger70.93.9F
348KDraper71.04.0F
349KMaltby70.83.2F
350BLebda70.14.3D
351JPominville70.93.7F
352PPrucha70.03.6F
353JJagr70.15.2F
354MSchneider60.01.9D
355JStumpel60.54.6F
356DVyborny60.83.9F
357CLarose60.84.6F
358LStempniak60.43.5F
359DBooth60.13.4F
360AHemsky60.04.8F
361CArmstrong60.82.5F
362TPlekanec60.44.0F
363KSkrastins60.31.4D
364ACogliano60.43.7F
365EMalkin60.04.7F
366TRuutu60.14.4F
367JRoenick60.13.3F
368MJohansson60.12.6D
369MCooke60.73.2F
370WSmith60.73.1F
371ABrunette60.03.9F
372CBrown60.41.3F
373VFilppula60.53.9F
374KAdams60.92.1F
375DPenner60.04.5F
376CSchubert60.42.2D
377DWinnik60.32.7F
378DDrake60.51.4F
379DStafford60.03.4F
380TZajac60.34.6F
381JPandolfo61.04.2F
382PElias60.54.2F
383SGomez60.14.8F
384PGaustad60.53.4F
385SBrylin60.73.4F
386MSatan60.23.8F
387MSillinger60.73.7F
388DTanabe60.01.3D
389MSt. Louis60.75.6F

November 7, 2007

Where have all the OTs gone?

If anyone can answer this simple question it would be great.

In 2003-2004 there were 47 overtimes in 209 games.
In 2005-2006 there were 44 overtimes in 210 games.
In 2006-2007 there were 46 overtimes in 210 games.
or 870 in 3690 games through 2003-2007 or 23.5%
In 2007-2008 there were 29 overtimes in 209 games. (13.9%)
This is a 40% reduction in overtimes!

Standard deviation = 6.

I noticed this in the first 100 games, but figured it could be an anomaly until it was repeated in the next 100 games. What is the NHL doing to prevent overtimes? Have team's incentives changed? Do teams see overtime as a bad thing because it gives the opponents a free point [more than last season]?

Something worth noting:
- scoring in the third period is the same as last year.

November 5, 2007

Corsi Numbers

Corsi numbers have popped up a few times in the last week. Due to the fact no one else was seeing if these numbers were relevant, I though I'd give it a go:

Corsi Number
Corsi number is the number of shots directed towards the net while the player is on the ice. The number can be broken down into whose net the shots are directed towards (their own net (-) and their opponent's net (+)) similar to the plus minus statistic. The hope of course is that the Corsi plus minus would correlate well with the regular plus minus, but because the numbers will be 16x larger than plus minus numbers they'll be about 4x more accurate than the plus minus numbers.

Team Regression:
If this statistic is really useful in predicting offense (or winning) it should correlate well with scoring, whether it be on a team by team basis or player by player. So I first look at the team using last season's results: Goals = 0.09*shots + 0.02*missed (where 0.02 is +/- 0.04, aka completely insignificant). First off, even if missed shots were significant, one missed shot is still only worth about 1/5 of an actual goal, so it would take 50 missed shots to make 1 goal.

Individual level:
The first question: are missed shots with regular shots a better predictor of offense than just regular shots? A. this is a resounding no, while missed shots don't seem to hurt the results too significantly they don't seem to add anything, except more variability to the model.

Are missed shots significant?
Again a regression with shots and missed shots, at this point in the season, are not a significant variable in the model. What was interesting is that missed shots were more important in a model that used "expected goals" as opposed to just shots.

The problem with Missed Shots:
The simplest most basic problem with the Corsi index is the fact that missed shots are by definition worse than a shot on goal. The only hope Corsi has, is that players who miss the net a lot are likely hitting the net a lot, and in the absence of a decent sample size this is a useful method as a missed shot is better than no shot at all.

Missed shot percentage (missed shots/(missed shots + regular shots)
The higher a player's missed shot percentage is the worse the player is (if a player is only hitting the net 10% of the time, they'll be sent back to the AHL or worse).

The problem is, that in a model where missed shots are included the missed shot percentage becomes a significant liability. That is to say that unless the missed shots are accompanied with actual shots they're worthless (this makes sense).

Missed Shots
Missed shots are a complicated variable that can be both a good thing and a bad thing. A team that chooses to shoot more shots haphazardly will likely struggle to score compare to a team that focuses on getting the puck on target. Missed shots can have any range depending on the score sheet recording a shot that missed by a few inches is quite different from one that misses by 3 feet.

Blocked Shots
Blocked shots are even more complicated than missed shots and similarly do not help predict offense better than regular shots on their own.

Conclusion:
I'll stick with expected goals. That being said, I've posted the Corsi index on my statistic site for those who think it is useful.

October 28, 2007

November is Divisional Play Month

For those who don't know, November will be the month in which teams play the most games against teams within their own division.

In fact there are a total of 136 inter-divisional games to be played in the month of November (about 9/team) and 63 games against other opponents. This works out to 68% of all games played in November are inter-divisional games. To put this number into perspective, there will be 146 inter-divisional games played in December, January and February combined (about 50 per month).

By the end of November we should have a good idea where teams stand within their own divisions, but it will still be difficult to tell how these divisions will fit into the overall standings.

Why the NHL choose to do things this way is beyond me. I would expect the NHL to want to evenly distribute these games throughout the year. Last season the NHL was much more balanced in regards to these games, but it appears the NHL wanted to load all the inter-divisional games into two months.

March is also a big inter-divisional month, with 108 games. So in November and March account for over 50% of the inter-divisional games, but only 1/3 of the season.

October 23, 2007

Goaltending

It's been an interesting season for goaltending. A number of goaltenders moved in the off season, which of course changes the style of defense for the goalies who move. However, there have been other surprise as well:

Vancouver: Luongo - 0.896, slow start
Minnesota: Seems every goalie on Minnesota does well, but are they actually good?
Calgary: Kiprosoff doesn't look so good without the nice defense in front.
Nashville: Rolled the dice and lost - Mason is no Vokoun.
Blue Jackets: Leclaire may actually be the real deal...
Pheonix: Sent down Aebischer who has their best save percentage. Auld and Tellqvist vie for #1, need I say more
L.A: Found out how to win when all you have is AHL goaltending - Allow 17 shots.
N.J: No defense - No Brodeur...
Philadelphia: Why was Biron used as a backup last season?
Pittsiburg: Don't worry Fleury is still the same goalie he was last year, except with a bit more experience.
Boston: Did Fernandez hide behind Minnesota's defense?
Toronto: One more bad game for Raycroft and he might see some AHL action...
Atlanta: Don't blame the goalies please.
Florida: This team need a lot more than decent goaltending to do well.
Tampa: No changes from last year - still bad goaltending, but great offense.

Early season expected-standings.

West
NameGDPCT
DET290.80
MIN150.72
STL100.63
CBJ70.59
CGY60.57
S.J40.56
DAL40.55
COL-10.48
VAN-30.46
PHX-30.45
ANA-100.37
CHI-140.34
L.A-130.33
NSH-130.31
EDM-250.20

East
NameGDPCT
OTT250.74
PHI130.69
CAR150.68
NYI110.64
BOS50.59
T.B60.58
MTL60.57
PIT30.53
BUF20.53
TOR-40.46
NYR-60.44
N.J-110.36
FLA-150.33
WSH-150.31
ATL-250.23

The above standings represent the expected winning percentage for each team based on the quality of shots for and against each team has had or generated. If a team has better than average goaltending then they should outperform the above predictions and if they have worse than average goaltending they should under perform the above expectations.

This does not account for strength of competition, but is simply calcualted by: EGF2/(EGF2+EGA2)
Where EGF = expected goals for, EGA = expected goals against.

This is posted mainly to show which teams may be higher ranked in the standings than they probably will do over the course of the season.

October 9, 2007

4 Playoff Team Division

James Mirtle posted a while back that it isn't realistic to expect 4 teams from 1 division to make the playoffs. Tom Benjamin responded that: "I do agree that the most probable outcome will be two or three playoff teams from each division, but I do think four teams making it from one division will happen more frequently than he thinks"

I was under the impression that the divisional schedule would significantly effect the chance that 4 teams make the playoffs, but now in two seasons we've had 4 teams make the playoffs in 2006-2007 and in 2005-2006 Toronto had 90 points (2 away from a playoff spot), which would have made it 4 that year as well.

So, I decided to look into the chance of this actually happening. I have a script that simulates the whole season to do season predictions. I can randomize team skill or choose a certain skill level manually. A random distribution of skill produced a 63% chance of a 4 playoff team division and unbalancing one division jumped that number to 68%. I then decided to make every team identical (50% chance for every game) and that produced a even larger 69% chance. Either way there will be 4 teams who make the playoffs from one division 2 times out of 3 years based on my best analysis. In other words it's more common to have 4 teams make it from one division than not.

October 8, 2007

Average Predicted Standings



NamePL
U
DET231
ANA361
S.J461
VAN591
CGY591
MIN693
COL6101
DAL7113
NSH9135
STL10128
L.A11138
EDM121510
CHI131511
CBJ131510
PHX151514


NamePL
U
OTT251
PIT241
NYR351
BUF471
CAR7141
N.J7113
TOR8124
T.B9162
FLA9153
PHI10145
ATL10154
MTL10156
NYI12156
WSH13159
BOS141511

I like NHL standings predictions, because I find it interesting how wrong we can be. The above standings represent the average (P) of several different standings I found on the web (if you know of more I am more than happy to add them).

I have also included the minimum standing spot (L) and maximum standing spot (U) based on the variance of the predictions. For example the New York Islanders have had a lot of different predictions (from average to great to terrible) as such they have a very large expected range (anywhere from 4th to 15th), where as Phoenix has a very small range (15th). No one has predicted Phoenix to do better than 15th.

The above standings is the average of several different sites:
Bookies
Mirtle
Mirtle's Playoff poll
My opinion
McKeen's Hockey
The Hockey News
Added:
Alain Chantelois
Gaston Therrien
Howard Berger
David Johnson

September 28, 2007

Adjusted bookie standings


I like "bookie" predictions due to the fact that they are backed by cash instead of just hot air. That being said, even bookie predictions have systemic problems or even systemic biases. Some can be fixed others cannot. Certain teams attract money from betters and other simply cannot. Due to the fact that very few people cheer for Nashville it may in fact suffer some devaluation.

However, some predictions are just stupid. For example, the Atlantic division last season was a joke, not only was Philadelphia the worst team by a far margin. Overall the division was below average or average. This season Pittsburgh doesn't get to play Philadelphia 8 times to stack their stats as Philadelphia will quite possibly make the playoffs. The bookies from the above prediction state that the Atlantic division will go from below average to the best division by a large margin in a matter of one season.

So, what I did is I adjusted the bookie's standings so that all the divisions performed the same this season as last season.

When I look at those standings I have a hard time finding fault with anything on there [except the four Northwestern team's making the playoff and only one central team and all four Northeastern teams making it].

I find it interesting either way...

September 25, 2007

Bigger Nets

"The NHL first discussed the idea of larger nets two years ago, when players and league executives met to debate ways of increasing scoring and opening up the game."

"The topic was revisited briefly in June when general managers met in Ottawa." [Luongo vows to quit over bigger nets].

How much would 1" change in net size (on all sides)? So what would happen if we moved the left post by 1" and the right post by 1" and increased the hight by 1"?

Since the NHL records how many goalposts (and crossbars) there are, we know how often the puck hits the 2 and 3/8th inch posts.

Last season the puck hit the frame 1480 times. So moving the post by 1" would mean taking the shots that hit the first 1" from the inside and counting those as goals and the pucks that hit the other 1 and 3/8th inch would still be posts (plus all the new posts) [simple logic, "works well enough"]. Essentially this would convert 1/(2+3/8) of the frame hits into goals or about 623 new goals per year for every 1" change in net dimensions. So it would take a 2" all around the net change to increase scoring per game by 1 goal.

Of course this is assuming goalies and teams don't adjust to the new system. Goalies would attempt to cut off shots even more (quite an adjustment for a goalie like Luongo).

September 23, 2007

My own formatted play-by-play

**UPDATED**
L.A @ ANA 2007-09-13
ANA @ L.A 2007-09-15
PHX @ ANA 2007-09-16
ATL @ STL 2007-09-16
WSH @ CAR 2007-09-16
NSH @ CBJ 2007-09-16
FLA @ CGY 2007-09-16
COL @ PHX 2007-09-17
ANA @ VAN 2007-09-17
PIT @ MTL 2007-09-17
FLA @ EDM 2007-09-17
STL @ DAL 2007-09-18
S.J @ L.A 2007-09-18
CHI @ CBJ 2007-09-18
PIT @ MTL 2007-09-18
TOR @ EDM 2007-09-18
S.J @ ANA 2007-09-19
L.A @ COL 2007-09-19
CGY @ VAN 2007-09-19
CBJ @ CHI 2007-09-19
DAL @ T.B 2007-09-19
COL @ DAL 2007-09-20
PHX @ TOR 2007-09-20
WSH @ OTT 2007-09-20
ATL @ NSH 2007-09-20
EDM @ VAN 2007-09-20
FLA @ CHI 2007-09-20
MIN @ DET 2007-09-20
N.J @ NYR 2007-09-21
NSH @ CAR 2007-09-21
ANA @ S.J 2007-09-21
CBJ @ BUF 2007-09-21
MIN @ CHI 2007-09-21
NYI @ MTL 2007-09-21
PIT @ DET 2007-09-21
TOR @ BOS 2007-09-22
CAR @ NSH 2007-09-22
DAL @ PHX 2007-09-22
OTT @ MTL 2007-09-22
STL @ ATL 2007-09-22
WSH @ T.B 2007-09-22
PHI @ NYR 2007-09-22
VAN @ S.J 2007-09-22
EDM @ CGY 2007-09-22
DET @ PIT 2007-09-22



Ok, this is a work in progress. Basically I want something that has the option to hide certain events and show other ones. If you click on SHOT, BLOCK etc. it will hide or show those events. This is really nice as you can break the play-by-play into just shots (what I care most about), or just face-offs. In the long run, this is just a great way to see if I have recorded the data correctly and where any problems are.

Also, I've color-coded the shots based on the likelihood of them going in. I wasn't sure if this would work, but it really gives you an idea of the flow of the game. Bright red = very good shot. Black goal = bad goal (low probability of going in).

I plan on publishing these (possibly live if I can figure out how to write a program that will download and parse them live). They are more in tune with the old style.

Note: the code runs slow in IE7 (not sure how it works in IE5.x or IE6).

I will likely attach a game summary part onto the top, which includes the goalie's save percentages and who scored and got assists. And a few other details. I also find the information a little overwhelming in this format so I'll be moving it around to see if it works better.

Nice CSS formatting was done by Chris Waycott.

Preseason Goaltending

NlastnameSQNS
1ANDERSON1.00055
2LUONGO0.95151
3JOHNSON0.93931
4HUET0.92831
5AUBIN0.92733
6KEETLEY0.92434
7VOKOUN0.92034
8HILLER0.91848
9BRYZGALOV0.91385
10DENIS0.91338
11MASON0.91253
12PAVELEC0.91037
13RINNE0.89644
14AEBISCHER0.89444
15PRICE0.89243
16ROLOSON0.88559
17MASON0.88341
18LALIME0.86852
19GARON0.86245
20THOMAS0.86236
21BERNIER0.85237
22KHABIBULIN0.84432
23GRAHAME0.83341
24TELLQVIST0.82842
25BUDAJ0.82049
26PATZOLD0.81032
27FLEURY0.80854
28BACKSTROM0.80741
29KIPRUSOFF0.79243
30TURCO0.68835

I'll update this list "regularly". Only includes games that the NHL published information for (obviously).

There are a significant number of shot from the wrong side of the ice in the NHL data I've assumed any shot >115' feet was a mistake and should have been recorded on the other half of the ice. Hopefully the NHL gets that sorted out before the beginning of the season.

Is Cloutier finished? (3-10, 3-18). He was able to stop all three SO opportunities.

For the most part I have the new NHL format in my database.

September 14, 2007

Adjusted Shot Quality Neutral Save Percentage

Alan Ryder has found systemic bias in the shot quality data leaving the results showing problems with the data. It is bet summarized by Ryder himself:
I have been worried that there is a systemic bias in the data. Random errors don’t concern me. They even out over large volumes of data. I seriously doubt that the RTSS scorers bias the shot data in favour of the home team. But I do think that it is a serious possibility that the scoring in certain rinks has a bias towards longer or shorter shots, the most dominant factor in a shot quality model. And I set out to investigate that possibility [Shot Quality Product Recall].

I did a rather simple way to fix the problem. I did a regression on SQ results for all games based on two factors: team shot quality and stadium shot quality or [RTSS shot quality]. This simply calculate how much off the RTSS scores are from the standard [how the team normally performs]. Preferably we want no effect from RTSS scores so all those variables should be 0. I found a rather long list of biases, most of them small, including: Calgary, St. Louis, Columbus, Chicago, Phoenix, New Jersey, New York Rangers, Philadelphia, Buffalo, Carolina, Washington. I have deliberately over chosen, so that list likely includes teams which are simply randomly different as opposed to actual bias, but it doesn't matter. Ideally, I would want to incorporate these issues into the model directly, but the shot quality model is time consuming to build and once you get the variables you have to go through the hassle of calculating the percentages for all 7000 shots.

Simple adjustment on shot quality and it's effect on goaltending:
NlastnamenewSQNoldSQNShots
1BACKSTROM0.9250.9261027
2VOKOUN0.9200.9211299
3DIPIETRO0.9200.9221917
4THIBAULT0.9200.921570
5MASON0.9190.9211244
6HUET0.9190.9191280
7GIGUERE0.9180.9181490
8LUONGO0.9180.9192169
9LEHTONEN0.9150.9152075
10ROLOSON0.9140.9141979
11BRODEUR0.9140.9132182
12KIPRUSOFF0.9140.9122190
13NABOKOV0.9140.9151227
14KOLZIG0.9130.9191771
15KHABIBULIN0.9130.9141668
16TURCO0.9120.9121554
17BURKE0.9120.913687
18FERNANDEZ0.9120.9131154
19AEBISCHER0.9110.910929
20LEGACE0.9110.9191177
21HASEK0.9110.9121309
22LUNDQVIST0.9100.9211927
23EMERY0.9100.9111691
24DUNHAM0.9100.910540
25NIITTYMAKI0.9080.9131562
26GRAHAME0.9080.912702
27MILLER0.9070.8991886
28SMITH0.9060.909510
29FLEURY0.9040.9051955
30BELFOUR0.9030.9041550
31NORRENA0.9030.9081420
32BUDAJ0.9030.9031499
33BRYZGALOV0.9020.903668
34THOMAS0.9020.9041985
35GERBER0.9000.901784
36AULD0.8990.899729
37GARON0.8990.901849
38JOSEPH0.8990.9041481
39TOIVONEN0.8990.897502
40THEODORE0.8980.898870
41WARD0.8980.9061625
42TOSKALA0.8970.899915
43LECLAIRE0.8940.900629
44JOHNSON0.8940.900894
45BIRON0.8940.899509
46SANFORD0.8930.901707
47RAYCROFT0.8910.8921939
48BIRON0.8870.886533
49TELLQVIST0.8870.892780
50HOLMQVIST0.8860.8901134
51DENIS0.8840.8841068
52CLOUTIER0.8780.880608
newSQN = adjusted for RTSS bias
oldSQN = no adjustment for RTSS bias.

I'm curious what the RTSS turnover is. That is to say, I wonder if the bias last year will be the same this year.

September 13, 2007

NHL Changes Data Format

Along with the Jersey changes it appears the NHL wanted to make their reporting of games a little more "user friendly". Hopefully they've managed to make sure all the files are the same, although I suspect they'll still have the French versions.

Positives:
+ There is ON ICE information for every event!
+ Nice break down of shots per situation.
+ Moved goalie info to Event Summary
+ Missed shots have distances.
+ Every event has a zone for where the action occurred.
+ Cloutier seems to be himself [3-10] [couldn't help myself]
+ Break down icetime by situation [NHL publishes 4v3 time and 5v3 time].

Negative:
- This could take weeks to figure out formatting to get information into my database
- They disabled left clicks for who knows what reason.
- The files are HUGE.
- Play by Play includes the header every so often, but doesn't explain why [looks like it's for com. break]
- Still don't include the X-Y cords. for shots.

Overall I think I can work with these, formatting looks simple and consistent.

September 12, 2007

Travel Schedule

Nlong_nameTripsKMKM/Trip
1Chicago Blackhawks63634401007
2New York Islanders6339587628
3New York Rangers6343336688
4Ottawa Senators6348097763
5Toronto Maple Leafs6349275782
6Pittsburgh Penguins6242485685
7Buffalo Sabres6243241697
8Atlanta Thrashers6260166970
9Tampa Bay Lightning62632171020
10St. Louis Blues61657251077
11Detroit Red Wings6159510976
12Columbus Blue Jackets61674141105
13Philadelphia Flyers6142989705
14New Jersey Devils6038815647
15Florida Panthers60618781031
16Washington Capitals6040934682
17Colorado Avalanche59758721286
18Boston Bruins5942769725
19Montreal Canadiens5944683757
20Vancouver Canucks58874401508
21Phoenix Coyotes58726931253
22Los Angeles Kings58699151205
23Carolina Hurricanes5844879774
24Nashville Predators57637751119
25San Jose Sharks57688691208
26Dallas Stars57676041186
27Anaheim Ducks57665211167
28Edmonton Oilers56830361483
29Minnesota Wild55678211233
30Calgary Flames55731801331
Here's the details on this season's schedule. Trips = number of days the team will be on a plane traveling between cities for an NHL game. KM = total kilometers to travel over the course of the upcoming season. KM/trip is just the average trip distance.

You can see the NHL does a pretty good job balancing the schedule. Also, there is a significant relationship between average trip length number of days on a plane.