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.

Nashville's Unfair Advantage.

NDateTeam
1Oct 4COL
2Oct 6DAL
3Oct 13CGY
4Oct 27FLA
5Nov 4CHI
6Nov 10CBJ
7Nov 15CHI
8Nov 17STL
9Nov 22DET
10Nov 24MIN
11Nov 29OTT
12Dec 1MTL
13Dec 6VAN
14Dec 8ANA
15Dec 10DET
16Dec 13COL
17Dec 22L.A
18Dec 29S.J
19Jan 3EDM
20Jan 12CBJ
21Feb 9S.J
22Feb 14CHI
23Feb 23DAL
24Mar 20DET
When I go through the NHL schedule for the first time there are two things I look at first: distance traveled and how many back to back games the team plays. I also look at back to back games from the perspective of the team who gets to play the tired team.

Typically a team sees about 12 back to back games and plays against 12 teams who played the day before. Nashville lucked out this season. They are scheduled to play 24 games (30% of the season) against tired teams [listed to the left] and play only 10 back to back games themselves.

Generally I argue that these games have a cost of about 5% in terms of winning percentage or 0.05 wins. So Nashville loses 0.50 games in the 10 games they play back to back and gains 1.2 wins in the 24 games they play against a back to back team. In essence the NHL has given Nashville 1.5 extra points. (this is worth approximately $700,000). Of course this isn't huge, but essentially Nashville has a 1.5 point advantage before the season starts.

It's interesting, but the majority of these games are in the first half of the season, possibly to get the team an early lead and to attract attention to the team. For example, Nashville plays tired teams for a whole week: Dec 6 to Dec 13.

Last season the NHL was very good at making sure back to back games were scheduled fairly and the largest difference was 5 (Col, S.J, NYI)

Mirtle Poll Standings.



James Mirtle posted a nice poll, which asked people to pick the teams that are expected to make the playoffs. I simply created a nice graphic for the predicted standings based on this poll. This graphic was made when there was 330 votes.

September 10, 2007

Player Save Percentage

I'm a big believer in "shot quality" as many readers have likely noticed. If players have an impact on the quality of shots a goalie sees, then they should affect how many pucks the goalie stops. If a goalie generally stops 90% of shots then a player who makes shots twice as easy to save should have a save percentage of 95%. Of course players have a much smaller impact of save percentage than goalies do.

In order to see if players can affect save percentage I did a season vs. season regression: I check if the players who out performed in 2005-2006 continued to outperform in 2008-2007. If there is a significant relationship between the two seasons then obviously players impact save percentage.

The regression does come out significant, which agrees with the theory that players can affect shot quality.
The regression is reasonably simple:
Difference in team's save percentage in 2006 vs. 2007 = D
Player's Save Percentage in 2006 = Save%
So:
Player's Save Percentage in 2006 = 0.7 + 0.235*Save% + 0.618*D
Basically the above equation is a regression towards the mean: it takes extreme values and brings them closer to the average (0.920).

Note: this data only includes even strength shots.

However, this data is worthless for predicting how a player will do next year due to the large amounts of variability. For example Crosby had a n excellent save percentage this season of 0.930, this system says he'll likely perform at 0.919 given identical goaltending as last season, however he could easily do as poorly as 0.890 with bad luck or as well as 0.950 with good luck. A 2% difference in save percentage on 600 shots works out to about 12 goals over the course of a season. So this large range of possible save percentages for players can have a large impact of a player's plus-minus, often due to just luck.

Results
Any way most people will want to see the results

NILastnameSave%Shots
1TGreen0.98885
2ASemenov0.982109
3SBates0.972216
4PSejna0.96893
5JKrog0.967122
6MHartigan0.96658
7CKobasew0.964274
8SParker0.96151
9BIsbister0.960126
10APeters0.95896
11CThorburn0.958143
12AEdler0.95792
13BMcgrattan0.95690
14APerezhogin0.955313
15JLangfeld0.95567
16RFitzpatrick0.954307
17RPetrovicky0.954109
18RMartinek0.954325
19JOrtmeyer0.954173
20RCallahan0.95263
21MNilson0.951286
22TPreissing0.950443
23SNichol0.950260
24MChouinard0.950139
25JHudler0.949256
26DMccarty0.94978
27CSchubert0.948424
28COrr0.947133
29PHarrold0.94757
30AHutchinson0.947150
31SMccarthy0.947187
32AMair0.946298
33CJanssen0.94693
34JVigier0.946297
35SSalo0.946445
36PBouchard0.946461
37JSlater0.945330
38MHossa0.945293
39JDowd0.945200
40JJokinen0.945361
41EBoulton0.944126
42JCowan0.944233
43SVeilleux0.944374
44KHuselius0.944425
45RRegehr0.943633
46MSillinger0.943509
47GLaraque0.943315
48FKaberle0.943140
49AWozniewski0.94387
50JGreen0.942226
51TWhite0.942467
52CMacarthur0.94286
53BRolston0.942481
54SHartnell0.941358
55CNeil0.941425
56PGaustad0.941237
57CSimon0.940318
58MModano0.940301
59JLeclair0.940117
60BRitchie0.940266
61JTambellini0.94083
62TVanek0.940531
63AFerence0.939577
64HSedin0.939494
65MMalik0.939477
66SBernier0.939310
67ABrooks0.93865
68JFranzen0.938308
69BLebda0.938370
70GZanon0.938418
71RGetzlaf0.938337
72MJohnson0.937510
73JLehtinen0.937366
74RHamrlik0.937636
75JKlemm0.937190
76BGuerin0.937427
77JZeiler0.93779
78BMezei0.937300
79NAlexeev0.936391
80DRoy0.936500
81HZetterberg0.936327
82VFiddler0.936389
83DMarkov0.936420
84CPronger0.936482
85CKelly0.936466
86GExelby0.936419
87DVyborny0.935480
88CPerry0.935325
89TLinden0.935339
90KCarney0.935462
91DLegwand0.935615
92KFoster0.935369
93NLidstrom0.935553
94DHeatley0.935643
95DHamel0.935153
96TSantala0.935107
97PStefan0.935168
98DSedin0.934488
99DBoyd0.93461
100AGreene0.934122
101BMuir0.934137
102AKastsitsyn0.934137
103AAsham0.934350
104BAllen0.934182
105BRafalski0.934743
106OTollefsen0.934393
107GMetropolit0.934332
108DMoore0.934346
109OSaprykin0.934376
110MLombardi0.933480
111JPandolfo0.933615
112VFilppula0.933255
113DPaille0.933150
114MPouliot0.933225
115BWitt0.933703
116MRozsival0.933538
117MAfinogenov0.933388
118ARadulov0.933313
119BHolik0.933506
120MGiordano0.933208
121LEriksson0.933282
122SHnidy0.933460
123ANasreddine0.933252
124DAlfredsson0.932548
125ELindros0.932222
126RFedotenko0.932503
127KTkachuk0.932487
128CChelios0.932383
129BGervais0.932324
130RZednik0.932265
131JSchultz0.932265
132MParrish0.932337
133JFriesen0.932336
134NWelch0.932146
135JModry0.931423
136SMontador0.931350
137PSchaefer0.931480
138MSkoula0.931669
139AVolchenkov0.931566
140KTimonen0.931624
141PKessel0.931434
142AYashin0.931405
143JHoggan0.931159
144PDatsyuk0.931419
145RWarrener0.931433
146BOrpik0.931519
147TPoti0.931634
148BBetts0.931389
149MErat0.930488
150SMellanby0.930330
151HTallinder0.930416
152PMarleau0.930516
153RHollweg0.930314
154GDe vries0.930699
155SCrosby0.930556
156AMarkov0.930641
157JRuutu0.930298
158PThoresen0.929326
159MStuart0.92985
160JStaal0.929354
161JMadden0.929566
162TKostopoulos0.929382
163MRycroft0.929240
164VKozlov0.929564
165THunter0.929437
166MJones0.929338
167SWeber0.929689
168BGionta0.929351
169ARourke0.92970
170DCleary0.928348
171TPlekanec0.928501
172TAmonte0.928417
173MWalker0.928264
174MOhlund0.928624
175MFisher0.928402
176TZajac0.928471
177MStraka0.928429
178MNaslund0.928498
179DFritsche0.928332
180DKasparaitis0.928166
181PKariya0.928650
182MGaborik0.928318
183MZidlicky0.928608
184JPavelski0.928221
185MSvatos0.927303
186NPratt0.927468
187AHilbert0.927399
188MMowers0.927440
189ASteen0.927467
190MSchneider0.927439
191BBurns0.927466
192JWoywitka0.927178
193TPyatt0.927397
194AZyuzin0.927246
195PMara0.927697
196JSim0.927355
197CPhillips0.927641
198JBlake0.927586
199BCoburn0.927327
200PAxelsson0.927422
201RLang0.926435
202RBonk0.926462
203TSelanne0.926461
204SGomez0.926447
205DLangkow0.926501
206JArnott0.926460
207SBrylin0.926527
208RGlobke0.92654
209MJurcina0.926472
210RWhitney0.926619
211JOduya0.926497
212SUpshall0.926188
213MBradley0.925308
214CRivet0.925629
215TDaley0.925495
216CSarich0.925548
217TNumminen0.925668
218LKukkonen0.925454
219JSpezza0.925493
220DKalinin0.925706
221SHill0.925706
222AEriksson0.925479
223SAvery0.925479
224SYelle0.925306
225FBeauchemin0.925585
226BMclean0.925411
227DBackes0.925212
228BMay0.925106
229JFinger0.925106
230CKunitz0.924450
231NSchultz0.924622
232IKovalchuk0.924569
233RSuter0.924567
234YStastny0.92479
235BYoung0.924105
236EBelanger0.924472
237KJohnsson0.924590
238DHale0.924236
239DSydor0.924485
240JSpacek0.924432
241MGreen0.924432
242SStaios0.924471
243JShelley0.924157
244KMclaren0.924510
245MRecchi0.923549
246JHeward0.923483
247WSmith0.923261
248RJohnson0.923313
249CWhite0.923625
250RMalone0.923364
251ATanguay0.923559
252MGoc0.923286
253BJones0.92352
254MMethot0.923117
255DBriere0.923649
256AFoote0.923493
257OVaananen0.923492
258JJacques0.923142
259BGordon0.923413
260RPolak0.922129
261BLukowich0.922605
262MHolmqvist0.922296
263BSeabrook0.922579
264WWalz0.922373
265KRachunek0.922463
266DHordichuk0.922128
267PRissmiller0.922320
268MComrie0.922371
269JLiles0.922370
270PMartin0.922727
271NBaumgartner0.92251
272JIginla0.922561
273CMacdonald0.922102
274KBarch0.92251
275MMichalek0.921433
276ACarter0.921331
277SDonovan0.921496
278WMitchell0.921483
279MOuellet0.921381
280CAdams0.921292
281KBieksa0.921660
282PTenkrat0.921368
283BShanahan0.921406
284MKomisarek0.921685
285BRichardson0.921342
286JStoll0.921266
287DWeight0.921456
288MDandenault0.921456
289SSullivan0.921354
290KHuskins0.921177
291ZParise0.921543
292CConroy0.921467
293DPhaneuf0.921719
294MHavlat0.921340
295CBrown0.921340
296SO'donnell0.921554
297KStewart0.921151
298AZhitnik0.920704
299VNieminen0.920176
300THolmstrom0.920314
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305ERasmussen0.920276
306AMeszaros0.920701
307DHamhuis0.920688
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309SRucchin0.920300
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311DBrashear0.920274
312JChimera0.920498
313JGorges0.920336
314DGirardi0.920199
315SHannan0.920609
316ASutton0.920410
317FMeyer iv0.920410
318SZubov0.919546
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321JLundqvist0.919161
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353RDavison0.91873
354MSmith0.918158
355JHecht0.918571
356BRadivojevic0.918425
357JStumpel0.918437
358CEhrhoff0.918461
359AMiettinen0.917315
360SPahlsson0.917436
361CBackman0.917436
362JJagr0.917557
363CGratton0.917448
364SRobidas0.917387
365PKubina0.917387
366JWilliams0.917387
367JRivers0.917181
368JThornton0.917482
369TKaberle0.917554
370PElias0.917445
371SMorrisonn0.917637
372BCampbell0.917661
373PBoucher0.917504
374MHejduk0.917492
375RRobitaille0.917468
376RPark0.917324
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378GPlatt0.917108
379NDempsey0.91772
380JNieuwendyk0.91772
381JDipenta0.917360
382MLehtonen0.91796
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384OJokinen0.917611
385ILaperriere0.916419
386TMoen0.916419
387YPerreault0.916383
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390AAlberts0.916669
391BMccabe0.916657
392KWellwood0.916238
393AVermette0.916464
394ZChara0.916737
395MCooke0.916451
396JHamilton0.916344
397TPock0.916249
398LRichardson0.91683
399GCampbell0.916308
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402RSmyth0.915473
403AMcdonald0.915473
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405MCammalleri0.915519
406GWesley0.915401
407RKlesla0.915613
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409RPotulny0.915153
410RHainsey0.915588
411MRibeiro0.915341
412BClymer0.915423
413BSalvador0.915505
414PNummelin0.915387
415SSamsonov0.915387
416DTanabe0.915398
417CColaiacovo0.915316
418JDumont0.914538
419AKarlsson0.914187
420EChristensen0.914292
421JO'neill0.914362
422MCullen0.914478
423RTorres0.914478
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425BWinchester0.914232
426BClark0.914637
427KMaltby0.914359
428NHavelid0.913682
429NHorton0.913520
430SBarnes0.913439
431DArkhipov0.913439
432MVan ryn0.913566
433RSalei0.913658
434VPeltonen0.913404
435TFedoruk0.913288
436JLundmark0.913357
437ROlesz0.913380
438RBlake0.913541
439SGonchar0.913633
440JHejda0.913310
441MZigomanis0.913344
442DMoss0.913172
443FSjostrom0.913401
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445DArmstrong0.913378
446MMalhotra0.912444
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449AMiller0.912636
450ASemin0.912488
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452PStastny0.912476
453RKesler0.912238
454MVlasic0.912578
455JBouwmeester0.912747
456LNagy0.912464
457KKlee0.912645
458BSmolinski0.912430
459EPerrin0.912509
460ALaaksonen0.912147
461JKlepis0.912147
462JTootoo0.912260
463MGrier0.912452
464MNorstrom0.911621
465JPominville0.911564
466DHinote0.911203
467JSakic0.911575
468CClark0.911518
469MEaton0.911259
470CStillman0.911281
471DDrake0.911303
472MTalbot0.911370
473JFahey0.91156
474SChistov0.911224
475YTremblay0.91156
476PDupuis0.911425
477MLapointe0.911369
478BHedican0.910402
479AHall0.910301
480JWisniewski0.910301
481SOtt0.91078
482EJovanovski0.910399
483JSmith0.910676
484JVandermeer0.910299
485GRoberts0.910454
486RJackman0.910155
487AHemsky0.910354
488SNiedermayer0.910575
489BMorrow0.910210
490MPeca0.910221
491LKrajicek0.909464
492NAntropov0.909298
493MBell0.909352
494EStaal0.909593
495AFrolov0.909549
496PEaves0.909417
497MSavard0.909647
498RScuderi0.909515
499SSouray0.909679
500JMcclement0.909416
501TTaylor0.909175
502MRupp0.909175
503FPisani0.908459
504MRoy0.90898
505TRuutu0.908381
506NKronvall0.908413
507LVisnovsky0.908554
508DMcammond0.908357
509APonikarovsky0.907400
510BGuite0.907162
511NTarnasky0.907205
512WRedden0.907550
513RVrbata0.907408
514JBulis0.907397
515CHiggins0.907397
516JMelichar0.907515
517BMorrison0.907460
518DMurray0.906128
519FTyutin0.906458
520PBrisebois0.906213
521BPothier0.906596
522TBertuzzi0.90685
523TLetowski0.906244
524MHandzus0.90653
525DBooth0.906180
526RClowe0.906254
527KSauer0.905327
528SBegin0.905274
529CCampoli0.905274
530AKopitar0.905474
531SHorcoff0.905558
532BSopel0.905431
533MSturm0.905473
534SKoivu0.905515
535KBallard0.905536
536JHalpern0.905399
537JImmonen0.90563
538WWolski0.905451
539BBattaglia0.905409
540TMarchant0.905283
541RBrind'amour0.905524
542MReasoner0.905377
543MRucinsky0.904293
544CArmstrong0.904449
545RBourque0.904261
546JMayers0.904480
547AOvechkin0.904697
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549AKovalev0.904499
550SDoan0.904478
551MPettinger0.904363
552GBrule0.903321
553GMurray0.903445
554MStreit0.903362
555MSundin0.903434
556MLashoff0.90393
557DTjarnqvist0.903258
558SWeiss0.903412
559WBelak0.903144
560PRanger0.903504
561MMurray0.90372
562JPitkanen0.903719
563IWhite0.903503
564SFedorov0.902441
565BStuart0.902727
566MRyder0.902480
567PCajanek0.902388
568DMaclean0.90251
569ABabchuk0.902367
570JVasicek0.902377
571ALilja0.902295
572BBoyes0.902518
573JNovotny0.902325
574DTucker0.901284
575ECole0.901446
576APicard0.901526
577TGilbert0.90191
578JPerrault0.901101
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580JNiinimaa0.901252
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591DPenner0.899367
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593NBoynton0.899395
594PBergeron0.899513
595DBrown0.899483
596DStafford0.898256
597CDrury0.898413
598JWard0.898354
599TFleischmann0.898118
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602AKotalik0.898362
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604PPrucha0.898381
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616MKnuble0.896405
617FKuba0.896588
618PBondra0.896154
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620TGleason0.895411
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622SThornton0.895295
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625DMorris0.895608
626DRichmond0.895133
627BAllen0.894635
628JLeopold0.894123
629NWallin0.894473
630PSykora0.894454
631MTjarnqvist0.894208
632JCheechoo0.894378
633MRyan0.894132
634RAbid0.89466
635ARoy0.894113
636NKapanen0.894386
637SGagne0.894489
638JTimonen0.893122
639SKapanen0.893477
640JYork0.893318
641ZStortini0.89384
642JPohl0.893298
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644MWeaver0.891229
645ABrunette0.891531
646BThomas0.891128
647JReich0.891128
648DClarkson0.89164
649JGratton0.890146
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651JWilliams0.890525
652DHatcher0.889714
653MCommodore0.889612
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657MGreene0.888529
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661BBell0.885234
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673JErskine0.879248
674LNycholat0.879132
675NZherdev0.879387
676DSeidenberg0.878337
677EMeloche0.87757
678DWestcott0.877146
679JTaffe0.87689
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681AAucoin0.875361
682OTverdovsky0.875120
683DSyvret0.875120
684DGauthier0.875279
685RJones0.874365
686JMckee0.873173
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688DScatchard0.870230
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690ASuglobov0.86853
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692MSt. pierre0.86552
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695KAdams0.854253
696ELisin0.841113
697BBerard0.84075
698DCarcillo0.82686

September 6, 2007

Southeastern Division Goaltending

The Falconer from Do The Thrashers Have Large Talons?. Requested I do a little summary on the goaltenders from the Southeast division. I figured I would post it here for all to see.






Atlanta - Lehtonen

Save %: 0.912, Career Save %: 0.911
Shot Quality Neutral Save %: 0.915
Wins: 34, Wins excluding SOW: 27
Career Wins: 58, Career Games: 110

#2 Pick overall in 2002. Lehtonen has had a great start to what should be a long career. He's young and has a lot of time to improve. Bob Hartley destroyed Lehtonen's confidence by replacing him with Hedberg in game #2 after he allowed 4 goals on almost 40 shots. He was put back into the fire for game #3 allowing 7 goals on 35 shots. I expect him to rebound in the coming season, but I never understood what Hartley was thinking.

Carolina - Ward
Save %: 0.897, Career Save %: 0.892
Shot Quality Neutral Save %: 0.906
Wins: 30, Wins excluding SOW: 30
Career Wins: 44, Career Games: 88

#25 overall in 2002. The only reason Ward become Carolina's starting goaltender was his amazing playoff run. However he has never really impressed me as an outstanding goalie. Ward was able to squeak in a 30 win season, but was limited to only 60 games.

Florida - Vokoun
Save %: 0.920, Career Save %: 0.913
Shot Quality Neutral Save %: 0.921
Wins: 27, Wins excluding SOW: 25
Career Wins: 161, Career Games: 384


Mason played well enough in Nashville to steal his spot leaving Vokoun, a very good goaltender, free for the taking. Many have commented that if Luongo couldn't get Florida into the playoffs, then why should a weaker goalie be able to? Vokoun has missed a lot of important hockey games due to injuries and most importantly the playoffs two years in a row. Florida almost made the playoffs last season, Vokoun should be able to put them over the edge.

Tampa Bay - Denis & Holmqvist
Denis
Save %: 0.883, Career Save %: 0.903
Shot Quality Neutral Save %: 0.884
Wins: 17, Wins excluding SOW: 13
Career Wins: 111, Career Games: 338


I commented once that both of Tampa's goaltenders were terrible and was criticized and told it's their defense that's terrible and the goalies are doing the best with what they got. Personally I felt Denis was a big reason Columbus slipped into mediocrity for so many years. Denis made the record for most losses for a goalie two seasons in a row, granted he played the most minutes and saw the most shots.



Holmqvist
Save %: 0.893, Career Save %: 0.882
Shot Quality Neutral Save %: 0.890
Wins: 27, Wins excluding SOW: 21
Career Wins: 27, Career Games: 52


"Lanky Holmqvist has the size to blot out the majority of the net from shooters and also has good technical ability and reflexes" (The Sports Forecaster 2001-02, p. 101). The one thing I will note of Holmqvist, despite his terrible stats he was able to play close to 50% hockey. Holmqvist doesn't even have a full season of experience to this point and the experience he does have is less than impressive. I don't see all that much positive in his game. John Tortorella choose to use Holmqvist over Denis in the playoffs last season.

Washington - Kolzig
Save %: 0.910, Career Save %: 0.927
Shot Quality Neutral Save %: 0.919
Wins: 22, Wins excluding SOW: 21
Career Wins: 276, Career Games: 657


Kolizg is getting older, but still may be the most important piece of a terrible Washington team. His skills are wasted on such a terrible team. Word on the street is that Washington is getting better, although not much. I think enough people know enough about Kolzig, there isn't much to add.

It would appear that there are three teams with excellent goaltenders (Florida, Atlanta and Washington - note these teams all have terrible defense) and two team's with terrible goaltenders (Tampa Bay and Carolina) with terrible defense as well, but they both have extreme offense to compensate with.