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.

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.

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.

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.

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

N I Lastname blocked sh ev P 1 A Volchenkov 79 1.3 5.1 D 2 M Komisarek 69 1.0 5.9 D 3 J Smith 61 0.9 4.8 D 4 G Zanon 60 1.4 4.8 D 5 M Eaton 50 1.4 5.1 D 6 B Clark 50 1.0 5.0 D 7 B Lukowich 48 0.9 4.5 D 8 B Witt 47 1.1 4.7 D 9 R Blake 46 1.3 4.6 D 10 N Havelid 46 1.2 5.2 D 11 R Hamrlik 44 0.9 5.7 D 12 L Kukkonen 44 0.6 4.6 D 13 C Phillips 43 1.4 5.6 D 14 K Klee 40 0.8 5.0 D 15 G Exelby 39 0.9 4.8 D 16 S Hannan 39 1.2 5.5 D 17 P Kubina 39 1.4 4.8 D 18 F Kuba 38 1.1 5.5 D 19 H Gill 38 1.9 5.4 D 20 A Foote 38 1.6 5.6 D 21 K Ballard 37 1.0 5.2 D 22 D Tarnstrom 36 0.9 4.6 D 23 T Lydman 36 1.2 4.6 D 24 K Timonen 36 1.1 5.1 D 25 P Ranger 36 1.1 6.5 D 26 P Boucher 35 0.9 5.3 D 27 R Martinek 35 1.0 4.8 D 28 A Ward 35 0.7 4.8 D 29 T Gleason 34 1.0 5.1 D 30 R Scuderi 34 1.0 5.2 D 31 B Orpik 34 0.3 5.1 D 32 F Tyutin 34 0.8 5.4 D 33 B Jackman 33 1.0 4.8 D 34 M Ohlund 33 0.8 4.9 D 35 G Wesley 33 1.2 4.3 D 36 A Alberts 33 0.9 5.4 D 37 B Hedican 33 0.7 5.2 D 38 S Gonchar 32 1.2 5.5 D 39 J Liles 32 0.0 4.2 D 40 A Greene 32 1.0 5.4 D 41 H Tallinder 32 1.4 5.0 D 42 J Modry 31 0.8 4.6 D 43 M Rozsival 31 1.0 5.7 D 44 A Wozniewski 31 1.0 4.2 D 45 D Keith 30 1.4 6.2 D 46 N Schultz 30 1.3 4.9 D 47 D Girardi 30 0.9 5.4 D 48 B Sopel 29 1.1 4.3 D 49 R Salei 28 1.1 6.4 D 50 B Salvador 28 0.9 3.7 D 51 A Markov 28 1.0 5.6 D 52 A Miller 28 0.9 4.2 D 53 J Johnson 27 0.7 5.6 D 54 B Mezei 27 0.9 4.1 D 55 M Vlasic 27 1.3 6.0 D 56 B Seabrook 27 1.0 4.9 D 57 K Johnsson 27 1.2 4.9 D 58 A Lilja 27 1.0 4.4 D 59 W Redden 27 0.8 4.9 D 60 I White 27 0.4 5.4 D 61 K Mclaren 27 0.9 5.6 D 62 A Meszaros 27 0.8 4.8 D 63 C Pronger 26 1.7 6.3 D 64 W Mitchell 26 1.3 5.2 D 65 J Finger 26 0.3 3.8 D 66 B Allen 26 0.9 4.7 D 67 R Suter 26 0.8 4.9 D 68 B Gervais 26 0.6 3.8 D 69 R Regehr 25 1.3 4.9 D 70 R Klesla 25 0.9 5.4 D 71 D Morris 25 0.9 4.8 D 72 B Mccabe 25 1.0 3.8 D 73 S O'brien 25 0.8 6.1 D 74 T Enstrom 25 0.8 4.5 D 75 M Jurcina 25 1.1 4.2 D 76 K Rachunek 25 0.4 4.3 D 77 L Visnovsky 24 0.3 5.1 D 78 J Bouwmeester 24 1.5 6.8 D 79 S Staios 24 1.1 6.0 D 80 F Kaberle 24 0.4 5.1 D 81 C Rivet 24 1.0 4.8 D 82 M Commodore 23 1.0 4.1 D 83 J Mckee 23 0.4 2.3 D 84 C Ehrhoff 23 0.6 4.5 D 85 N Lidstrom 23 1.6 5.7 D 86 T Poti 23 0.8 3.6 D 87 N Paetsch 23 0.2 3.9 D 88 V Vishnevski 23 0.7 4.6 D 89 P Mara 23 0.6 4.1 D 90 A Sutton 23 0.7 3.6 D 91 D Seidenberg 22 0.3 3.2 D 92 T Gilbert 22 0.5 5.7 D 93 K Russell 22 0.1 3.4 D 94 Z Chara 22 0.9 5.4 D 95 S Robidas 20 0.9 4.8 D 96 M Norstrom 20 0.9 3.7 D 97 N Kronvall 20 0.6 3.7 D 98 D Hamhuis 20 1.3 5.2 D 99 A Eriksson 20 0.5 3.1 D 100 A Zhitnik 20 0.7 4.6 D 101 T Daley 20 0.9 5.1 D 102 J Hejda 19 1.2 4.7 D 103 M Jones 19 0.6 4.3 D 104 T Kaberle 19 0.4 5.9 D 105 L Smid 19 0.7 3.1 D 106 J Wisniewski 19 0.5 4.8 D 107 D Murray 19 0.4 3.0 D 108 C Sarich 19 1.1 4.7 D 109 B Coburn 19 0.5 5.2 D 110 R Jones 19 0.8 3.8 D 111 G De%20vries 19 1.0 4.9 D 112 R Getzlaf 18 0.5 4.3 F 113 F Beauchemin 18 1.5 6.6 D 114 B Stuart 18 1.0 4.9 D 115 G Campbell 18 0.9 3.4 F 116 D Sydor 18 0.2 4.1 D 117 J Pavelski 18 0.4 3.3 F 118 T Mitchell 18 0.7 3.7 F 119 M Skoula 18 1.1 5.2 D 120 S Morrisonn 18 1.3 4.6 D 121 C Drury 18 1.1 4.4 F 122 D Wideman 18 0.5 4.2 D 123 M Van%20ryn 18 0.8 4.5 D 124 K Huskins 17 0.7 5.4 D 125 A Aucoin 17 0.9 5.5 D 126 D Steckel 17 1.4 2.8 F 127 C Backman 17 0.4 4.1 D 128 N Boynton 17 0.7 4.2 D 129 D Grebeshkov 17 0.4 3.7 D 130 M Carle 17 0.2 3.9 D 131 R Johnson 17 1.0 2.5 F 132 L Krajicek 17 0.2 2.0 D 133 B Burns 17 1.1 5.4 D 134 M Parrish 17 0.3 3.4 F 135 M Zidlicky 17 0.2 4.8 D 136 S Zubov 17 1.0 5.5 D 137 D Roy 17 0.8 4.2 F 138 N Wallin 17 0.6 2.9 D 139 P Martin 17 1.1 4.7 D 140 J Oduya 17 0.8 5.1 D 141 M Grier 17 1.1 3.8 F 142 B Rafalski 17 0.8 5.5 D 143 J Lehtinen 17 0.8 4.2 F 144 D Phaneuf 16 1.1 5.8 D 145 M Greene 16 0.7 2.9 D 146 A Burish 16 0.8 3.0 F 147 J Halpern 16 0.9 3.9 F 148 J Ward 16 0.8 3.6 F 149 A Ovechkin 16 0.0 5.2 F 150 B Campbell 16 1.0 5.3 D 151 M Staal 16 0.6 4.9 D 152 M Stuart 16 0.3 3.4 D 153 S Hnidy 15 0.6 4.4 D 154 C Murphy 15 0.1 3.5 D 155 R Hainsey 15 0.4 5.1 D 156 M Streit 15 0.2 4.2 D 157 R Whitney 15 0.6 4.0 D 158 S Wagner 15 0.5 3.8 D 159 R Kesler 15 1.0 3.8 F 160 B Guite 15 0.7 2.5 F 161 K Foster 15 0.4 3.6 D 162 B Pothier 15 0.4 5.0 D 163 C Campoli 15 0.2 3.8 D 164 M Cullen 15 0.3 4.0 F 165 R Brind'amour 15 1.2 5.0 F 166 B Shanahan 15 1.0 4.0 F 167 N Antropov 15 1.2 4.5 F 168 A Ference 15 0.6 3.9 D 169 M Mottau 15 0.4 3.2 D 170 J Carter 15 0.4 4.0 F 171 M Malik 15 0.6 2.9 D 172 P Brisebois 15 0.2 4.0 D 173 M Weaver 15 0.5 2.9 D 174 S Ozolinsh 15 0.0 2.4 D 175 A Kopitar 14 0.7 4.5 F 176 M Handzus 14 1.1 3.6 F 177 M Lombardi 14 1.1 4.0 F 178 E Brewer 14 0.7 3.7 D 179 J Staal 14 1.2 4.1 F 180 S Crosby 14 0.2 5.0 F 181 M Lundin 14 0.3 4.2 D 182 J Madden 14 1.1 4.4 F 183 R Umberger 14 0.7 4.0 F 184 S Kapanen 14 0.6 3.0 F 185 M Sundin 14 0.5 4.8 F 186 S O'donnell 13 1.5 4.8 D 187 R Zednik 13 0.0 4.2 F 188 S Yelle 13 1.2 2.2 F 189 J Williams 13 0.9 4.7 F 190 K Sauer 13 0.8 4.6 D 191 M Tjarnqvist 13 0.9 2.8 F 192 E Jovanovski 13 0.3 4.8 D 193 S Begin 13 0.7 2.7 F 194 F Bouillon 13 0.6 3.4 D 195 R Warrener 13 0.5 2.7 D 196 B Radivojevic 13 0.2 4.3 F 197 S Mccarthy 13 0.2 2.6 D 198 J Schultz 13 1.0 3.4 D 199 J Spacek 13 0.5 2.5 D 200 C Higgins 13 0.3 4.4 F 201 G Metropolit 13 0.7 3.5 F 202 R Park 13 0.8 2.4 F 203 D Brown 12 0.8 4.2 F 204 J Slater 12 0.3 2.5 F 205 J Mcclement 12 0.7 3.3 F 206 M Reasoner 12 1.1 3.3 F 207 A Hall 12 1.1 2.8 F 208 T Kostopoulos 12 0.7 2.9 F 209 K Chipchura 12 0.6 2.5 F 210 C Kilger 12 0.7 3.5 F 211 J Stoll 12 1.0 3.9 F 212 J Vandermeer 12 0.7 4.1 D 213 C Kelly 12 1.3 3.5 F 214 A Hilbert 12 0.5 3.2 F 215 A Sekera 12 0.2 2.4 D 216 C Adams 12 0.8 2.7 F 217 Z Parise 12 0.1 4.7 F 218 D Hatcher 12 0.6 2.5 D 219 M Fisher 12 0.9 4.2 F 220 R Dvorak 11 0.9 4.0 F 221 N Backstrom 11 0.0 4.1 F 222 B Laich 11 0.9 2.8 F 223 Z Michalek 11 0.5 3.2 D 224 R Niedermayer 11 1.2 3.9 F 225 K Brodziak 11 0.9 3.0 F 226 D Heatley 11 0.1 5.1 F 227 S Veilleux 11 0.7 3.4 F 228 M Nilson 11 0.9 2.4 F 229 M Richards 11 1.0 4.2 F 230 R Fedotenko 11 0.3 4.0 F 231 P Axelsson 11 0.9 3.6 F 232 O Jokinen 10 0.1 5.5 F 233 J Smithson 10 0.9 3.4 F 234 J Erskine 10 0.6 2.3 D 235 T Letowski 10 0.6 2.2 F 236 M Popovic 10 0.0 2.1 D 237 E Perrin 10 0.7 3.9 F 238 A Rourke 10 0.0 0.6 D 239 J Pitkanen 10 0.3 2.3 D 240 A Burrows 10 0.9 2.8 F 241 S Reinprecht 10 0.1 3.6 F 242 M Niskanen 10 0.2 4.4 D 243 T Pyatt 10 0.0 3.5 F 244 R Holik 10 0.3 3.8 F 245 M Bergeron 10 0.1 2.5 D 246 J Strudwick 10 0.2 2.4 D 247 N Dawes 10 0.1 3.0 F 248 T White 10 0.9 4.4 F 249 D Krejci 10 0.1 2.4 F 250 T Hunter 10 0.6 3.4 F 251 R Fitzpatrick 10 0.2 2.0 D 252 D Kalinin 10 0.3 1.7 D 253 M Moulson 9 0.1 1.2 F 254 P O'sullivan 9 1.0 3.7 F 255 V Peltonen 9 0.8 4.6 F 256 C Conroy 9 0.7 4.3 F 257 K Klein 9 0.1 1.0 D 258 J Ortmeyer 9 0.8 3.3 F 259 B Gordon 9 1.4 3.3 F 260 P Dupuis 9 0.7 3.5 F 261 M Hanzal 9 0.3 3.7 F 262 N Horton 9 0.3 5.0 F 263 R Torres 9 0.1 4.9 F 264 S Horcoff 9 1.0 5.0 F 265 K Bieksa 9 0.6 3.0 D 266 R Bourque 9 0.7 3.0 F 267 M Koivu 9 1.1 4.4 F 268 A Steen 9 1.1 4.4 F 269 P Datsyuk 9 0.6 4.7 F 270 P Nummelin 9 0.0 3.1 D 271 R Whitney 9 0.2 4.3 F 272 B Betts 9 1.0 3.0 F 273 S Hartnell 9 0.0 3.9 F 274 D Janik 9 0.0 1.5 D 275 J Hlavac 9 0.0 5.0 F 276 V Prospal 9 0.0 5.2 F 277 J Corvo 9 0.0 3.4 D 278 J Hamilton 9 0.0 2.6 F 279 P Schaefer 9 0.4 4.2 F 280 B Morrison 9 0.4 3.4 F 281 B Devereaux 9 1.0 3.4 F 282 C Kobasew 9 0.7 4.2 F 283 T Connolly 9 0.7 2.7 F 284 S Brookbank 9 0.4 2.8 D 285 J Zeiler 8 0.1 2.3 F 286 C Kunitz 8 0.4 4.8 F 287 A Miller 8 0.2 3.0 F 288 D Moss 8 0.4 2.7 F 289 D Legwand 8 0.7 3.9 F 290 M Malhotra 8 0.8 4.0 F 291 R Nash 8 0.7 4.3 F 292 M Pettinger 8 1.1 3.5 F 293 M Green 8 0.0 4.6 D 294 E Staal 8 0.1 5.1 F 295 K Tkachuk 8 0.0 3.6 F 296 R Vrbata 8 0.5 4.0 F 297 S Weiss 8 0.4 4.6 F 298 B Mclean 8 0.8 2.3 F 299 R Malone 8 0.6 4.4 F 300 J Gorges 8 0.2 1.2 D 301 B Ritchie 8 0.7 2.3 F 302 M Zigomanis 8 1.0 2.9 F 303 M Stajan 8 1.1 4.5 F 304 B Smolinski 8 0.6 3.8 F 305 S Bernier 8 0.0 3.6 F 306 D Fritsche 8 0.1 3.3 F 307 R Lang 8 0.2 4.5 F 308 M Naslund 8 0.0 4.0 F 309 M Goc 8 0.6 1.9 F 310 J Thornton 8 0.3 5.2 F 311 P Demitra 8 0.4 2.3 F 312 D Cleary 8 0.9 3.7 F 313 S Gagner 8 0.0 3.7 F 314 R Bonk 8 0.2 4.0 F 315 J Arnott 8 0.1 4.3 F 316 D Alfredsson 8 1.0 4.7 F 317 A Vermette 8 1.1 3.1 F 318 W Walz 8 0.3 2.2 F 319 J Novotny 8 0.8 3.4 F 320 B Dubinsky 8 0.0 3.5 F 321 S Salo 8 0.1 1.1 D 322 J Lupul 8 0.6 4.5 F 323 M Knuble 8 0.3 4.2 F 324 M Sturm 8 0.5 3.9 F 325 B Berard 8 0.1 1.3 D 326 S Pahlsson 8 1.2 3.8 F 327 T Marchant 7 1.1 4.0 F 328 V Koistinen 7 0.0 2.7 D 329 J Mayers 7 0.9 2.9 F 330 N Kapanen 7 0.1 3.1 F 331 M Talbot 7 1.0 3.1 F 332 P Stastny 7 0.5 4.2 F 333 J Leopold 7 0.2 1.7 D 334 A Ponikarovsky 7 0.2 4.1 F 335 S Koivu 7 0.3 4.3 F 336 M Dandenault 7 0.5 3.3 D 337 P Sharp 7 1.0 3.9 F 338 A Zyuzin 7 0.1 1.3 D 339 M Michalek 7 0.3 3.7 F 340 D Setoguchi 7 0.0 2.3 F 341 D Weight 7 0.1 3.4 F 342 M Ouellet 7 0.0 3.8 F 343 I Laperriere 7 0.5 3.2 F 344 J Cheechoo 7 0.1 4.3 F 345 P Rissmiller 7 0.7 2.9 F 346 B Rolston 7 0.9 4.3 F 347 E Belanger 7 0.9 3.9 F 348 K Draper 7 1.0 4.0 F 349 K Maltby 7 0.8 3.2 F 350 B Lebda 7 0.1 4.3 D 351 J Pominville 7 0.9 3.7 F 352 P Prucha 7 0.0 3.6 F 353 J Jagr 7 0.1 5.2 F 354 M Schneider 6 0.0 1.9 D 355 J Stumpel 6 0.5 4.6 F 356 D Vyborny 6 0.8 3.9 F 357 C Larose 6 0.8 4.6 F 358 L Stempniak 6 0.4 3.5 F 359 D Booth 6 0.1 3.4 F 360 A Hemsky 6 0.0 4.8 F 361 C Armstrong 6 0.8 2.5 F 362 T Plekanec 6 0.4 4.0 F 363 K Skrastins 6 0.3 1.4 D 364 A Cogliano 6 0.4 3.7 F 365 E Malkin 6 0.0 4.7 F 366 T Ruutu 6 0.1 4.4 F 367 J Roenick 6 0.1 3.3 F 368 M Johansson 6 0.1 2.6 D 369 M Cooke 6 0.7 3.2 F 370 W Smith 6 0.7 3.1 F 371 A Brunette 6 0.0 3.9 F 372 C Brown 6 0.4 1.3 F 373 V Filppula 6 0.5 3.9 F 374 K Adams 6 0.9 2.1 F 375 D Penner 6 0.0 4.5 F 376 C Schubert 6 0.4 2.2 D 377 D Winnik 6 0.3 2.7 F 378 D Drake 6 0.5 1.4 F 379 D Stafford 6 0.0 3.4 F 380 T Zajac 6 0.3 4.6 F 381 J Pandolfo 6 1.0 4.2 F 382 P Elias 6 0.5 4.2 F 383 S Gomez 6 0.1 4.8 F 384 P Gaustad 6 0.5 3.4 F 385 S Brylin 6 0.7 3.4 F 386 M Satan 6 0.2 3.8 F 387 M Sillinger 6 0.7 3.7 F 388 D Tanabe 6 0.0 1.3 D 389 M St. Louis 6 0.7 5.6 F

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.

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.

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.

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 Name GD PCT DET 29 0.80 MIN 15 0.72 STL 10 0.63 CBJ 7 0.59 CGY 6 0.57 S.J 4 0.56 DAL 4 0.55 COL -1 0.48 VAN -3 0.46 PHX -3 0.45 ANA -10 0.37 CHI -14 0.34 L.A -13 0.33 NSH -13 0.31 EDM -25 0.20

East Name GD PCT OTT 25 0.74 PHI 13 0.69 CAR 15 0.68 NYI 11 0.64 BOS 5 0.59 T.B 6 0.58 MTL 6 0.57 PIT 3 0.53 BUF 2 0.53 TOR -4 0.46 NYR -6 0.44 N.J -11 0.36 FLA -15 0.33 WSH -15 0.31 ATL -25 0.23

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.

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.

Average Predicted Standings

Name P L U DET 2 3 1 ANA 3 6 1 S.J 4 6 1 VAN 5 9 1 CGY 5 9 1 MIN 6 9 3 COL 6 10 1 DAL 7 11 3 NSH 9 13 5 STL 10 12 8 L.A 11 13 8 EDM 12 15 10 CHI 13 15 11 CBJ 13 15 10 PHX 15 15 14

Name P L U OTT 2 5 1 PIT 2 4 1 NYR 3 5 1 BUF 4 7 1 CAR 7 14 1 N.J 7 11 3 TOR 8 12 4 T.B 9 16 2 FLA 9 15 3 PHI 10 14 5 ATL 10 15 4 MTL 10 15 6 NYI 12 15 6 WSH 13 15 9 BOS 14 15 11

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

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...

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).

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

N lastname SQN S 1 ANDERSON 1.000 55 2 LUONGO 0.951 51 3 JOHNSON 0.939 31 4 HUET 0.928 31 5 AUBIN 0.927 33 6 KEETLEY 0.924 34 7 VOKOUN 0.920 34 8 HILLER 0.918 48 9 BRYZGALOV 0.913 85 10 DENIS 0.913 38 11 MASON 0.912 53 12 PAVELEC 0.910 37 13 RINNE 0.896 44 14 AEBISCHER 0.894 44 15 PRICE 0.892 43 16 ROLOSON 0.885 59 17 MASON 0.883 41 18 LALIME 0.868 52 19 GARON 0.862 45 20 THOMAS 0.862 36 21 BERNIER 0.852 37 22 KHABIBULIN 0.844 32 23 GRAHAME 0.833 41 24 TELLQVIST 0.828 42 25 BUDAJ 0.820 49 26 PATZOLD 0.810 32 27 FLEURY 0.808 54 28 BACKSTROM 0.807 41 29 KIPRUSOFF 0.792 43 30 TURCO 0.688 35

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.

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:

N lastname newSQN oldSQN Shots 1 BACKSTROM 0.925 0.926 1027 2 VOKOUN 0.920 0.921 1299 3 DIPIETRO 0.920 0.922 1917 4 THIBAULT 0.920 0.921 570 5 MASON 0.919 0.921 1244 6 HUET 0.919 0.919 1280 7 GIGUERE 0.918 0.918 1490 8 LUONGO 0.918 0.919 2169 9 LEHTONEN 0.915 0.915 2075 10 ROLOSON 0.914 0.914 1979 11 BRODEUR 0.914 0.913 2182 12 KIPRUSOFF 0.914 0.912 2190 13 NABOKOV 0.914 0.915 1227 14 KOLZIG 0.913 0.919 1771 15 KHABIBULIN 0.913 0.914 1668 16 TURCO 0.912 0.912 1554 17 BURKE 0.912 0.913 687 18 FERNANDEZ 0.912 0.913 1154 19 AEBISCHER 0.911 0.910 929 20 LEGACE 0.911 0.919 1177 21 HASEK 0.911 0.912 1309 22 LUNDQVIST 0.910 0.921 1927 23 EMERY 0.910 0.911 1691 24 DUNHAM 0.910 0.910 540 25 NIITTYMAKI 0.908 0.913 1562 26 GRAHAME 0.908 0.912 702 27 MILLER 0.907 0.899 1886 28 SMITH 0.906 0.909 510 29 FLEURY 0.904 0.905 1955 30 BELFOUR 0.903 0.904 1550 31 NORRENA 0.903 0.908 1420 32 BUDAJ 0.903 0.903 1499 33 BRYZGALOV 0.902 0.903 668 34 THOMAS 0.902 0.904 1985 35 GERBER 0.900 0.901 784 36 AULD 0.899 0.899 729 37 GARON 0.899 0.901 849 38 JOSEPH 0.899 0.904 1481 39 TOIVONEN 0.899 0.897 502 40 THEODORE 0.898 0.898 870 41 WARD 0.898 0.906 1625 42 TOSKALA 0.897 0.899 915 43 LECLAIRE 0.894 0.900 629 44 JOHNSON 0.894 0.900 894 45 BIRON 0.894 0.899 509 46 SANFORD 0.893 0.901 707 47 RAYCROFT 0.891 0.892 1939 48 BIRON 0.887 0.886 533 49 TELLQVIST 0.887 0.892 780 50 HOLMQVIST 0.886 0.890 1134 51 DENIS 0.884 0.884 1068 52 CLOUTIER 0.878 0.880 608

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.

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.

Travel Schedule

N long_name Trips KM KM/Trip 1 Chicago Blackhawks 63 63440 1007 2 New York Islanders 63 39587 628 3 New York Rangers 63 43336 688 4 Ottawa Senators 63 48097 763 5 Toronto Maple Leafs 63 49275 782 6 Pittsburgh Penguins 62 42485 685 7 Buffalo Sabres 62 43241 697 8 Atlanta Thrashers 62 60166 970 9 Tampa Bay Lightning 62 63217 1020 10 St. Louis Blues 61 65725 1077 11 Detroit Red Wings 61 59510 976 12 Columbus Blue Jackets 61 67414 1105 13 Philadelphia Flyers 61 42989 705 14 New Jersey Devils 60 38815 647 15 Florida Panthers 60 61878 1031 16 Washington Capitals 60 40934 682 17 Colorado Avalanche 59 75872 1286 18 Boston Bruins 59 42769 725 19 Montreal Canadiens 59 44683 757 20 Vancouver Canucks 58 87440 1508 21 Phoenix Coyotes 58 72693 1253 22 Los Angeles Kings 58 69915 1205 23 Carolina Hurricanes 58 44879 774 24 Nashville Predators 57 63775 1119 25 San Jose Sharks 57 68869 1208 26 Dallas Stars 57 67604 1186 27 Anaheim Ducks 57 66521 1167 28 Edmonton Oilers 56 83036 1483 29 Minnesota Wild 55 67821 1233 30 Calgary Flames 55 73180 1331

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.