Apr 042007
 

It seems that we as fans just love to lay blame on a variety of players for bad plays that lead to scoring opportunities and every team seems to have one or two players that fans seem to love to pick on. But is it really justified? In an attempt to find the answer to that I decided to take a look at every players giveaways and whether they led to a shot on goal, or worse yet, a goal. In order to eliminate as much of the bias that exists in the giveaway stat that results from game monitors in every city seemingly having a different definition of what a giveaway is, I decided to look only at road giveaways. It probably still isn’t perfect but will eliminate a lot of the problems with the giveaway stat. From there, I summed up how many shots and goals were scored within 8 seconds of the giveaway or until play stopped. Using 8 seconds is pretty arbitrary but seems like a reasonable amount of time to attribute negative results directly to the giveaway.

Let’s start off with the team results. The following table shows how many road giveaways each team committed, how many shots resulted from those turnovers and how many goals resulted from those shots. The table is sorted by shots.

Num Team Road
Giveaways
Road
Giveaway
Shots
Road
Giveaway
Goals
1 Boston 417 64 9
2 Toronto 379 55 11
3 Carolina 344 53 6
4 San Jose 371 53 10
5 Anaheim 349 51 5
6 Buffalo 361 49 8
7 Montreal 339 49 8
8 Vancouver 320 47 7
9 Colorado 309 46 6
10 Minnesota 352 46 8
11 NY Rangers 407 46 7
12 Tampa Bay 327 46 13
13 Ottawa 359 44 5
14 Washington 317 43 8
15 Columbus 278 42 6
16 Detroit 333 41 3
17 Phoenix 346 41 7
18 St. Louis 268 41 5
19 New Jersey 322 40 12
20 Calgary 314 39 7
21 Dallas 284 39 8
22 Atlanta 340 38 5
23 Edmonton 346 38 7
24 Chicago 324 37 8
25 NY Islanders 334 36 6
26 Pittsburgh 369 36 5
27 Nashville 296 35 6
28 Florida 320 33 7
29 Los Angeles 290 33 4
30 Philadelphia 296 33 5

For me I think there are a few surprises. The first is probably how few shots and goals seem to result from giveaways. The most any team has given up is 13 by Tampa which isn’t even one goal by a turnover every 3 games. Another surprise is New Jersey with 12 giveaway goals on only 40 shots. Brodeur is a great goalie but it seems he isn’t so good at making up for mistakes by the players in front of him. Another surprise is Philadelphia. Who’d have thought they would have given up the fewest shots on giveaways. The only thing I can think of is they never have the puck to give it away.

For the record, the save percentage for the above shots on goal is 0.838 which is a fair bit below typical save percentages.

The following table lists all 57 players whose giveaways have resulted in 5 or more shots on goal.

Num Player Team Road
Giveaways
Road
Giveaway
Shots
Road
Giveaway
Goals
1 ZDENO CHARA Boston 61 13 3
2 BRYAN MCCABE Toronto 38 9 2
3 ALEXANDER SEMIN Washington 31 9 1
4 SERGEI ZUBOV Dallas 32 8 3
5 CHRIS CHELIOS Detroit 27 8 1
6 BRIAN ROLSTON Minnesota 28 8 0
7 ANDREW FERENCE Boston 29 8 1
8 SCOTT NIEDERMAYER Anaheim 52 8 0
9 DMITRI KALININ Buffalo 36 7 0
10 PAUL RANGER Tampa Bay 29 7 2
11 NICLAS HAVELID Atlanta 31 7 0
12 SCOTT HANNAN San Jose 21 7 2
13 ANDREW ALBERTS Boston 32 7 0
14 ANDREI MARKOV Montreal 36 7 2
15 HAL GILL Toronto 31 6 0
16 PETER BUDAJ Colorado 23 6 0
17 BRIAN POTHIER Washington 30 6 2
18 ALEXANDER OVECHKIN Washington 39 6 1
19 MIKE GREEN Washington 22 6 0
20 MICHAEL NYLANDER NY Rangers 21 6 1
21 DAN BOYLE Tampa Bay 41 6 2
22 VYACHESLAV KOZLOV Atlanta 35 6 0
23 LUKAS KRAJICEK Vancouver 20 6 0
24 DEREK MORRIS Phoenix 23 6 1
25 CHRISTIAN EHRHOFF San Jose 28 6 2
26 JONATHAN CHEECHOO San Jose 24 6 2
27 PAUL MARA NY Rangers 22 6 2
28 MIKE VAN RYN Florida 23 6 2
29 SHELDON SOURAY Montreal 29 6 1
30 BRENT BURNS Minnesota 19 6 1
31 RYAN MILLER Buffalo 20 5 1
32 BRIAN CAMPBELL Buffalo 29 5 0
33 ERIK COLE Carolina 25 5 0
34 ALEXEI PONIKAROVSKY Toronto 17 5 0
35 KEN KLEE Colorado 21 5 2
36 JAROMIR JAGR NY Rangers 69 5 0
37 DOUG JANIK Tampa Bay 22 5 1
38 MARC-ANDRE FLEURY Pittsburgh 16 5 3
39 JOSEF MELICHAR Pittsburgh 19 5 0
40 RYAN WHITNEY Pittsburgh 37 5 0
41 MATHIEU SCHNEIDER Detroit 20 5 0
42 JASON WILLIAMS Chicago 26 5 0
43 JOSEF VASICEK Carolina 8 5 0
44 KIM JOHNSSON Minnesota 19 5 0
45 ROBYN REGEHR Calgary 17 5 2
46 DENNIS WIDEMAN Boston 21 5 2
47 BRYCE SALVADOR St. Louis 13 5 0
48 VILLE NIEMINEN St. Louis 9 5 1
49 MATTHEW CARLE San Jose 27 5 1
50 PATRICK RISSMILLER San Jose 19 5 0
51 BRAD LUKOWICH New Jersey 14 5 2
52 PATRIK ELIAS New Jersey 25 5 0
53 ANTON BABCHUK Carolina 13 5 0
54 JASON CHIMERA Columbus 15 5 1
55 BRAD STUART Calgary 28 5 0
56 ALEX KOVALEV Montreal 24 5 2
57 CARLO COLAIACOVO Toronto 18 5 1

Well, looks like Chara is easily running away with the lead in this statistic with 13 shots given up on 61 turnovers. Otherwise, not a lot of surprises really. There are a lot of defensemen near the top of the list with probably makes sense since they are more likely to turn over the puck in their own zone and less likely to have someone else backing them up. They also get the most ice time so inherently should have more opportunities to give away the puck. It is also interesting that a few goalies made the list.

Dec 152006
 

The NHL keeps track of a large variety of statistics, many of which are subjective in nature. The most subjective might be the hits statistic. What really is a hit? There is the pound the opposing player through the glass hit and then there are the almost incidental bumps which depending on severity may be classified a hit by some and not a hit by others. And looking at the stats, there is no real standard because some stats keepers in some cities seem to give players credit for a ton of hits while other cities are far more stingy in crediting players with a hit. One way to eliminate the bias teams will have by playing in their home arena 41 times a year is to just look at their road hits as a more unbiased indication of how frequently they hit opposing players.

In the table below you will find each teams home and road hits per game, as well as the visiting teams hits per game and the average of all visiting teams road hits per game (exp. hits). The hit bias gives an indication of how many more or fewer hits are credited to players in games in that city which is calculated by (home/road + visitor/exp.visitor)/2.

Team Home Road Visitor Exp.Vis HitBias
Anaheim 13.26 22.07 8.84 15.52 0.59
Atlanta 18.07 18.94 13.13 17.16 0.86
Boston 13.56 17.69 11.88 16.01 0.75
Buffalo 15.43 15.47 14.57 16.95 0.93
Carolina 28.77 14.79 24.38 16.41 1.70
Calgary 16.80 16.00 14.33 15.30 0.99
Chicago 16.71 16.06 12.86 15.82 0.93
Columbus 17.36 16.00 10.43 14.48 0.91
Colorado 9.81 8.87 14.12 15.98 0.96
Dallas 24.44 15.06 26.38 15.47 1.66
Detroit 18.93 12.27 18.53 16.07 1.32
Edmonton 15.59 16.79 15.00 15.12 0.96
Florida 20.69 20.17 14.50 16.94 0.95
Los Angeles 19.30 15.08 17.40 16.09 1.18
Minnesota 11.44 11.07 10.69 14.96 0.85
Montreal 26.18 17.36 18.47 15.81 1.35
Nashville 14.33 13.74 11.50 14.84 0.90
New Jersey 14.29 17.44 13.21 16.54 0.81
NY Islanders 23.75 20.77 16.69 16.15 1.10
NY Rangers 23.40 19.00 22.00 16.44 1.28
Ottawa 27.77 16.60 20.77 15.40 1.52
Philadelphia 14.19 15.53 13.81 16.52 0.87
Phoenix 19.81 15.47 15.38 16.34 1.11
Pittsburgh 18.31 13.79 16.50 16.83 1.14
San Jose 18.13 18.33 14.67 14.71 0.99
St. Louis 14.94 16.93 12.94 15.47 0.86
Tampa Bay 16.18 11.20 18.24 17.74 1.19
Toronto 22.81 16.00 19.56 16.92 1.29
Vancouver 16.76 14.60 22.35 16.12 1.27
Washington 18.31 17.71 18.25 16.89 1.06

As an example, Anaheim gets credited with 13.26 hits per home game and 22.07 hits per road game. Teams visiting Anaheim get credited with 8.84 hits pre game when playing in Anaheim and they average 15.52 hits per game in all of their road games. Those sets of numbers indicate a huge difference between how Anaheim game monitors credit hits than the rest of the league. Overall, hits are credited to players in Anaheim at a rate about 40% less than the average other city. On the opposite end of the spectrum,teams playing in Carolina get credited with 70% more hits than the average city.

So, armed with this information we can adjust team and players hit totals accordingly. Below is a table showing each teams adjusted hit totals which should give a much more realistic representation of a teams level of physical play.

Num Team Hits Adj. Hits
1 Anaheim 583 744
2 Florida 694 697
3 Atlanta 593 592
4 NY Islanders 650 592
5 San Jose 602 573
6 NY Rangers 674 549
7 Ottawa 693 546
8 Montreal 688 545
9 Toronto 621 522
10 Washington 541 521
11 Chicago 491 520
12 Los Angeles 582 514
13 Columbus 499 513
14 Phoenix 549 508
15 St. Louis 476 501
16 New Jersey 479 494
17 Edmonton 500 490
18 Boston 447 486
19 Carolina 655 483
20 Philadelphia 460 483
21 Calgary 476 467
22 Dallas 632 451
23 Buffalo 479 449
24 Vancouver 504 443
25 Pittsburgh 486 442
26 Nashville 433 442
27 Detroit 468 400
28 Tampa Bay 443 387
29 Minnesota 349 371
30 Colorado 290 285

The above table can also be found by clicking on the link in the left menu and that table will be updated manually. You will also find each players adjusted hits in the left menu as well. I will be updating these tables semi-regularly (hopefully at least once a week) so you can all see how your favourite teams and players are doing in the physical play department.

Nov 222006
 

This may show my age here but I thought it might be interesting for everyone to see one of my first ventures into statistical analysis of hockey statistics.

Back in March of 1996, the Leafs traded Darby Hendrickson, Sean Haggerty, Kenny Jonsson and a first round pick (turned out to be Roberto Luongo) to the Islanders for Wendel Clark, Mathieu Schneider and D.J. Smith. Clearly the first round pick and the lost chance at Roberto Luongo made this a bad trade but at the time Toronto media and especially Maple Leafs fans were outraged at the Leafs trading of young defenseman Kenny Johnsson. Many fans thought he was an up and coming Borje Salming (mostly because both are Swedish and Salming also played for the Leafs) and a future Norris Trophy candidate. While Jonsson turned out to be a good defenseman (who had his career shortened due to concussions and I believe is now playing in Sweden) he was no Borje Salming.

My view back then was that Jonsson was unlikely to become the next Borje Salming and I took to statistics to try to make my point and posted my analysis on the USENET news groups. Google has had these groups archived and available to anyone who wants to read them. You can read my analysis and follow on discussion here and some more here. I have included my main posts here as well. I am not sure if I will be able to get any time to do it but it would be interesting to revisit some of the work I did here and update it with some of the defensemen who have played in the NHL since then.

If you are not interested in reading it all, the short and sweet conclusion was that the first couple years of a defenseman’s career pretty much define that player for his whole career. If a defenseman isn’t a top offensive defenseman in his first few years, he likely never will be.

Continue reading »

Nov 022006
 

Yesterday I presented my even strength player ranking system and rankings for last season which clearly created some controversy. Hopefully when I present this years rankings I can clear up a bit more of the concerns that people have with the system. I know some people found some of the results difficult to believe but if I can show some consistency from year to year I think that should help show the value that this rating system can have.

So first let me start off by showing the top 40 rated players from this season.

Player Team Offense Rating Offense Rank Defense Rating Defense Rank Overall Rating Overall Rank
PIERRE-MARC BOUCHARD Minnesota 1.89 23 5.13 2 3.51 1
KEITH YANDLE Phoenix 0.48 417 6.18 1 3.33 2
MATTHEW LOMBARDI Calgary 1.75 34 4.68 5 3.22 3
JAROSLAV MODRY Dallas 1.63 52 4.72 4 3.17 4
DANIEL SEDIN Vancouver 2.31 6 3.90 6 3.10 5
STEPHANE ROBIDAS Dallas 0.79 326 4.75 3 2.77 6
NIKLAS HAGMAN Dallas 1.36 101 3.90 7 2.63 7
MIIKKA KIPRUSOFF Calgary 1.66 47 3.36 10 2.51 8
NIKITA ALEXEEV Tampa Bay 2.14 12 2.62 16 2.38 9
BRIAN ROLSTON Minnesota 1.72 36 2.92 12 2.32 10
ALEXANDER PEREZHOGIN Montreal 1.71 38 2.75 15 2.23 11
BRETT MCLEAN Colorado 0.90 276 3.49 9 2.20 12
SCOTT HARTNELL Nashville 1.05 216 3.04 11 2.04 13
ILJA BRYZGALOV Anaheim 0.33 444 3.75 8 2.04 14
THOMAS VANEK Buffalo 2.60 2 1.35 93 1.98 15
ROBERT LANG Detroit 1.72 37 2.19 22 1.96 16
OWEN NOLAN Phoenix 2.51 3 1.26 115 1.88 17
KEITH TKACHUK St. Louis 1.22 146 2.53 18 1.87 18
JASON WILLIAMS Detroit 1.35 106 2.35 19 1.85 19
SIDNEY CROSBY Pittsburgh 1.84 27 1.83 34 1.84 20
ROBERTO LUONGO Vancouver 2.32 5 1.36 89 1.84 21
RADEK MARTINEK NY Islanders 0.89 280 2.79 14 1.84 22
MARK RECCHI Pittsburgh 1.02 226 2.61 17 1.82 23
PAVEL DATSYUK Detroit 2.04 15 1.58 55 1.81 24
STEVE SULLIVAN Nashville 2.79 1 0.83 303 1.81 25
MIKE JOHNSON Montreal 1.27 131 2.35 20 1.81 26
SCOTT GOMEZ New Jersey 2.21 7 1.32 101 1.77 27
TOM PREISSING Ottawa 1.89 24 1.57 58 1.73 28
DANNY MARKOV Detroit 1.57 64 1.84 33 1.71 29
STEVE MCCARTHY Atlanta 0.50 409 2.85 13 1.68 30
MARC-ANDRE FLEURY Pittsburgh 2.15 11 1.15 150 1.65 31
MIKE DUNHAM NY Islanders 1.11 193 2.17 23 1.64 32
SAMI SALO Vancouver 1.19 164 2.06 26 1.62 33
NICLAS WALLIN Carolina 2.51 4 0.71 364 1.61 34
WOJTEK WOLSKI Colorado 2.01 18 1.22 123 1.61 35
MICHAEL NYLANDER NY Rangers 2.19 8 1.04 191 1.61 36
MILAN MICHALEK San Jose 1.69 41 1.49 71 1.59 37
SAMUEL PAHLSSON Anaheim 1.27 132 1.92 29 1.59 38
NICKLAS LIDSTROM Detroit 1.80 30 1.35 94 1.58 39
MICHAEL CAMMALLERI Los Angeles 1.29 127 1.86 31 1.57 40

The first thing one will notice is that the ratings are much higher than those for last season. This is because we are working with much less data this season than last so one bad or good game will make a huge difference. This is not ideal because essentially it means we may not have enough data to reliably evaluate players. For example, I don’t expect Keith Yandle to remain at the top of the list as the season goes on but I must also say that to play over 100 minutes of even strength ice time with the Coyotes and only have one goal scored against you is pretty impressive as Phoenix is a horrible defensive team.

So, it is still early in the season to rely too much on these rankings but I think there is some value in comparing players with their values of last season to see if there is much consistency showing up. Here is a list of each players overall ratings comparing last years with this years.


Gelinas 1.70 last year vs 1.43 this year
Donovan: 1.67 vs 0.85
Zetterberg: 1.66 vs 1.05
Thornton 1.64 vs 0.81
Cheechoo 1.64 vs 0.51
Nylander 1.64 vs 1.61
Schneider 1.63 vs 1.53
Armstrong 1.61 vs 1.43
Jagr 1.57 vs 1.15
Selanne 1.56 vs 1.18
A. Markov 1.55 vs 1.27
Weber 1.55 vs 1.05
Legwand 1.54 vs 1.29
Shanahan 1.53 vs 1.1
Sakic 1.51 vs 1.21
Komisarek: 1.45 1.17
P. Bergeron: 1.45 vs 1.17
Kalinin 1.44 vs 0.92
Crosby: 1.44 vs 1.84
P. Boucher: 1.40 vs 0.85
Morrow: 1.38 vs 0.87
Rozsival: 1.38 vs 0.96
Ponikarovsky: 1.36 vs 1.31
Gionta: 1.36 vs 1.51
Zhitnik: 1.35 vs 0.98
Pothier: 1.35 vs 1.36
Malik: 1.35 vs 1.31

Clearly there are some significant differences (Donovan, Thorton, Cheechoo, Boucher, Kalinin, Morrow, Zhitnik) but clearly many of them haven’t gotten off to good seasons. Thornton and Cheechoo haven’t gotten anything going yet. There are many similarities too. Most other top rated players from last season are still rated well above the 1.00 mark. Last years highest rated player in Gelinas is also fairly highly rated this year. Brian Pothier who changed teams has an almost identical rating. It might also be worth pointing out that Chara also has an almost identical rating this year compared to last year (0.72 vs 0.82).

While we are on the Senators lets take a look at their defensemen from last season more closely. Here are last seasons and this seasons overall ratings.


Chara 0.72 0.82
Redden 1.22 1.05
Meszaros 0.95 0.73
Phillips 0.85 1.16
Pothier 1.35 1.36
Volchenkov 0.98 1.07

Pretty good consistency really as Phillips is the only one who really changed their ratings significantly. But what I really wanted to point out that in the playoffs last year most people said that Redden and Pothier were Ottawa’s best defensemen and Chara and Meszaros were big disappointments. In fact, Chris McMurtry gave Redden and Pothier a B grade, Chara an F and Meszaros a D when he handed out grades on each players playoff performance. Phillips got a C+ and Volchenkov a C. I find it quite interesting that Chris’s analysis agrees almost perfectly with my ranking system. Maybe 5-on-5 (which is generally more important in playoff hockey) Chara isn’t the stud defensemen everyone thinks he is and a good skating, puck moving defenseman like Pothier is what matters most. Certainly Chara joining the Bruins hasn’t done a lot for them in the standings.

Once this years ratings stabilize more (say in a month or so) I’ll do a more formal analysis but what I see so far is that there is some consistency in ratings from year to year.

Nov 012006
 

When developing my player ranking system I wanted to isolate the ability of an individual player as much as I can and best factor out both who the player is playing with and who they are playing against. I don’t know how many times I hear things like ‘but he has to face the opponent’s best players’ and things like that when people try to analyze how a player is doing. So I am trying to eliminate that.

For now I am going to spare you all of the gory details of the process I took but basically I looked at every shift every player has played and who they played with and against for those shifts. Thankfully the NHL makes this information available on their website. I also looked at how many goals for and goals against each player was on the ice for. By combining the goals for and goals against with the shift data of who the player was playing with and against I came up with an expected goals for and goals against. By that I mean, based on a players linemates and opponents, how many goals can one expect that he will be on the ice for and against. To get a defensive rating I divided how many goals he was expected to be on the ice for by how many goals he actually was on the ice for. I also eliminated the effect of players playing different amounts of ice time by adjusting the numbers to a per 20 minutes of ice time basis (i.e. how many goals were scored for every 20 minutes that the player was on the ice). A number greater than 1 means he was on the ice for fewer goals than expected and thus can be concluded that the player is a better than average defensive player. A number less than 1 would indicate he was on the ice for more goals than expected and is a below average defensive player. To calculate an offensive rating I took the number of goals his team scored while he was on the ice by the number of goals that were expected to be scored while he was on the ice. A number greater than one indicates more goals were scored by his team than expected and thus he is a better than average offensive player. Conversely, if the number is less than 1 the player is a less than average defensive player. I then calculated an overall rating by averaging the offensive and defensive ratings. For the time being I have just looked at even strength situations. In the future I plan on developing power play and penalty kill ratings as well or maybe finding a way to develop a combined rating system. I am just not sure how to do that yet so for now just even strength ice time was used.

The interesting thing about this method of rating players is that it doesn’t take into account how many goals and assists that that player tallied. It only takes into account how many goals were scored while he was on the ice. This is interesting because it allows us to compare forwards and defensemen and even goalies directly. We know that forwards score more goals and get more points but that doesn’t mean defensemen don’t contribute the same offensively. We also know that defensemen and goaltenders are important in stopping goals, but that doesn’t mean that forwards aren’t equally important. By not looking at goals and assists a player tallied we aren’t biasing the analysis towards point producing forwards.

Ok, I think it is time to look at some results. The NHL started making shift data available on January 18th of last season so I have ranked players using data from January 18th through the end of last season. That’s a total of 549 games or almost 45% of the season. Here are the top ranked players overall. Only players with 200+ minutes of even strength ice time are listed.

Player Team Offense Rating Offense Rank Defense Rating Defense Rank Overall Rating Overall Rank
MARTIN GELINAS Florida 1.83 13 1.56 28 1.70 1
SHEAN DONOVAN Calgary 0.85 398 2.48 1 1.67 2
HENRIK ZETTERBERG Detroit 1.77 15 1.55 30 1.66 3
JOE THORNTON San Jose 2.30 2 0.98 250 1.64 4
JONATHAN CHEECHOO San Jose 2.09 6 1.19 112 1.64 5
MICHAEL NYLANDER NY Rangers 2.23 3 1.06 180 1.64 6
MATHIEU SCHNEIDER Detroit 2.02 11 1.23 95 1.63 7
COLBY ARMSTRONG Pittsburgh 2.16 5 1.07 172 1.61 8
J-SEBASTIEN AUBIN Toronto 1.74 21 1.45 37 1.59 9
JAROMIR JAGR NY Rangers 1.70 26 1.44 38 1.57 10
TEEMU SELANNE Anaheim 2.31 1 0.82 413 1.56 11
ANDREI MARKOV Montreal 1.24 125 1.85 11 1.55 12
SHEA WEBER Nashville 1.23 129 1.88 8 1.55 13
DAVID LEGWAND Nashville 1.11 201 1.97 5 1.54 14
JAMIE MCLENNAN Florida 1.30 94 1.77 15 1.54 15
BRENDAN SHANAHAN Detroit 1.52 40 1.55 31 1.53 16
JOE SAKIC Colorado 2.03 8 1.00 228 1.51 17
MIIKKA KIPRUSOFF Calgary 0.66 511 2.36 2 1.51 18
MARK CULLEN Chicago 1.60 31 1.40 44 1.50 19
MICHAEL KOMISAREK Montreal 0.77 447 2.14 3 1.45 20
PATRICE BERGERON Boston 2.07 7 0.82 414 1.45 21
DMITRI KALININ Buffalo 1.66 27 1.21 105 1.44 22
SIDNEY CROSBY Pittsburgh 2.17 4 0.70 523 1.44 23
WILLIE MITCHELL Dallas 0.71 484 2.11 4 1.41 24
PHILIPPE BOUCHER Dallas 1.75 16 1.04 197 1.40 25
VESA TOSKALA San Jose 1.74 22 1.03 204 1.39 26
AARON JOHNSON Columbus 1.75 17 1.02 213 1.38 27
ANDY HILBERT Pittsburgh 1.84 12 0.92 295 1.38 28
BRENDEN MORROW Dallas 1.75 18 1.00 229 1.38 29
MICHAL ROZSIVAL NY Rangers 1.41 65 1.34 60 1.38 30
MIKE DUNHAM Atlanta 2.03 9 0.73 500 1.38 31
NIK ANTROPOV Toronto 1.37 76 1.39 47 1.38 32
FREDRIK SJOSTROM Phoenix 1.32 87 1.43 40 1.37 33
MARK MOWERS Detroit 1.41 66 1.32 66 1.37 34
ALEXEI PONIKAROVSKY Toronto 1.48 53 1.23 96 1.36 35
BRIAN GIONTA New Jersey 2.03 10 0.69 529 1.36 36
CRISTOBAL HUET Montreal 0.81 425 1.91 7 1.36 37
RICK DIPIETRO NY Islanders 1.40 69 1.33 63 1.36 38
ALEXEI ZHITNIK NY Islanders 1.30 95 1.39 48 1.35 39
BRIAN POTHIER Ottawa 1.04 255 1.65 20 1.35 40

Yeah, I know what you are thinking. Martin Gelinas the best overall even strength player? Are you kidding. No. I am not kidding. He was on the ice for 40 goals his team scored even strength which is two more than top Panther scorer Olli Jokinen was on for at even strength, and he was also on the ice for just 13 goals against. That’s an awfully good track record. On the season he was a +27 which is a whopping 12 points higher than his closest teammate (wouldn’t surprise me if this was a league best differential). Shean Donovan is also very surprising but mostly because hardly any goals were scored against the Flames when he was on the ice. In fact, when he was on the ice just 5 goals were scored against since January 18th. That’s pretty phenomenal really. We also need to remember that these ratings don’t represent a player’s net value. A player who is rated slightly less but gets more ice time will have a higher net value. What these ratings are trying to do is compare players when all things (teammates, opponents, and ice time) are equal. After those first two surprised in Gelinas and Donovan come some probably more expected names in Zetterberg, Thornton, Cheechoo, Nylander, Schneider and Armstrong.

I’ve kept the goalies included but their ratings are somewhat suspect because it ends up largely being a comparison with the other goalies on the team. J.S. Aubin is the top rated goalie because he was so much better than Belfour and Tellqvist but I am not sure he was the best goalie in the NHL. I plan on developing a more refined goalie ranking system but for now it is interesting to see how the goalies rate using this system.

Nik Antropov gets a lot of criticism by Toronto media and fans but I have been a big defender of his. And because of this I am glad to see that he ranked very well at 32nd overall.

At the bottom of the list were Marcel Hossa, Clarke Wilm, Eric Weinrich (while in Vancouver), Tim Taylor, Mike Ricci, Alyn McCauley, Alexander Khavanov, Martin Lapointe, Tyson Nash, and Kirk Maltby. There are a few surprising names there but most of them are lower tier players.

Evaluating a player ranking system is a difficult thing to do but one method of doing so is to look at how consistent it is. By that I mean how closely do players get ranked before and after trades and from season to season. Tomorrow I’ll look at some season to season comparisons but for now lets look at some players that were traded at the trade deadline last year and see how things look. I’ve listed offensive, defensive and overall ranking numbers for each player

Mark Recchi (Pittsburgh): 1.10, 0.76, 0.94
Mark Recchi (Carolina): 0.54, 0.97, 0.76

Martin Skoula (Dallas): 1.02, 0.74, 0.88
Martin Skoula (Minnesota): 0.66, 1.18, 0.92

Keith Carney (Anaheim): 0.76, 0.98, 0.87
Keith Carney (Vancouver): 0.88, 0.83, 0.86

Eric Weinrich (St. Louis): 1.49, 0.67, 1.08
Eric Weinrich (Vancouver): 0.56, 0.49, 0.52

Brent Sopel (Islanders): 0.84, 0.74, 0.79
Brent Sopel (Los Angeles): 0.52, 0.95, 0.73

Brendan Witt (Washington): 0.98, 1.44, 1.21
Brendan Witt (Nashville): 1.24, 0.62, 0.93

Brad Lukowich (NY Islanders): 0.91, 0.99, 0.95
Brad Lukowich (New Jersey): 1.46, 0.74, 1.10

Willie Mitchell (Minnesota): 1.14, 0.73, 0.94
Willie Mitchell (Dallas Stars): 0.71, 2.11, 1.41

There is some consistency in the overall numbers as Skoula’s, Carney’s and Sopel’s overall numbers are almost identical before and after the trade and Lukowich’s is fairly close. Overall I would have liked to see more consistency but most players got worse with their new teams which I think is a testament to how difficult it is to learn a new system and learn to play with new line mates.

Oct 312006
 

Hockey is, maybe more than any other sport, a team sport. A hitter in baseball can be evaluated based on a series of one on one battles with pitchers which are largely independent of the ability of the hitters teammates. In hockey this is not the case. Jaromir Jagr may be a great goal scorer but if he didn’t have quality teammates around him the number of goals he scores would be significantly impacted. A great defensive play by Marek Malik causing a turnover in the defensive zone followed by a great breakout pass might be just as important in scoring a goal as Jaromir Jagr being in the right spot at the right time to chip in a rebound. Conversely, a good forcheck by Peter Forsberg might be just as important for keeping the puck out of his teams net as a poke check by Antero Niittymaki taking away a shot and a scoring opportunity. As some say, the best defense is a good offense. But there is no stat that “adequately” rewards either Malik’s great defensive play resulting in an offensive opportunity nor Forsberg’s great offensive play limiting the number of offensive opportunities against.

I say “adequately” because +/- attempts to do that and though it has some use as a statistic it is seriously flawed because it doesn’t isolate a players worth independent from his teammates. Conceptually it is a good statistic. Winning games is all about scoring more goals than you give up and +/- measures how many goals are scored by your team while you are on the ice in relation to how many goals are scored by the opposition while you are on the ice. Problem is, if you play on a really good team you are likely to have a decent +/- regardless of how good or bad you actually are. Conversely, if you play on a really bad team you are likely to have a poor +/- regardless of how good or bad you really are. Was Vaclav Varada at +21 really significantly better than Sidney Crosby at -1? No, of course not. There is a reason why Varada is no longer in the NHL.

So the question is, how best can we isolate a players individual ability in such a team oriented sport like hockey. Is it even possible? I believe that this can be done and I believe that I have developed an algorithm that does so adequately. Tomorrow I will describe in more detail what I have done and present some player rankings from last season and Thursday I’ll post this seasons player rankings. I am sure the results with be both surprising and controversial but most of all I hope that are thought provoking and make you think about certain players a little differently.

Apr 252006
 

Jame Mirtle has an interesting post regarding the increase in offense from 2003-04 to this season and where the goals came from. James concludes:

It’s also no wonder Gary Bettman wants to keep the number of penalties called per game high for the postseason — without the increased scoring generated on the power play, the notion of a radically transformed, higher-scoring (read: better) league goes up in smoke.

Now, as much as I love bashing Gary Bettman digging deeper into the numbers will bring out a different picture. According to James, power play goals have increased 48.2%, short handed goals increased 30.3% and even strength goals increased just 5.12%. He also calculates that of the 1125 additonal goals in the NHL this season, 80.2% are accounted for via the extra PP and SH goals. From these numbers it makes his conclusion seem obvious. But, this is one of those cases where the surface statistics don’t tell you the whole story. Let me dig further.

What James didn’t account for is that because there is more PP time, one should expect more PP and SH goals. Also, because there is more PP time there is naturally less even strength time. Grabbing some ice time statistics from mc97hockey.com we can draw more accurate conclusions.

(Note: mc97hockey.com only has ice time by situation stats through to the Olympic break so I have prorated them to the full year)

What we will find is that teams played 39% more time on the PP (or PK) this season than in 2003-04. Combined PP and SH increased 46%. When we factor out the ice time difference PP and SH goal scoring only increased a measly 5%.

We will also find that even strength ice time dropped to 88.3% of what it was in 2003-04 while even strength goals were up 5.12%. When we adjust for the ice time difference we find that even strength goals are in fact up 19%.

Why the difference? I suspect the reason why even strength goals are up more than PP/SH goals after adjusting for ice time is because power plays are usually just played inside the blue line and the benefits of no red line and the crack down on neutral zone obstruction has very little influence on the PP.

In conclusion, while the greatest net increase in goals this season has been because of the increase in penalties, the crack down on obstruction and the other rule changes have had a much more significant impact on the rate of goals being scored even strength.

Mar 052006
 

The push for the playoffs is on and with the extremely unabalanced schedules it becomes important to look at which teams have the more favourable schedules between now and the end of the season to see who has the best chance of making the playoffs. So what I did was look at how many points a team has as of games through last night and compare that to the difficulty of their schedule so far and then based on the number of games and the difficulty of their remaining schedule make a prediction on how many points they should get. From that, here are the predicted final standings for the eastern and western conferences.

Eastern Conference Predicted Standings

Pos Team GP Pts Schedule Strength Future GP Future SchedStr Pred Pts Total Pts
1 Carolina 60 88 0.471 22 0.435 34 122
2 Ottawa 59 85 0.496 23 0.472 34 119
3 Buffalo 59 81 0.486 23 0.524 29 110
4 NY Rangers 60 80 0.464 22 0.491 27 107
5 Philadelphia 61 76 0.467 21 0.481 25 101
6 New Jersey 61 71 0.477 21 0.466 25 96
7 Montreal 59 66 0.511 23 0.491 26 92
8 Tampa Bay 60 68 0.472 22 0.471 24 92
9 NY Islanders 59 58 0.495 23 0.429 26 84
10 Atlanta 61 62 0.472 21 0.490 20 82
11 Toronto 60 59 0.513 22 0.495 22 81
12 Boston 61 60 0.505 21 0.527 19 79
13 Florida 60 54 0.482 22 0.482 19 73
14 Washington 59 46 0.486 23 0.500 17 63
15 Pittsburgh 61 39 0.501 21 0.472 14 53

Western Conference Predicted Standings

Pos Team GP Pts Schedule Strength Future GP Future SchedStr Pred Pts TotalPts
1 Detroit 60 87 0.484 22 0.464 33 120
2 Dallas 60 81 0.518 22 0.507 30 111
3 Calgary 60 77 0.515 22 0.539 26 103
4 Nashville 60 76 0.487 22 0.488 27 103
5 Colorado 62 74 0.531 20 0.489 25 99
6 Vancouver 62 75 0.517 20 0.553 22 97
7 Edmonton 60 70 0.526 22 0.538 25 95
8 Los Angeles 62 73 0.516 20 0.539 22 95
9 Anaheim 59 67 0.516 23 0.539 25 92
10 San Jose 59 64 0.520 23 0.522 24 88
11 Phoenix 61 60 0.537 21 0.491 22 82
12 Minnesota 62 63 0.526 20 0.550 19 82
13 Columbus 60 48 0.509 22 0.491 18 66
14 Chicago 59 47 0.505 23 0.511 18 65
15 St. Louis 58 43 0.502 24 0.531 16 59

Of course the Rangers would get the #3 playoff seed in the east. Overall there isn’t really much change from the current standings but there are some interesting things to note.

1. If everything goes according to what one would expect from the above analysis there really won’t be much of a playoff race in the eastern conference. Tampa and Montreal would end up tied for the final 2 playoff spots a full 8 points ahead of the NY Islanders who push their way into 9th based on a very easy remaining schedule.

2. The playoff race in the west will be a bit closer with Edmonton and Los Angeles tied just 3 points ahead of 9th place Anaheim and 5 ahead of 10th place San Jose. One must also consider than San Jose has played better since the Joe Thornton trade so they should be able to close the gap even further.

3. Most importantly we must remember that things never happen according to expecations and that all of the above predictions are subject to change depending on what trades teams make or what injuries teams suffer. And don’t assume that all trades for good players are for the better. Nashville has been pretty mediocre since picking up Sillinger and Carolina has been no better either since the Weight trade. Weight has so far contributed very little (8 games, 1 point, -4).

I’ll post updated predictions every week or two so you can all keep up to speed on how your team is doing and it’s playoff outlook.

Feb 032006
 

To win the Stanley Cup, you have got to win in the playoffs and that means beating the best of the rest. That led me to ask the question, who is the best at beating the best?

To answer that question I started with my power ranking calculation based on games through last night. Just for reference, here are the updated power rankings.

Rank Last Week Team AdjWinP SchedStr Power Rank
1 1 Ottawa 0.696 0.511 0.723
2 4 Dallas 0.604 0.522 0.639
3 2 Colorado 0.565 0.538 0.634
4 3 Carolina 0.683 0.476 0.632
5 5 Calgary 0.594 0.518 0.615
6 6 Vancouver 0.575 0.523 0.607
7 8 Buffalo 0.647 0.478 0.595
8 7 Detroit 0.657 0.479 0.594
9 9 Edmonton 0.519 0.528 0.561
10 12 Nashville 0.593 0.484 0.547
11 13 San Jose 0.520 0.517 0.543
12 10 Los Angeles 0.509 0.524 0.542
13 11 Toronto 0.500 0.518 0.538
14 14 Minnesota 0.491 0.525 0.531
15 15 Philadelphia 0.604 0.461 0.525
16 16 Phoenix 0.473 0.534 0.521
17 17 Anaheim 0.490 0.518 0.507
18 20 Boston 0.472 0.508 0.491
19 19 Tampa Bay 0.528 0.473 0.489
20 18 Montreal 0.441 0.519 0.484
21 22 NY Rangers 0.537 0.453 0.453
22 21 NY Islanders 0.433 0.496 0.448
23 23 Atlanta 0.462 0.474 0.433
24 24 New Jersey 0.472 0.469 0.433
25 25 Florida 0.396 0.482 0.383
26 26 Columbus 0.370 0.500 0.372
27 27 Chicago 0.337 0.500 0.334
28 28 Washington 0.324 0.486 0.319
29 29 St. Louis 0.269 0.495 0.265
30 30 Pittsburgh 0.241 0.490 0.244

AdjWinP is a teams winning percentage when shootouts are considered ties and there are no points awarded for overtime losses
SchedStr is an indication of a teams relative difficulty of schedule
Power Rank is the teams expected winning percentage if team played all .500 teams

The next thing I did was determined each teams record against the top 10 power ranked teams listed above. Here is what resulted.

Team Games Wins Loss OTW SOW OTL SOL Points AdjPts AdjWinP
Carolina 8 5 3 1 0 0 1 11 11 0.688
Ottawa 12 7 5 0 0 1 1 16 15 0.625
Dallas 19 13 6 1 4 0 0 26 22 0.579
Calgary 24 14 10 1 2 2 1 31 27 0.563
Nashville 17 9 8 2 0 1 0 19 18 0.529
Colorado 27 11 16 1 0 1 4 27 26 0.481
Edmonton 25 13 12 3 2 0 0 26 24 0.480
Atlanta 12 6 6 0 1 1 0 13 11 0.458
Florida 12 5 7 1 0 0 1 11 11 0.458
Los Angeles 24 12 12 1 2 1 0 25 22 0.458
Tampa Bay 12 5 7 1 0 0 1 11 11 0.458
New Jersey 13 6 7 0 2 0 1 13 11 0.423
Vancouver 24 10 14 1 2 2 2 24 20 0.417
Phoenix 24 10 14 1 2 0 2 22 20 0.417
San Jose 20 7 13 2 1 2 3 19 16 0.400
Philadelphia 13 4 9 1 1 1 3 12 10 0.385
Detroit 17 6 11 0 0 3 1 16 13 0.382
NY Rangers 12 5 7 1 1 0 0 10 9 0.375
Buffalo 9 3 6 0 0 0 0 6 6 0.333
Anaheim 18 5 13 0 1 1 2 13 11 0.306
Boston 15 4 11 1 0 1 1 10 9 0.300
NY Islanders 18 5 13 0 1 0 1 11 10 0.278
Montreal 18 5 13 0 1 2 0 12 9 0.250
Chicago 23 6 17 0 1 1 0 13 11 0.239
St. Louis 19 4 15 0 1 0 2 10 9 0.237
Toronto 17 3 14 1 1 0 3 9 8 0.235
Columbus 24 5 19 1 2 0 2 12 10 0.208
Washington 10 2 8 0 0 0 0 4 4 0.200
Pittsburgh 13 1 12 0 0 2 1 5 3 0.115

Games-# of games played
Wins-All wins including overtime and shootout wins
Loss-All losses including overtime and shootout losses
OTW-Overtime win
OTL-Overtime loss
SOW-Shootout win
SOL-Shootout loss
Points-Points as awarded by NHL
AdjPoints-2 points for a win in regulation or overtime and 1 point for a shootout win or loss
AdjWinP- Adjusted winning percentage calculated using AdjPoints/(2*Games)

Only 5 teams have an adjusted winning percentage (which doesn’t count points for an overtime or shootout loss and treats a shootout game as a tie for both teams and 1 point each) over .500 and one would have to consider that these teams are the top 5 favourites to win the Stanley Cup. The usual suspect sit among the bottom of the standings including St. Louis, Columbus, Pittsburgh, Washington, and Chicago but with the addition of Toronto. Toronto has struggled mightily against the top teams in the league.

To take the analysis a bit further I also looked at each teams records against the bottom 10 and middle 10 teams in the league.

Bottom 10:

Team Games Wins Loss OTW SOW OTL =SOL Points AdjPts AdjWinP
Toronto 14 13 1 2 0 0 0 26 26 0.928
Dallas 10 9 1 0 1 0 0 18 17 0.850
Ottawa 12 10 2 0 0 0 0 20 20 0.833
Los Angeles 10 8 2 0 0 0 0 16 16 0.800
Detroit 22 18 4 1 1 1 0 37 35 0.795
Philadelphia 28 22 6 5 1 2 1 47 44 0.785
Carolina 26 21 5 1 2 0 0 42 40 0.769
San Jose 10 7 3 0 0 0 1 15 15 0.750
Nashville 21 15 6 0 1 1 2 33 31 0.738
Anaheim 11 8 3 0 1 1 0 17 15 0.681
Buffalo 22 15 7 2 1 0 1 31 30 0.681
Columbus 15 11 4 3 2 0 0 22 20 0.666
Atlanta 22 13 9 1 0 0 3 29 29 0.659
Vancouver 11 7 4 0 0 0 0 14 14 0.636
NY Rangers 29 19 10 1 3 3 1 42 36 0.620
Tampa Bay 26 16 10 2 2 1 2 35 32 0.615
NY Islanders 22 14 8 2 3 0 2 30 27 0.613
Edmonton 11 5 6 0 0 1 3 14 13 0.590
Boston 17 9 8 2 0 1 2 21 20 0.588
Calgary 12 6 6 0 0 0 2 14 14 0.583
Phoenix 9 5 4 1 0 1 0 11 10 0.555
Colorado 9 5 4 0 2 0 1 11 9 0.500
Florida 22 11 11 2 1 2 1 25 22 0.500
New Jersey 27 14 13 1 2 3 1 32 27 0.500
Chicago 16 7 9 3 1 3 2 19 15 0.468
Montreal 16 7 9 3 1 0 1 15 14 0.437
St. Louis 18 6 12 0 1 2 2 16 13 0.361
Pittsburgh 25 8 17 2 0 1 2 19 18 0.360
Washington 22 6 16 0 3 1 2 15 11 0.250

What you quickly notice is that Toronto is now on the top of the list. It appears that Toronto is an excellent example of a team who almost always beats the teams they should but rarely steps up and beats teams better than they are.

Middle 10:

Team Games Wins Loss OTW SOW OTL SOL Points AdjPts AdjWinP
Detroit 14 11 3 2 0 0 0 22 22 0.786
Buffalo 20 15 5 2 2 0 2 32 30 0.750
Vancouver 18 13 5 0 0 0 1 27 27 0.750
Colorado 18 13 5 1 0 0 0 26 26 0.722
Ottawa 27 18 9 1 2 0 2 38 36 0.667
Calgary 17 10 7 0 0 0 2 22 22 0.647
Montreal 17 11 6 2 0 3 0 25 22 0.647
Carolina 18 11 7 1 2 3 0 25 20 0.556
Anaheim 23 11 12 1 0 3 3 28 25 0.543
Edmonton 17 10 7 0 3 2 1 23 18 0.529
San Jose 21 10 11 2 0 0 2 22 22 0.524
Dallas 24 14 10 1 3 2 0 30 25 0.521
Boston 21 10 11 1 0 2 1 23 21 0.500
NY Rangers 13 7 6 0 1 4 0 18 13 0.500
Phoenix 22 12 10 3 2 0 0 24 22 0.500
Washington 19 10 9 1 3 1 1 22 18 0.474
Nashville 15 8 7 0 3 1 1 18 14 0.467
New Jersey 13 7 6 0 2 1 0 15 12 0.462
Tampa Bay 15 7 8 0 1 0 0 14 13 0.433
Los Angeles 22 10 12 1 2 3 1 24 19 0.432
Toronto 21 9 12 2 1 1 1 20 18 0.429
Philadelphia 12 5 7 1 0 1 0 11 10 0.417
Chicago 13 4 9 1 0 0 1 9 9 0.346
NY Islanders 12 4 8 1 0 0 0 8 8 0.333
Columbus 15 6 9 0 2 0 0 12 10 0.333
Atlanta 19 4 15 1 0 1 1 10 9 0.237
Florida 19 4 15 1 0 3 1 12 9 0.237
St. Louis 15 3 12 0 1 1 1 8 6 0.200
Pittsburgh 16 3 13 1 1 4 0 10 5 0.156

Another interesting team to look at is the Philadelphia Flyers. Many people have criticized my power rankings and used the Flyers as an example saying that they should be ranked much higher. But these results don’t back that up. Philadelphia plays fairly poorly against the top 10 teams (.385), not much better against the middle 10 (.417) but does real well against the bottom 10 (.785). Add it all up and it doesn’t make sense to have them much higher than a middle of the pack team. All totaled, 47 of Philadelphia’s 70 points (67%) have come against the worst 10 teams in the NHL. The New York Rangers show a similar pattern with 60% of their points coming against the leagues 10 worst teams.

Jan 272006
 

It is a very quiet day on the hockey schedule today so I figured it would be a perfect time to unveil version 2 of my prediction and power ranking algorithms which I have spent the last day or two tweaking.

The new algorithms are much more robust and have allowed me to improve my success rates by a couple percent as well as allow me to make predictions on all games. That means no more pick-em games. Here are my (would be) success rates under the old and new systems.

Old:
Strong: 94 of 129 – 72.9%
Good: 144 of 222 – 64.9%
Some: 93 of 177 – 52.5%
163 games unpredicted

New:
Strong: 87 of 109 – 79.8%
Good: 129 of 201 – 64.2%
Some: 208 of 372 – 55.9%

Overall I am fairly pleased with the improvements and I am especially pleased that I can now make predictions on all games and get reasonable success rates which are in fact better than the old ‘some’ confidence success rates.

Before I get on to the changes in the power ranking system, here are the predictions for tonights games using the new prediction algorithm.

Home Team Road Team Predicted Winner Confidence
Columbus Minnesota Columbus Some
Florida New Jersey Florida Good

I have completely revamped the power ranking system too and because the predictions are based on the same theory used in this power ranking system, I am confident that it is a good indicator how good a team is. Here are the new power rankings as of this morning using this new algorithm.

Rank Last Week Team AdjWinP SchedStr Power Rank
1 1 Ottawa 0.719 0.521 0.771
2 2 Calgary 0.590 0.532 0.640
3 4 Colorado 0.569 0.537 0.632
4 3 Carolina 0.670 0.478 0.627
5 5 Dallas 0.590 0.520 0.614
6 6 Vancouver 0.569 0.526 0.606
7 7 Detroit 0.667 0.476 0.597
8 9 Buffalo 0.633 0.481 0.580
9 12 Edmonton 0.520 0.533 0.569
10 8 Los Angeles 0.528 0.523 0.564
11 10 Toronto 0.500 0.528 0.556
12 11 San Jose 0.521 0.517 0.545
13 16 Minnesota 0.500 0.526 0.540
14 13 Nashville 0.588 0.479 0.533
15 14 Phoenix 0.490 0.527 0.528
16 15 Philadelphia 0.620 0.458 0.527
17 17 Montreal 0.469 0.522 0.514
18 18 Anaheim 0.480 0.516 0.494
19 19 Tampa Bay 0.530 0.472 0.490
20 20 Boston 0.460 0.510 0.478
21 21 NY Rangers 0.520 0.455 0.443
22 22 NY Islanders 0.418 0.500 0.437
23 23 Atlanta 0.480 0.467 0.435
24 24 New Jersey 0.451 0.465 0.406
25 25 Florida 0.392 0.479 0.373
26 28 Chicago 0.340 0.500 0.338
27 26 Columbus 0.340 0.495 0.336
28 27 Washington 0.316 0.484 0.310
29 29 St. Louis 0.276 0.487 0.264
30 30 Pittsburgh 0.255 0.483 0.251

AdjWinP is a teams winning percentage when shootouts are considered ties and there are no points awarded for overtime losses
SchedStr is an indication of a teams relative difficult of schedule
Power Rank is the teams expected winning percentage if team played all .500 teams
————–
(Note: Last week rank is the rank as of Wed. morning)

The big changes I have made are mentioned in the notes immediately above. Because shootouts are kind of a gimmick and that shootouts aren’t necessarily reflective of how good a team is from top to bottom I have considered shootouts to be a tie game. Also, because overtime losses are reflective of a teams ability to win games (or lose games) I have not considered any points for overtime losses (this is the same as the old algorithm). But the most significant difference is how I account for strength of schedule. I have made significant changes in this regard and because of this significant, and somewhat surprising, results have popped up.

Generally western conference teams have improved their rankings (western conference teams had very good records against eastern conference teams in interconference games) as well as any teams playing in particularly difficult divisions. Teams playing in weak divisions saw their rankings drop. Some of the surprising results are:

Philadelphia being ranked 16th in the NHL and being only slightly above .500 if they had a perfectly average strength of schedule. Montreal is ranked 17th so I guess Montreal beating the Flyers the other day might not have been that much of an upset.

Nashville has also dropped dramatically, partly due to their weak schedule but partly because of the number of shootouts and overtime losses they have played in (same for Philly for that matter).

The NY Rangers are another team that has benefitted dramatically from an easy schedule and ovetime loss points and shootouts as they are ranked 21st in the NHL under my power ranking system. Division rivals Islanders and New Jersey also drop significantly in the rankings.

Calgary, Vancouver, Colorado, Edmonton and Minnesota make up the toughest division in the NHL and as a result all saw their rankings improve dramatically and all are ranked in the top 13 in the NHL.

Some of these results may be quite surprising but I do have confidence in them. I do because my prediction algorithm seems relatively good at predicting games. An example is Montreal defeating Philadelphis mentioned above. Other examples are Minnesota defeating Nashville last night. Just looking at the standings you wouldn’t think Montreal or Minnesota would have a chance but if you look at the power ranks above you’ll see they aren’t that far apart. Upsets will always happen (like Chicago defeating Calgary last night) but some upset aren’t as big of upsets as one might initially think.

You can find other statistics based power ranking systems here and here.