Detroit vs. Pheonix

DET PHX # G EG S% G EG SV% Game 1: 4 3.9 90.5 2 2.1 89.7 Game 2: 4 2.9 90.9 3 3.3 86.2 Game 3: 4 2.8 90.9 2 2.2 85.7 Game 4: 6 3.3 84.2 3 1.9 84.8 Series [4-0] 18 12.9 89.5 10 9.5 86.8

DET PHX Winner Even Strength GF 2.77 2.6 EGF 2.78 2.29 GA 2.65 2.23 EGA 2.49 2.58 SV% 89.4% 91.3% Power Play GF 10.55 5.85 EGF 10.52 6.53 GA 0.8 0.67 EGA 0.52 0.77 SV% 90.8% 90.1%

DET PHX Winner H. Zetterberg S. Doan P. Datsyuk M. Hanzal J. Franzen E. Belanger T. Bertuzzi R. Whitney V. Filppula L. Stempniak D. Cleary T. Pyatt T. Holmstrom R. Vrbata J. Hudler V. Fiddler J. Abdelkader L. Korpikoski D. Helm K. Turris M. Modano M. Boedker

DET PHX Off. Winner Def. Winner B. Stuart K. Yandle J. Ericsson A. Aucoin N. Lidstrom D. Morris B. Rafalski M. Rozsival R. Salei E. Jovanovski

DET PHX Winner J. Howard I. Bryzgalov J. Macdonald J. Labarbera

Hockey Numbers Update

I've created mini series summaries. To the right you will see my series summary (just a little smaller than normal). You can get a general overview of series by looking at those images. To get the detailed summary, just click on the image.

Playoff Matchups

I hadn't updated my code for the new SOW tie breaker system before today. So this should be correct now.

vs. 100% vs. 100% vs. 100% vs. 100% vs. 100% vs. 100% vs. 100% vs. 100% vs. 100% vs. 100% vs. 100% vs. 100% vs. 100% vs. 100% vs. 100% vs. 100%

And it's all over! - Now the real fun begins.

New Jersey finally eliminated...

This was an interesting finale to what was a terrible year for New Jersey. You wonder why they even tried to make the playoffs. Not only did they blow the season, they will also end up around 24th place, which wont even get them a great first round pick.

This is the first time they have missed the playoffs since 1995-1996

Canucks poor record

As always, I compile the record of the playoff bound teams vs. each other. This gives a more even sampling of performance, because some teams get to play bad teams more often than others. The Canucks have been blessed with six games vs. a division with only one playoff bound team.

So here's the short summary:
Canucks are 14 - 13 (3 - 7 in OT) vs. current playoff bound teams (Stars and Hurricanes are not included). The Canucks averaged 2.7 GF and 2.6 GA. Not terrible numbers, but not great either.

In contrast, the San Jose Sharks went 17 - 11 (6 - 5 OT) vs. playoff bound teams.

Gregory Campbell & Referees

After reading a little bit about the mess Colin Campbell put himself in. I couldn't help but wonder a few things:

- Did Dean Warren know Colin was out to get him when he refereed Campbell's son's game?

- How many other times has Colin asked Stephen to discuss a call about his son with the referee who called it?

- Do the referees think that these calls could impact their employment/advancement?

- Are there other player's that result in similar concerns from Colin?

With that said, I compiled a quick list of the number of games that referees were involved in with Gregory Campbell (GP) and the number of calls against Gregory Campbell in those games. (ERR = 2 standard deviations of the Call%).

There's a little more variation in these results than would be expected, especially since these results should be less variable (due to the fact the NHL uses 2 refs and they are randomly matched). I don't really want to say more than that, but interestingly, Stephen has 0 calls against Greg. Kerry and Dean have the most and they're gone (although I don't think that Kerry's retirement had anything to do with Colin's son).

In the AHL Gregory averaged 1.37 Penalty minutes per game, in the NHL he's averaged 0.92. Although I believe it is common to have fewer penalties in the NHL compared to the AHL.

Update: Sorry, I just loved this exchange on TSN:

Duthie: But, do you believe the emails to former referee-in-chief Stephen Walkom were inappropriate?

Campbell: No they weren't inappropriate...

...

Campbell: Well, it is inappropriate.

New Jersey

Updated Prediction Model

I've done quite a bit of work on my prediction algorithm's and I am extremely happy with the results: With less than dozen games per team so far, I have a pretty good idea of the probabilities of a team making the playoffs. Although my model is designed to predict individual games and not outcomes over the course of a season it seems to do a reasonable job at doing so anyway.

Results

I used my algorithm to review the 2009 - 2010 results and grouped teams into 3 categories:

Top 10, Middle 10 and Bottom 10 after 154 games were played.

Top 10: 8.4 teams in this group were expected to be in the playoffs, 8 made it (Dallas & NYR didn't make it)

Middle 10: 6.1 were expected to make the playoffs in this group. 5 actually did.

Bottom 10: 1.5 were expected to make the playoffs in this group. 3 made it: Detroit 35%, Boston 32%, Nashville 7%.

Overall these results suggest that my model does not differ significantly from the true results (although my model is based on 4 seasons, which include last season)

My model says they have a 3% chance of making the playoffs

And on that note I can say with a bit of confidence that: New Jersey is probably not going to be in the playoffs (namely, they are statistically out of the playoffs at this point). Right now their win percentage is 23%. They're Pythagorean percentage sits at 18% (GF^2/(GF^2 + GA^2). I should mention, they have been a little unlucky in the neighborhood of -15 GF. Including these goals in their totals would only improve their winning percentage to 41%. Now with the injury to Parise they have an up hill battle for the rest of the season.