Clark vs. Perron

When I saw the story about MacArthur my first question was: who? I try to keep on top of all major NHL contributors and that one I had never heard of. So the obvious next question was: why on earth is a no name NHL contributor get $2.4 million from an arbitrator? Then came a story shortly after Perron signed for $2.15M, which is a name I certainly do know.

MacArthur (25):
G: 16
A: 19
Salary: $2.5M

Perron (22):
G: 20 (+25%)
A: 27 (+40%)
Salary: $2.15M

First off Atlanta did the right thing to abandon a player that is worth about $1.5M. MacArthur shows no signs of improvement. His PP stats are terrible. David Backes is getting paid $2.5 for an extra 12 assists compared to MacArthur. Callahan is making $2.3 for similar numbers. Fehr (#18 overall) is making $2.1. Disappointing Bernier is getting $2M. Stafford (much more potential) is making $1.9. Steen $1.7. Ladd $1.6M etc.

Presumably Atlanta wanted to sign him this year. It's not like they really needed his services at the deadline. That's what makes this arbitration award so bad: Atlanta traded a 3rd & 4th rounder for nothing because the arbitrators were generous.

Of course the Thrashers could have shot themselves in the foot by not providing good alternatives to what MacArthur's agent provides. There are plenty of players in this category (no shortage of supply).

Kovalchuk Mess

New Jersey finally pushed the CBA rule makers to their limits. By signing a contract that is almost guaranteed to not be completed the NHL did the right thing to step in and say no.

Of course that made people ask the simple question:

Why now?
The answer to this is probably that Kovalchuk's contract is well over the 40 mark, a point where very few players continue to play (especially snipers who typically retire early: Bure, Naslund, Sundin).

Also, the NHL is aware Kovalchuk wanted $10M/year. If he plays 10-11 years and then retires, he'll have that.

A Better Way?
Of course there is a much easier way of dealing with these contracts. That is to institute a "maximum salary cap hit" for long term SPC. Any contract year's cap over 4 years would be subject to these maximums (that is to say a 36 year old signing a 4 year contract would not be impacted at all, but a 5 year contract would, but would only apply to the last year). Say $4M for players over 35, $2M for players over 37 and $1M for players over 39. However the sum of the contract would still be the same, so the difference would be applied to the years there there is no maximum:

Proposed Cap Schedule	Current Cap Schedule
$9.56	$6.00
$9.56	$6.00
$9.56	$6.00
$9.56	$6.00
$9.56	$6.00
$9.56	$6.00
$9.56	$6.00
$9.56	$6.00
$9.56	$6.00
$4.00	$6.00
$4.00	$6.00
$2.00	$6.00
$2.00	$6.00
$1.00	$6.00
$1.00	$6.00
$1.00	$6.00
$1.00	$6.00
=$102	=$102

I'm sure others could see how it would affect players like Luongo and Hossa. But at least it would create a consistent system as opposed to this ad-hoc system full of surprises. All of the sudden these long terms contracts are worthless!

What will the NHL accept?
Obviously the Devils will go back the drawing board, however, I'm not sure how much cap space they can give up. I'm sure N.J and the NHL will take a lot about this over the next few days and a slightly modified contract will be approved (and this whole process will turn out to be a joke).

Northwest Expecations - 2010

I'm still tweaking these, but this is a start. I've adjusted my technique. I've pulled 5 years worth of data to generate these tables. The "young" player problem still exists (because I lack data to differentiate between a good 19 year old or a average 19 year old).

Notation:
Name [GFScore@EV, GAScore@EV]

I've separated out PP & PK now. However these values are based on the player's past performance over the last 5 years. The first 7 rows only included EV goals. there are still some bugs (eg. Johnny vs. John...), which I am working on (I have to find all of them first).

Note: the percentage besting the team's name is the percentage of salary cap used.



















The little arrows indicate whether there is a substantial change in the team's expected points from last season (more than 10 difference).

Note: These include RFA's who haven't signed yet.

Central Expectations - 2010

I'm still tweaking these, but this is a start. I've adjusted my technique. I've pulled 5 years worth of data to generate these tables. The "young" player problem still exists (because I lack data to differentiate between a good 19 year old or a average 19 year old).

Notation:
Name [GFScore@EV, GAScore@EV]

I've separated out PP & PK now. However these values are based on the player's past performance over the last 5 years. The first 7 rows only included EV goals. there are still some bugs (eg. Johnny vs. John...), which I am working on (I have to find all of them first).

Note: the percentage besting the team's name is the percentage of salary cap used.


















The little arrows indicate whether there is a substantial change in the team's expected points from last season (more than 10 difference).

Note: These include RFA's who haven't signed yet.

Pacific Expectations - 2010

I'm still tweaking these, but this is a start. I've adjusted my technique. I've pulled 5 years worth of data to generate these tables. The "young" player problem still exists (because I lack data to differentiate between a good 19 year old or a average 19 year old).

Notation:
Name [GFScore@EV, GAScore@EV]

I've separated out PP & PK now. However these values are based on the player's past performance over the last 5 years. The first 7 rows only included EV goals. there are still some bugs (eg. Johnny vs. John...), which I am working on (I have to find all of them first).

Note: the percentage besting the team's name is the percentage of salary cap used.



















The little arrows indicate whether there is a substantial change in the team's expected points from last season (more than 10 difference).

Note: These include RFA's who haven't signed yet.

Atlantic Expectations - 2010

I'm still tweaking these, but this is a start. I've adjusted my technique. I've pulled 5 years worth of data to generate these tables. The "young" player problem still exists (because I lack data to differentiate between a good 19 year old or a average 19 year old).

Notation:
Name [GFScore@EV, GAScore@EV]

I've separated out PP & PK now. However these values are based on the player's past performance over the last 5 years. The first 7 rows only included EV goals. there are still some bugs (eg. Johnny vs. John...), which I am working on (I have to find all of them first).

Note: the percentage besting the team's name is the percentage of salary cap used.



















The little arrows indicate whether there is a substantial change in the team's expected points from last season (more than 10 difference).

Note: These include RFA's who haven't signed yet.

Northeast Expectations - 2010

I'm still tweaking these, but this is a start. I've adjusted my technique. I've pulled 5 years worth of data to generate these tables. The "young" player problem still exists (because I lack data to differentiate between a good 19 year old or a average 19 year old).

Notation:
Name [GFScore@EV, GAScore@EV]

I've separated out PP & PK now. However these values are based on the player's past performance over the last 5 years. The first 7 rows only included EV goals. there are still some bugs, which I am working on (I have to find all of them first).

Note: the percentage besting the team's name is the percentage of salary cap used.



















The little arrows indicate whether there is a substantial change in the team's expected points from last season (more than 10 difference).

Note: These include RFA's who haven't signed yet.

Southeast Expecations - 2010

I'm still tweaking these, but this is a start. I've adjusted my technique. I've pulled 5 years worth of data to generate these tables. The "young" player problem still exists (because I lack data to differentiate between a good 19 year old or a average 19 year old).

Notation:
Name [GFScore@EV, GAScore@EV]

I've separated out PP & PK now. However these values are based on the player's past performance over the last 5 years. The first 7 rows only included EV goals. there are still some bugs (eg. Johnny vs. John...), which I am working on (I have to find all of them first).

Note: the percentage besting the team's name is the percentage of salary cap used.



















The little arrows indicate whether there is a substantial change in the team's expected points from last season (more than 10 difference).

Note: These include RFA's who haven't signed yet.

Goalie Statistics for 5 years

So, here is a compilation of all the saves/goals/shots etc. each goalie faced over the last 5 years (inc. playoffs). I may be missing some games, but there is a lot of data here. The "Cred" column is just adjusting the shot quality neutral save percentage based on number of shots faced (regressing to the mean).

EG = Expected Goals
G = Actual Goals
SQN = shot quality neutral save percentage
= 1- 0.0926*G /EG
SV% = real save percentage
= 1 - G / S

Note: The expected goals are adjusted for site based biased shot information.
Few things of note
- Save percentages over 0.920 are not really sustainable (8 goalies in 2010).
- I didn't realize how good Hiller is.
- Raycroft is really bad (allowed almost 100 more goals than average)

N name C EG G SVPCT SQN Cred 1 Henrik Lundqvist 10175 1010 847 0.917 0.922 0.919 2 Jonas Hiller 4178 402 323 0.923 0.926 0.918 3 Tomas Vokoun 9550 853 753 0.921 0.918 0.915 4 Jaroslav Halak 3677 348 297 0.919 0.921 0.913 5 Roberto Luongo 11234 995 929 0.917 0.914 0.911 6 Craig Anderson 4857 452 405 0.917 0.917 0.911 7 Timothy Thomas 8388 726 684 0.918 0.913 0.910 8 Dominik Hasek 3899 363 332 0.915 0.915 0.909 9 Cam Ward 8739 836 813 0.907 0.910 0.907 10 Cristobal Huet 6549 581 559 0.915 0.911 0.907 11 Chris Mason 6652 604 579 0.913 0.911 0.907 12 Martin Brodeur 10354 871 861 0.917 0.909 0.906 13 James Howard 2464 213 195 0.921 0.915 0.906 14 Dan Ellis 3198 289 275 0.914 0.912 0.905 15 J.S Giguere 7767 693 685 0.912 0.908 0.905 16 Pekka Rinne 3203 290 276 0.914 0.912 0.905 17 Ilja Bryzgalov 7851 665 667 0.915 0.907 0.904 18 Miikka Kiprusoff 11244 954 972 0.914 0.906 0.904 19 Dwayne Roloson 8565 769 772 0.910 0.907 0.904 20 Kari Lehtonen 6679 596 595 0.911 0.908 0.904 21 Manny Fernandez 3557 313 304 0.915 0.910 0.904 22 Nikolai Khabibulin 6651 629 641 0.904 0.906 0.903 23 Niklas Backstrom 6662 543 549 0.918 0.906 0.903 24 Ryan Miller 10516 862 884 0.916 0.905 0.903 25 Steve Mason 3451 321 320 0.907 0.908 0.902 26 Martin Biron 7202 623 651 0.910 0.903 0.901 27 Evgeni Nabokov 9187 794 833 0.909 0.903 0.901 28 Rick Dipietro 5960 539 558 0.906 0.904 0.901 29 Josh Harding 2251 194 190 0.916 0.909 0.901 30 Jason Labarbera 3163 287 290 0.908 0.906 0.900 31 Marty Turco 9132 811 854 0.906 0.903 0.900 32 M.A Fleury 9616 839 887 0.908 0.902 0.900 33 Carey Price 4539 395 408 0.910 0.904 0.900 34 Manny Legace 4112 379 389 0.905 0.905 0.900 35 Martin Gerber 5169 461 486 0.906 0.902 0.899 36 Ray Emery 5114 451 478 0.907 0.902 0.899 37 Pascal Leclaire 4363 394 420 0.904 0.901 0.898 38 Jonathan Quick 3371 298 313 0.907 0.903 0.898 39 Ty Conklin 3512 300 316 0.910 0.902 0.898 40 Alexander Auld 5245 473 513 0.902 0.900 0.897 41 Peter Budaj 5170 460 502 0.903 0.899 0.896 42 Mathieu Garon 5314 473 518 0.903 0.899 0.896 43 Brian Elliott 2334 210 222 0.905 0.902 0.896 44 Chris Osgood 5058 446 485 0.904 0.899 0.896 45 Jose Theodore 6974 635 700 0.900 0.898 0.896 46 Antero Niittymaki 5913 527 578 0.902 0.898 0.896 47 Joey Macdonald 2220 212 223 0.900 0.902 0.896 48 Brian Boucher 2586 245 263 0.898 0.900 0.895 49 Brent Johnson 3375 299 331 0.902 0.898 0.894 50 Johan Hedberg 4299 386 430 0.900 0.897 0.894 51 Curtis Sanford 2352 215 234 0.901 0.899 0.894 52 Vesa Toskala 6217 558 632 0.898 0.895 0.893 53 Scott Clemmensen 2311 195 216 0.907 0.898 0.893 54 Ed Belfour 3026 279 309 0.898 0.897 0.893 55 Olaf Kolzig 5425 476 543 0.900 0.894 0.892 56 Mike Smith 3805 312 354 0.907 0.895 0.892 57 David Aebischer 2435 220 245 0.899 0.897 0.892 58 Curtis Joseph 3768 341 393 0.896 0.893 0.891 59 Fredrik Norrena 2410 210 244 0.899 0.892 0.889 60 Patrick Lalime 3116 280 327 0.895 0.892 0.889 61 Mikael Tellqvist 2636 227 270 0.898 0.890 0.887 62 Andrew Raycroft 4553 406 500 0.890 0.886 0.885 63 Marc Denis 2779 247 305 0.890 0.886 0.884 64 John Grahame 2675 237 301 0.887 0.882 0.882 65 Johan Holmqvist 2466 199 268 0.891 0.875 0.876

Stanley Cup Final

CHI PHI # G EG S% G EG SV% Game 1: 6 3.2 83.9 5 3.1 81.3 Game 2: 2 2.3 96.4 1 2.8 91.3 Game 3: 3 3.0 89.7 4 3.9 90.0 Game 4: 3 3.7 86.2 5 2.9 91.9 Game 5: 7 2.7 84.6 4 2.6 77.8 Series [3-2] 21 14.9 88.2 19 15.3 86.6

Philadelphia is doing a lot better than I would have anticipated. If it wasn't for the difference in goaltending this series could easily be tied (or have gone the other way).

CHI PHI Winner Even Strength GF 2.9 2.44 EGF 2.83 2.78 GA 2.35 2.43 EGA 2.19 2.53 SV% 89.2% 90.4% Power Play GF 6.54 6.64 EGF 6.55 8.69 GA 0.49 2.42 EGA 0.6 0.8 SV% 92.3% 89.6%