June 26, 2009

Top Pair of Forward - Contracts

I was wondering what teams generally pay for their top pairing forwards: the forwards who have the highest number of points per game, so I quickly made a table. The reason I was wondering is to see what sort of contract the Sedin's should qualify for as a pair as opposed to being two individuals. What it looks like is teams generally pay about $5 million per point/gp, so the Sedin pair average about 2 points per game are worth about $10 million per year. Of course you can see by the list, some teams pay more (Ottawa, New York) for their top players on a per point basis, but on average teams pay approximately $5 million. Some comments about the table above
<22 - is one of the top 2 players under 22 (in which case they may still be playing on entry level contract)
<26 - is one of the top 2 players under 26 (would be considered RFA's and have cheaper contracts)
<26$ - 0 if a player is <26 otherwise it is the team's price per point/gp
Sedin? - would the team have cap space and would be interested in paying $10+ million to sign Sedin's. Many teams already have a top pair they are happy with and have no interest in the Sedin's.

Long Term SPC

This article assumes that:

For players that the team has filed an LTI exception, the team is allowed to exceed the cap by up to the amount of the injured player's salary with as many replacement players as needed, provided that when the injured player is activated the team comes into compliance with the cap immediately.

In this article I am only referencing situations where the LTI (long term injury) is in fact a permanent injury where there is no possibility to return. This means that while the salary counts towards the cap it also increases the cap by the injured player’s salary, which is equivalent to the salary not counting towards the cap and the cap being unchanged.

Why do teams sign long term contracts?

There have been a number of extremely long contracts signed in the NHL since the lockout largely due to salary cap rules. The Vancouver Sun had an interested article written about precisely that on June 19, 2009 (image to the left). The article doesn't provide an explanation why teams would sign these contracts or why these contracts are able to reduce the salary cap hit. The first long term contract was signed by New York Islanders and Charles Wang was criticized at length by fans and sports writers for such an irrationally long contract. Even after DiPietro’s significant injury(s?) other teams are now singing very similar contracts with their players. Why would teams do what many outside of the hockey business community think are irrational?

12-year vs. 1-year

Let’s assume a very simple world where two things are true: player has the options to sign a very long contract or a 1-year contract. We'll also assume, for simplicity's sake, that there is no inflation, or no price changes from year to year. As a result if a player is just as good as they were last year then they will receive the same salary as they did last year.

Let’s work with the Sedin’s contract: As per the article attached the Sedin’s are willing to sign for 5.25 million per season for 12 years, this means that so long as the Sedin’s are willing to play for the Canucks (they do not retire) they will receive 5.25 million per year.

In contrast imagine if the Sedin’s decided to sign 1 year agreements for 12 years instead, how much they would require for the 1-year contract to be equivalent to the 12 year contract?

Now, before you jump to the conclusion that this amount would be the same as a 12-year contract imagine now that after 1 season one of the Sedin’s had a career ending injury. If the player had signed a 12 year contract he would continue to receive his annual salary, if the player had signed a 1 year contract instead his compensation would cease after 1 year and he would have significantly less money than if he had signed a 12 year contract.


Of course, the player can choose to insure this lost income (so long as they can find a willing insurance company who wants to risk millions of dollars on a dangerous sport). The willing partner could insure the player lost income if the team chooses not to sign him due to injury. The player of course would have to pay a premium to do so, but the insurance could guarantee his income for 12 years as the long term contract did automatically.

Below I have attached a table that summarizes these two worlds:P(active) = probability the player is not injured
- Note I used a 5% chance the player has a career ending injury, this might be a little high, but it makes the numbers look better.

Money = Average amount of money paid by the team to that particular player.

CAP = Average cap hit to team as a result of this particular player + replacement player if injured

Diff = Average loss to player as a result of getting a career ending injury

Ins. = Insurance – The average cost associated with finding a replacement player when the player has a career ending injury.

In the 12-year contract the team pays both replacement player salary (if the signed player is injured) and the injured player’s salary. In the 1-year situation the team only pays the non-injured players salary, if the player wants to insure their earning that is their problem. To make both situations equivalent (player guaranteed $63 million after 12 years) the player should buy an insurance policy for their lost earnings.

In my example, a 12-year contract results in the team having to pay:

$63 million to Signed player

$14.7 million to replacement player due to injuries.


$77.7 million net cost of signing.

However, the $14.7 million would not matter in terms of cap concerns though…

In my example, a 1-year contract results in the team having to pay:

$63 million to signed player

$19.2 million to replacement player due to injuries.


$82.2 million net cost, but entire amount counts towards the cap.

The team who chooses the longer term contract not only pays essentially the same total cost for player salaries, but in the long term contract the injury replacement do no effect the cap, whereas the replacement do effect the cap in the short term contracts. The difference is essentially who is buying the insurance. If the team pays for the insurance it is not charged against the cap, whereas if the player buys the insurance it is charged against the cap. I’m not sure if players actually buy these injury insurance policies or if they just self insure the risk.

So if anyone ever asks you why teams would sign such long term contracts the answer is simple: insurance is part of the cap for short term contracts and is not part of the cap in the long term contracts.

My Opinion

My belief is that these contracts are bad for the NHL in general. I think it create stagnation in teams. These rules make teams boring as there is very little change. It means that the stars will stay in one place for a long time without the possibility of going to a different team. Player’s however benefit significantly from these agreements: they have guaranteed wages and know where they will be living for the few years and can settle down (buy and house, move kids to a new school they might graduate in). That’s not to say players wouldn’t be nervous about these contracts. Specifically they would worry about management changing priorities and the being stuck in the NHL’s worst team for 12 years.

I still think the costs associated with these contracts are a turn off for many General Managers. Unless the General Manager has an unlimited budget it’s hard to a owner asking to sign off on a $63 million dollar contract (that could backfire).

I think there should be enough natural disincentives to these contracts to keep them becoming the norm, but teams who sign them will be rewarded with the extra cap space they need to sign better players.

June 19, 2009

Some goalie statistics...

I have been compiling data since 2003-2004 and felt now was a good time to join it all together in one large database so I can produce statistics that you see below. The tables below include data from playoffs and regular season from 2003-2004 2005-2006 to 2008-2009 (excluding the 2003-2004 playoffs). The database contains over 300,000 shots in over 6000 games.

Top 10 - Total Shots Against.
1Miikka Kiprusoff0.9070.912920880883022
2Roberto Luongo0.9170.9198923723835112
3Martin Brodeur0.9120.918822367873860
4Ryan Miller0.9020.9138201717705-12
5Henrik Lundqvist0.9160.916806068077898
6Tomas Vokoun0.9190.9217513597709112
7Marc-andre Fleury0.9050.90975056826886
8Marty Turco0.9030.9057500713707-6
9Cam Ward0.9090.905732969372835
10Tim Thomas0.9160.919716758066585

Top 10 - Goals Prevented.
1Tomas Vokoun0.9190.9217513597709112
2Roberto Luongo0.9170.9198923723835112
3Henrik Lundqvist0.9160.916806068077898
4Tim Thomas0.9160.919716758066585
5Cristobal Huet0.9160.919545444450763
6Martin Brodeur0.9120.918822367873860
7Dominik Hasek0.9180.915389833239058
8Jonas Hiller0.9270.926230317122554
9J.S. Giguere0.9100.913673858863042
10Niklas Backstrom0.9130.922503039143140

Top 10 - Save Percentage (+3000 shots)
1Niklas Backstrom0.9130.922503039143140
2Tomas Vokoun0.9190.9217513597709112
3Cristobal Huet0.9160.919545444450763
4Roberto Luongo0.9170.9198923723835112
5Tim Thomas0.9160.919716758066585
6Martin Brodeur0.9120.918822367873860
7Henrik Lundqvist0.9160.916806068077898
8Dominik Hasek0.9180.915389833239058
9Manny Fernandez0.9130.915355730433430
10Ilja Bryzgalov0.9050.91456354874958

Top 10 - Shot Quality Neutral Save Percentage (+3000 shots)
1Tomas Vokoun0.9190.9217513597709112
2Dominik Hasek0.9180.915389833239058
3Roberto Luongo0.9170.9198923723835112
4Cristobal Huet0.9160.919545444450763
5Henrik Lundqvist0.9160.916806068077898
6Tim Thomas0.9160.919716758066585
7Niklas Backstrom0.9130.922503039143140
8Manny Fernandez0.9130.915355730433430
9Martin Brodeur0.9120.918822367873860
10J.S. Giguere0.9100.913673858863042

All the data for all goalies can be found here.
SQN - shot quality neutral save percentage - a save percentage that adjusts for the difficulty of the shots (If a goalie faces a lot of easier shots then their SQN will be lower than their save percentage. Similarly, if a goalie faces more difficult shots (rebounds, powerplay, etc.) they will have a higher SQN than their Save percentage
SV - Save percentage = 1-Goals/Shots
S - Shots against
G - Goals against
EG - Expected goals - The number of goals that should be scored against a goalie given how difficult the shot is to stop.
D = EG - G - Goals Prevent - how many goals a goalie stopped compared to how many you would expect him to stop.

UPDATE: As per a request Even Strength statistics:
1Tomas Vokoun8692.393.25761394
2Roberto Luongo6691.893.06507454
3Tim Thomas6091.892.95548393
4Henrik Lundqvist5491.692.56038451
5Dominik Hasek5292.693.32819188
6J Giguere4691.792.84854349
7Martin Brodeur3991.392.66371469
8Kari Lehtonen3691.592.54757359
9Cristobal Huet3091.592.74021294
10Rick Dipietro2191.292.04252340
11Manny Fernandez1891.392.42727207
12Miikka Kiprusoff1790.992.46715508
13Ilja Bryzgalov1591.092.44357330
14Niklas Backstrom1591.192.73948289
15Chris Osgood1090.992.03459278
16Cam Ward990.891.45535475
17Ray Emery990.991.83287268
18Nikolai Khabibulin890.891.44502388
19Martin Gerber690.891.83922323
20Marc-andre Fleury590.792.15562440
21Carey Price490.892.12469195
22Chris Mason390.792.03753299
23Ty Conklin090.692.12137169
24Mathieu Garon-190.691.73274271
25Manny Legace-290.591.62497209
26Jason Labarbera-690.391.52037174
27Alexander Auld-690.391.32510218
28Ed Belfour-790.291.32148187
29Evgeni Nabokov-890.491.85058414
30Ryan Miller-890.492.46288481
31Brent Johnson-890.191.52025173
32Martin Biron-990.491.94870396
33Pascal Leclaire-990.291.62402202
34Dwayne Roloson-1190.491.65316444
35Marty Turco-1290.491.85631463
36Mike Smith-1289.992.02014162
37Vesa Toskala-1590.291.34167361
38Peter Budaj-1890.091.33683320
39Curtis Joseph-2189.790.82720251
40Johan Hedberg-2289.590.82225204
41Antero Niittymaki-2589.790.83264299
42Marc Denis-2989.090.22071203
43Olaf Kolzig-3089.791.23788333
44John Grahame-3188.990.12018199
45Jose Theodore-3789.690.84144380
46Andrew Raycroft-4289.090.43047294

June 10, 2009

Stanley Cup Final

Game 1:32.096.713.085.0
Game 2:32.296.813.186.4
Game 3:22.481.341.691.7
Game 4:23.585.242.794.3
Game 5:53.310001.284.8
Game 6:
Series [3-3]1615.692.41214.589.7

Game 1: If Detroit plays like that for the entire series they probably wont be able to win.
Game 2: Much of the same. Excellent chances for Pittsburgh, but too many goal posts.
Game 3: That was closer to what I was expecting this series to be like.
Game 4: ?
Game 5: Detroit certainly is better with Datsyuk.

Even Strength
Power Play

Not exactly sure when Detroit's injuries will recover. If they all recover Detroit should win this series, otherwise it's anyone's series.