## December 3, 2009

### Shots - Home vs. Away

Back in March Jilken's posted an article on "shot recording bias" for the home team. It an interesting concept. Jilken's most convincing evidence was a excel table that showed bias patterns highlighted in yellow. The table didn't give me any perspective as there were no indication of what one should expect the table to look like in a perfectly random environment. I ended up ignoring it for the time being. Sunny Mheta's recent post went into more details (along with 25 comments - which are quite detailed as well). Sunny started mostly with a subjective techniques, actually counting the shots himself. He also used some not so subjective techniques, showing the viewer of a shot that wasn't recorded.

This made me wonder, is there really a significant "home bias" effect for counting shots. Who could have thought such a simple task would be subject to such variations?

So let's start with the last 4 years of shot data. The graph below shows a "Matrix Plot" of the ratio of Shots@home / Shots@road (only first 3 periods). All four seasons have some sort of positive correlation (I didn't check significance though for each). The reader should notice quite quickly that most of the values are greater than 1. Or that shots @ home is generally greater than shots on the road. This should not be a huge surprise - home teams also get more goals.
Clicking on the image will bring up a much
bigger copy with the names of teams
Before I get too far ahead of myself I should explain the above plot. A Matrix Plot is used to compare multiple variables at the same to to see if any of them are related. In the above plot the "rows" represent a season variable and the "columns" another season variable. So for example, the graph in the first row and last column represents "Ratio 2005 vs. Ratio 2008" or is a scatter plot along with a regression for the 2005 season and the 2008 season. Similarly, if you move over one column you'd have "Ratio 2005 vs. Ratio 2007". The reason the diagonal doesn't contain any graphs is because "Ratio 2005 vs. Ratio 2005" is not a very interesting graph (just a bunch of points along the 45 degree line).

On a separate note, I did do a regression based on the average of the first 3 seasons:
Ratio in 2008 = 0.42 + 0.61 x Ratio Average (2005,2006,2007) [p-value=0.001]
or
Ratio in 2008 = 1.07 + 0.61 x (Ratio Average - 1.07)

I've also included a data summary for all the ratio observations (4 seasons x 30 teams = 120 observations) to give the reader an idea of how the data is distributed.

Conclusion:
There is obviously something going on here (sorry I'm trying to keep this short). It's worth noting that Colorado elevation provides them with a natural home advantage. However, even when you remove C0lorado from the data you get similar results.

## September 29, 2009

### Popular Predictions

I quickly copied all the Polls on Mirtle's site: From the Rink. And used the information to generate a simple prediction. How I got the Pts column is I took an average points for each position and multiplied by the percentage of votes for that position. So for example over the past few years the team in 8th position in the standings has average 92 points. So every 8th place vote gets 92 points. So first place votes (116 points) are worth a lot more than second place votes (110 points). It doesn't make a huge difference if you vote 7th (93.5 points) or 6th (94.5 points) as those positions are quite similar. I threw out votes for 15th or 1st depending on the team's average standings.

## August 21, 2009

### Heatley

"This is a straight hockey decision. I have nothing against the fans of Ottawa, or the city of Ottawa. I would like the opportunity to go somewhere where I can play to the best of my capabilities and be the player that I can be." [Heatley]

I found the story about Heatley just a little bit troubling. As you are likely aware, several months ago Heatley requested to be traded. Slightly ironically he had requested a no trade clause when he signed his extension meaning that Heatley would have to be consulted first before any trade could be made (that is to say Heatley would have to waive his no trade clause). There is a long list of comments in the article many of which are hoping that he ends up in their city.

However, I do not want him on my team: Vancouver. Or if you're a fan of any team you shouldn't want this player. I wish this article wasn't about his character as a person, but Heatley from his past and now his present his actions show he is a selfish player off the ice, I assume these tendancies are seen on the ice, but I haven't seen enough of him to know.

My complaint with Heatley is best summarized by Murray:
"We signed [Heatley] to a long-term deal and we expect him to honour it," Murray said on Tuesday. "At this point in time he's a Senator."
I can only imagine what people would think, if they signed the best contractor to work on their house and two weeks into a 2 month project the guy comes to you and tells you that you just can't get along and he wants to have his contract traded to a different home to do the same work. You would be stuck trying to offload this crummy annoying contractor to someone else: who would want him? So my argument goes: by requesting a trade he is hurting his own value as a player, but more importantly he is hurting the value of his current contract with Ottawa and thereby hurting Ottawa the most. Certainly Ottawa would get a better trade offer if instead Heatley was traded when it best suited Ottawa not when it best suited Heatley.

Anyway to the main point. Heatley's argument: I was relegated to smaller role on the team. I want to be #1. No one is better than me! Does Heatley have a point or was Clouston re-dsitributing talent to try to balance an unbalanced team? Well lets look at the breakdown:

What I find most enlightening is the "Team minus Heatley" numbers. Clouston improves scoring efficiency by 30% at even strength and 60% for the power play by shifting Heatley around without affecting Heatley's offensive numbers (some even go up). There of course is not enough information to distinguish whether these results are correct or simply random variations. However, these results do not surprise me. A while ago I did a study that absolute offense generally doesn't change much as a result of small changes in ice time. It looks like Heatley might be a bit of a selfish player; caring about his own performance more than that of the team. That's a guy I really want.

Even if Heatley is right, who wants a player who leaves as soon as things go south?

## July 17, 2009

### Southeast Expecations - 2009

I tried these little tables last season with a little success (and some failures). That being said, I think these tables give a good impression (even if you don't like how the numbers line up) of what each team will look like. I've color coded this year: red = 10% below average for that category. Green = 10% above average for that category. I've excluded PK, Goaltending though.

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 - 2009

I tried these little tables last season with a little success (and some failures). That being said, I think these tables give a good impression (even if you don't like how the numbers line up) of what each team will look like. I've color coded this year: red = 10% below average for that category. Green = 10% above average for that category. I've excluded PK, Goaltending though.

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 - 2009

I tried these little tables last season with a little success (and some failures). That being said, I think these tables give a good impression (even if you don't like how the numbers line up) of what each team will look like. I've color coded this year: red = 10% below average for that category. Green = 10% above average for that category. I've excluded PK, Goaltending though.

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 - 2009

I tried these little tables last season with a little success (and some failures). That being said, I think these tables give a good impression (even if you don't like how the numbers line up) of what each team will look like. I've color coded this year: red = 10% below average for that category. Green = 10% above average for that category. I've excluded PK, Goaltending though.

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.

### Northwest Expectations - 2009

I tried these little tables last season with a little success (and some failures). That being said, I think these tables give a good impression (even if you don't like how the numbers line up) of what each team will look like. I've color coded this year: red = 10% below average for that category. Green = 10% above average for that category. I've excluded PK, Goaltending though.

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 - 2009

I tried these little tables last season with a little success (and some failures). That being said, I think these tables give a good impression (even if you don't like how the numbers line up) of what each team will look like. I've color coded this year: red = 10% below average for that category. Green = 10% above average for that category. I've excluded PK, Goaltending though.

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.

## 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

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.

Insurance

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.
 N Name SQN SV S G EG D 1 Miikka Kiprusoff 0.907 0.912 9208 808 830 22 2 Roberto Luongo 0.917 0.919 8923 723 835 112 3 Martin Brodeur 0.912 0.918 8223 678 738 60 4 Ryan Miller 0.902 0.913 8201 717 705 -12 5 Henrik Lundqvist 0.916 0.916 8060 680 778 98 6 Tomas Vokoun 0.919 0.921 7513 597 709 112 7 Marc-andre Fleury 0.905 0.909 7505 682 688 6 8 Marty Turco 0.903 0.905 7500 713 707 -6 9 Cam Ward 0.909 0.905 7329 693 728 35 10 Tim Thomas 0.916 0.919 7167 580 665 85

Top 10 - Goals Prevented.
 N Name SQN SV S G EG D 1 Tomas Vokoun 0.919 0.921 7513 597 709 112 2 Roberto Luongo 0.917 0.919 8923 723 835 112 3 Henrik Lundqvist 0.916 0.916 8060 680 778 98 4 Tim Thomas 0.916 0.919 7167 580 665 85 5 Cristobal Huet 0.916 0.919 5454 444 507 63 6 Martin Brodeur 0.912 0.918 8223 678 738 60 7 Dominik Hasek 0.918 0.915 3898 332 390 58 8 Jonas Hiller 0.927 0.926 2303 171 225 54 9 J.S. Giguere 0.910 0.913 6738 588 630 42 10 Niklas Backstrom 0.913 0.922 5030 391 431 40

Top 10 - Save Percentage (+3000 shots)
 N Name SQN SV S G EG D 1 Niklas Backstrom 0.913 0.922 5030 391 431 40 2 Tomas Vokoun 0.919 0.921 7513 597 709 112 3 Cristobal Huet 0.916 0.919 5454 444 507 63 4 Roberto Luongo 0.917 0.919 8923 723 835 112 5 Tim Thomas 0.916 0.919 7167 580 665 85 6 Martin Brodeur 0.912 0.918 8223 678 738 60 7 Henrik Lundqvist 0.916 0.916 8060 680 778 98 8 Dominik Hasek 0.918 0.915 3898 332 390 58 9 Manny Fernandez 0.913 0.915 3557 304 334 30 10 Ilja Bryzgalov 0.905 0.914 5635 487 495 8

Top 10 - Shot Quality Neutral Save Percentage (+3000 shots)
 N Name SQN SV S G EG D 1 Tomas Vokoun 0.919 0.921 7513 597 709 112 2 Dominik Hasek 0.918 0.915 3898 332 390 58 3 Roberto Luongo 0.917 0.919 8923 723 835 112 4 Cristobal Huet 0.916 0.919 5454 444 507 63 5 Henrik Lundqvist 0.916 0.916 8060 680 778 98 6 Tim Thomas 0.916 0.919 7167 580 665 85 7 Niklas Backstrom 0.913 0.922 5030 391 431 40 8 Manny Fernandez 0.913 0.915 3557 304 334 30 9 Martin Brodeur 0.912 0.918 8223 678 738 60 10 J.S. Giguere 0.910 0.913 6738 588 630 42

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:
 N name D SQN SV S G 1 Tomas Vokoun 86 92.3 93.2 5761 394 2 Roberto Luongo 66 91.8 93.0 6507 454 3 Tim Thomas 60 91.8 92.9 5548 393 4 Henrik Lundqvist 54 91.6 92.5 6038 451 5 Dominik Hasek 52 92.6 93.3 2819 188 6 J Giguere 46 91.7 92.8 4854 349 7 Martin Brodeur 39 91.3 92.6 6371 469 8 Kari Lehtonen 36 91.5 92.5 4757 359 9 Cristobal Huet 30 91.5 92.7 4021 294 10 Rick Dipietro 21 91.2 92.0 4252 340 11 Manny Fernandez 18 91.3 92.4 2727 207 12 Miikka Kiprusoff 17 90.9 92.4 6715 508 13 Ilja Bryzgalov 15 91.0 92.4 4357 330 14 Niklas Backstrom 15 91.1 92.7 3948 289 15 Chris Osgood 10 90.9 92.0 3459 278 16 Cam Ward 9 90.8 91.4 5535 475 17 Ray Emery 9 90.9 91.8 3287 268 18 Nikolai Khabibulin 8 90.8 91.4 4502 388 19 Martin Gerber 6 90.8 91.8 3922 323 20 Marc-andre Fleury 5 90.7 92.1 5562 440 21 Carey Price 4 90.8 92.1 2469 195 22 Chris Mason 3 90.7 92.0 3753 299 23 Ty Conklin 0 90.6 92.1 2137 169 24 Mathieu Garon -1 90.6 91.7 3274 271 25 Manny Legace -2 90.5 91.6 2497 209 26 Jason Labarbera -6 90.3 91.5 2037 174 27 Alexander Auld -6 90.3 91.3 2510 218 28 Ed Belfour -7 90.2 91.3 2148 187 29 Evgeni Nabokov -8 90.4 91.8 5058 414 30 Ryan Miller -8 90.4 92.4 6288 481 31 Brent Johnson -8 90.1 91.5 2025 173 32 Martin Biron -9 90.4 91.9 4870 396 33 Pascal Leclaire -9 90.2 91.6 2402 202 34 Dwayne Roloson -11 90.4 91.6 5316 444 35 Marty Turco -12 90.4 91.8 5631 463 36 Mike Smith -12 89.9 92.0 2014 162 37 Vesa Toskala -15 90.2 91.3 4167 361 38 Peter Budaj -18 90.0 91.3 3683 320 39 Curtis Joseph -21 89.7 90.8 2720 251 40 Johan Hedberg -22 89.5 90.8 2225 204 41 Antero Niittymaki -25 89.7 90.8 3264 299 42 Marc Denis -29 89.0 90.2 2071 203 43 Olaf Kolzig -30 89.7 91.2 3788 333 44 John Grahame -31 88.9 90.1 2018 199 45 Jose Theodore -37 89.6 90.8 4144 380 46 Andrew Raycroft -42 89.0 90.4 3047 294

## June 10, 2009

### Stanley Cup Final

 DET PIT # G EG S% G EG SV% Game 1: 3 2.0 96.7 1 3.0 85.0 Game 2: 3 2.2 96.8 1 3.1 86.4 Game 3: 2 2.4 81.3 4 1.6 91.7 Game 4: 2 3.5 85.2 4 2.7 94.3 Game 5: 5 3.3 100 0 1.2 84.8 Game 6: 1 2.2 93.1 2 2.9 95.5 Series [3-3] 16 15.6 92.4 12 14.5 89.7

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.

 DET PIT Winner Even Strength GF 3.01 2.99 EGF 2.69 2.35 GA 2.57 2.53 EGA 2.31 2.38 SV% 88.9% 89.4% Power Play GF 9.94 8.34 EGF 9.04 7.27 GA 0.48 1.37 EGA 0.67 0.84 SV% 87.8% 90.4%

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