"Cost certainty" refers to teams in aggregate. It obviously does not refer to teams individually, because not all teams produce the same level of revenues. Some teams are going to spend a higher percentage of revenues on players salaries, and some teams are going to spend less. The key though is that teams should still have a better idea of what to expect rather than knowing that 10 teams will spend significant sums of money on player salaries [and be responsible for over half of the total outlays on player salaries] while some teams will spend minimal amounts to basically have a viable NHL team and little else. The opportunity to be competitive improves, but it's still no guarantee.
And really, in the non-cap system it would be easier for teams to maximize profits [which has been mentioned a few times here and numerous times in other cap-related threads], because teams really could choose to control spending on their terms. The fact is, in a non-cap system teams chose not to attempt to maximize profits in any way; a capped system provides a better chance of realizing profits in general, but very arguably makes it more difficult for lower-revenue teams to realize profits even after considering revenue sharing contributions, much less maximize them.
Now back to the ongoing conversation, already in progress.
Straw man, straw man, straw man. That seems to be all you can ever say.
You literally say we can't state there are discounts, and there would be a stronger correlation if tax discounts existed, which is blatantly not true. You set up an analysis that was destined to be correlated like this, and then tried to use that weaker correlation as justification for your conclusion, knowing that your data actually shows that there is a significant difference between the no-tax areas and high-tax areas.
You literally have run away from answering any of the points in this thread criticizing your methods.
All you do is throw personal attacks at me and then project that onto me.
The only one lying and misrepresenting here is you. Stop saying this without backing it up.
Hahaha, you're not fooling anybody.
I've literally tried to make this about your data and address the data and analysis, and all you have done is run away from it and attack me. You're the one making this about me because you don't want to answer to anything.
Because I literally have no interest in having any sort of discussion with you whatsoever. I'm just calling you out on your lies because I've noticed that other people seem to believe them.
How so? I've yet to see anybody give an explanation why.
My so-called "analysis" is just using his own data to look at the impacts on no-tax and high-tax areas, creating a bigger sample than any one individual team.
How so? I've yet to see anybody give an explanation why.
My so-called "analysis" is just using his own data to look at the impacts on no-tax and high-tax areas, creating a bigger sample than any one individual team.
Besides narrowing down the data set for no apparent reason, you cherry picked a cutoff that gave you a favorable result. Had you chosen the top 6 and bottom 6, the difference in your averages would have been less than 7%. You also averaged percentages, which you should never do, because the data that is being divided has different magnitudes. You needed to add the top 5 cap hits and divide the sum by the sum of the projected cap hits. Had you done that, your top 5 and bottom 5 would have been 1.08% and 0.99%. Again, that's with a cherry picked cutoff. Had you done the top and bottom 10 with that kind of calculation, you would actually see the trend in reverse. Top 10 'averages' out to 107.61%, bottom 10 is 121.68%. Colorado has the 6th "best" tax situation and a 130.68% cap hit ratio, yet was conveniently omitted from your calculation.
The way the OP did his analysis shows the trend as accurately as the data allows. Your analysis omits some of the data and results in a distortion of the trend.
Because I literally have no interest in having any sort of discussion with you whatsoever. I'm just calling you out on your lies because I've noticed that other people seem to believe them.
I have not lied about a single thing in this thread, and I have repeatedly tried to engage in discussion related to the thread. The only one throwing out personal attacks and lying here is you, and it is clearly because you have no answer to my points.
I have not lied about a single thing in this thread, and I have repeatedly tried to engage in discussion related to the thread. The only one throwing out personal attacks and lying here is you, and it is clearly because you have no answer to my points.
1. Binning is not a good idea as shown above.
2. @hairylikebear described quite clearly the issue with your analysis.
To elaborate on one point; the "averaging of averages". Here is what the data looks like if you actually sum all the cap hit and projected cap hit for the teams in question:
Cap Hit/Projected in 2019:
DAL, FLA, NSH, TBL, VGK: 99.07% of projected cap hit.
ANA, LAK, MTL, OTT, SJS, TOR: 108.95% of projected cap hit.
And, just like the R^2 value, the new 2018 data also gives us an extremely similar result:
Cap Hit/Projected in 2018:
DAL, FLA, NSH, TBL, VGK: 100.34% of projected cap hit.
ANA, LAK, MTL, OTT, SJS, TOR combined: 108.62% of projected cap hit.
And remember, this is with your cherry picked set of teams. And what you're also ignoring here is that the no state tax teams are literally paying projected value in both cases.
But there's no reason that we should exclude teams like Arizona from the analysis. Why would we? That already omits data from a sample size that you yourself have said is not very large. Surely, if there are significant differences between teams in the 50% range and teams in the 40% range, there should be differences between teams in the <45% range and teams in the >49% range, no? Why not compare Pittsburgh, Detroit, Arizona, Boston, etc. to Winnipeg and Vancouver?
No I didn't. I put the cutoff as the places that have always been talked about regarding the effect of taxation. All of the no state tax areas (~40%), and then all of the areas where taxes push over 50%. This is where the taxation effect would be most visible, and there were 5 of each. Most of the others were within 44-47%.
Why would I include cities that have been taxed differently into my sample evaluating the effect on those specific markets? All of the no-tax states were within 0.34% of each other, and you want me to include Colorado in that sample that's taxed 4% higher? That doesn't make any sense.
No I didn't. I put the cutoff as the places that have always been talked about regarding the effect of taxation. All of the no state tax areas (~40%), and then all of the areas where taxes push over 50%. This is where the taxation effect would be most visible, and there were 5 of each. Most of the others were within 44-47%.
Why would I include cities that have been taxed differently into my sample evaluating the effect on those specific markets? All of the no-tax states were within 0.34% of each other, and you want me to include Colorado in that sample that's taxed 4% higher? That doesn't make any sense.
1. Binning is not a good idea as shown above.
2. @hairylikebear described quite clearly the issue with your analysis.
To elaborate on one point; the "averaging of averages". Here is what the data looks like if you actually sum all the cap hit and projected cap hit for the teams in question:
Cap Hit/Projected in 2019:
DAL, FLA, NSH, TBL, VGK: 99.07% of projected cap hit.
ANA, LAK, MTL, OTT, SJS, TOR: 108.95% of projected cap hit.
And, just like the R^2 value, the new 2018 data also gives us an extremely similar result:
Cap Hit/Projected in 2018:
DAL, FLA, NSH, TBL, VGK: 100.34% of projected cap hit.
ANA, LAK, MTL, OTT, SJS, TOR combined: 108.62% of projected cap hit.
And remember, this is with your cherry picked set of teams. And what you're also ignoring here is that the no state tax teams are literally paying projected value in both cases.
But there's no reason that we should exclude teams like Arizona from the analysis. Why would we? That already omits data from a sample size that you yourself have said is not very large. Surely, if there are significant differences between teams in the 50% range and teams in the 40% range, there should be differences between teams in the <45% range and teams in the >49% range, no? Why not compare Pittsburgh, Detroit, Arizona, Boston, etc. to Winnipeg and Vancouver?
again. You spend pages on stats..... but undergrad stats is not research. If you Really want to do “research” you have to present your thesis AND how it fits in with current evidence.
You have to present the actual state of the understanding of the problem and what you expect.
The introduction of any argument would provide expert opinions. Quotes from GMs. And the tax specialist who actually provided specific numbers and quotes for actual contracts.
But you never addressed the actual evidence from actual people. You pretended it didn’t exist. Then made a thread that made it sound like it was all fan speculation and not direct quotes from actual pros.
You completely misrepresented the arguments. Made up a study and decided that it was true?
Again. If we pretend it was done properly.... do you think this over rides their actual evidence?
again. You spend pages on stats..... but undergrad stats is not research. If you Really want to do “research” you have to present your thesis AND how it fits in with current evidence.
You have to present the actual state of the understanding of the problem and what you expect.
The introduction of any argument would provide expert opinions. Quotes from GMs. And the tax specialist who actually provided specific numbers and quotes for actual contracts.
But you never addressed the actual evidence from actual people. You pretended it didn’t exist. Then made a thread that made it sound like it was all fan speculation and not direct quotes from actual pros.
You completely misrepresented the arguments. Made up a study and decided that it was true?
Again. If we pretend it was done properly.... do you think this over rides their actual evidence?
What the hell? No it wouldn't. Nobody cares as much about that stuff as you do.
NHL GMs and "experts" constantly talk about how valuable hits are, and how you need to be big and physical to win in the playoffs. For example, look at the research which is done on things the correlation between hitting/physicality and wins. Yet if you look at the research that is done on hitting and the correlation that it has with winning, quotes from GMs are absent.
The purpose of this thread was not to ask whether there was a pre-conceived notion of correlation. The purpose of this was to research whether or not there actually is any tangible correlation that we can put our hands on. Just through the research that I conducted, the answer is "yes, but it's very weak".
More specifically, if you look at just the teams with no state taxes, there doesn't seem to be a consistent trend. 3 of the 5 teams with no state taxes (Tampa Bay, Florida, and Dallas) actually paid more than projected to their players.
Surely, if there are significant differences between teams in the 50% range and teams in the 40% range, there should be differences between teams in the <45% range and teams in the >49% range, no? Why not compare Pittsburgh, Detroit, Arizona, Boston, etc. to Winnipeg and Vancouver?
The smaller the difference in range, the harder it will be to see differences caused by the taxation. Also, you're complaining about averaging two sets of 5 teams saying it's too small, and now you want me to average out 2 teams? How does that make any sense?
Even if I did do the 44-45% teams in your way, it would be under 100%, even with the extreme outlier that is Colorado included and taking up almost 1/3 of the sample.
Let's do a bigger analysis with a bigger sample of teams using your methods of cap hit/projected cap hit:
2019:
All teams between 40-45% taxes: 100.4%
All teams between 49-53% taxes: 105.2%
2018:
All teams between 40-45% taxes: 101.7%
All teams between 49-53% taxes: 109.3%
What the hell? No it wouldn't. Nobody cares as much about that stuff as you do.
NHL GMs and "experts" constantly talk about how valuable hits are, and how you need to be big and physical to win in the playoffs. For example, look at the research which is done on things the correlation between hitting/physicality and wins. Yet if you look at the research that is done on hitting and the correlation that it has with winning, quotes from GMs are absent.
The purpose of this was to research whether or not there actually is any tangible correlation that we can put our hands on. The purpose of this thread was not to ask whether there was a pre-conceived notion of correlation.
Nobody cares about the actual quotes and experiences of the actual professionals who make the contracts, sign the contracts, and advise the players on the tax implications of the contracts?
They care about your “study” based on a series of uncontrolled IVs? On a topic you know nothing about? You realize that to actually do a study like this you would have to actually control for factors right?
Ie: Low tax team. Just happens to be in perpetual
Bankruptcy. Differing RFAS vs UFAs. Arbitration decisions. Etc. All those would effect the expected
Contract values.
You could also look at top ufa contracts at
Each position for age and stats appropriate players. And see how they fit with comparables.
Price at 14% and Vasilevskiy at 11.5%
Tavares at 14% and stamkos at 11.5%
Kane and at 15% and kucherov at 11.5%
O wow! A correlation of course there is no sample size worth talking about here. But of course there is also no statistical power in your “study”’to find a correlation that overrides actual people in the actual rooms. And even at that. You actually did. Which proves the point. A small signal in excessive noise is actually noteworthy in stats.
A little bit of stats is dangerous. Like the fact that you are somehow equating “hits and wins”. With ACTUAL peoples thoughts and beliefs in negotiations is just wow
Question: how influential are a GMs thoughts and opinions on hits in any given NHL game?
Do you think a GM thinking hitting is good effects
Players on the ice? Do they skate faster? Shoot harder?
Now how important do you think an agents advice is when negotiating a contract?
How important do you think a tax accountants analysis is to NHL clients who actually make decisions?
Are those factors not relevant to the decision making process?
Oh yeah, he made a great point, cause there are so many examples of salary caps that fluctuate from team to team rather than being the same for everyone.
I mean, it would be great if you actually explained yourself instead of just saying this with nothing to back it up, but if you look above, I even did it in the way that OP wants (adding up the cap hits/projected cap hits) and included twice as many teams, and it still shows a significant difference between the high-tax and low-tax areas.
Oh yeah, he made a great point, cause there are so many examples of salary caps that fluctuate from team to team rather than being the same for everyone.
This has come up a lot lately, and it's really tough to say what the truth is. When discussing this, an overwhelming amount of weight is put on anecdotal evidence by both sides; one side will mention that Nikita Kucherov took $9.5M, and the other side will mention that Sergei Bobrovsky took $10M. Confirmation bias is also heavily at play here; if you already believe that teams that play in states without state income taxes get heavy discounts, then William Karlsson signing for less than $6M on an 8-year term in Vegas will help confirm what you believe. If you already believe that these teams don't get heavy discounts, then Andrei Vasilevskiy signing for $9.5M on an 8-year term will help confirm what you believe.
What I wanted to do was take an objective look at all of the data available. I didn't want to just bicker over one, two, or even 5 contracts. So I looked at every free agent (UFA or RFA) skater who was signed to a contract with an AAV of at least $1M, and and added together the full cap hit that each team had signed their players to. (Extensions signed that take place at the start of 2020-2021 are not present here.) Then, using Evolving Hockey's contract projections, I entered their projected cap hit on the same term that they signed for. Then I created a new variable, titled "Cap hit/Projected Cap hit", which divides the team's cap hit by their projected cap hit, and attempts to discern whether or not certain teams are getting heavy discounts or not. A team below 100% would be doing well, since they paid less than full value for their players, where as a team above 100% is doing worse than full value.
Here is the data for every player:
Player
Position
Signed as
Age
Team
Term
Cap Hit
Projected Cap Hit
Cap Hit Over Projected
Lawson Crouse
F
RFA
22
ARI
3
$1,533,000
$1,823,399
($290,399)
Danton Heinen
F
RFA
23
BOS
2
$2,800,000
$2,819,150
($19,150)
Brett Ritchie
F
UFA
25
BOS
1
$1,000,000
$794,944
$205,056
Jeff Skinner
F
UFA
27
BUF
8
$9,000,000
$8,341,377
$658,623
Marcus Johansson
F
UFA
28
BUF
2
$4,500,000
$3,143,553
$1,356,447
Jake Mccabe
D
RFA
25
BUF
2
$2,850,000
$2,685,410
$164,590
Evan Rodrigues
F
RFA
25
BUF
1
$2,000,000
$1,262,744
$737,256
Zemgus Girgensons
F
RFA
25
BUF
1
$1,600,000
$1,389,466
$210,534
Johan Larsson
F
RFA
26
BUF
1
$1,550,000
$1,238,189
$311,811
Sebastian Aho
F
RFA
21
CAR
5
$8,454,000
$8,773,423
($319,423)
Ryan Dzingel
F
UFA
27
CAR
2
$3,375,000
$4,171,461
($796,461)
Brock Mcginn
F
RFA
25
CAR
2
$2,100,000
$2,195,805
($95,805)
Gustav Nyquist
F
UFA
29
CBJ
4
$5,500,000
$5,577,975
($77,975)
Ryan Murray
D
RFA
25
CBJ
2
$4,600,000
$3,128,864
$1,471,136
Scott Harrington
D
RFA
26
CBJ
3
$1,633,333
$1,366,977
$266,356
Sam Bennett
F
RFA
23
CGY
2
$2,550,000
$2,407,183
$142,817
David Kampf
F
UFA
24
CHI
2
$1,000,000
$1,293,504
($293,504)
Ryan Carpenter
F
UFA
28
CHI
3
$1,000,000
$1,680,243
($680,243)
J.T. Compher
F
RFA
24
COL
4
$3,500,000
$3,428,686
$71,314
Joonas Donskoi
F
UFA
27
COL
4
$3,900,000
$3,326,413
$573,587
Andre Burakovsky
F
RFA
24
COL
1
$3,250,000
$2,079,146
$1,170,854
Nikita Zadorov
D
RFA
24
COL
1
$3,200,000
$2,458,808
$741,192
Colin Wilson
F
UFA
29
COL
1
$2,600,000
$1,247,204
$1,352,796
Pierre-Edouard Bellemare
F
UFA
34
COL
2
$1,800,000
$1,424,773
$375,227
Joe Pavelski
F
UFA
34
DAL
3
$7,000,000
$7,408,595
($408,595)
Esa Lindell
D
RFA
25
DAL
6
$5,800,000
$5,709,883
$90,117
Mattias Janmark
F
RFA
26
DAL
1
$2,300,000
$2,091,628
$208,372
Roman Polak
D
UFA
33
DAL
1
$1,750,000
$1,675,162
$74,838
Corey Perry
F
UFA
34
DAL
1
$1,500,000
$1,102,330
$397,670
Andrej Sekera
D
UFA
33
DAL
1
$1,500,000
$1,008,539
$491,461
Patrik Nemeth
D
UFA
27
DET
2
$3,000,000
$2,213,156
$786,844
Valtteri Filppula
F
UFA
35
DET
2
$3,000,000
$2,943,253
$56,747
Alex Chiasson
F
UFA
28
EDM
2
$2,150,000
$2,567,505
($417,505)
Markus Granlund
F
UFA
26
EDM
1
$1,300,000
$1,338,082
($38,082)
Jujhar Khaira
F
RFA
24
EDM
2
$1,200,000
$1,138,249
$61,751
Anton Stralman
D
UFA
32
FLA
3
$5,500,000
$4,475,273
$1,024,727
Brett Connolly
F
UFA
27
FLA
4
$3,200,000
$3,648,650
($448,650)
Noel Acciari
F
UFA
27
FLA
3
$1,666,667
$1,505,589
$161,078
Alex Iafallo
F
RFA
25
L.A
2
$2,425,000
$2,402,877
$22,123
Mats Zuccarello
F
UFA
31
MIN
5
$6,000,000
$6,015,461
($15,461)
Ryan Donato
F
RFA
23
MIN
2
$1,900,000
$1,746,496
$153,504
Ryan Hartman
F
UFA
24
MIN
2
$1,900,000
$2,703,931
($803,931)
Brett Kulak
D
RFA
25
MTL
3
$1,850,000
$2,365,692
($515,692)
Joel Armia
F
RFA
26
MTL
2
$2,600,000
$2,918,875
($318,875)
Ben Chiarot
D
UFA
28
MTL
3
$3,500,000
$2,766,359
$733,641
Jordan Weal
F
UFA
27
MTL
2
$1,400,000
$1,848,538
($448,538)
Nick Cousins
F
UFA
25
MTL
1
$1,000,000
$841,415
$158,585
Mike Reilly
D
RFA
25
MTL
2
$1,500,000
$1,294,063
$205,937
Nate Thompson
F
UFA
34
MTL
1
$1,000,000
$905,746
$94,254
Wayne Simmonds
F
UFA
30
N.J
1
$5,000,000
$1,938,597
$3,061,403
Will Butcher
D
RFA
24
N.J
3
$3,730,000
$3,327,467
$402,533
Matt Duchene
F
UFA
28
NSH
7
$8,000,000
$7,675,778
$324,222
Colton Sissons
F
RFA
25
NSH
7
$2,857,143
$3,905,167
($1,048,024)
Anders Lee
F
UFA
28
NYI
7
$7,000,000
$6,558,841
$441,159
Jordan Eberle
F
UFA
29
NYI
5
$5,500,000
$5,654,091
($154,091)
Brock Nelson
F
UFA
27
NYI
6
$6,000,000
$4,895,832
$1,104,168
Artemi Panarin
F
UFA
27
NYR
7
$11,642,000
$10,482,359
$1,159,641
Jacob Trouba
D
RFA
25
NYR
7
$8,000,000
$6,873,268
$1,126,732
Pavel Buchnevich
F
RFA
24
NYR
2
$3,250,000
$2,874,385
$375,615
Ron Hainsey
D
UFA
38
OTT
1
$3,500,000
$2,264,619
$1,235,381
Anthony Duclair
F
RFA
23
OTT
1
$1,650,000
$1,222,631
$427,369
Kevin Hayes
F
UFA
27
PHI
7
$7,140,000
$6,579,110
$560,890
Travis Sanheim
D
RFA
23
PHI
2
$3,250,000
$2,859,126
$390,874
Brandon Tanev
F
UFA
27
PIT
6
$3,500,000
$3,821,772
($321,772)
Zach Aston-Reese
F
RFA
24
PIT
2
$1,000,000
$1,268,315
($268,315)
Erik Karlsson
D
UFA
29
S.J
8
$11,500,000
$9,720,403
$1,779,597
Timo Meier
F
RFA
22
S.J
4
$6,000,000
$4,852,639
$1,147,361
Kevin Labanc
F
RFA
23
S.J
1
$1,000,000
$1,893,645
($893,645)
Joel Edmundson
D
RFA
26
STL
1
$3,100,000
$4,021,205
($921,205)
Oskar Sundqvist
F
RFA
25
STL
4
$2,750,000
$2,348,432
$401,568
Carl Gunnarsson
D
UFA
32
STL
2
$1,750,000
$1,001,246
$748,754
Braydon Coburn
D
UFA
34
T.B
2
$1,700,000
$2,084,100
($384,100)
Cedric Paquette
F
RFA
25
T.B
2
$1,650,000
$1,436,654
$213,346
Jan Rutta
D
UFA
28
T.B
1
$1,300,000
$934,481
$365,519
Cody Ceci
D
RFA
25
TOR
1
$4,500,000
$3,057,622
$1,442,378
Andreas Johnsson
F
RFA
24
TOR
4
$3,400,000
$3,672,590
($272,590)
Alex Kerfoot
F
RFA
24
TOR
4
$3,500,000
$4,216,644
($716,644)
Kasperi Kapanen
F
RFA
22
TOR
3
$3,200,000
$2,884,599
$315,401
Tyler Myers
D
UFA
29
VAN
5
$6,000,000
$5,536,915
$463,085
Alex Edler
D
UFA
33
VAN
2
$6,000,000
$5,738,872
$261,128
Micheal Ferland
F
UFA
27
VAN
4
$3,500,000
$4,106,404
($606,404)
Jordie Benn
D
UFA
31
VAN
2
$2,000,000
$2,741,134
($741,134)
Josh Leivo
F
RFA
26
VAN
1
$1,500,000
$1,133,691
$366,309
William Karlsson
F
RFA
26
VGK
8
$5,900,000
$7,567,491
($1,667,491)
Tomas Nosek
F
UFA
26
VGK
1
$1,000,000
$886,753
$113,247
Nathan Beaulieu
D
UFA
26
WPG
1
$1,000,000
$1,435,683
($435,683)
Neal Pionk
D
RFA
23
WPG
2
$3,000,000
$2,934,707
$65,293
Andrew Copp
F
RFA
24
WPG
2
$2,280,000
$2,176,861
$103,139
Richard Panik
F
UFA
28
WSH
4
$2,500,000
$4,795,203
($2,295,203)
Jakub Vrana
F
RFA
23
WSH
2
$3,350,000
$3,100,747
$249,253
Carl Hagelin
F
UFA
30
WSH
4
$2,750,000
$3,309,399
($559,399)
Garnet Hathaway
F
UFA
27
WSH
4
$1,500,000
$2,062,733
($562,733)
[TBODY]
[/TBODY]
You'll notice that Anaheim is missing because they didn't sign a single player to a contract with an AAV of at least $1M.
After I put all of this data together for each team, I used data from CapFriendly to get every team's estimated tax rate.
TEAM
Estimated Tax Rate
Cap Hit
Projected Cap Hit
Cap Hit/Projected Cap Hit
Arizona Coyotes
44.02%
$1,533,000
$1,823,399
84.07%
Boston Bruins
44.41%
$3,800,000
$3,614,094
105.14%
Buffalo Sabres
47.23%
$21,500,000
$18,060,739
119.04%
Calgary Flames
47.46%
$2,550,000
$2,407,183
105.93%
Carolina Hurricanes
44.73%
$13,929,000
$15,140,689
92.00%
Chicago Blackhawks
44.16%
$2,000,000
$2,973,747
67.26%
Colorado Avalanche
43.97%
$18,250,000
$13,965,030
130.68%
Columbus Blue Jackets
46.05%
$11,733,333
$10,073,816
116.47%
Dallas Stars
40.54%
$19,850,000
$18,996,137
104.49%
Detroit Red Wings
44.67%
$6,000,000
$5,156,409
116.36%
Edmonton Oilers
47.46%
$4,650,000
$5,043,836
92.19%
Florida Panthers
40.20%
$10,366,667
$9,629,512
107.66%
Los Angeles Kings
51.52%
$2,425,000
$2,402,877
100.92%
Minnesota Wild
48.66%
$9,800,000
$10,465,888
93.64%
Montreal Canadiens
52.91%
$12,850,000
$12,940,688
99.30%
Nashville Predators
40.28%
$10,857,143
$11,580,945
93.75%
New Jersey Devils
47.50%
$8,730,000
$5,266,064
165.78%
New York Islanders
47.26%
$18,500,000
$17,108,764
108.13%
New York Rangers
47.23%
$22,892,000
$20,230,012
113.16%
Ottawa Senators
52.93%
$5,150,000
$3,487,250
147.68%
Philadelphia Flyers
47.50%
$10,390,000
$9,438,236
110.08%
Pittsburgh Penguins
44.87%
$4,500,000
$5,090,087
88.41%
San Jose Sharks
51.52%
$18,500,000
$16,466,687
112.35%
St. Louis Blues
45.42%
$7,600,000
$7,370,883
103.11%
Tampa Bay Lightning
40.27%
$4,650,000
$4,455,235
104.37%
Toronto Maple Leafs
52.93%
$14,600,000
$13,831,455
105.56%
Vancouver Canucks
49.26%
$19,000,000
$19,257,016
98.67%
Vegas Golden Knights
40.47%
$6,900,000
$8,454,244
81.62%
Washington Capitals
45.00%
$10,100,000
$13,268,082
76.12%
Winnipeg Jets
49.96%
$6,280,000
$6,547,251
95.92%
[TBODY]
[/TBODY]
Then, I looked to try and find a correlation between cap hit/projected cap hit and estimated tax rates.
There is a slight correlation here, meaning that teams with higher tax rates are paying more than projected value to their players. However, the correlation isn't nearly strong enough to draw any conclusion, with an R^2 value of only 0.0764. And just looking at the chart, the data really is all over the place.
More specifically, if you look at just the teams with no state taxes, there doesn't seem to be a consistent trend. 3 of the 5 teams with no state taxes (Tampa Bay, Florida, and Dallas) actually paid more than projected to their players.
Just based on this data we have here, I don't think we can really conclude that teams in states with lesser tax rates get significant discount. I know that people will have some issues with the contract evaluation model, and so do I. I think some of the projected cap hits are fairly wonky for some of these players. For example, Matt Duchene in Nashville was widely regarded as a major discount, yet his projected contract on a 7-year term was slightly less than what he actually signed for. Duchene is definitely an outlier, and the model has a few.
Having said that, even with a model that isn't perfect, we would probably still observe a much stronger correlation between discounts and low estimated tax rates if the correlation was extremely strong.
UPDATE: I just did these same calculations using Matt Cane's 2018 contract projection model, along with contracts signed in 2018.
What's interesting is that when using data from both years, you get a slight correlation with almost identical R^2 values. Both in that 0.06-0.08 range, which is not significant on its own, but the fact that this is repeated through two samples with two different models is very interesting.
I like the way you present your data. I think it’s a very interesting way of at least getting an idea of what kind of tax impact we are seeing.
Matthews is missing from your chart for Leaf players I believe but he’s calculated in the team portion, correct? What was his projected cap hit?
I’d like to see some data taking into account several more years so that we can have a further understanding. UFA/RFA splits would be nice too. Maybe even an arbitrary salary cutoff such as contracts between $1 million and $5 million, and $5 million and upwards to get an idea of the impact a “quality” player might have. That’s just an example, though, as there’s probably better suggested brackets to look at.
I can understand some criticism of the method having read through this whole thread, but from my perspective it doesn’t seem like you’re misrepresenting anything intentionally. Good work.
I like the way you present your data. I think it’s a very interesting way of at least getting an idea of what kind of tax impact we are seeing.
Matthews is missing from your chart for Leaf players I believe but he’s calculated in the team portion, correct? What was his projected cap hit?
I’d like to see some data taking into account several more years so that we can have a further understanding. UFA/RFA splits would be nice too. Maybe even an arbitrary salary cutoff such as contracts between $1 million and $5 million, and $5 million and upwards to get an idea of the impact a “quality” player might have. That’s just an example, though, as there’s probably better suggested brackets to look at.
I can understand some criticism of the method having read through this whole thread, but from my perspective it doesn’t seem like you’re misrepresenting anything intentionally. Good work.
Matthews is missing because he was already signed by the time that the 2019 projections were first made. There is no official projected cap hit for Matthews at the time of signing, but they do have "projected cap hits" for every player in the league if they signed a contract right now. Matthews' projection on a 5-year term is $9,935,390. However, this is skewed by two things. One, it doesn't include the games that Matthews played after signing his contract. Two, their model is heavily based on comparables and because the Auston Matthews already existed in the database and had signed for a 5-year term at $11.634M, the projection for Auston Matthews (a player with an extremely similar statistical profile) was skewed by those numbers. They actually touched up on this here:
The model does actually include "projected contracts" for every single player in the league, but those projected contracts have various flaws. I did not include him in the team or the player side of things.
I do agree with you that it would be a great help if we had more contract projection models, and if we had more data. I really wasn't trying to misrepresent anything, and honestly didn't have a conclusion in my mind when I started this; I was expecting roughly the correlation that we actually got.
One thing that I want to do is create my own contract projection model. I've wanted to do it for a while, but haven't got around to it and I'm not sure I have the computers available to do it. One thing I might try to do in the future is just make a super simple contract prediction model based on just a few variables and run a multi linear regression to determine which variables are most closely correlated with cap hit. From that point, I could create a basic contract projection model and just use that to see which teams significantly exceed it.
Matthews is missing because he was already signed by the time that the 2019 projections were first made. There is no official projected cap hit for Matthews at the time of signing, but they do have "projected cap hits" for every player in the league if they signed a contract right now. Matthews' projection on a 5-year term is $9,935,390. However, this is skewed by two things. One, it doesn't include the games that Matthews played after signing his contract. Two, their model is heavily based on comparables and because the Auston Matthews already existed in the database and had signed for a 5-year term at $11.634M, the projection for Auston Matthews (a player with an extremely similar statistical profile) was skewed by those numbers. They actually touched up on this here:
The model does actually include "projected contracts" for every single player in the league, but those projected contracts have various flaws. I did not include him in the team or the player side of things.
I do agree with you that it would be a great help if we had more contract projection models, and if we had more data. I really wasn't trying to misrepresent anything, and honestly didn't have a conclusion in my mind when I started this; I was expecting roughly the correlation that we actually got.
One thing that I want to do is create my own contract projection model. I've wanted to do it for a while, but haven't got around to it and I'm not sure I have the computers available to do it. One thing I might try to do in the future is just make a super simple contract prediction model based on just a few variables and run a multi linear regression to determine which variables are most closely correlated with cap hit. From that point, I could create a basic contract projection model and just use that to see which teams significantly exceed it.
Okay, I see. I haven’t looked at contract projections before so I wasn’t quite sure. Thank you for the clarification.
It really is an interesting area of discussion, and any extension to the data would be terrific to look at. I’d love to see your own model and how it correlates with what we already know.
Nobody cares about the actual quotes and experiences of the actual professionals who make the contracts, sign the contracts, and advise the players on the tax implications of the contracts?
They care about your “study” based on a series of uncontrolled IVs? On a topic you know nothing about? You realize that to actually do a study like this you would have to actually control for factors right?
Ie: Low tax team. Just happens to be in perpetual
Bankruptcy. Differing RFAS vs UFAs. Arbitration decisions. Etc. All those would effect the expected
Contract values.
You could also look at top ufa contracts at
Each position for age and stats appropriate players. And see how they fit with comparables.
Price at 14% and Vasilevskiy at 11.5%
Tavares at 14% and stamkos at 11.5%
Kane and at 15% and kucherov at 11.5%
O wow! A correlation of course there is no sample size worth talking about here. But of course there is also no statistical power in your “study”’to find a correlation that overrides actual people in the actual rooms. And even at that. You actually did. Which proves the point. A small signal in excessive noise is actually noteworthy in stats.
A little bit of stats is dangerous. Like the fact that you are somehow equating “hits and wins”. With ACTUAL peoples thoughts and beliefs in negotiations is just wow
Question: how influential are a GMs thoughts and opinions on hits in any given NHL game?
Do you think a GM thinking hitting is good effects
Players on the ice? Do they skate faster? Shoot harder?
Now how important do you think an agents advice is when negotiating a contract?
How important do you think a tax accountants analysis is to NHL clients who actually make decisions?
Are those factors not relevant to the decision making process?
You are comparing two RFA contracts to three UFA contracts. And the one UFA contract that you are comparing to another UFA contract - the one signed in a high tax state was signed by a player on the open market. You do realize that, right? Those aren't comparable contracts whatsoever.
I'm also disappointed that you completely missed the mark on what I had to say regarding hits and wins. Read it again.
I don't necessarily agree that this is a better way to do it, but this still shows a significant difference.
I don't necessarily agree that this is a better way to do it, but again, this still shows a significant difference, for the second year in a row.
Good lord, it's literally the set of teams that you separate in your own analysis:
Now suddenly it's how dare I use the set of teams that have always been separated from the others that you yourself used.
The smaller the difference in range, the harder it will be to see differences caused by the taxation. Also, you're complaining about averaging two sets of 5 teams saying it's too small, and now you want me to average out 2 teams? How does that make any sense?
Even if I did do the 44-45% teams in your way, it would be under 100%, even with the extreme outlier that is Colorado included and taking up almost 1/3 of the sample.
Let's do a bigger analysis with a bigger sample of teams using your methods of cap hit/projected cap hit:
2019:
All teams between 40-45% taxes: 100.4%
All teams between 49-53% taxes: 105.2%
2018:
All teams between 40-45% taxes: 101.7%
All teams between 49-53% taxes: 109.3%
I do not know why you are so insistent on using bins, either. I have already shown you why binning in the method that you are doing it is not a good idea either. In fact, it is a horrible idea.
Okay, I see. I haven’t looked at contract projections before so I wasn’t quite sure. Thank you for the clarification.
It really is an interesting area of discussion, and any extension to the data would be terrific to look at. I’d love to see your own model and how it correlates with what we already know.
Well, I'm glad to hear that you liked reading it. I do think it's a very interesting area of discussion, and I don't think we have a definitive answer either way yet. But what this little bit of research has shown me is that it's probably somewhere in the middle. The tax advantage is probably not so substantial that you can say that contracts signed in states without state taxes can't be used as comparables, but it's also not completely non-existent.
Oh yeah, he made a great point, cause there are so many examples of salary caps that fluctuate from team to team rather than being the same for everyone.
I do not know why you are so insistent on using bins, either. I have already shown you why binning in the method that you are doing it is not a good idea either.
You have shown me nothing. I literally did it in the exact way that you did (adding together all of the cap hits), included the teams you wanted, showing that over the last 2 years, there is still a significant differences between low tax and high tax areas, and this is all you have to say?
2019:
All teams between 40-45% taxes: 100.4%
All teams between 49-53% taxes: 105.2%
2018:
All teams between 40-45% taxes: 101.7%
All teams between 49-53% taxes: 109.3%
You have shown me nothing. I literally did it in the exact way that you did (adding together all of the cap hits), included the teams you wanted, showing that over the last 2 years, there is still a significant differences between low tax and high tax areas, and this is all you have to say?
2019:
All teams between 40-45% taxes: 100.4%
All teams between 49-53% taxes: 105.2%
2018:
All teams between 40-45% taxes: 101.7%
All teams between 49-53% taxes: 109.3%
I literally did it in the exact way that you did (adding together all of the cap hits) and included the teams you wanted. Now you see the results and suddenly it's useless? Even I would have expected more from you.
2019:
All teams between 40-45% taxes: 100.4%
All teams between 49-53% taxes: 105.2%
2018:
All teams between 40-45% taxes: 101.7%
All teams between 49-53% taxes: 109.3%
There is still a significant differences between low tax and high tax areas.
I live in Austin, and while state income taxes are low, property taxes are ridiculous. We have one of the highest in the nation, and it isn't getting any better anytime soon. The rent on my apartment that was just under 800 square feet was jumping from around 700 to 1,000 dollars. And I believe it has gotten higher since I moved out 4 years ago. Our rent increases here are among the highest in Texas. I hope you have a pretty good job, like 25 dollars an hour - because that's roughly how much it'll cost to live in Austin for a two bedroom apartment/house.
You are comparing two RFA contracts to three UFA contracts. And the one UFA contract that you are comparing to another UFA contract - the one signed in a high tax state was signed by a player on the open market. You do realize that, right? Those aren't comparable contracts whatsoever.
I'm also disappointed that you completely missed the mark on what I had to say regarding hits and wins. Read it again.
Of course it is a better way to do it. Averaging averages is a terrible idea.
I do not know why you are so insistent on using bins, either. I have already shown you why binning in the method that you are doing it is not a good idea either. In fact, it is a horrible idea.
Well, I'm glad to hear that you liked reading it. I do think it's a very interesting area of discussion, and I don't think we have a definitive answer either way yet. But what this little bit of research has shown me is that it's probably somewhere in the middle. The tax advantage is probably not so substantial that you can say that contracts signed in states without state taxes can't be used as comparables, but it's also not completely non-existent.
1.). Actual evidence of people who actually make the decisions and advise players on decisions IS evidence. These things are clearly intrinsically linked to the actual negotiations. You have completely ignored the actual evidence and current state of affairs and just decided it is confirmation bias. You have to realize that is intellectually dishonest
There is a reason why introductions are generally longer than results. Maybe you haven’t got there yet.......
2.) you pretend you didn’t have an actual theory of what would happen. But you clearly did. You argued about it for pages.
3.) you made no effort to actually look at different situations (arbitration/no trade clauses/RFA vs ufa... performance etc). You just lumped everyone together. Ie. Players who don’t babe trade protection may not take less because then they sign a contract and get traded.
Take smoking and cancer. If you just look at all those who have smoked a cigarette in the past
Year and people with lung cancer. You would
Probably get a small effect. There are tons of young people who smoke. They won’t have it. That dampens the effect. People may not be diagnosed. There are a tons of variables. That mask the signal and the noise. Now because you looked at it in one way. You just decide that it’s not as risky as people say?
Again. Do you honestly think that you lumping everything together and STILL coming up with a small correlation proves something over the words of the people who are actually involved?
You think it makes sense to draw conclusions from this that contradict the experts?
I'm not sure how this can really be debated. Taxes do influence contracts. It's a simple, obvious fact.
I think the bigger question is: Is the benefit worth changing rules over? I see a team like Tampa and it looks like they have an advantage, but I also wonder how much is culture/region. It's hard to really say without players coming out and disclosing this info.
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Nobody cares as much about that stuff as you do.
