A  new study published in  New Scientist contains this sentence "In previous work, they found that hockey goalies and forwards, and soccer strikers and goalkeepers, tended to be more attractive than their teammate". Here's a link to the original study (pdf)  called " Does the face reveal athletic flair? ".Positions in team sports and facial attractiveness. Personality and Individual Differences. I suspect that this study was completed long

after Gump retired.

via ourhistory.canadiens.com

Draft picks are coin of the realm among NHL general managers, hoarded, traded, exchanged. Given that they represent pure probability, trading them is like exchanging win/place/show tickets on a horse race that is far off in the future. Their value seems to change from trade to trade and is often insanely difficult to calculate as part of individual trades.

One approach developed by Victor Wang (in Hardball Times) to quantify the economic  value of baseball prospects seems promising. Wang's methods are described below but basically he compares the salary savings of a drafted player during the years in which that player is controlled by an organization (six years in MLB, until age 27 in the NHL) with the cost of a obtaining a comparable player in free agency. The product is a weighted result that Wang calls surplus value* which he then uses to evaluate trades involving prospects and which others have used to rank teams'  farm systems (See this example from SBN's Beyond the Boxscore).

It seems to me that this "surplus value" approach may have even more relevance to the NHL than to MLB. MLB teams can't trade draft picks: MLB teams don't operate under a "Hard" salary cap like NHL teams. Both rich and poor teams in the NHL need to maximize the "surplus value" of draft picks to create an efficient team salary structure.

I've developed two approaches based on Wang's work to quantify the value of NHL draft picks and prospects. The first approach, which I call Optimal Payroll Surplus (OPS-a nod to the SABR origins of Wang's work), is for prospects. The second, Probable Payroll Surplus (PPS), is intended to apply to draft picks. It discounts OPS by the percentage of draft picks that never play in the NHL.

Numbers and comparisons are in the table below. For those unmoved by stats, here are three narrative examples of how these theories might be applied:

• The Chicago Blackhawks had 12 players 23 years or younger on their 2008-9 roster, resulting in a savings versus expected payroll of about 17 million. Fourteen million of that came from five drafted forwards and two prospects acquired in trade (Versteeg and Fraser). Based on draft position, these seven forwards have a projected PPS of 37 million in their cost-controlled years. The Blackhawks are well on their way to fully realizing those savings. In the days before the salary cap, that would have meant more money in the late William Wirtz's piggybank: instead the payroll  savings allowed Dale Tallon to overpay  for Brian Campbell and snafu his way  into a  ridiculously expensive goalie tandem. But Tallon's profligacy is not the point here, rather, the payroll savings gave Tallon the flexibility to make those moves and now give him the flexibility to recover from his mistakes. Which is not the case in my next example 
• The noose of  unrealized PPS is tightening around Glen Sather and the New York Rangers. In 2004, Sather, realizing he needed forwards, used 11 of his 13 picks on them. Add in the ill-fated first round pick of Hugh Jessiman in 2003 and you get a PPS of $38 million. So far the Rangers have realized only about 8 million in savings (Dubinsky, Callahan). The core of the problem is the three risky first round picks (Al Montoya, Lauri Korpikoski and Jessiman). Had just one of  those picks been a forward who met expectations (Zajac, Parise, Getzlaf, Richards etc ) Sather might well have avoided tying up 15 million in Drury and Gomez and would have the flexibility to make trades and  sign free agents this summer  possibly  vaulting  the Rangers into a genuine Cup contender.
• One of the many trades at the 2008 NHL Draft was this one: the Ducks  traded pick # 28 to the Coyotes in exchange for 2 picks, #s 25 and 39. The Coyotes  obviously had  their eye on Victor Tikhonov and he has  made a good start towards providing that cash-poor franchise with significant OPS, playing 61 games at age 20. Whether he ever reaches his full OPS potential will depend on whether he raises his game to the level of a second or at least third line forward. On the other hand,  if Brian Burke was thinking that two picks were better than one ,he forgot to do his math homework: The Coyotes one pick, with a 52% probability of success,  was worth more than the two picks received, with a 50%  chance of one pick succeeding (general rule of addition) Burke did acquire an 8% probability that both would be successful . And, because second round picks have a significantly lower probability of becoming stars or first line forwards, the PPS Burke traded away- 4.6 million- is actually greater than the 2.9 million PPS of  the two forwards Burke drafted.(All of this is academic since Burke’s successor, Bob Murray, traded Eric O’Dell (who was pick # 39) to the Thrashers for Eric Christensen. And the trade on draft day  actually had its genesis in February 2007 when the Kings traded Mattias Norstrom to Dallas:try flow charting this series of trades (5 teams are involved so far) and you’ll understand the what I mean when I say trades can be  “insanely difficult” to value.

Finally, for those who see this  approach as  bean-counterish and reductive, I tend to agree. It's not for everyone. Islanders fans  won’t be  thinking  of  the more than $30 million in PPS Garth Snow brings to the draft on Friday (although Charles Wang probably should be). They’ll be cheering the actual hockey players who  might someday help  their team, they'll be debating  the skills those prospects display on the ice, not their possible contributions to the bottom line.

I plan on using these theories as a performance measure to evaluate GMs, sort of like ROI in finance. And maybe quantitative values will help shed some light on the often bizarre trades of draft picks that take place every year.This post at Under Review "Moving Up in the Draft" is a sample (hint-don't play this game with Doug Wilson).

I don't plan on rating the economic value of each organization's prospects but I think that might be a good thing to do and I would be interested in the results. Too many prospect lists read like Garrison Keillor's description of Lake Wobegon-"all the children are above average"-and there's a need for some kind of objective standard.

* "Surplus value" should have a familiar ring to students of history. The term is central to Karl Marx's theories on the exploitation of labor. Is Victor Wang suggesting prospects are exploited? I don't know but that term's  historic associations make me nervous so I've substituted my own.

** Notes-Wang began by using WARP (wins over replacement player) to categorize prospects and comparable free agents. He has since switched to an even more exotic SABR formula. I grouped free agents/prospects into 6 groups(Star-Ist Line through 4th-marginal) based on TOI, PPG, historical depth charts. Projecting players salaries over their cost-controlled years is probably the weakest point here, not enough data and recent trends which give young players long term contracts affect this fairly dramatically. The OPS for picks 31 through the end are so similar because the distribution of types of forwards (mostly 3rd and 4th line) picked in those rounds is so similar. The PPS is so low because it reflects the much lower probability of success. I used a constant entry age of 20 years: I fully realize that lower round picks enter the NHL later (less salary savings) but more research is needed to find the appropriate break points. All my calculations are based on salary not cap hit and are for forwards only.

Team selections in the NHL Draft are usually a product of a consensus decision within each organization, but when the general manager of each team steps to the podium in Montreal this year to announce his team's selections, those selections will be indelibly linked to his name and his record, his success or his failure.

This report is the first I know of to systematically track and evaluate draft success or failure by general managers rather than  by teams. Sixteen current general managers have records that are substantial enough to be evaluated: each draft  pick they have made, as GM for any team, since 1990 has been recorded ,analyzed for tendencies (position,nationality, league source), and their overall record has been ranked by comparing  their records of success with the average expected  success rate by draft position  over  fifteen years of draft history (1990-2004). Success is measured by the number of players selected who play 200+ NHL games.* Each general manager has then been assigned  a  +/- rating versus the average expected  success rate by draft position. Full reports  (22 general managers in all) are at Under Review. Charts like these are included:

via 2.bp.blogspot.com

SCORECARD

1.Lou Lamoriello +12 2.Darcy Regier +8 3. Bob Gainey +5 4. Don Maloney +4  5. Dean Lombardi +3 6. Ken Holland +2, Bryan Murray +2 Jim Rutherford +2 9. Larry Pleau +0, Doug Wilson +0 11. Brian Burke -1, Darryl Sutter -1 13. Don Waddell -2  14. George McPhee -3 15. Glen Sather -6 16. David Poile -10

Note-Both Poile and Sather had successful drafts prior to 1990. So this survey is somewhat unfair to them:in fact, the year this survey begins (1990) Sather had perhaps the worst draft in history at Edmonton:none of his 11 picks ever played a single NHL game. At least we now know the price the Hockey Gods extract for beginners’ luck (Sather took Coffey and Kurri with  two of his first three picks ever in 1980): it’s 15 years of sub-par drafts.
Lamoriello’s success would seem to refute theories about the randomness  of the draft: in 15 years he has drafted 22 more players with 200+ games than David Poile (one less draft). That’s essentially a complete NHL roster and a pretty good one. On the other hand, Lou’s more recent picks are suspect (HF ranks the Devils 28th in organizational strength) suggesting possible regression toward the mean.

The following ratings and tendencies may be of interest to those compiling Mock Drafts (as should the full reports):

Best and Worst %-Forwards-Best-Wilson 33% Worst-Sutter-8% Defensemen-Best-Lombardi-40% Worst-Sather-8% Goalies- Best-Gainey-30% Worst-Several, 0%  Highest # of Picks By Position-F-Sutter 69% D-Waddell-35% G-Wilson-20% Best Success Above Pick #180-Pleau-21% Worst-Sutter-0%(note-Holland is at 4%)

Tendencies- Europhile-Holland-52% Europhobe-Sutter 13% Highest Pct by Country and League- Canada-Sutter-69% US-Wilson-38% OHL-Poile-38% WHL-Sutter-40% QMJHL-Burke,Regier-15% NCAA-Tallon-20% US High School-Wilson-18%

* Other ranking systems use 100 NHL games as a threshold. That would add 137 players (over 15 drafts) to the list and would not change the rankings very much (except to boost Lou L.'s lead even more). My judgement is that the vast majority of players with between 100-200 games played are below replacement value statistically. It's worth noting that at least 137  undrafted free agents have entered the NHL during the 15 years surveyed and many of those non-draftees have had careers exceeding 200 games. Using 200 games as a standard, in short, is an attempt to ascertain the quality as well as quantity of a GM's drafting.

With the NHL playoffs set to begin on Wednesday, sports writers, and bloggers are focusing  on  the regular season  series between  the teams that are matched up in the first round,. While analyzing personnel, coaching style, and  statistical data from the season series may provide pundits with a basis for  plausible predictions, head to head records in a regular season  series  have proven to have  low predictive power, especially in determining a winner in the first round of the playoffs.

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In a press release last week, IIHF president René Fasel trumpeted a "substantial increase" in attendance  at European club hockey games. What Fasel didn't say and a closer look at the attendance figures reveals is that attendance actually declined for 37 of the 64  teams  for which year over year comparisons are possible.

Most of the declines were small, to be sure, and seem related to poor on-ice performance by the individual clubs. Still, the Finnish Sm-Liga must be concerned about declines at 12 of its 14 clubs, the Swedish Elite League by declines at 9 of the 11 clubs  for which year over year comparisons are available.  The Russian KHL which blustered  on to the scene last summer was a lot like its predecessor, the Russian Super League, except that its average per game attendance was actually lower for reasons explained below.

Fasel points to growth in attendance and the opening of larger arenas over the nine years that the IIHF has surveyed European attendance. That is certainly true and it's true that overall attendance at the five leagues for which strict comparisons can be made increased by 2.8%. But that increase can be more than accounted for by changes in 3 cities: in Berlin where the 14,200 seat O2 Arena replaced a much smaller arena and in two promotions, EHC Biel in the Swiss NLA and Rogle in the Swedish Elite League.

A fairer statement  about European hockey attendance would go something like this:Attendance at European  club hockey has plateaued at slightly over 70% of arena capacity and is at risk of trending  downward .

Here are the numbers for the six major European leagues

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