2009 NHL Review. Alan Ryder HockeyAnalytics.com

Transcription

1 2009 NHL Review Alan Ryder HockeyAnalytics.com Copyright Alan Ryder 2009

2 2009 NHL Review Page 2 Table of Contents Introduction 3 Player Contribution... 3 PC Basics... 3 Threshold Performance... 4 Situational PC... 5 The Currency of PC... 6 Team Performances 8 Lucky and Unlucky Teams... 8 Offense... 9 Shots and Shot Quality Defense Goaltending The Shootout Top Individual Performances 21 Forwards Defensive Forwards Defensemen Defensive Defensemen Goaltenders Clean Play Rookies Shootout All Star Contributions 44 NHL East West Rookie Green Grey Offense Defense Even Handed Power Play Short Handed Most Valuable Performances All Cap Roster Hall of Fame Watch 59

3 2009 NHL Review Page 3 Introduction This review is focused on the most outstanding individual performances in the NHL during the ( 2009 ) season. But I will also comment on certain aspects of team performance since individual performances are difficult to assess without understanding the team context. My tool for measuring individual player impact is Player Contribution (PC). When I developed PC I put it out in the public domain 1 so that (a) people would know that it was there and (b) it could be critiqued and therefore enhanced over time. People certainly know it is there. My original PC paper is probably the most downloaded hockey analytics how to piece, and my series of annual NHL reviews is probably the most downloaded piece of hockey analysis, on the internet. For those of you anxiously awaiting my analysis, I am sorry that I am so late with this. As the NHL does not publish a decent statistical database or set of reports, the process of cobbling together the data (and PC uses everything that I can get my hands on that seems relevant) is enormous and I have a day job. The constructive evolution of PC through public debate has not happened. What has happened, instead, is use and abuse of my work. I don t mind the use of my analysis. That s why I publish it in a public forum. I do mind the abuse by hackers, amateurs and thieves. PC has evolved considerably since I first developed it. Some of those enhancements have been documented by me. But my most recent improvements have not been publicly described. If you want the most refined view of individual player performance you will have to read on 2. PC Basics The PC method is a system of credits and debits. The credits are for the observed elements of individual performance that aggregate to team success. That part is easy to understand. The debits are to subtract the marginal aspects of performance more on that below. PCO is PC from offense, based on goals created in excess of a threshold level of performance. To determine PCO a player is credited for creating goals but debited for ice time (greater ice time, especially for forwards and on the power play, means greater offensive expectations). 1 PC is described in 2 There are other individual performance measurement systems out there. The better ones are actually built from the same principles as PC. The differences are largely in the details.

4 2009 NHL Review Page 4 PCD is PC from defense, based on goals prevented in excess of a threshold level of performance. To determine PCD players get credited for ice time (greater ice time, especially for defensemen and on the penalty kill, means more defensive responsibility and exposure to goals against) but debited for goals allowed while on ice. PCG is PC from goaltending, again based on goals prevented in excess of a threshold level of performance. Goaltender contribution is essentially measured by save percentage (credit) in excess of a threshold (debit), factoring in shots faced. The assessment of goaltending is adjusted for various team defense factors. These adjustments find their way back into the assessment of defense. PC is also determined for penalty taking and drawing. In this case, however, the benchmark is essentially the average propensity to take/draw penalties (rather than the marginal propensity). In this sense it is best interpreted as an adjustment to the other PC scores. Threshold Performance In the PC calculations threshold performance is determined mathematically, by inference. It is determined by observing that (a) the marginal impact of more/fewer goals on wins/points is virtually linear 3 over the normal performance range of teams and (b) the slope of that linear relationship is the average number of goals scored per game. Marginal or threshold performance is determined by figuring out what level of performance predicts zero contribution to winning when using this linear relationship. The notion of threshold performance is critical to the analysis of individuals. If an AHL goaltender gets promoted to the NHL and posts an.890 save percentage, we should think of an NHL regular with an.893 save percentage as not contributing a performance of very much value, there being a large number of others (minor leaguers) lined up to play nearly as well. Although not really true, you can think of a marginal player as a borderline NHLer (my AHL goaltender). It is difficult to be precise about where the borderline is, but the PC method draws a line in the sand somewhere near that line. Why subtract out borderline performance? Because performance at that level is worth nothing. Borderline players sit on the end of the bench and / or spend a great deal of time in transit to / from the minors. Marginal performance is so far from average as to be zero valued. 3 This is critical to the success of any measurement of individual contributions to team success. Linearity means that individual contributions are additive. Every goal scored or prevented has the same impact on winning percentage. Non-linearity (curvature) would mean that not all goals have the same impact on winning percentage and, by extension, that the attribution of team success to individuals would involve very complex assessments of the nature of the curvative relationship between goals and wins. The relationship between goals and wins is curved for teams winning 70% or more of the time (or losing 30% or more) but those are very rare teams. PC does make an adjustment for whatever curvature (or statistical noise) is detected so that individual performances do sum to team performance.

5 2009 NHL Review Page 5 The assessment of marginal (valueless) performance based on the contribution of a replacement player (my AHL goaltender) is a common approach used in baseball analytics where most players are either (a) regulars or (b) replacement players. The application of that thought process (and various rules of thumb derived from it) is tempting but, for hockey, the notion of replacement level performance is much trickier. Other than in goal, especially with player changes every seconds, it would be difficult to sort players into the categories of regular and replacement. A third line forward or third pairing defender is used regularly, frequently in special roles. In fact the notion of a replacement player is quite tricky in hockey. When the first line centre is injured, the second line centre gets a promotion (gains more ice time). Or perhaps the third line centre is a better fit on the first line. Yes, somebody from the fourth line is going to get more ice time (and a call up player will dress). But it may not be the centre and players can and do shift position. And it may not be much ice time as second and third line players my get incremental special team duty. A hockey team is not comprised of regulars and replacement players. Instead there is an allocation of ice time along the spectrum of superior to inferior players. The cascading effect of replacing a player is difficult to model and highly situational. With all of this in mind I stick with my approach to the assessment of marginal performance. And it tests out just fine. Players with any amount of playing time and PC scores of around zero get deployed by coaches as if they were replacement players. Situational PC Where we can, PC is measured separately for even handed (EH), short handed (SH), power play (PP) and shootout (SO) situations. This ensures that specialty team performance is assessed relative to marginal performance on specialty teams. In other words, a player who runs up big offensive numbers on the power play is judged against other power play performances while a penalty killing specialist has his offense judged against other penalty killers. Perhaps more importantly, assessing contribution by situation permits a much better assessment of overall performance. On defense, short handed situations are further subdivided into penalty killing (PCDSHK) and penalty taking (PCDSHO). On offense, power play situations are further subdivided into power play production (PCOPPP) and penalty drawing (PCOPPO). To the right is a summary of the component parts of PC. Components of Player Contribution Situation Offense Defense Goaltending Even Handed PCOEH PCDEH Power Play Opportunities PCOPPO PCDPP Production PCOPPP PCGRO Short Handed Opportunities PCDSHO PCOSH Penalty Killing PCDSHK Shootout PCOSO PCGSO TOTALS PCO PCD PCG

6 2009 NHL Review Page 6 The Currency of PC Since advancing in the standings (wining) is the one and only team objective, PC is denominated in points in the standings (wins). Goals created/prevented are translated into points in the standings on the same basis that teams do so. A PC point is scaled to be 1/10 th of a standings point and the PC points allocated to a team are therefore 10 x points in the standings. The end result is that offense, defense and goaltending (including shootout performance) are all on the same page, in the same currency. This denomination of player contribution is also inflation proof it is unaffected by changes in scoring levels (but is affected by schedule length). To get a lot of PC points one needs to both (a) play a lot and (b) play well. As (a) and (b) tend to be correlated, PC is also a measure of talent. However, coaches tend to over play top talent with a resulting distortion of apparent relative value. As a rough rule of thumb it takes 100 or more PC points for a skater to be an all-star candidate (the story with goaltenders is different). At 80 points you would consider a skater to be a team star, 60 is a team leader, 40 is a solid contributor and 20 is a weak link. With a salary cap of $56.7 million (all figures U.S.) for the 2009 season, a rough guide to player value is $56,700 per annum per PC point or $1,134,000 for every 20 PC points). This is based on a team spending the cap amount and targeting a 100 point season (a comfortable target for a berth in the playoffs). A serious Stanley Cup aspirant will need to target a lower cost per PC point. And, of course the market value of a player may be different due to supply and demand and other factors. As an illustration I have shown to the right the cap costs (all dollars are US), PC scores (rounded to the 2009 Boston Bruins Player $ Cap Cost PC Cost per PC Point Tim Thomas 1,100, ,111 Dennis Wideman 3,937, ,083 Zdeno Chara 7,500, ,458 Marc Savard 5,000, ,696 David Krejci 883, ,886 Phil Kessel 2,200, ,555 Michael Ryder 4,000, ,934 Manny Fernandez 4,333, ,013 Blake Wheeler 2,825, ,965 Chuck Kobasew 2,333, ,665 Matt Hunwick 750, ,265 Mark Stuart 1,300, ,159 Patrice Bergeron 4,750, ,682 Aaron Ward 2,500, ,384 Shane Hnidy 757, ,433 P.J. Axelsson 850, ,768 Andrew Ference 1,400, ,548 Milan Lucic 850, ,191 Mark Recchi 1,500, ,567 Stephane Yelle 750, ,172 Marco Sturm 3,500, ,534 Tuukka Rask 3,200, ,580 Martin St. Pierre 500, ,866 Shawn Thornton 516, ,338 Byron Bitz 740, ,292 Petteri Nokelainen 850, ,055 Matt Lashoff 850, ,892 Steve Montador 800, ,886 Johnny Boychuk 500, ,137,504 Mikko Lehtonen 800, ,999 Martins Karsums 883, ,999 Vladimir Sobotka 750, ,999

7 2009 NHL Review Page 7 nearest integer) and the dollar cap costs per PC point (I have shown negative cost per PC points results as 999,999) for the Boston Bruins. Note that cap costs in this table are (a) annualized (the actual cap hit depends on days on the roster, which is a big factor for those at the bottom of the table) and (b) not salary (cap costs are the average salary/bonus over the contract). Boston was the NHL s second best team in the regular season (one point behind San Jose), finishing with 116 points. One can easily see that this was achieved on the back of Tim Thomas and his bargain basement contract. But there were some other inexpensive performances, most notably from Krejci and Kessel and very few contracts that were way off performance levels (Chara s contract continues to be difficult to live up to).

8 2009 NHL Review Page 8 Team Performances Although I want to focus on individual performance it sets the stage best if I first look at some team metrics using a marginal goals analysis. Marginal goals, a building block for Player Contribution, are simply: a. goals scored in excess of a threshold, plus b. goals allowed subtracted from a threshold. Lucky and Unlucky Teams Wins are about 94% predicted by goals for and against (or by marginal goals totals). When a team wins in spite of a low marginal goal performance it is either very skilled at winning close games or it is lucky. Historical analysis suggests that this is mostly luck. I would not completely rule out some intangible, but nobody has found it yet. Lucky teams tend to regress the following season (and vice versa). But these teams may also be systemically able to win tight games. To the right is a table of the marginal goals per point during the conventional part of the game ( skating time ) and during the shootout. The five most efficient/lucky teams are highlighted in green and the five least efficient/lucky teams are highlighted in red. Scoring was up in 2009 and an average point in the standings was more expensive as a result. During skating time it cost, on average, 2.71 goals to generate a point (versus 2.60 in 2008). It also required more goals to resolve a shootout in 2009 (an average of 2.25 per shootout) than in 2008 (2.21). During skating time the Coyotes were the most efficient team in the NHL, requiring only 2.49 marginal goals per point. Toronto (2.55) repeated from 2008 as one of the league s luckiest teams. Other lucky teams were Calgary (2.51), Washington (2.51) and San Jose (2.57). Marginal Goals per Point Team In Skating Time per Skating Point In Shootout per Shootout Win ANA ATL BOS BUF CAL CAR CBJ CHI COL DAL DET EDM FLA LA MIN MON NAS NJ NYI NYR OTT PHI PHO PIT SJ STL TB TOR VAN WAS AVG

9 2009 NHL Review Page 9 The Minnesota Wild flipped from being one of the most unlucky teams in 2007 to being one of the luckiest ones in 2008 to being the NHL s most unlucky team in 2009 (2.98 marginal goals per win). Buffalo (2.97) and Chicago (2.85) repeated as a unlucky teams. Atlanta (2.96) went from lucky in 2008 to unlucky in Colorado (2.84) rounded out the bottom five in skating time winning efficiency. Florida ranked as the luckiest team in the shootout. They had three shootout wins on just 3 marginal (shootout) goals. Carolina repeated as a lucky shootout team (requiring only 1.70 goals per win. The Lightning (1.16), Islanders (1.38) and Kings (1.64) were other lucky shootout teams in Ottawa was the NHL s unluckiest shootout team, requiring 2.96 marginal goals per win. Joining Ottawa to repeat from 2008 as one of the NHL s unluckiest shootout teams was Phoenix (2.81). Buffalo s bad luck during skating time continued in the shootout (2.86), making it the NHL s most unlucky team over the past two years. Other unlucky shootout performances came from Anaheim (2.79) and Dallas (2.78). Notwithstanding a few repeat performances there was no correlation between marginal goal efficiency in 2008 and This is part of the evidence that (in) efficiency is just (bad) luck and most likely will not repeat next season. Note that PC attempts to allocate team performance, whether lucky or skilled, to individuals. It does not set out to determine whether a performance is from luck or skill. It translates a player s marginal goals into PC points using these marginal goal factors. The implicit assumption here is that these observed team performances are a result of skill. This means that a goal scored (or prevented) by a Coyote is worth more than a goal scored (or prevented) by a member of the Wild. Offense A marginal goals analysis helps us to deconstruct offenses. Below is a summary of marginal goals from offense (MGO) by situation even handed (MGO EH ), power play production (MGO PPP ), power play opportunities (MGO PPO ) and short handed (MGO SH ). Also shown is the change from As we already know, the Red Wings were the NHL s top offense. This translates to 153 marginal goals comprised mainly of a league leading 99 even handed marginal goals and a league leading 51 marginal power play goals. While the Bruins won the East on the basis of their outstanding goaltending, they were still a very potent offense and ranked second in the NHL. This team improved by a stunning 58 marginal goals over their performance in the prior season (ranking third in even handed and tied for fourth in power play offense). Pittsburgh had the NHL s second best even handed offense (95 marginal goals) and Washington was the second strongest power play team. With just 77 marginal goals, the Senators offense fell off badly (-51 goals) in The Avalanche also fell off the offensive cliff with 40 fewer goals from offense.

10 2009 NHL Review Page 10 Marginal Goals - Offense Team vs 2008 MGO MGO EH MGO PPP MGO PPO MGO SH DET BOS WAS CHI PHI PIT CAL SJ ATL TOR VAN BUF MON ANA NJD CAR FLA EDM STL DAL CBJ MIN OTT NAS TB PHO LA NYR NYI COL Columbus had a big improvement (+24 goals) on offense notwithstanding a woeful power play (just 5 marginal goals). Short handed offense varies considerably from year to year. This year s hot team was Philadelphia with 11 marginal goals while short handed. In general, weak offensive teams were not playoff teams (the Rangers being a notable exception) and strong offensive teams were. This tendency was a little stronger this year than in the past.

11 2009 NHL Review Page 11 MGO PPO measures a team s relative ability to generate power play opportunities (draw penalties). Montreal and Carolina (with 4 such marginal goals) earned an extra one or two points by their ability to draw penalties. This was the fourth year in a row for a strong performance by the Hurricanes in this metric. Anaheim, New Jersey and Florida (each with MGO PPO of -4) were the teams least able to draw penalties. Defense Shots and Shot Quality Marginal goals analysis is an even more helpful tool for assessing defenses. But, to get at this, it is necessary to separate goal prevention into defense and goaltending. To identify a team s contribution from goaltending I compare its goals against, adjusted for shot quality, to a threshold level (based on the shots allowed). A goaltender facing no shots cannot make a contribution. When he faces a high number of shots he can make a high contribution. So the number of shots faced is a significant factor in assessing goaltending contribution. For a given number of shots a high (shot quality neutral) save percentage implies a big goaltending contribution (and a low shot quality neutral save percentage implies a small goaltending contribution). And whatever is not goaltending must be attributable to defense. This is clearest with shots (low shots allowed suggest a strong defense). If you do this math you are attributing to defense the responsibility for the number and quality of shots on goal. To the right are the shots and shot quality leader boards for the 2009 season. Average shots on goal per game is a familiar metric. In the table below we see Detroit, San Jose, Columbus and Dallas with strong performances again in Los Angeles made the biggest move to reduce shots allowed (third worst to fourth best). Florida, Atlanta, Philadelphia, Montreal and Edmonton continued to allow league worst shot totals. Tampa s decline continued, sagging from near the top of this list in 2007 Defensive Measures Team Avg Shots Team Shot Quality SJ 27.2 NJD DET 27.7 BUF CBJ 27.8 PHO LA 28.1 ANA DAL 28.1 ATL OTT 28.5 PHI STL 28.5 MIN CHI 28.6 COL COL 29.0 TB VAN 29.2 WAS NAS 29.4 CBJ NJD 29.5 STL WAS 29.5 OTT CAR 29.5 NYI NYR 29.7 SJ CAL 29.8 BOS TOR 30.3 CHI PIT 30.3 DET ANA 30.5 VAN MIN 30.7 NYR BOS 30.8 EDM BUF 31.4 CAL PHO 31.6 MON MON 31.7 NAS EDM 32.5 PIT PHI 32.5 FLA ATL 32.7 CAR TB 32.9 LA NYI 33.5 DAL FLA 34.7 TOR 1.124

12 2009 NHL Review Page 12 to a middling performance in 2008 to third worst in Shot totals up in 2009 with average shots on goal per game increased about 3% from to 29.1 to The dispersion of team results reduced considerably. Last season s team shots allowed per game ranged from 23.5 (Detroit) to 33.9 (Atlanta). This season that span was 27.2 (San Jose) to 34.7 (Florida). I don t know what this means, but the development is, as Spock would say, fascinating. It suggests that defense is becoming more homogeneous across the NHL and, by extension, less valuable. Or it could just be statistical noise. Shot quality is based on an assessment of the characteristics of each shot allowed 4. The expected goals from this assessment, normalized for variations in shots on goal, can be compared to average. My shot quality factors are the ratio of these normalized expected goals to average (a shot quality factor of means that shots taken are, on average, 5% less likely to result in goals). There is a clear shot distance recording bias 5 in certain arenas. The worst such reporting bias is in Madison Square Garden where the raw data suggests that the Rangers give up much shorter shots than average (shorter shots are more likely to be goals). But further analysis reveals the distance recording bias. The truth is that the Rangers are closer to an average shot quality team. There are other issues with the quality of reported shots. This year my shot quality factors adjust for any reporting biases I have been able to measure with confidence. Any comparison of these results with those of prior years is therefore a bit tricky. Shot quality leaders in 2009 were New Jersey, Buffalo, Phoenix and Anaheim. This was a huge turnaround for the Ducks notwithstanding their continued propensity to take penalties (shot quality goes up materially while shot handed). The laggards were Toronto, Dallas, Los Angeles and Carolina. Defense Marginal goals provides a sophisticated look at team defense. Below is a summary of marginal goals from defense (MGD) by situation even handed (MGD EH ), power play (MGD PP ), short handed opportunities or penalty taking (MGD SHO ) and penalty killing (MGD SHK ). Also shown is the change in MGD from San Jose (MGD of 131) repeated as the NHL s top defensive team, especially while even handed (MGD EH of 91). As noted above, the Sharks lead the league in (fewest) shots allowed. But a Marginal Goals analysis shows a contributing factor was that they were 4 My original approach to Shot Quality is described in 5 See

13 2009 NHL Review Page 13 quite disciplined (MGD SHO of 8), behind only Phoenix and Minnesota in short handed opportunities granted. Improving from seventh to record the second best defense in 2009 were the Devils (MGD of 125). While even handed they were about the same team as the Sharks. New Jersey was a little less effective on the penalty kill (MGD SHK of 25 versus 31) and a little less disciplined (MGD SHO of 4) but they had superior defense on the power play (yes Martha there is such a thing as power play defense). Proving that they were not a defensive fluke in 2008, the Blue Jackets repeated as the NHL s third best defensive team (MGD of 131). The Blue Jackets were third in even handed defense (MGD EH of 88) and tied for third in MGD SHK (with 35). Marginal Goals - Defense Team vs 2008 MGD MGD EH MGD PP MGD SHK MGD SHO SJ NJD CBJ DET STL COL CHI OTT WAS BUF ANA PHO VAN MIN NYR LA BOS DAL CAL NAS CAR PHI PIT ATL MON TB EDM NYI TOR FLA

14 2009 NHL Review Page 14 The most improved defense in 2009 (going from awful to average) belonged to Los Angeles (+38 MGD). But in hockey so many things are tradeoffs. This improvement in defensive play may have resulted from a change in emphasis (MGO was off 30 goals). It was a similar story in Ottawa where MGD bounced back (+31) but MGO sagged (-51) badly. Chicago s defensive developed considerably (+30), but not at the expense of offense (+20). Detroit s defense deteriorated considerably (-29) in 2009, but improving offense (+38) more than made up the difference. Notwithstanding that deterioration, the Red Wings still ranked fourth in defense. Other large deteriorations belonged to Calgary (-22) and, just when fans thought it couldn t get worse, Tampa Bay(-21). The Lightning had had a terrible swing in penalty taking with its MGD SHO going from +13 in 2008 to -15 in This 28 goal swing is the equivalent of about 10 points in the standings and possible evidence of a coaching mess. Repeating with the NHL s worst defensive team were the Florida Panthers (MGD of 46). There is a now long legacy of superior goaltending (first Luongo and now Vokoun) in Miami which may be contributing to the openness of their game. In hockey there is a trade-off between offense and defense. The Florida situation tells us clearly that a topnotch netminder can indirectly improve offense. After an extreme makeover, including a new coach, Toronto sagged (from 22 nd in 2008) to deliver the second worst defensive performance. The Leafs and Panthers had the worst penalty killing in the NHL. While Toronto s penalty killing record was obviously awful (a PK percentage of 74.7%), Florida s weak penalty killing was hidden by great goaltending. This analysis sorts that out for you. Conventional analysis says that the Rangers had the league s best penalty killing (87.8%). However their MGD SHK score of 37 was bested by the Blues (40). St. Louis had the third best penalty killing percentage (83.8%) but did so in front of less effective goaltending. Minnesota also slides in the penalty kill rankings after you consider goaltending. Philadelphia (MGD of 89) and Pittsburgh (87) continue to believe that you don t have to play defense to be a playoff team. The Penguins Stanley Cup, more so that the Wings championship of the prior year, may encourage NHL coaches to believe less in defense. Phoenix led the NHL in MGD SHO (12) based on the second lowest total in short handed opportunities (293). This was worth 4 5 points in the standings. While Minnesota allowed the least number of short handed opportunities (293), superior goaltending diminished the value of this performance (MGD SHO of 9). Tampa Bay, Washington, Philadelphia and Anaheim (again) were the NHL s least disciplined teams, each giving up four or more points by taking penalties.

15 2009 NHL Review Page 15 Goaltending Isolating shots and shot quality lets one better assess goaltending. The impact of goaltending is highest when a strong goalie allows few goals notwithstanding a high number of shots faced and / or shots of high quality. To the right is a table of the marginal goals from goaltending (MGG) by team (excluding the shootout which is discussed below). Although it is certainly possible, you won t normally find impactful goaltending behind a great defense. It just does not get the opportunity to shine. So it is not so surprising to see the goaltending of San Jose, New Jersey and Columbus in the bottom half of this list. This does not mean that goaltending for these teams is necessarily weak, just that it did not contribute much to overall team success. In 2007 Nashville was on top by some distance. In 2008 it was Florida (by a nose). This season it was Florida (by many lengths). The common thread was Tomas Vokoun and bad defense. While in Nashville he played behind a weak defense. But in Florida he has suffered through the NHL s worst defense. The confluence of really bad defense and really good goaltending (.925 team save percentage) resulted in a very high MGG for Florida (in fact 42% of Florida s success was attributable to goaltending). Boston was ranked second in MGG. The Bruins league average defense offered Tim Thomas less opportunity to shine. A marginal goals analysis compares performance to a threshold. The threshold is based on my estimate of zero-value performance. You can see from the table above that Colorado barely cleared that threshold. There was also an unimpressive goaltending contribution in Atlanta. The Red Wings should have a look at the company they keep on this list. Here is a team inches away from back to back Stanley Cups with nearly no goaltending. Marginal Goals - Goaltending Team vs FLA BOS MIN CAR NYR EDM 9 54 VAN MON NAS PIT PHI CHI 9 48 LA SJ NJD BUF ANA CAL STL OTT NYI CBJ DAL TB WAS PHO TOR 3 16 DET ATL COL -34 2

16 2009 NHL Review Page 16 Let s look at the biggest swings in goaltending. First the biggest improvements: The Carolina (+47 MGG) story was mainly the emergence of Cam Ward. Over the past few seasons he has carried the goaltending load for the Hurricanes. But the former Conn Smyth Trophy winner has spent some time getting his legs underneath him. In 2007 he had a save percentage of.897. In 2008 that improved notably to.904. But this past season his save percentage soared to.916. To complete the picture one needs to note the performance of the backups. Over the past two seasons backups have worked about 1000 minutes. This season Michael Leighton delivered a useful.901 save percentage. In 2008 John Grahame delivered most of these minutes at.875. In Minnesota (+40) Niklas Backstrom went from stunning (.929) in 2007 to simply stellar (.920) in 2008 to some-where-in-between (.923) in 2009 (in more playing time). But, again, backups can matter a great deal. Josh Harding delivered stunning (.929) in 870 minutes in This compares to.908 in 2008 over 1571 minutes. The profile in Boston (+36) was similar to that of Carolina improvement all around. Thomas went from a stellar save percentage of.921 in 2008 to a stunning.933 in 2009 (playing a similar number of minutes). In 2008 backup goaltending had a save percentage of.904. This season it was.914. A year ago I said this about Thomas: There is a rich history in the NHL of late blooming goalies. But expect his performance to regress next season. Oops! I ll try to repair that damage by saying that I am a big fan of the underdog so I am a big fan of Thomas (I was then and I still am). But I still expect regression. As warm as it is there, Tampa Bay (+35) has been Siberia for goaltenders since the departure of Khabibulin. In 2008 they had negative goaltending (-13 MGG). You can forgive the giddiness of the team based on the magnitude of the improvement, but this team still ranked 24 th in goaltending contribution and has some distance to go. The redeemer was Mike Smith, who posted a Cam Ward like.916 save percentage (behind worse defense). The good news for the Lightning for the upcoming season is that he only played 2,471 minutes in Florida was +21 on the basis of a weaker defense and an improvement in team save percentage. Vokoun improved his save percentage from.919 to.926 but played fewer minutes (3,324 versus 4,031). Although his performance fell off year over year (.924 versus.935), his increase in playing time meant that Craig Anderson still contributed materially to the overall team improvement. The Panthers have lost his contribution for the 2010 season to the Avalanche. With the team save percentage improving from.908 to.912, San Jose was +21 in MGG. This was not because of Nabakov, who delivered a.910 save percentage two seasons in a row. Actually his playing time was reduced in favour of Brian Boucher who logged a.917 save percentage in 1,291 minutes of play. In 2008 Nabakov was a Vezina Trophy finalist. What got him there was a league leading

17 2009 NHL Review Page wins. Of course he got a lot of wins. He played 77 games for the second best team in the NHL. In 2009 he posted the same save percentage for the same team (OK first overall) but reduced playing time (62 games) meant fewer wins (41). On the basis of fewer games played, he slumped to fifth in the Vezina voting (for the best goaltender). Goaltender wins is the most useless statistic in the NHL!!! General Managers vote for the Vezina. I cannot believe how dumb they are. And the largest deteriorations: Atlanta (-22) has seen goaltending fall off badly over the past two seasons (the Thrashers were -40 MGG in 2008). The number one guy, Kari Lehtonen, played a bit worse in 2009 (save percentage of.911 versus.916 in 2008 but similar to.912 in 2007). This is a better than average performance but his playing time was again modest for the main man (2,624 minutes in 2009, not too different from 2,707 in 2008). Backup goaltending is the main culprit here. Johan Hedberg continues to accumulate significant playing time (1,717 minutes in 2009, 1,927 minutes in 2008) without stopping pucks (save percentage of.886 in 2009 and.892 in 2008). Phoenix (-24) has ridden the roller coaster of goaltending fortunes. Last season the savior was anointed Ilya Bryzgalov arrived from Anaheim and delivered a.921 save percentage in 55 games. But this season Bryz proved to be merely mortal (.906). Colorado (-34) suffered a white out in net. I suppose the writing was on the wall when they signed Toronto cast-off Andrew Raycroft. He played better in 2009, but his save percentage of.892 was still quite marginal. The former Calder Trophy winter has moved on to Vancouver for the coming season and Canuck fans should pray for a healthy Roberto Luongo. That Raycroft got 1,722 minutes of playing time says that Peter Budaj was a disappointment as well. His save percentage in 2008 (.903) should not have inspired much hope and he delivered more-or-less as expected in 2009 (.899). The loss of Jose Theodore s 2008 performance (.910 save percentage) turned out to be quite painful (but note that he only delivered a.900 save percentage in Washington). For 2010 Craig Anderson has been acquired to lead the Avalanche out of the blizzard. I think there is considerable upside potential there. In Anaheim (-46) goaltending went from exceptional in 2008 (.922 team save percentage) to merely average in 2009 (.909). J-S Giguere fell off badly, posting an unremarkable.900 save percentage in 2009 (versus.922 in 2008). As a consequence his role as the premier puck stopper first eroded and then was lost (his playing time reduced from 3,310 minutes in 2008 to 2,486 minutes last season). With a quicker hook Jonas Hiller would have mitigated the problem. Although it also fell from 2008, he posted a not-to-shabby.919 save percentage in 2009.

18 2009 NHL Review Page 18 There was not much change in Ottawa (-4) including change itself. But it is sure is fun to follow the bouncing puck. Where should I start? How about Martin Gerber was acquired for the 2007 season to succeed Domenic Hasek who was acquired to lead the Senators out of goaltending purgatory to the Promised Land (the story is much too long to start earlier). The problem with Gerber was that he got off to a rough start. Ray Emery surprised and won the starting job. The 2008 season opened with the Senators seeking to trade Gerber but an injury to Emery made Gerber the go-to guy in the early season. He was white hot so Sugar Ray sat for a while and began to pout. He got his chance as Gerber cooled considerably, but his performance was not compelling and he returned to the bench (and to pouting). Emery s behavior got him sent to Siberia (no, not Tampa Bay) for Enter, stage right, Alex Auld, who posted a respectable.911 save percentage, and, stage left, Brian Elliott, (.902). As we look forward to the 2010 season Hasek is retired, Gerber is in Siberia, Emery is in Philadelphia, Auld is in Dallas, Pascal Leclaire arrived from Columbus (where he was displaced by rookie of the year Steve Mason) and Elliott is back on the end of the bench. Are you following this? Or should I go over it again? The Shootout My method for assessing shootout performance for goaltenders is the same as for skating time. To get marginal shootout goals saved (MGG SO ) I compare save percentages to a threshold and then multiply the difference by the number of attempts faced. For skaters I use the same kind of logic to derive MGO SO 6. To the right is some data from the shootout from its inception. You can see that the number of shootouts has been relatively stable (this is a function of what is going on during the previous 65 minutes of skating time ). The average number of attempts per shootout was down slightly in This may reflect the number of Shootout Statistics Statistic Shootouts Attempts Goals Attempts per Shootout Goals per Shootout Shooting Percentage Shooting Threshold Save Threshold Goalie Attribution shootout marathons, which tend to favour the goaltender as the rules prohibit shooters from multiple attempts. Goals per shootout were up slightly in The history of the shootout is brief. The trend line that had emerged through 2008, that goaltenders were getting smarter faster 6 For a full description of my method see

19 2009 NHL Review Page 19 than shooters, reversed in 2009 when the shooting percentage reverted to 2006 levels. The observed fluctuation of the relative success of goaltenders and shooters is likely nothing more than randomness at work. The shooting and save thresholds I have used in my calculations are also shown. They were similar in 2006 and 2007 and then changed rather dramatically to different levels in 2008 and The driver of this is the final statistic goalie attribution. In 2006 and 2007 I attributed about 40% of shootout results to goaltending. But in 2008 and 2009 I attributed about 60% of the shootout to goalies. Goalie attribution is a measure of the relative team-to-team variation in shooting percentages and save percentages. In the extreme, no team-to-team variation in save percentages would imply that shootout success was determined 100% by the shooters. Likewise no team-to-team variation in shooting percentages would imply that shootout success was determined 100% by the goaltenders. Recent history has revealed less team-toteam variation in shooting percentages relative to team-to-team variation in save percentages than in the early years of the shootout. This higher variation in goaltending means that goaltending is relatively more valuable in the shootout and my Player Contribution results reflect that. To the right is a summary, by team, of the shootout in 2009 shootout wins and marginal goals from offense and goaltending. So far as I can tell the shootout is a lottery. In 2008 nearly one Edmonton game in four was decided by a shootout. And this was good for the Oilers as they went In 2009 the Oilers were less fortunate, winning 6 and losing 4 and dropping 6 points in the standings. Atlanta had the NHL s best shootout performance, winning 7 of 8 (on 18 marginal goals). The Thrashers goaltenders posted the best save percentage in the shootout (.829). Lehtonen (.773) faced most of the shots while the previously slagged Hedburg posted a not-too-shabby.923 save percentage. The Rangers topped the NHL in shootout wins (10 of 16 matches). This success was Marginal Goals - Shootout Team SW MGOSO MGGSO ANA ATL BOS BUF CAL CAR CBJ CHI COL DAL DET EDM FLA LA MIN MON NAS NJD NYI NYR OTT PHI PHO PIT SJ STL TB TOR VAN WAS 4 2 6

20 2009 NHL Review Page 20 largely attributed to goaltending (15 of 23 marginal goals) that faced a league leading 52 attempts. Buffalo (14) and Anaheim (14) also had valuable goaltending contributions in the shootout. Tampa Bay logged the NHL s worst goaltending performance in the shootout (.500 save percentage). But their shooters were not so hot either (.244 shooting percentage). The net result was just 3 wins in 13 shootouts (from just 4 marginal goals). This was a repeat of their dismal performance in 2008 (just 2 shootout wins on 4 marginal goals). Florida was the NHL s luckiest shootout team with 3 wins in 11 tries notwithstanding a league worst shooting percentage of.188 and a save percentage of.567. This profile attributed their meager success (more than) fully to goaltending. Colorado had a league best.512 shooting percentage, winning 9 of 13, followed by the Devils (.480).

21 2009 NHL Review Page 21 Top Individual Performances Hart Trophy The Hart Memorial Trophy is awarded to the player judged to be the most valuable to his team. This year s finalists (the top three vote getters) for both trophies were Pavel Datsyuk, Evgeni Malkin and Alexander Ovechkin. Although a literal read of this clearly means that a goaltender must win this prize each year, the award has typically (nearly 90% of the time) been presented to the NHL s most impactful skater, as judged by the voters. In fact the Hart Trophy has usually gone to a forward (about 80% of the time). In their voting for the Lester B. Pearson Award (for the most outstanding player ) the players have shown an even greater bias towards forwards than do the hockey writers who choose the Hart winner. I will leave this debate alone and go straight to a discussion of my Forwards Here is the high level case for each of the Hart candidates: Malkin was the NHL s leading point scorer (113 points), snatching the Art Ross Trophy from Ovechkin in the process. Ovechkin, the 2008 Art Ross winner, was the (repeat) winner of the Maurice Richard Trophy with 56 goals. Datsyuk was voted the NHL s top defensive forward and finished fourth in scoring (97 points). Alexander Ovechkin (with 1,264 voting points) was the Hart Trophy winner by a pretty clear margin over Malkin (787) and Datsyuk (404). He also won the Pearson Trophy. But my analysis says sees this quite differently. PC has Datsyuk ahead of Ovechkin (by decimal places) for my Wayne Gretzky Award, as top forward (Malkin was ranked 7 th ). Furthermore, with fewer marginal goals per point than Detroit, Washington was an efficient team. If you treat this as luck rather than skill a PC gap opens up and Datsyuk emerges as the clearly more impactful player (by about 9 PC points). And he works for less money too. The lens of Player Contribution enables us to break down these performances and Wayne Gretzky Award Top Forward Player Team PC Pavel Datsyuk DET 124 Alexander Ovechkin WAS 124 Zach Parise NJ 119 Rick Nash CBJ 105 Patrick Marleau SJ 100 Alexander Semin WAS 100 Mike Richards PHI 100

22 2009 NHL Review Page 22 determine who was the NHL s most valuable forward and why. Shown below are the details of the PC calculation for the top 30 forwards Player Contribution Forwards (items may not total due to rounding) Player Team POS PC EH PPP PPO SH SO PCO EH PP SHK SHO PCD Pavel Datsyuk DET C Alexander Ovechkin WAS LW Zach Parise NJ LW Rick Nash CBJ LW Patrick Marleau SJ LW Alexander Semin WAS RW Mike Richards PHI C Evgeni Malkin PIT C Marian Hossa DET RW Jarome Iginla CAL RW Sidney Crosby PIT C Patrik Elias NJ LW Henrik Zetterberg DET C Daniel Alfredsson OTT RW Nicklas Backstrom WAS C Jeff Carter PHI C Jason Blake TOR LW Jonathan Toews CHI C Simon Gagne PHI LW Joe Pavelski SJ C Eric Staal CAR C Shane Doan PHO RW Loui Eriksson DAL LW Daniel Sedin VAN LW Jamie Langenbrunner NJ RW Martin St. Louis TB RW Brad Boyes STL RW Derek Roy BUF C Joe Thornton SJ C Devin Setoguchi SJ RW Ovechkin was clearly the top offensive threat in the NHL. By conventional standards his 56 goals and 54 assists ranked him second in scoring (13 points behind Malkin). But nobody but the NHL gives an assist equal weight in assessing offense (hence his trophy collection). PC awarded him 116 points for offense and nobody else was close. He was the top even handed offensive threat (PCO EH of 69) and had the league s most impactful power play performance (PCO PPP of 40). PCO PCD

23 2009 NHL Review Page 23 That sounds like an MVP performance. Datsyuk trailed by 18 PCO points while even handed (PCO EH of 51) and then slipped behind another 13 points on the power play (PCO PPP of 40). But he significantly out-performed in the shootout (Ovechkin was 2 for 7 while Datsyuk was 5 for 10). On the penalty kill the two players had similar contributions notwithstanding much more ice time for Datsyuk. Ovechkin was not far off Datysuk s even handed defensive pace. Ovechkin s failure to seal the deal was mainly due to his propensity to take penalties (36 minors versus 11, PCD SHO of -8 versus 12). This is not an insignificant thing and a big, sloppy turnaround from last season (PCD SHO of 9). As a rule of thumb the cost of a minor penalty is about 0.25 goals (both extra goals allowed and forgone offense). Voters miss the shootout, undervalue defense and completely ignore the penalty taking story. When you put these things into the mix it deflates Ovechkin a great deal and promotes the work of Datsyuk. PC reports a virtual tie between these two players. But Washington was the NHL s second luckiest ( most efficient ) team in translating goals to points in the standings. Knowing that PC credits Ovechkin with some of Washington s luck, I give the nod to Datsyuk for my Wayne Gretzky Award. Based on the second best offensive performance in the NHL (PCO EH of 60), Zach Parise ranked third among forwards. Helping his overall profile was a strong shootout performance (4 goals in 8 attempts for 11 PCO SO points) and passable defense (PCD of 22). He was (properly) selected to the second all-star team at left wing and finished 5 th in Hart Trophy voting. What is Malkin, the NHL s leading scorer, doing in 7 th? Let s compare him to Datsyuk. At a high level these two looked like similar offensive players. Malkin collected 35 goals and 78 assists while Datsyuk was 32 and 65. On the power play Malkin was 14 and 27 whereas Datsyuk was 11 and 25. The edge would seem to go to the Pittsburgh centre until you look at ice time: Time on Ice EH PP SH TOT Pavel Datsyuk 1, ,566 Evgeni Malkin 1, ,846 On the power play Malkin chewed up a lot of minutes to collect just 3 goals and 2 assists more. PC says that Datsyuk s performance was therefore more valuable (by 7 PC points). While even handed Malkin s contribution (19 goals, 51 assists) was seen by PC to be only a little ahead of that of Datsyuk (20, 39) because he took an extra 150 minutes to achieve the differential. Where Malkin excelled was in drawing penalties. Only LA s Dustin Brown was fouled more often than the Pittsburgh centre (who drew 58 minors for 11 PCO PPO points). But Malkin gave this back to Datsyuk by going just 2 for 7 in the shootout. Where Malkin fell far off the pace was on defense (7 PCD points), most notably due to penalty taking (PCD SHO of -6).