Shootout at the Oval Corral

Home , Jere Lehtinen, Johan Hedberg, Jussi Jokinen, Martin Brodeur, Mikko Koivu, Paul Kariya

How the Shootout Impacted the Game in 2006

Introduction

The lockout was a seminal moment for professional hockey. The new CBA changed the economics of the game. On the ice we saw dozens of changes designed to improve the game but the largest laboratory experiment of the 2005-06 (“2006”) NHL season was the introduction of the shootout. It did not take long for one thing to be clear – the fans loved it. Nobody left the building when the shootout was imminent. In fact the atmosphere was generally electric.

Over the course of the season there were 145 shootout competitions. That means that there were 145 extra points up for grabs. Shootout results clearly affected the standings. This paper examines the performance of teams and individuals in the shootout and, especially, the impact of the shootout on our view of relative player values.

Coin Toss? 2006 Shootout Results There were 170 games that were unresolved after “skating time” (ties) TEAM SW SL SWP Dallas 12 1 92% during the 2003-04 season, twenty-five Los Angeles 6 1 86% more than in 2006. Why the reduction? Carolina 8 2 80% Other rule changes helped increase NY Islanders 9 3 75% scoring and resolve games earlier. But Columbus 8 3 73% there also appeared to be motivation on New Jersey 9 4 69% the part of most teams to avoid the Nashville 6 3 67% shootout. Was it the case that the NY Rangers 7 4 64% Minnesota 5 3 63% shootout was seen by coaches and /or Tampa Bay 6 4 60% players as a lottery? Detroit 4 3 57% Phoenix 4 3 57% If you think that the teams that have a Washington 7 6 54% tendency to win during skating time Atlanta 5 5 50% would also have a propensity to win in Buffalo 5 5 50% shootouts, you would be wrong. For the Vancouver 4 4 50% 2006 season, the correlation coefficient Florida 4 5 44% Edmonton 7 9 44% between winning percentages in the Philadelphia 4 6 40% shootout and those in the opening 65 Montreal 2 3 40% minutes of competition (treating games St. Louis 4 8 33% going to the shootout as half a win) was Colorado 3 6 33% 0.22. This means that only about 5% Anaheim 3 7 30% (0.22 squared) of shootout success is Toronto 3 7 30% Chicago 2 6 25% explained by success in skating time. In Ottawa 2 6 25% statistical terms this is mighty small and Calgary 2 7 22% this means that we have a lot of Boston 2 8 20% explaining to do. Pittsburgh 1 6 14% San Jose 1 7 13%

Let’s test the assumption that the shootout is pure luck. If there was no special skill involved in the shootout, then the results would resemble that of a coin toss. Above is a summary of team shootout results in 2006, showing shootout wins (SW), shootout losses (SL) and the winning percentage in shootouts (SWP).

To the naked eye these results look plausibly like the results of a series of coin tosses, but to really know you have to run them through the binomial theorem. When you do that, two teams have low (less than 5%) probability records - Dallas and San Jose. In the case of Dallas the chance of a record as good as 12 – 1 was less than 0.2%. While there might have been something going on in Dallas (more on that later), I would say that, at the team level, the evidence is marginal that the shootout is anything other than a coin toss .

Shooting Versus Blocking

The shootout pits a lone skater against a 2006 Shootout lonely goaltender. You might be inclined to Shooting Percentages think that this is a 50/50 competition. But that’s not the case. TEAM Offense Defense Anaheim 26% 39% Consider a shootout competition in which Atlanta 31% 32% teams take turns facing the same neutral Boston 17% 33% goaltender (or even a goalie robot). A Buffalo 38% 37% Calgary 21% 46% common goalie (or a machine) would Carolina 50% 28% provide a constant test for shooters. There Columbus 35% 20% would be zero variation in goaltender Chicago 32% 48% performance from team to team. And, Colorado 20% 35% clearly, the shooters would have to have Dallas 57% 22% attributed to them 100% of shootout success Detroit 48% 36% Edmonton 36% 43% or failure. Florida 26% 29% Los Angeles 50% 14% Next consider a shootout competition in Minnesota 29% 23% which goaltenders take turns facing the same Montreal 29% 27% neutral forward (or a puck shooting Nashville 48% 32% machine). A common shooter would provide New Jersey 40% 27% a constant test for goaltenders. There would NY Islanders 46% 27% be zero variation in shooting performance NY Rangers 37% 30% Ottawa 17% 38% from team to team. And, clearly, the Philadelphia 33% 44% goaltenders would have to have attributed to Phoenix 29% 28% them 100% of success or failure. Pittsburgh 21% 48% San Jose 24% 48% So, to work out the relative impact of St. Louis 26% 43% shooting and blocking you have to study Tampa Bay 44% 33% the variation of performance of the skaters Toronto 17% 42% Vancouver 30% 38% and compare it to the variation in Washington 33% 30% performance of goaltenders . More Average 33.6% 33.6% precisely the relative impact of goaltenders Variance 1.219% 0.778%

(“Shootout Goaltending Attribution” or “SGA”) would be:

SGA = Variance(Defensive Success) / (Variance(Offensive Success) + (Variance(Defensive Success)) where the variance is taken across all teams, “Offensive Success” is the shooting percentage of the skaters and “Defensive Success” is the allowed shooting percentage.

Above are the shooting percentage summaries for offense and defense (in the case of defense this is 1 – Save Percentage, but you would reach the same conclusion if you just used save percentages) for the 2006 season. If you apply the formula above to this data you determine that shooting was about 61% (SGA = 39%) of the battle last season.

Marginal Goals – Allocating Shootout Success

I have applied the reasoning used in “Step 3” of my Player Contribution 1method to allocate shootout success between shooting and blocking and, in fact, amongst individual skaters and goaltenders. The mathematics is as follows:

Let: ShC = Shootout Chances ShG = Shootout Goals ShSh = Shootout Shooting Percentage = ShG / ShC ShSv = Shootout Save Percentage = 1 – ShSh ShSh< = League Average ShSh = 0.336 (for 2006)

Then: MGGS = Marginal Goals Goaltending Shootout = ShC x (ShSv - TSSv) where TSSv = Threshold Shootout Save Percentage

There is only one way to select TSSv such that goaltenders are attributed 39% of shootout success:

TSSv = 1 – (1 + SGA) x ShSh< = 0.553

Applying the same thinking to shooters we get:

MGOS = Marginal Goals Offense Shootout = ShC x (ShSh - TSSh) where TSSh = Threshold Shootout Shooting Percentage = SGA x ShSh< = 0.133

Note that the formulae can also be written as:

MGOS = Shootout Goals – ShC x TSSh), goals scored in excess of a threshold given the number of chances and MGGS = Shootout Saves – ShC - TSSv), saves made in excess of a threshold given the number of chances

1 See http://www.hockeyanalytics.com/Research_files/Player_Contribution_System.pdf

In English this says that we will give credit to team (or individual) goaltenders to the extent that their save percentages exceed 0.553 (for 2006) and we will give credit to team (or individual) shooters to the extent that their shooting percentages exceed 0.131 (for 2006). The selection of these threshold levels will attribute 39% of shootout success across the NHL to goaltenders 2006 Shootout Results and 61% to shooters, while keeping total marginal goals equal to the actual TEAM SW MGOS MGGS MG/SW number of goals. Anaheim 3 3.9 2.5 2.1 Atlanta 5 6.3 4.9 2.2 Boston 2 1.3 4.8 3.1 Buffalo 5 7.8 3.0 2.2 Allocating Calgary 2 2.2 0.2 1.2 Shootout Points Carolina 8 12.5 6.8 2.4 Chicago 2 9.4 11.6 2.6 The goal of the shootout is to win (and Colorado 3 5.9 - 0.4 2.7 Columbus 8 2.1 3.5 1.9 collect a point). The currency of the Dallas 12 18.5 10.2 2.4 shootout is wins rather than goals. So Detroit 4 8.7 2.7 2.9 the remaining step is to translate Edmonton 7 12.1 2.2 2.0 shootout goals into shootout points. Florida 4 3.9 5.5 2.4 Los Angeles 6 7.4 6.8 2.4 To do this we observe that there were Minnesota 5 4.9 7.0 2.4 330 goals scored in 145 shootouts Montreal 2 2.2 3.0 2.6 (worth one point each). This means Nashville 6 8.7 3.7 2.1 New Jersey 9 12.1 9.0 2.3 that, on average, it took 2.3 goals to NY Islanders 9 13.6 8.6 2.5 win a shootout. As this “cost” varied NY Rangers 7 11.6 8.4 2.9 by team, we need to translate MGGS Ottawa 2 1.0 1.8 1.4 and MGOS into shootout points team Philadelphia 4 6.7 1.0 1.9 by team based on the team’s ratio of Phoenix 4 3.9 4.7 2.1 (MGGS + MGOS) / Shootout Wins. Pittsburgh 1 2.3 - 0.4 2.0 San Jose 1 3.2 - 0.4 2.8 This approach allocates success, St. Louis 4 5.0 1.3 1.6 whether based on intangibles or luck. Tampa Bay 6 8.5 3.6 2.0 Toronto 3 0.9 1.2 0.7 In order to integrate this calculation Vancouver 4 4.0 2.2 1.6 with my Player Contribution (“PC”) Washington 7 10.9 9.6 2.9 method I will also multiply by a scaling Total 145 201.4 128.6 2.3 factor (in this case 10) 2.

In other words:

PCOS = Player Contribution Offense Shootout = 10 x MGOS / ((MGOS + MGGS) / Shootout Wins) and PCGS = Player Contribution Goaltending Shootout = 10 x MGGS / ((MGOS + MGGS) / Shootout Wins)

2 The Player Contribution method allocates 10 “PC” points per point earned in the standings, so divide PC points by 10 if you want to express a player’s contribution in standings points.

For teams, these formulae would be applied as written. For individuals the bold part of the formulae is team data.

Above is a table summarizing raw material of those calculations, shootout wins (“SW”), MGOS, MGGS and marginal goals per shootout win (“MG/SW”). For instance Toronto had the worst raw performance in the shootout, generating only 2.1 marginal goals (0.9 from shooting, 1.2 from goaltending). Yet the lucky Leafs somehow won three shootouts. To translate the Leafs’ marginal goals into shootout points you need to divide them by 0.7. This means that, for Toronto, we allocate 1.3 shootout points to skaters and 1.7 shootout points to goaltenders.

Boston was the unluckiest team in the shootout. The Bruins generated 6.1 marginal goals but claimed only two shootout wins. To allocate shootout points to individual Bruins’ players we need to divide their marginal goals by 3.1. That would mean 0.4 shootout points are allocated to skaters and 1.6 shootout points to goaltenders.

Yes, each of Boston’s shooting and goaltending was better than that of the Leafs, but with this method I am allocating the results rather than measuring performance, and Boston’s results were worse. With larger sample sizes the results and performance might converge (unless, of course, you believe that goaltenders stand in the crease mumbling something like “here comes a really critical shot, I think I will make an extra effort to block this attempt”).

Individual Performances – Shooters

So how does this shake out for skaters? Below is a table showing the top 20 shootout contributions (“PCOS”) by shooters in 2006.

Shootout contribution reduces to “chances” x “performance” x “impact”. The highest number of “chances” belonged to Ales Hemsky, who went to the net 14 times (in 16 Edmonton shootouts). He delivered 5 goals, but his shooting percentage of 0.357 was the lowest on the leaderboard.

“Performance” is the shootout shooting percentage in excess of the threshold. The lowest number of chances on the leaderboard was 5 for Ray Whitney. But, of the leaders, his shootout shooting percentage of 0.800 was second only to that of Petr Sykora.

“Impact” is the adjustment to translate the result into shootout wins (worth one point in the standings). What is Tony Amonte doing on this list? His 8 chances gave him a chance to be on the leaderboard. But he would not seem to belong with 8 chances and only 3 goals. Amonte was among the shootout contribution leaders because Calgary won 2 shootouts with only 2.4 marginal goals. As Amonte contributed 1.95 marginal goals to the effort, he deserves the lion’s share of the Flames’ success (and this method allocates 1.7 points, or 17 PC points, to him).

2006 Player Contribution Shootout Results - Shooters

Player Team ShC ShG ShSh% MGOS PCOS Jussi Jokinen DAL 13 10 0.769 8.30 35 Viktor Kozlov NJD 12 8 0.667 6.43 27 Brad Richards TB 9 6 0.667 4.82 24 Miroslav Satan NYI 10 7 0.700 5.69 23 Sergei Zubov DAL 12 7 0.583 5.43 23 Matt Cullen CAR 9 6 0.667 4.82 20 Paul Kariya NAS 7 5 0.714 4.08 20 Jaroslav Balastik CBJ 9 6 0.667 4.82 18 Slava Kozlov ATL 7 5 0.714 4.08 18 Tony Amonte CAL 8 3 0.375 1.95 17 Ales Hemsky EDM 14 5 0.357 3.17 15 Trent Hunter NYI 9 5 0.556 3.82 15 Petr Sykora NYR 6 5 0.833 4.21 15 Alexander Ovechkin WAS 13 6 0.462 4.30 15 Jason Williams DET 7 5 0.714 4.08 14 Vincent Lecavalier TB 9 4 0.444 2.82 14 Ray Whitney CAR 5 4 0.800 3.34 14 Mikko Koivu MIN 6 4 0.667 3.21 13 Michael Nylander NYR 11 5 0.455 3.56 12 Olli Jokinen FLA 9 4 0.444 2.82 12

Some of these names are hardly name brand:

• Rookie Jussi Jokinen’s number was called in each of the Stars’ thirteen shootouts and he did not disappoint (he collected 10 goals). The flying Finn scored 17 goals and collected 38 assists over the course of the season, respectable numbers but reflecting 353 power play minutes. But his 35 PC points (3.5 points for Dallas in the standings) from thirteen shootouts say that his shootout contribution was actually greater than his offensive contribution during 1099 minutes of skating time (33 PC points) over 81 games (note that he contributed 17 points on defense).

• Mikko Koivu was another Finnish rookie. He appeared in 6 of Minnesota’s 8 shootouts, scoring 4 goals and contributing 1.3 points (13 PC points). His total PC score was only 16! During skating time he delivered offense at marginal levels (6 goals and 15 assists, about half of that on the power play). Less than half way through the current season Koivu is emerging as the Jussi Jokinen of 2007.

• Who the heck is the Balastik weapon of the Blue Jackets? He appeared in 9 of Columbus’ 11 shootouts (6 goals) and collected 18 PC points in the shootout. He collected only another 6 PC points on offense during skating time (12 goals, 10 assists in 847 minutes, much of which was on the power play).

• The Devils’ Viktor Kozlov collected 27 of his 36 offensive PC points on the shootout, where he appeared in 12 of 13 contests (8 goals). During 934 minutes of skating time (over 200 minutes on the power play) he scored 12 goals and collected 13 assists.

• And then there is defenseman Sergei Zubov. Of the 981 shootout chances last season exactly 48 were given to defensemen. Zubov appeared in 12 of the Stars’ 13 shootouts, scoring 7 goals and collecting 23 PC shootout points. This contributed to making the unheralded Dallas defender the league’s most valuable skater in 2006 .

During skating time, over an 82 game season, an average skater develops a PC score of about 40 points while an all-star skater typically develops a score between 100 and 130 points (goalies can contribute more). These leading shootout contributions are huge relative to these benchmarks.

Some big names were missing from the leaderboard. Each of Jaromir Jagr (NYR – 3 PCOS), Jonathan Cheechoo (SJ – 1) and Eric Staal (CAR – 2) made only a small contribution. But the following players had negative PCOS scores: Joe Thornton (SJ – -1 based on 0 goals in 3 attempts), Patrick Marleau (SJ – -2, 0 goals on 5 tries), Ilya Kovalchuk (ATL – -1, 1 goal in 10 attempts), Jarome Iginla (CAL – -2, 1 out of 9) and, with the worst shutout streak last season (0 for 7), Joe Sakic (COL – -5).

Individual Performances – Goaltenders

Individual shooters are only permitted to try once per shootout. But the goalie can stay in place to face multiple shots. Although I determined that goaltending accounts for less than half of the battle, the impact of individual goaltenders can therefore be higher than that of individual skaters.

Below is a table showing the top 10 goaltending contributions in the 2006 shootout.

2006 Player Contribution Shootout Results - Goaltenders

Player Team ShC ShG Saves ShSv% MGGS PCGS Martin Brodeur NJD 38 9 29 0.763 8.76 3.7 Henrik Lundqvist NYR 37 9 28 0.757 8.30 2.9 Rick DiPietro NYI 41 12 29 0.707 7.17 2.9 Kari Lehtonen ATL 20 3 17 0.850 6.35 2.8 Michael Morrison EDM 24 6 18 0.750 5.22 2.6 Roberto Luongo FLA 27 7 20 0.741 5.62 2.4 Martin Gerber CAR 33 10 23 0.697 5.43 2.2 Marty Turco DAL 24 6 18 0.750 5.22 2.2 Alexander Auld VAN 22 7 15 0.682 3.28 2.1 Johan Hedberg DAL 17 3 14 0.824 4.95 2.1

Again, shootout contribution reduces to “chances” x “performance” x “impact”. In this case “performance” is save percentage and this table is roughly ordered by saves.

I estimate that Martin Brodeur contributed about 3.7 of the Devil’s 9 shootout points last season, making him the shootout MVP with 37 PCOS points. Some people think I am a Brodeur basher, but I just follow the numbers. In the shootout Brodeur was on his own, unshielded the NHL’s top defensive voodoo. These numbers say that he was a significant part of the Devils’ post-skating success.

Henrik Lundqvist had a similar shootout record but there are two items worthy of note:

• The Rangers were among the unluckiest teams in the shootout (requiring 2.9 goals per win); but

• Offsetting that was Lundqvist’s participation in the shootout marathon with Washington (an extended shootout exposes the goaltender to less polished shooters).

Kari Lehtonen had an awesome 0.850 save percentage to claim more than half of Atlanta’s PC points in the shootout. With more chances he could have been the shootout MVP. Alexander Auld’s performance (0.682 save percentage on just 22 chances) makes the top 10 list because of Vancouver’s relatively efficient performance in the shootout (the Canucks required only 1.6 goals per win). He got credited with a little over half of Vancouver’s shootout success.

Case Study

Dallas was the only team to place two goaltenders on the goalie leaderboard. Taken together, Turco and Hedburg outperformed Brodeur. The Stars had two of the top five shooter performances in 2006, a Finnish rookie and a defenseman. The Stars went 12-1 in the shootout. Something was going right in Dallas.

The heavy reliance on Zubov and Jokinen made Dallas coach Dave Tippett look smart rather than lucky. This is not the way most coaches approached the shootout in its first season. Dallas had two thirty goal scorers last season, Jere Lehtinen (33 goals) and Jason Arnott (32). Most coaches would have put them at the top of their shootout depth chart. In fact, these two snipers were given exactly two chances (no goals) in the Stars’ thirteen shootouts last season.

Making Dallas’ coaching look better was the Star’s goaltending, which had the NHL’s second best shootout save percentage. Making the coach look competent was the way he went about making those decisions. “We did a lot of things in practice, just to try to get an idea of who our shooters would be”, said Tippett last season. This is something that 29 other coaches did not seem to do as well.

The largest single on-ice change for the 2006 NHL season was the introduction of the shootout. Only one team seemed to latch on quickly. Most coaches kept going to their go-to guys with results that might have resulted from a coin toss. But the performance of Dallas was outstanding and indicative of (a) individual skills and (b) coaching.

While it appears that there was something special going on in Dallas, I would say that, at the team level, the evidence is marginal that the shootout in 2006 was anything other than a coin toss. Furthermore, as coaches learn from experience, team results are increasingly likely to look like a lottery.

There was a great deal at stake in the shootout (145 additional points) last season. And a limited number of players had a very significant impact. On an individual level, many performances looked nothing like a coin toss .

The shootout materially changed the relative values of certain players. Special offensive skills got to stand out. The shootout increased the overall impact of goaltending and reduced the relative impact of defense. Sergei Zubov … move to the front of the class.

2006-07 Post Script

At the time of writing (December 2006) shootout scoring is down. Goaltending variation (team to team) is also down. Goalies are winning the battle.