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Shootout at the Oval Corral Shootout at the Oval Corral How the Shootout Impacted the Game in 2006 Copyright Alan Ryder, 2006 Hockey Analytics www.HockeyAnalytics.com Shootout at the Oval Corral Page 2 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% Copyright Alan Ryder, 2006 Hockey Analytics www.HockeyAnalytics.com Shootout at the Oval Corral Page 3 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% Copyright Alan Ryder, 2006 Hockey Analytics www.HockeyAnalytics.com Shootout at the Oval Corral Page 4 (“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 Copyright Alan Ryder, 2006 Hockey Analytics www.HockeyAnalytics.com Shootout at the Oval Corral Page 5 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.
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