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Determining Goalie Value to New Teams By Danger Zone

Determining Goalie Value to New Teams By Danger Zone
With Robin Lehner, Eddie Lack and Cam Talbot all expecting to be traded, InGoal took a closer look at how their statistics might translate to new teams. (Photos used in illustration by Scott Slingsby and Clint Trahan)

With Robin Lehner, Eddie Lack and Cam Talbot all expecting to be traded, InGoal took a closer look at how their statistics might translate to new teams. (Photos used in illustration by Scott Slingsby and Clint Trahan)

What difference can a goalie make on a new team? What effect would playing elsewhere have on a goaltender’s stats? How would this keeper have done in another’s place?

With the draft upon us and free agency set to open shortly, several NHL general managers are scrambling to answer these hard questions. The Rangers’ Cam Talbot, Canucks’ Eddie Lack, and Senators’ Robin Lehner stand, in that order, as the most desirable available solutions.

None of these goaltenders is a sure bet.

Including playoffs, none has even played in 90 NHL games, with Talbot failing to break 60. This means any analytical work involving the three will have to be done with limited information; like the general managers who make the final decisions, we have to make the best possible use of the imperfect data we have.

The problems with relying on traditional measures like wins, shutouts, goals-against average and raw save percentage are well documented. A poor team can destroy a goalie’s numbers, while a great team can make them glow.

Steve Valiquette’s shot-quality tracking work featuring red shots (poor chances) and green shots (great chances) based on several factors, including pre-shot movement, offers a very thorough analysis and in a perfect world we’d be able to compare how each available goaltender did against each type of scoring chance and cross-reference that with how many of those he’d face on a potential new team. However, because this data is not publically available, we have to work harder to dig up the details we need.

Mining the Danger Zones

The excellent War on Ice website divides all shots against into three categories based on location: high danger, medium danger, and low danger:

War on Ice Zones

Zone Chart from the WarOnIce.com.

Blue = High Danger (HD)

Red = Medium Danger (MD)

Yellow = Low Danger (LD)

Though factors like shot speed and pre-shot movement are not evaluated, shot location alone can show us how much harder life is for goalies on different teams.

Teams give up not only different numbers of shots, but also different ratios of HD, MD, and LD shots.

If two teams surrender 2500 shots in a season, and both give up the same number of goals, you would be tempted to assume their goalies were about equally good. But if you knew one team’s shots were 30 percent HD, while the other team’s were 25 percent, you’d start to consider one far better then the other, based on the increased difficulty of the shots.

War on Ice tracks every goalie’s save percentage for each danger zone; this means we can establish how many of each type of shot a team surrenders, and use that to try and determine how many goals another goalie would allow behind that team. We can also take one goalie’s shots faced and evaluate how a different goalie would have done with the same danger distribution. In both cases, we can see how a goalie’s raw save percentage, goals-against average, and goals allowed would change playing for another team, or substituted for another goalie.

Skating a Mile in Each Other’s Pads: Comparing Talbot, Lack, and Lehner

If we look at the danger-zone-specific career save percentages for the three goalies, it’s clear that Talbot stands out. This is especially true for HD, which best differentiates goaltending quality:

Name Raw SV% LD Sv% MD Sv% HD Sv%
Cam Talbot 93.05 96.94 93.42 85.61
Eddie Lack 91.53 96.69 93.05 82.35
Robin Lehner 91.41 96.1 91.79 82.9

When we take each goalie’s save percentage in each zone and apply it to the shots faced by the others, we get a picture of how one would do in the other’s place:

Goalies Added Goals New SV% SV% Change
Talbot as Lack -27 92.69 -0.36
Talbot as Lehner -42 92.96 -0.09
Lack As Talbot 18 91.96 0.43
Lack As Lehner -12 91.87 0.34
Lehner as Talbot 25 91.51 0.10
Lehner as Lack 10 91.10 -0.31

If Talbot had faced Lack’s career shots in each danger zone, given Talbot’s career save percentage for each zone, he would have saved an additional 27 goals. If Lack had faced Talbot’s career shots, he would have given up an additional 18 goals.

Notice the change in raw save percentage switching circumstances makes.

Talbot and Lack have a raw save percentage more than 1.5 points apart to begin. If they switched places, that gap would narrow significantly: Talbot falls to a 92.69, while Lack rises to a 91.96.

Also interesting is how different Lehner begins to look from the others.

Initially, his save percentage is very near Lack’s. However, when the two switch places, Lack’s numbers rise, while Lehner’s fall, indicating Lack’s workload was more difficult.

Looking at the distribution of shots by danger zone, it’s clear which goaltender has had the most challenging time:

Name HD% of total MD% of total LD% of total
Cam Talbot 26.75 24.52 48.73
Eddie Lack 29.07 27.06 43.86
Robin Lehner 26.84 26.62 46.54

Talbot has better overall numbers, but Lack’s ratio of high to low danger chances is easily the greatest of the three. This use of danger zones to put a goalie in another’s place is able to reveal important differences that raw save percentage conceals.

New Goalie, New Team

The same principles can be used to see how a given goalie would fare if he had faced the shots allowed by a specific team. Since Edmonton, San Jose, and Buffalo are all actively pursuing stable goaltending, let’s see how our trio of tenders would have done last season compared to the goaltenders each team already had.

For this comparison, I’ve scaled everything to sixty games, since that represents the workload of a solid starting goaltender. The team averages are what the goaltender in question is being compared to.

Let’s start with Edmonton:

Edmonton Oilers

Name Added Goals New SV% SV% Change Change in GAA
Cam Talbot -60 92.52 -0.53 0.36
Eddie Lack -38 91.32 -0.21 0.28
Robin Lehner -30 90.89 -0.52 0.08

All three goalies represent a significant statistical upgrade for the Oilers: allowing 30 to 60 fewer goals in a season would make a massive difference to any team (though whether any goaltender could maintain his zone-specific save percentages in such a difficult environment is an open question).

Notice the significant dip in raw save percentage, and the uniform increase in goals-against average. More than 30 percent of Edmonton’s shots allowed are high danger.

Buffalo Sabres

Name Added Goals New SV% SV% Change Change in GAA
Cam Talbot -36 92.8 -0.25 0.68
Eddie Lack -12 91.68 0.15 0.65
Robin Lehner -2 91.23 -0.19 0.47

If you expected our intrepid trio to help Buffalo like they did Edmonton, you will be disappointed. Buffalo did not finish poorly because of poor goaltending and a bad HD shot ratio. Buffalo simply allowed a horrific mass of shots. This accounts for the sharp spike in goals-against average, with less effect on save percentage. In fact, Lack would have posted a slightly better save percentage in Buffalo than he did in Vancouver!

San Jose Sharks

Name Added Goals New SV% SV% Change Change in GAA
Cam Talbot -26 92.72 -0.33 0.28
Eddie Lack -5 91.55 0.02 0.19
Robin Lehner 2 91.13 -0.28 -0.02

At this point, Talbot’s remarkable numbers are beginning to look unbeatable – San Jose is not a terrible team, so saving them 26 goals is incredible. In fact, if I were a general manager looking at this analysis, I’d be asking myself how sustainable Talbot’s totals are. He is either a remarkable talent who simply hasn’t had much chance to prove himself, or he’s a good goalie benefiting from his circumstances in New York.

San Jose has to be more careful in courting a goaltender than either Edmonton or Buffalo. Lack’s contribution, while positive, would be minimal, and Lehner’s would actually be negative (though his goals-against average would decrease). If there was a team who didn’t likely need much of an upgrade in goal, it was the Sharks.

More Uses for the Danger Zones

Even though the sustainability of any goalie’s performance level is difficult to predict, the danger zone method gives us some useful distinctions that raw save percentage cannot. It also provides information on the way teams allow shots, and may say something about the systems a given team or coach employs (relative to the talent of the players on that team).

Is it perfect? No. As noted above, being able to conduct the same type of comparisons using metrics like the ones being developed by Valiquette would be another step. But it adds a new layer of context to the trade talk.

Comparisons between more established goaltenders whose career numbers have levelled off would be more reliable and could be especially useful in comparing goaltenders for salary negotiation purposes. The same goes for Vezina Trophy voting: instead of simply considering raw save percentage and (perhaps) goals saved above average, general managers could access more detailed and direct comparisons.

In an arena where teams claw and scratch for the tiniest advantage, the kind of additional information danger zone comparisons provide could prove invaluable.

About The Author

Paul Campbell

Paul Campbell is a writer at InGoal, and a former CIS goaltender and women's goaltending coach for Mount Allison University. He occassionally moonlights as a university literature instructor.

3 Comments

  1. Barret

    Should Talbot’s added goals in the SJ Sharks section be -26 instead of 26? The commentary notes that he’d have saved 26 additional goals, while “26”, I believe, indicates he’d have let 26 more into the net.

    • Paul Campbell

      Nice catch: we’d reversed the minuses in 2 of those tables. Should be right now. Thanks!

  2. Ben

    Great article! Love the use of advanced statistics to evaluate potential goalie upgrades.