Who Were the Top Goaltenders of Each Decade?

Image from Rick Dikeman via Wikimedia Commons

Image from Rick Dikeman via Wikimedia Commons

I set out to measure the top goaltenders of each decade using a simple measure that adjusts for different environments of the years played.

The measure used is Saves Above League Average which is a measure of how many pucks a goalie stops relative to league average from that season. It is computed as:

salaformula

I compiled the top 10 and bottom 10 for the cumulative totals for the decades and the most extreme single-season marks.

If you want to test yourself before looking at the results, I made them into Sporcle quizzes: 2000s / 1990s / 1980s

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Ryan Johansen – How much is he worth on a Bridge Deal?

Courtesy of Wikimedia.org

The Ryan Johansen contract dispute is the biggest contract talk going in the NHL as training camps begin, as you might expect.  Johansen is young, with a great pedigree (4th overall in 2010), and is coming off a seemingly breakout season that also led Columbus to its first playoff relevance in quite a while.  And yet Johansen is unsigned and is unlikely to be so going into camp, with the Columbus front office opening fire this week on Johansen’s agent.

Both sides have seemingly agreed at least to a bridge deal – Johansen at first was seriously against such a deal, but has since agreed a 2 year deal is acceptable.  But they are WAY apart on the money – reports have Columbus at 3 to 3.5 Million over 2 years while Johansen is sitting at 6 to 6.5 Million.  But what is reasonable for a player’s first two RFA years?

A quick note:  This article is considering how much those years are worth in the league’s current economic structure, which is team friendly and makes RFA deals less pricey than UFA ones.  You can argue whether this is unfair to the player or not (it’s not), but if a player wants to fight the system is recourse is to either go to the KHL or not sign and “hold out” – see Ryan O’Reilly a few years ago for an example.  Since nearly all players don’t want to do that and accept the league’s economic framework eventually, that’s the framework we’ll be using for this post.

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What to Expect When You’re Expecting: Does Switching NHL Head Coaches Make a Difference?

Bruce Boudreau

Photo by Matthew Miller, via Wikimedia Commons; altered by author

How good do you feel because your team has a new coach? I mean, really…it’s almost like a new-car smell. So many possibilities – This time, things will be different. With the exception of coaching changes due to disastrous, unexpected things, the typical hockey fan was ready for that moment, and were happy to see the coach go. But is that eagerness for change based on real results?

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Scoring talent influence on goal differentials and statistical double dipping

Screen shot 2014-09-09 at 1.20.24 AM

In August, I wrote an article on how you can translate Corsi differential values in terms of the average expected goal differential given for a players of similar average ice time.

In the article, I used an example of how this information could be used:

For example, Matt Halischuk and Eric Tangradi are two players who averaged 4th line minutes on the Winnipeg Jets. Tangradi finished the season with a 53.9% Corsi, while Halischuk was at 44.0%. Over the span of a season, forwards with those Corsi% would be expected to have on average of -1.04 and a -4.77 goal differential respectively. Therefore, on average, a 53.9% Corsi fourth line forward is worth 3.73 goals more than a 44.0% Corsi forward. Another option is comparing these players to the 46.8% Corsi% of an average fourth line player. The goal differentials can then be used to estimate win values using Pythagorean relationships.

There is a caveat with using raw Corsi% to estimate goal differentials; all effects -such as zone starts- still apply. The estimated goal differentials would be no more predictive than Corsi is; however, you can now easily and more accurately measure Corsi impact in terms of goals and wins.

Now, I used the example of Halischuk and Tangradi for a few reasons. The main one being that they are familiar to me as Winnipeg Jets players. They are two fourth line players that have experienced similar usage but have very polar opposite shot metrics. But, there is another reason… an interesting one.

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To Draft or Not To Draft Goalies – Not Really the Right Question

On Monday, Kyle Alexander and CAustin (aka the Puckologist) wrote a post on Raw Charge titled “It’s still okay for an NHL team to draft goaltenders.”  This is a topic that isn’t exactly new in the hockey analytics community – on this site alone Garret and myself have written a few posts about how unpredictable goalies are and the general consensus in the hockey analytics community being that goalies are simply not worth drafting in the early rounds of the draft, due to the variability on their results compared to other skaters (particularly forwards).

The Raw Charge guys in their post don’t totally disagree, but do think the talk of avoiding goalies is a bit exaggerated by some, concluding:

However, the gap between goalie drafting and forward drafting isn’t nearly as stark as it’s been made out to be. It’s much more worthwhile to make drafting and development at all positions better than to attempt to specialize in elite forwards to the exclusion of other positions.

Essentially, the Raw Charge guys argue:
1.  The Gap between skaters and goalies’ success and failure rates isn’t as big as people think – most evaluative measures used in such studies disfavor goalies by using metrics such as GP by a certain age, where goalies rarely get opportunities to meet such thresholds.
2.  The response to whatever gap there actually is should be to try and improve goalie evaluation – similar to how Swedish and Finnish goalie federations’ improved early goalie training to improve their goalie crop – rather than to eschew goalies altogether.
3.  The failure of goalies may also have to do with poor development processes rather than bad evaluation.

While all three points do have merit, I think they’re both quite a bit overstated.

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An early look into some of the new numbers available

From Wikipedia Commons: A graph showing the minimum value of Pearson’s correlation coefficient that is significantly different from zero at the 0.05 level, for a given sample size.

There are two new and very exciting frontiers being explored by the hockey analytics blogosphere. There is the manual tracking of zonal statistics, such as zone entries and exits. This area of research was first pioneered by Eric Tulsky and Corey Sznajder. Then there is the splicing of Corsi into microstates, such as looking at shot attempt differentials momentarily after face off wins or loses in particular zones. The early workers on these numbers were Tyler Delow and Muneeb Alam.
(side note: it should not be a surprise that one of each group was recently picked up by a NHL team this summer)

I recently was able to get data from the non-NHL hires named above (and will enjoy their contact while I can until they are picked up too). Sznajder provided me with zone entry and exit data for just over 60% of the NHL. If you would like to check out his project and contribute, check this link. Alam sent over shot attempt events 10 seconds after a defensive zone face off, which was further separated into wins and losses.

I originally received this data for study of the Jets and noticed what appeared to the eye to be a relationship, and wished to delve in further.

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Save Percentage vs the Experts: Do shots against inflate a goaltender’s save percentage?

Curtesy of Wikipedia Commons

I’ve seen many statistical articles look at different ways to determine whether or not shot volume inflates a goaltender’s save percentage; however, I’ve never been satisfied with the methods used, regardless of the outcomes. So, I finally went and looked at the data myself.

It’s been seven months since I’ve written anything on save percentage. With all that wait, you’d think I’d give you a big, long, and in-depth article… but I won’t. 

I had one planned, but accidentally lost all my data. Of course, errors always come in clumps. Instead of recovering the lost data, I ended up permanently removing it. To make matters worse, extraskater.com going black made the information a hassle to manually extract again. I probably could write a code (or get someone else) to draw up the information again… but I still have one piece remaining from the original data: the graph.

What is this graph of? What does it mean? Continue reading

Value of Corsi possession measured in goals

The average on-ice shooting and save percentages a player experiences tends to be influenced by their average time on ice per game. This relationship likely occurs due to a combination of factors: shooting talents of linemates and opponent, defensive talents of linemates and opponent, system and psychological effects, and an effect I like to call “streak effects”.
(See bottom for discussion on these effects)

Regardless of the reasons why, these effects indicate that not all Corsi percentages are created equal in impact. This has been discussed previously on Hockey-Graphs both here and here. So, can we measure this difference in impact? Continue reading

How well do Plus Possession Rookie D-Men do in their next few years?

There is nothing perhaps more encouraging to fans of struggling teams than to see a rookie D-Man come up and put up big numbers right out of the gate.  I speak of course, not just about goals and assists – in this case I refer to good possession #s (Corsi, Fenwick, and the relative versions thereabout).  Fans of the Oilers (Marincin), Leafs (Rielly), Isles (de Haan, Donovan), etc, all seem to have higher hopes than they might’ve otherwise due to how well their rookie D has performed.  After all, a top pair D Man (under control for cheap for years to come) can have such a great impact and they are extremely hard to find on the free market (or trade market).

But can these standout rookie D keep up their great performances?  After all, we always hear about the so-called “sophomore slump” and it’s not like players disappointing after great rookie years is that uncommon.  How certain can we be about the futures of rookie D-Men who come up and right away show strong possession #s?  Let’s see how similar rookie D the last few years did.

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Twenty Players to Expect a Shooting Percentage Bounceback from in 2014-15

Photo from Michael Miller via Wikimedia Commons

Photo from Michael Miller via Wikimedia Commons

Goals are such a small sample stat that even over a full season you’ll see some raw figures that may not be overly indicative of ability. As a general rule, you can normally expect a player’s goal total to bounce back from a down season if the player is still producing shots on goal but suffered a significant drop in shooting percentage.

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