Practical Concerns: On Anchoring, Delight And The Frederik Andersen Contract

One of the things I am trying to work on this summer is to be more self-critical about the way I treat and act on information. Frederik Andersen’s trade from the Anaheim Ducks to the Toronto Maple Leafs, and his subsequent signing of a five-year, $25 million contract proved to be a good opportunity in that sense.

Initially, I cringed a bit at the term and cap commitment Toronto made to Andersen. Five years is a long time and $5M per year is a big money for a guy who is not guaranteed to play all that well.

But I could be very wrong on that.

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How Hard is It to Find Good NHL Goaltending?

Image courtesy Flickr user Harold Cecchetti. Use of this image does not imply endorsement

Is Jacob Markstrom still good?

Whether you come at hockey from the numbers or from traditional scouting, finding NHL-quality goaltending is a challenge. In order to have a good sense of a goalie’s talent (as measured by even-strength Sv%), you need to observe about 4,000 shots worth of work. On average, a goalie needs to play over three seasons as a starter (or eight seasons as a backup) to see that many shots. If they play poorly, few netminders will ever get close to that amount of playing time and most goalies are entering age-related decline by the time they’ve seen that many shots. As such, teams usually make decisions on goaltenders long before they’ve seen 4,000 shots and, unsurprisingly, teams make mistakes.

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xSV% is a better predictor of goaltending performance than existing models

This piece is co-authored between DTMAboutHeart and asmean.

Analysis of goaltending performance in hockey has traditionally relied on save percentage (Sv%). Recent efforts have improved on this statistic, such as adjusting for shot location and accounting for goals saved above average (GSAA). The common denominator of all these recent developments has been the use of completed shots on goal to analyze and predict goaltender performance.

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HOCKEY GRAPHS PODCAST EPISODE 10: GOALIES ARE VOODOO

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On this week’s episode, Rhys and Garret talk about Michael Hutchinson’s recent struggles, Jacob Markstrom’s inability to make the NHL transition, the Canucks signing of prospect Ben Hutton, Corey Pronman’s trolling of Rhys, and some Alberta Major Bantam talk to top it all off. Join us on the other side of the break to listen!

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Hockey Talk: Thoughts on Save Percentage and Shot Quality

(Image courtesy of Wikimedia)

Welcome to a brand-new, semi-regular segment where I -Garret Hohl- will touch on a few trending topics in hockey statistics in a less mathematical and more discussion format.

This week we will explore the debate on shot quality impacts on save percentage.

So let’s begin.

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2015 Midseason Goalie Projections using Hockey Marcels

Last Year, I unveiled a hockey version of the baseball Marcels forecasting system in an attempt to forecast the future performance of goalies.  The idea behind Marcels is simple: we take the last few years of a player’s performance and then weight more recent numbers higher than older numbers.  In addition, we regress the player’s #s to the mean (with a player who has a larger sample being regressed less than one with a smaller sample) and, if we’re projecting for the future, we adjust the overall #s for aging.  Again, this is a very very basic projecting system, but its’ been proven to be incredibly well founded for baseball, and probably for hockey as well.

So let’s take a look at how things have changed now that we have data from the most recent season.  We now have a few goalies with enough data to run Marcels on that we didn’t previously (although barely in most cases) and a few goalies have had large turns in one way or another in their career, which changes the projections.

Again, as a reminder, here is our methodology:
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Goaltender Performance vs Rest

Photo by Michael Miller, via Wikimedia Commons

Photo by Michael Miller, via Wikimedia Commons

I couldn’t find this data (if it’s out there, please point me to it), so I went back to 1987 and pulled goaltender performance vs games rest. We knew goalies did poorly in the second game of a back-to-back pair, but I’m surprised to see such a large gap for two and three games. (The overall dataset is roughly 40000 games.)

Days between Games % of Games Mins (G1) Mins (G2) Shots Vs (G1) Shots Vs (G2) Sv% (G1) Sv% (G2) W% (G1) W% (G2)
1 9.5 54.7 55.0 28.9 29.7 0.905 0.897 0.498 0.421
2 35.6 57.0 56.8 28.7 28.7 0.908 0.901 0.522 0.486
3 19.2 57.1 56.7 29.0 29.0 0.905 0.900 0.514 0.481
4 12.1 56.7 56.3 29.2 28.7 0.899 0.898 0.477 0.487
5 7.2 55.4 55.2 29.0 28.8 0.892 0.899 0.440 0.448

There are lots of systematic issues here (e.g. most back-to-back games are on the road) but simplistically, this would mean goalie rest obscures the bulk of a goaltender’s value. That seems implausible and worth looking at in more detail…

The State of Save Percentage

Image from Wikimedia commons

Currently save percentage is the single best statistic for evaluating goaltenders… which is unfortunate as save percentage is extremely rudimentary and a suboptimal statistic.

There are two important factors for a statistic to be useful: that it impacts wins and the individual can either control or push the needle. Save percentage has both. Continue reading

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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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