Quick Post: Do Past Sv% Variables Predict Future Sv% Variables?

The usefulness of on-ice save percentage (and derivative metrics such as Sv% Rel and Sv% RelTM) has been the source of many, many heated debates in the analytics blogosphere. While many analysts point to the lack of year-over-year repeatability that these metrics tend to show (past performance doesn’t predict future performance very well) as evidence of their limitations, others (primarily David Johnson of HockeyAnalysis.com) have argued that there are structural factors that haven’t been accounted for in past analyses that artificially deflate the year-to-year correlations that we see.

David’s point is a fair one – a lot can change about how a player is used between two samples, it’s not unreasonable to think that those changes could impact the results a player records. But we don’t just have to speculate about the impact those factors have – we can test the impact, by building a model that includes measures of how these factors have changed and seeing how it changes our predictions.

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Practical Concerns: Garret Sparks, Emotions & My New Favorite Hockey Movie

Garret Sparks of the Toronto Maple Leafs made history in his NHL debut after being drafted in the 7th round and working his way up from the ECHL. By all accounts, he did it on merit by maintaining a .924sv% since turning pro, including playing for .940 in the past two years in the minors.

He’s earned his big break, but in a way he is lucky to be playing for an organization which values performance and statistical trends as much as the Leafs. I’m not sure his story would have unfolded quite this way had he been born a couple of years earlier, or had he belonged to team which only tries out a young goalie if he’s over 6’5″. But we’ll get back to that.

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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 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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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

The Futility of Predicting Playoff Series Goaltending

Goaltending is a devilishly difficult thing to predict at the best of times. In smaller samples, even the most powerful forecasting tools fall victim to variance and luck. Playoff performances, and to a greater extent single series, represent such samples and we’re frustratingly inefficient at predicting them using traditional methods. I’m excluding more refined models such as @Garik16’s Marcels, which may very well do a better job of it. I compared regular season 5v5 Sv% over different intervals of time, both total and strictly on the road, for all playoff goalies over the past three seasons and how they matched up with playoff 5v5 Sv%. Here are the results:
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