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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10 Rules Of Thumb For Hockey Analysts

  1. The point of hockey is to create goal differential. The point of hockey analysis is to find ways to improve it.

  2. Shot differentials today is goal differentials tomorrow.

  3. 100-10-1. 100 minutes of your time to create data, 10 minutes of the coach’s time to digest the data, 1 minute of the player’s time to absorb the data.

  4. Optimise workload, reduce uncertainty.

  5. If your findings are either always or never surprising, then review your methodology. 80/20 (in favor of confirming existing beliefs) is a good place to start.

  6. Your priority is to help the coach get a better night’s sleep and to help players maximise their experience.

  7. Know your place, but stand your ground.

  8. We are all on the same team.

  9. Who you compete against influences your results; who you work with dictates your destiny.

  10. The job is only done when you’ve trained someone to make you expendable.

 

Jack Han is the Video & Analytics Coordinator for the McGill Martlet Hockey team (not his full-time job). He also writes occasionally about the NHL for Habs Eyes on the Prize. You can find him on Twitter or on the ice at McConnell Arena.

Tactalytics: Using Data to Inform Tactical Neutral Zone Decisions

Breakout - Against 2-1-2...2

Last time, I showed how using data and video evidence can be combined to inform tactical offensive zone decisions. Today, I’m going to do the same thing in the neutral zone. Neutral zone play is something that has been a hot topic among analysts for many years, going back to this paper written by Eric Tulsky, Geoffrey Detweiler, Robert Spencer, and Corey Sznajder. Our own garik16 wrote a great piece covering neutral zone tracking. Jen Lute Costella’s work shows that scoring occurs sooner with a controlled entry than an uncontrolled entry.

However, for all the work that goes into zone entries, there have been few efforts to account for how predictable these metrics are. At the end of the day, what matters is how we can better predict future goal-scoring. Also, in looking at our passing data, what can we also learn about how actions are linked when entering the zone? Does simply getting into the offensive zone matter? Does it matter whether it’s controlled or not? Or, does what happen after you enter the zone matter exponentially more? Lastly, what decisions can we make to improve the team’s process using this data?

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Practical Concerns: The Analyst’s Plight

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(Image Wikipedia Commons)

Recently, the statistical analyst of an NHL team was let go in the aftermath of an underwhelming regular season and a puzzling decision involving one of the team’s most productive and iconic players. Rights and wrongs aside, the episode illustrated an uncomfortable fact: the analyst’s job is perhaps the most fragile one of all.

Imagine the tightrope walker, balancing him/herself atop a fine metal wire between two buildings. The job is a difficult one on the best of days, requiring a lifetime of practice and undivided focus. Randomness is not the tightrope walker’s friend. A gust of wind, a slight mis-step or even a meeting with an errand low-flying pigeon could yield deadly consequences.

While the physical stakes are different, an analyst’s career prospects (and personal well-being) are similarly affected by things out of his or her control. While job security in any field is dependent on market conditions, things are especially dire for the technical worker responsible for uncovering Truths, but ranked too low in the corporate hierarchy to effect real change.

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Measuring Single Game Productivity: An Introduction To Game Score

Who had the best game last night?

That’s a rhetorical question obviously… because it’s July, but when hockey is actually being played from October to June it’s an important question to ask – one that’s currently not very easy to definitively answer.

Some will look at points, some will look at shot differentials, some will watch the game, but rarely is there any consensus. Different people value different things. At the top and bottom of the spectrum the answer is sometimes obvious. If Connor McDavid has a five point night, he was very likely the best guy on the ice. If Pekka Rinne lets in five goals against on 21 shots he was very likely the worst. But for many games the answer is neither obvious or simple and is generally up for debate depending on an observer’s personal value system.

What we don’t have in hockey is a standardized measurement for single game productivity. It’s not something that will end any debate, but it can provide a much better framework to answer the question over what’s currently available. And that’s what I’m going to introduce in this post.

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Tactalytics: Using Data to Inform Tactical Offensive Zone Decisions

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Much of the gains made in the field of hockey analytics has to do with player evaluation and roster construction. Identifying and quantifying a player’s on-ice shot differential while accounting for the context (score state, quality of teammates, quality of competition, deployment, etc.) is something the community has largely been successful at doing. When teams sign or trade for a player, we’re at a good enough place to determine if that was a positive or negative signing, for the most part. There have even been improvements in scouting and drafting that are analytical in nature.

However, we still are lacking in areas of quantifying a team’s system and how they play. We have made strides concerning two important phases of the game, namely the work done here on zone entries by Eric Tulsky, Geoffrey Detweiler, Robert Spencer, and Corey Sznajder, and also work done here by Jen Lute Costella on zone exits. These two pieces, among others written on these subjects, demonstrate a data-driven approach that can influence the tactical decisions a team can make on the ice. However, these are isolated incidents at the blue lines and structured play in the offensive zone remains difficult to quantify.

I attended the NHL Coaching Clinic held in Buffalo, NY the day before this year’s draft. During a presentation from Davis Payne, an assistant coach with the Los Angeles Kings, I noticed two distinct plans of attack being demonstrated and wanted to quantify them as best I could with our passing data. The decision on how to set up and attack in the offensive zone is largely determined by the coach. They will establish a structure within which their players have some latitude to create offense. Rarely do we see this aspect of the game quantified as it’s incredibly fluid and difficult to pin down. However, today we’re going to do just that.

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Neutral Zone Playing Styles

Player A is a sniper. Player B is a playmaker. Quick: If the two of them get a 2-on-1 break, what do you expect each of them to do? Odds are you would expect the playmaker to pass and the sniper to shoot. You may not know how good each of these players is, but the monikers give you a rough idea of this player’s relative strengths and how they generally try to succeed.

We have plenty of different names that explain a player’s general “role”. We use words like sniper, dangler, two-way player, and power forwards (even if we can’t agree on what that last one actually means). However, these names are usually limited to the offensive zone. We have no easy way to describe what a player does in the neutral zone.

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Entry Generation and Suppression

Intro

Hockey analysts have repeatedly shown the value of neutral zone play. If a player performs well in the neutral zone, he or she is helping generate offense for their team and limiting the opponent’s chances. In addition, neutral zone play is repeatable, and the player is likely to continue to drive possession for their team. If you can identify players who thrive in the neutral zone, you are in a position to help your team improve.

But while neutral zone play is important, we still have a very limited understanding of it. Between the distance from the goal, the fluidity of play, and the relative scarcity of data, most people don’t know how players perform in the middle third of the ice. Furthermore, we don’t even have a complete idea of how to make those evaluations. When figuring out how good a player is in the neutral zone, should offense and defense be evaluated separately, or are overall results enough? What skills translate to strong neutral zone play? What playing styles?

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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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SEAL-Adjusted Scoring and why it matters for prospects

While the primary focus of the hockey analytics community has been around roster optimization, there has been a small subset of the community that has worked a great deal on prospect analytics. This includes the work of Gabriel Desjardins’ on NHL Equivalent scoring, Josh Weissbock and Cam Lawrence’s work on Player Cohort Success (since purchased by the Florida Panthers), and Rhys Jessop’s work on adjusted scoring metrics.

As a big fan of prospect scouting and analytics, I wanted to add to the community by expanding upon the work done by Jessop.

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