Revisiting Relative Shot Metrics – Part 2

In part 1, I described three “pen and paper” methods for evaluating players based on performance relative to their teammates. As I mentioned, there is some confusion around what differentiates the relative to team (Rel Team) and relative to teammate (Rel TM) methods (it also doesn’t help that we’re dealing with two metrics that have the same name save four letters). I thought it would be worthwhile to compare them in various ways. The following comparisons will help us explore how each one works, what each tells us, and how we can use them (or which we should use). Additionally, I’ll attempt to tie it all together as we look into some of the adjustments I covered at the end of part 1.

A quick note: WOWY is a unique approach, which limits it’s comparative potential in this regard. As a result, I won’t be evaluating/comparing the WOWY method further. However, we’ll dive into some WOWYs to explore the Rel TM metric a bit later.

Rel Team vs. Rel TM

Note: For the rest of the article, the “low TOI” adjustment will be included in the Rel TM calculation. Additionally, “unadjusted” and “adjusted” will indicate if the team adjustment is implemented. All data used from here on is from the past ten seasons (’07-08 through ’16-17), is even-strength, and includes only qualified skaters (minimum of 336 minutes for Forwards and 429 minutes for Defensemen per season as estimated by the top 390 F and 210 D per season over this timeframe).

Below, I plotted Rel Team against both the adjusted and unadjusted Rel TM numbers. I have shaded the points based on each skater’s team’s EV Corsi differential in the games that skater played in:

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Revisiting Relative Shot Metrics – Part 1

Relative shot metrics have been around for years. I realized this past summer, however, that I didn’t really know what differentiated them, and attempting to implement or use a metric that you don’t fully understand can be problematic. They’ve been available pretty much anywhere you could find hockey numbers forever and have often been regarded as the “best” version of whatever metric they were used for to evaluate skaters (Corsi/Fenwick/Expected Goals). So I took it upon myself to gain a better understanding of what they are and how they work. In part 1, I’ll summarize the various types of relative shot metrics and show how each is calculated. I’ll be focusing on relative to team, WOWY (with or without you), and the relative to teammate methods.

A Brief Summary

All relative shot metrics whether it be WOWY, relative to team (Rel Team), or relative to teammate (Rel TM) are essentially trying to answer the same question: how well did any given player perform relative to that player’s teammates? Let’s briefly discuss the idea behind this question and why it was asked in the first place. Corsi, and its usual form of on-ice Corsi For % (abbreviated CF%) is easily the most recognizable statistic outside of the standard NHL provided boxscore metrics. A player’s on-ice CF% accounts for all shots taken and allowed (Corsi For / (Corsi For + Corsi Against)) when that player was on the ice (if you’re unfamiliar please check out this explainer from JenLC). While this may be useful for some cursory or high-level analysis, it does not account for a player’s team or a player’s teammates.

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The 2018 NHL Trade Value Rankings

Most years, the NHL trade deadline is basically the equivalent of an annual Y2K party: Much Ado About Nothing. The issue comes from the underlying inertia the permeates most of the league’s landscape.

The best players almost never switch teams in their prime (Seriously, who was the last top 10 player to leave their current team? Marian Hossa?)

Even when a trade does get made, there’s often no rhyme or reason to how it plays out. Sometimes you trade your team’s top disgruntled forward and get Seth Jones. Sometimes you get Adam Larsson.

So, to give the league’s decision makers a little kick in the butt, I’ve put together a trade model that identifies the trade value of every regular NHL player and determines what would be a fair return in a trade.

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An Introduction To New Tracking Technology

The first significant breakthrough in hockey analytics occurred in the mid-2000’s when analysts discovered the importance of Corsi in describing and predicting future success. Since that time, we’ve seen the creation of expected goals, WAR models, and more. Many have cited that the next big breakthrough in hockey analytics will come once the NHL is able to provide tracking data. We’ve already seen some of the incredible applications of the MLB’s Statcast data and the NBA’s SportVu data. Unfortunately, the NHL has no immediate plans to publicly provide this data and as such, many analysts have decided to manually obtain the data.

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How Much Do NHL Players Really Make?

Under the provisions of the collective bargaining agreement between the NHL and the NHLPA, a player’s cap hit and the salary they are paid can be two very distinct values in any given year. But even when you understand those differences, how much do NHL player actually take home?

Players’ actual earnings are diminished by a number of factors including escrow, agent fees, and taxes. Agent fees can range from 2-6% depending on representation agreements and services rendered. Tax rates vary throughout the NHL depending on the country, state, and city a team and player reside and play in. But of all the deductions from their income, escrow might be considered the greatest annoyance, as it’s a mechanism to ensure that the owners collect a greater share of hockey-related revenues (HRR) than they have in previous collective bargaining agreements (CBA).

So what is escrow, how much does it actually deplete a player’s salary, and why has it contributed to the tensions between players and owners?

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Women’s Olympic Hockey Predictions

It’s the Olympics again, which means it’s time for everyone’s favorite activity: watching Canada underperform at ice-hockey! And while Hilary Knight breaking the hearts of Canadians is fun for everybody, the only thing that’s more fun is watching Hilary Knight break the hearts of Canadians while you have a statistical model that predicts each team’s likelihood of winning a medal! That’s right, Hockey Graphs is taking on the challenge of predicting the Women’s Olympic Hockey Tournament results.[1]

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Goal Scorer Cluster Analysis

“They don’t ask how. They ask how many.”

-Hockey Proverb

“But seriously though… how?”


To state the obvious: goal-scoring is an essential skill for a hockey team. Players have made long careers by putting the puck in the net.

But how do players create goals? Skaters rely on all sorts of skills to score; some are fast, some have a huge shot, and some know how to be in the right place for an easy tap-in. But we don’t have a rigorous view of what those skills are, how they fit together, and which players rely on which ones.

In this piece, I take 100 of the top NHL goal-scorers and apply unsupervised learning techniques to group them into specific goal scoring types. The result is a classification that buckets the scorers into 5 categories: bombers, rushers, chance makers, chaos makers, and physical forces. These can help players understand how to apply their skill set to goalscoring. It can also help teams make sure that their system is putting their top players in a position to score.

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Hockey-Graphs Podcast Episode 9: Erik Karlsson and Market Value

Chris Watkins joined Adam Stringham to discuss some of his new work and Erik Karlsson’s recent comments. Is the NHL entering a new age of superstar transition? Will the leagues best players start jumping around in free agency? Any comments are appreciated, the goal is to produce a podcast that people want to hear. Please subscribe to the podcast on iTunes!

Estimating Shot Assist Quantities for Skaters


Hockey fans and analysts have always appreciated the importance of passing. But until the passing project led by Ryan Stimson, we couldn’t quantify that importance. His work supported by a team of volunteers and other analysts has established that the passing sequence prior to a shot is a significant predictor of the likelihood of the shot becoming a goal. His work also showed that measuring shots and shot assists combined as shot contributions is a better predictor of future performance for both players and teams than shots alone.

Knowing that, the logical next step is to use passing data in analysis whenever possible. Unfortunately, the NHL does not provide passing data so it must be manually tracked by people like Corey Sznajder. Corey’s work is invaluable and I encourage you to support him but he’s only one person.

This article attempts to estimate a player’s quantity of shot assists in a given sample using publicly available data to help fill in gaps where tracked data doesn’t exist.

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