FQG: Using Goals Above Replacement to Measure Injury Impact

 

Injuries are an inevitable part of the NHL. An 82 game schedule guarantees that all teams are going to deal with injuries during the season but not all teams deal with them equally. Quantifying the impact of injuries is difficult. The introduction of better individual player impact stats gives us some new tools with which to approach this concept. In particular, DTMAboutHeart‘s Goals Above Replacement stat seems a useful place to start because it allows for estimating how many goals above replacement a team loses while a player is injured.

All injury data in this post comes from NHL Injury Viz. GAR data comes via DTMAboutHeart. Games played data comes from Corsica and standings data is via Hockey-Reference.

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Measuring Playoff Excitement

Most of us are by now familiar with the concept of win probability. The current state of the game has many implications on the way the game is played and I’ve been a proponent of using it to adjust statistics as an alternative to using just the score, since win probability itself is simply a function of score and time remaining.

In the spirit of the playoffs today I want to use win probability and corresponding statistic leverage to measure ‘excitement’. Leverage is the total win probability added (and for the opposing team, lost) on account of a particular goal. If a team scores a goal in the last second of the third period, the win probability added would be about 0.5: they went from essentially 0% to 50% chance of winning the game.

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Behind the Numbers: Scientific Progress and Diminishing Returns in Hockey Statistics

Every once-in-a-while I will rant on the concepts and ideas behind what numbers suggest in a series called Behind the Numbers, as a tip of the hat to the website that brought me into hockey analytics: Behind the Net.

As the hockey analytics community pushes for validation of current metrics and their value, I think it is sometimes lost that we do understand these statistics have their weaknesses. We do wish and try to improve upon these weaknesses.

I also think an often underlooked fact is that each incremental improvement diminishes the potential value from every subsequent improvement.

Let’s take a look at what I mean…

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A New Look at Aging Curves for NHL Skaters (part 2)

In part 1 of this series, I looked at how NHL skaters age using the delta method with Dawson Sprigings’ WAR model. As mentioned in my previous article, there is still one major problem with the delta method that needs to be addressed: survivorship bias. The “raw” charts presented in part 1 are quite informative, but they’re missing a correction for this bias. Before we can draw conclusions about what this new WAR metric tells us about NHL skater aging, we need to figure out how to correct for survivorship bias.

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Identifying Playing Styles with Clustering

One of the aspects of player performance that is discussed ad nauseam is chemistry. How well do certain players elevate their performance with one player or another due to some inherent ability to find the other on the ice? To know what a teammate is going to do? However, very little has been done to analyze this phenomenon. In this piece, I posit that by identifying playing styles, something that’s been done in the NBA, we can quantify how well certain players will complement one another.

All data is from 5v5 situations from the 2015 – 2016 and current season, totaling almost 900 games from the Passing Project volunteers and Corey Sznajder. Special thanks to Asmae for her guidance throughout this piece.

I want to stress that this is a first foray into this type of analysis and simply because a player has a different style than what I’ve named (which are relatively arbitrary) it doesn’t mean they are necessarily better than another player. Players may have similar styles, but some will simply be more effective due to their ability. Finally, given that each day we accumulate more data, a player with a smaller sample size could find themselves in a different cluster in future analysis.

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A Defense of WAR from a WAR-Skeptic

Note: This was originally intended to be a tweet-thread which grew far too long and unmanageable, so you’re getting a poorly-written post instead. Apologies in advance.

Recently, David Johnson, owner of the awesome puckalytics.com has been on a bit of a warpath (pun intended) against the use of WAR/GAR. Most of David’s arguments can be found here and here, but there are some other comments in this thread.

I consider myself a bit of a WAR skeptic. I think Dawson’s work is great, but I think there are limitations/issues with it. A good summary of some of my concerns can be found in another ill-advised and long tweet thread.

With that being said though, I still think it’s extremely useful as a first pass to start discussion. WAR can be broken down into 5 useful components to see where a players impact derives from.

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Attention: Scouts Wanted

Here at Hockey Graphs, we can be a little bit intimidating to some scouts, with our differences in approach.

Although our new additions will increase the amount of video analysis we have on this site, this is mostly an analytics site. Even though most, if not all of us, believe both stats and scouting can and should be combined to make decisions, we primarily focus on one aspect: the stats.

For players playing in juniors and in Europe, there is almost no data available besides simple box score stats like points and shots. Although we can form solid models on the little information we have available, we still need to blend in scouting. Scouts can evaluate a player’s skating, stick handling, shooting, positioning, among other things that are inputs into future production and strong advanced stats. The information obtained by scouts is vital to the drafting and signing process in order to fill in those blanks.

The goal for any person who applies math or science to sports management is to decrease human error. Scouts, like everyone, have many biases which need to be accounted for, and it is close to impossible to adjust for them. Even when people are made aware of their bias, they cannot overcome it. So it is alright to be a scout that tends to notice tall players first, we just need to find a way to adjust for that in order to get the fairest assessment.

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Friday Quick Graphs: The Dangers of Binning Data

If you’ve ever read a little math, you likely know the dangers of binning continuous data when testing relationships between two variables. It is one of the easiest and most common mistakes that an amateur statistician might make, largely because, intuitively, it seems like it should make sense.

But it doesn’t, and here’s why.

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Exploiting Variance in the NHL Draft

Recently, one of my VanHAC slides was used to justify the Bruins’ decision to take Trent Frederic over Alex DeBrincat (and many others) in the first round of the 2016 NHL Draft. Needless to say, I haven’t slept soundly since then.

Unfortunately, crafting a solid rebuttal isn’t as easy as saying “DeBrincat has a higher ceiling.” To that end, I present a framework for evaluating these types of draft decisions. There are two basic questions to consider:

  1. What are the outcomes for each player?
  2. How can we appropriately value these outcomes?

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Friday Quick Graphs: Points Per Goal

In my last post, I based some work on The Numbers Game by Chris Anderson and David Sally. It was a fun book about analytics in soccer even though I do not have much of a background in soccer. There was one other section of the book I found particularly applicable to hockey. They created a few charts on the expected number of points a team gets depending on how many goals they score in that game. I went through every regular season game from 2007 – 2016 to produce the below version for the NHL:

Plot 1 - PPG Overall

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