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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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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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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Friday Quick Graphs: Navigating the Trade Deadline’s Hype

This year’s trade deadline was uneventful. March 1st was filled with a bunch of small trades that we probably made a bigger deal out of than we should have. However, just a little over two weeks have gone by and people are already looking for a winner. As a follower of analytics, it would be unfair of me to decide less than ten games in who won the deadline. Mainstream media gets a ton of clicks for those posts though, so let’s evaluate them.

A post from Sportsnet found that the last trade of the deadline held the most value. The Bruins traded a 6th round pick to the Jets for Drew Stafford. Stafford has had the worst season of his career. His -3.38 rel CF% is by far the worst of his career, his all situations 1.74 points per 60 is below career average, and he has suffered from the second lowest shooting percentage of his career. The question is: where is the value in Drew Stafford?

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Friday Quick Graphs: League Wide Report

 

The All-Star break is now in the past. The trade deadline is less than two weeks away. Teams across the NHL have a pretty good idea of who they are. They know their strengths and weaknesses. The possible outcomes for their seasons are narrowing. Some teams are already locked into playoff spots and only have to worry about positioning. Others will have to slowly accept the reality that this isn’t their year and consider how that impacts their approach at the deadline. This is a perfect time to take a high-level view of the league and look at each team using a series of simple metrics to help get a grasp on where all thirty teams are sitting.

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FQG: Were Julien’s Bruins Gaming their Corsi?

This week, the long-speculated dismissal of Boston Bruins’ coach Claude Julien finally happened. After 759 games, 419 wins, a Stanley Cup, and a Jack Adams trophy over his almost 10-year run in Boston, Julien is a free agent coach, free to mull options like the Vegas Golden Knights, the New York Islanders, and a slew of other head coach positions that are almost certain to be offered to him as the season goes on.

Every coach gets fired sometime. Julien, great as he was, wouldn’t escape this fate either.

But the fallout since his dismissal has been intriguing. The Bruins led the NHL in adjusted Corsi for percentage under Julien this season but sunk to 28th in the in team shooting percentage and 24th in team save percentage this week.

How can we reconcile these contrasting stats?

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NWHL Shot Leaders

In anticipation of the NWHL All-Star Game (Feb 11-12), I wanted to look at which NWHL players contribute the highest % of their team’s shots on goal. This is simply the number of shots the player has taken, divided by the number of shots the player’s team has taken.

nwhl-shots-bar-plot-team-colors

As the graph shows, Brianna Decker and Shiann Darkangelo lead the league in % of team shots at 19% each. Haley Skarupa, a rookie, leads the Conneticut Whale at 15% of the team’s shots. This is doubly impressive, as the Whale also lead the league in shots. Madison Packer leads the New York Riveters at 12%.

The code for this graph can be found on my Github page.

Friday Quick Graphs: Marginal Gains for Forwards

Screen Shot 2017-01-19 at 1.30.05 PM.png

How many goals is improving a team’s first line worth versus your fourth line?

The above graph shows the number of goals over a season a team should expect in improving their player’s shot differential talent, here described in percentiles of talent.

The blue line is first liners with 2nd, 3rd, and 4th liners falling next with red, yellow, and green.

The blue line is the steepest, suggesting that moving from a 55th percentile player to 60th percentile player on the top line will improve a team’s goal differential by about twice that of a 2nd or 3rd line player. (This is not to be confused with improving from a 55% Corsi player to a 60% Corsi player)

What is interesting is that the marginal gains in improving a 2nd line player and 3rd line player is about equal.

The next question one should ask is: what are the costs in salary and cap hit for making said improvements?

Method:

  1. All forwards over all available full seasons were sorted by 5v5 TOI/GP
  2. Players binned into four groups of equal number of games played
  3. Each bin then sorted by Corsi%, and binned into percentiles
  4. Goal differentials are extrapolated to full season given average TOI per season for each line (so differing rates in injuries and pressbox banishment is being included)

Friday Quick Graphs: Total Player Charts, Revived

Bringing back an older concept…a few years ago, I was spurred by Tom Awad’s “Good Player” series to put together these radar charts of player ice-time. I’d always felt, for fantasy hockey purposes, it is important to know the boxcars (goals, assists, points) come from the ice-time as much as anything, and so the initial creation of what I called “Total Player Charts,” or TPCs, was to portray precisely that. It ended up that they gave intriguing portrayals of players that we felt had strong seasons. See Jamie Benn’s above; an Art Ross Trophy, sure, and much of it came from near the top share of playing time at evens and on the powerplay, league-wide. You can also get a sense of just how valuable a defenseman like T.J. Brodie is:

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Friday Quick Graphs: Are the 2014-15 Buffalo Sabres the Worst Team of All-Time?

This is part-opportunity to finally explore this question, and part-opportunity to tout some existing and upcoming data visualizations for HG. Travis Yost has been following the absolutely terrible Sabres season all year, and has raised some questions about whether it’s an all-time worst team. He’s only been able to reach back to the admittedly bad early 2000s Atlanta Thrashers, but the historically bad team by which all others need to be measured is the 1974-75 Washington Capitals squad. Using an historical metric like 2pS%, or a team’s share of all on-ice shots-for in the first 2 periods (expressed as a percentage), we can bring the 2014-15 Sabres together with the 74-75 Caps to see where both teams stand. Note: I used the cumulative version of the measure below, and added lines for one standard deviation below league-average in both seasons.

For as bad as Buffalo has been, they haven’t quite matched the futility of the 74-75 Capitals…nor should they. The Capitals were an expansion team that year, and unlike in other years the NHL did not really reach out to ensure the expansion teams in 1974-75 were given a good base to build from. These were also the peak years of the World Hockey Association, which made professional level talent even more diffuse than normal. The other expansion team in 74-75, the Kansas City Scouts, lasted two years before moving to Colorado to become the Rockies (the team subsequently moved to New Jersey in 1982-83 and changed their name to the Devils).

I included the standard deviations for the leagues in 1974-75 and 2013-14 (I haven’t compiled the data for 2014-15 yet, but this should be close enough), and even by those markers the Capitals compared markedly worse to their league than did the Sabres. But once again, the Capitals had a reasonable excuse, while the Sabres have walked into this situation with eyes wide open.

For those interested, I also put together 2-period shots-for and shot-against rates (and stretched them out to per 60 minutes) to get a rough sense of offense-versus-defense for both teams.

I added a couple extra filters to the charts, league-averages and standard deviations as well as 20-game moving averages in all the measures I used, which you can select by clicking on the grey “Team” bars and clicking on “Filter.”