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

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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.”

Spread of NHL Team Shooting Performances, Year-to-Year 1952-53 through 2013-14

Sort of a mid-week quick graph…I’ve been compiling data for a different project and curiosity got the best of me to see what the spread in team shooting percentages was in NHL history. We all know that shooting percentage in the NHL went up substantially during the 1980s, but what you’re seeing above is one of the reasons why we theorize that shot quality and team shooting talent might have figured more greatly in outcomes in the 1980s than it does today. With some exceptions, the standard deviation seems to have settled from about 1996-97 to the present at just under 1%, which suggests our expectations from one year to the next should only allow a team that much of a bump above or below league-average. It’s worth noting that sample will affect this measure, hence why our line is so spiky during the Original Six era, and why 1994-95 and 2012-13 might have not been as characteristic of a trend. Incidentally, this is shooting percentage for all situations.

Note: As mentioned by a reader, increased scoring is going to work together with this standard deviation to accentuate the differences between teams. League-wide, the shooting percentage and standard deviation move well enough together to cause this effect, usually portrayed by coefficient of variance, to regress heavily from 1965 to the present. The exceptions, though muted, would be the early 1980s and the more recent years of Dead Puck, so the standard deviation fairly accurately represents our variance above. CoV data:
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Friday Quick Graph: Player Career Charting by Percentage of Team Shots, 1967-68 to 2012-13

Embedding interactive graphs into blog posts, especially blogs with a narrow runner like ours, is frequently an awkward process. Just about the time things look good, you tinker with it and it looks bad. Nevertheless, I had a bunch of old data I put together, once upon a time, and I wanted to get it out there in a form that you could tinker with. Basically, in the past I have used the percentage of team shots in the games a player participated (%TSh; explanation here) as a way to capture a player’s contribution to the shot load; I also think it strongly implies a player’s involvement and contribution to team offense overall.

In the case of today’s graph, I took %TSh and looked at aging curves with a multitude of players from 1967-68 through 2012-13 (like I said, the data is a little old). I prepared this with a selected group of players available for the filter, the majority of whom are stronger, more familiar players of the years covered. I also included some players that struggled by the metric, for the sake of comparison. To filter, click on the “Name” bar, click on “Filter,” and let your imaginations run wild. Feel free to download if you wish.

Note: I believe I set the cut-off at 20 GP before I would record the point of data. It’s old. I’m old. We’re all getting older.

NHL Player Size From 1917-18 to 2014-15: A Brief Look

Image by Erich Schutt, via Wikimedia Commons

Image by Erich Schutt, via Wikimedia Commons

As any person interested in hockey stats should do, I’ve been gradually building my own personal database of player information that I can use when Y3K robs my future post-human self of cloud data for 3 seconds. To that end, player size wasn’t a huge priority but I knew eventually I’d want to have it, if only to think about how normal-sized I’d be in the 1920s NHL. In the process of bringing in all that data, I decided to do a little demographic work on player height and weight. We all know the players are bigger now than they were before, but by how much? And is there greater variance in size now or in the past?
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Friday Quick Graph: Does puck possession affect penalty differentials?

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Using data from War-On-Ice.com, I grabbed the penalty and Corsi differentials for all teams for 5v5 score tied minutes. The whole point was to look at whether or not possession plays a role in a team’s penalty differential.

Above we see a weak but real relationship, with about 6.7% of penalty differentials being explained via possession.

From the regression curve, we estimate the average impact difference between a top and bottom possession team is about 11 penalties drawn per a season for 5v5 score-tied minutes. Of course, there is the opportunity to draw penalties for other team strengths and score situations. (The bottom/top difference is using the 40-60 rule)