The 2015 OHL Final: Oshawa Generals Passing Network

Last time, I took a look at the passing network for the Erie Otters during the 2015 OHL Final. Today, we’ll take a look at the passing network for the Oshawa Generals. Below you’ll see their network constructed using Gephi.

Oshawa_Network

Another reminder on how to read the visual: The larger and darker the node (player), the higher number of edges (connections or passes) to another player or goal (shots) that player had. Rather than simply total up the passes and shots, I’ve assigned weights based on whether that pass led to an actual shot on goal, was a scoring chance, was a danger zone pass (from behind the end line or across the Royal Road), resulted in a goal, etc. The edges (lines) between players are weighted as well, so you can tell which players were setting up a higher number of chances for specific shooters.

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How can we measure a goalie’s rebound control? Examining Pekka Rinne and James Reimer.

Embed from Getty Images

Pekka Rinne is good at controlling his rebounds. I know this, because people on the internet have made their opinions abundantly clear. Scouts and fanalysts alike credit Rinne’s quick glove hand with helping him catch a significantly higher volume of shots than most other goalies, leaving few opportunities behind for lurking opponents to deposit into his net.

James Reimer is not good at controlling his rebounds. I know this, once again, because people on the internet have made their opinions abundantly clear. Reimer’s (supposed) inability to prevent the shots he’s saved from bouncing into dangerous areas is often cited as one of the main reasons for why he should be the #2 goalie behind Jonathan Bernier on the Leafs’ depth chart.

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Revisiting Imbalanced Drafting Strategies

Photo by user

Photo by user “Tsyp9”, via Wikimedia Commons.

At Hockey-Graphs, we like to provide data-based answers to questions. It’s what we do. But it’s also good to recognize issues in the analytics world that haven’t yet been addressed. Sometimes that’s the case because we don’t have the data we need available, and sometimes it’s because the question has yet to be properly framed. It’s important to know what we don’t know, and to talk about it regardless.

There has been some great draft work done at our site and elsewhere in the last few years, and one of the findings has been the volatility of drafting defensemen relative to forwards. Couple that with claims that forwards have more of an impact on shot rates than defensemen, and one would be tempted to claim that avoiding defensemen altogether would be a solid draft strategy (though I’ll note that most analysts think this is taking the conclusion too far).

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Why linemate and competition metrics may not be as simple as we think

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Competition Histograms by Eric Tulsky from NHL Numbers, Sep 23, 2012

ABSTRACT

We know that linemates have a larger impact on results than competition on the average. This has caused many to change player deployment chart axis from QoC to QoT metrics.

However, it’s not quite that simple.

The area of contextually nuanced studies with numbers like competition and teammate metrics is still well in its infancy. We have a general idea of what’s going on but there is a lot of information in the details.

We show here that a 1 percentage point change in teammate and competition Corsi% has an equal but opposite impact on observed output, but there are some differences. The distribution in the NHL is much smaller with competition. However, unlike with competition Corsi%, teammate Corsi% impact is not the same for all players.

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Paul Bissonnette is Wrong and Right

Photo by Michael Wifall, via Wikimedia Commons; altered by author

Photo by Michael Wifall, via Wikimedia Commons; altered by author

From the outset, I want to say the Player’s Tribune, conceptually, is a wonderful thing. To have players guest post or answer questions without the emotions of a post-game presser or rigid formality of a journalist interview provides great insight to their personalities. And just like anybody we’d encounter in daily life, they say things we agree with, things we don’t agree with, or things we might’ve worded differently. Take, for instance, today’s “Mailbag” with Paul Bissonnette. A majority of the interview, which were questions from readers, were your general enforcer interview questions: best fight, worst fight, scary fight, do you like to fight, etc.

But then there was this final question, which I can only assume came from Mark Spector:

Bissonnette Players Tribune II

Bissonnette’s response, his longest of the interview, was chock full of wrong, with plenty of right on the side.

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Sunday Notes: September 13, 2015

Math lecture at TKK.JPG
Math lecture at TKK” by Tungsten – photo taken by Tungsten. Licensed under Public Domain via Commons.

Welcome to Sunday Notes, where we try to rehash important developments occurring on Hockey Graphs and elsewhere in the CORSI twitter league in less than 500 words. I’m sorry if we forgot about your post, or misconstrued what you said. We don’t care. Don’t @ us. Just do better next time. – Asmaen

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The Pressures of Parity

File:Balanced scale of Justice (blue).svg

Two nights ago, when no one was looking, I tweeted out a telling statistic to understand how teams have reacted to the salary cap post-lockout.

Boulerice wasn’t the only one scraping the bottom of the barrel in 2005-06; Colton Orr was nearby with his 2:49 per game, and you didn’t have to look much further to see Andrew Peters (3:15) and Eric Godard (3:27). In fact, 19 skaters played over 20 games that season and recorded even-strength TOI/G lower than Peluso’s from this year. Teams have realized that, in a salary-capped league, even league-minimum dollars can’t justify players who cannot be trusted with regular minutes.

This was a fairly stark evolution of player usage, but it led me to wonder if there were any other things we could see by looking at finer-grained data from 2005-06 to the present. The salary cap was a game-changer because it pushed teams at the top and bottom closer together, and that compelled teams to stop employing players they couldn’t trust at evens; what are some other areas we see the pressure of parity?

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Why The Los Angeles Kings Missed the Playoffs: An Open Email

I’ve been asked by a couple of people how a team with a normal PDO and strong metrics could have missed the playoffs entirely. It’s an important question to address, particularly because the playoffs are so much more important than worrying about whether you’re lucky enough to win the Stanley Cup. I composed an email response, and felt good enough about it to open it up. While this doesn’t comprise the whole of the explanation (certainly, there’s some “blame” that goes to Calgary & Winnipeg), they’re points that I’m not seeing made elsewhere.

Hi XXXXX,

A couple of things really hurt the Kings. One is a cruel fact of a low-scoring league: if more games are going to be decided by one or two goals, it increases the likelihood that a fluky goal can impact a team in the standings. The Kings had the most overtime losses in the Western Conference; last year they were tied for the second least in the West. The second thing is the tank battle…the West had two teams with historically bad records – add in games against Buffalo, and we have three teams that will end the season with point totals that were typically reserved for the sole worst team in the league in other seasons. On the flip side, that creates a rising tide for all the other ships in the league, and raises the bar for getting into the playoffs. I mean, needing to get nearly 100 points to get in? Last year, the bottom team in the West, Dallas, had 91 points. A nearly identical record to this year got Los Angeles into the playoffs in the 8th seed in 2011-12.

Maybe the closest comparable circumstance was 2010-11, when the West again had two sad-sack teams (Colorado, Edmonton), and the East was noticeably weaker than the West. It took Chicago 97 points to get in. Also, look at 2006-07…Colorado didn’t make it with 95 points, having gone 44-31-7 during the season. If the West is considerably stronger than the East, as it was back then, you could also end up with a tougher path to making the playoffs. In ’06-07, every team in the Western Conference, save the 8th seed (Calgary, with 96 points), had 104 points or more!

Anyway, this year’s league created a scenario where a good team, by any measure, might not get in. The Kings went 39-27-15, outscored their opponents by 12 goals (in fact, they tied for 2nd in the league in goal differential at even strength), and could get 95 points and not make the playoffs. In the loser point era, there were only two seasons that was even possible, and both occurred in the stronger Western Conference. It’s a successful season by anything except the fluid marker of the playoffs, which unfortunately for them is all-important to reach.

Hope this helps,

Best,

Ben

Note: One critique I’d like to address – yes, all teams in the league are theoretically dealing with the tank battle, but tanking doesn’t occur across the entire season, which means that teams that have already played most or all of their games against tanking teams earlier in the year won’t have the benefit. Additionally, those same teams might have the resulting, added pressure of a more-difficult set of opponents through the latter portion of the season. If the difference between making the playoffs versus not is a matter of a few points, the difference in scheduling can become all the difference in the world.

Who are NHL Coaches Playing More with the (Big) Lead?

Photo by Michael Miller, via Wikimedia Commons

Johan Larsson’s TOI% jumps 8.5% when the Sabres are leading by 2 or more goals (which is never). Photo by Michael Miller, via Wikimedia Commons

Right out the gates, I knew two things: 1) I wanted to take TOI% data from close scores and subtract it from TOI% data from 2+ goal leads, and 2) that it would automatically tell us that perceived poorer players are given more playing time with the lead. Why? Because they tend to play less when the score is close, which increases the likelihood that a differential with 2+ goal time on-ice will show they get to play more with a big lead. That said, I wanted to run a quick study to see just how much of a difference that time swing could be, and which players come out of the woodwork on either end.

But first, I want to whittle away the small sample players, and to do that I’m going to run a quick test to see at what # of games played this TOI 2+ minus TOI Close differential (let’s call it “TOI Lead Diff”) stabilizes.

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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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