Improving Opposition Analysis by Examining Tactical Matchups

On Monday, I introduced some work on quantifying and identifying team playing styles, which built upon my earlier work on identifying individual playing styles. Today we’re going to discuss how to make this data actionable.

What are the quantifiable traits of successful teams? What plays are they executing that makes them successful? How can we use data to then build a style of play that is more successful than what we’re currently doing? The way we bridge the gap between front office and behind the bench is by providing data to improve their matchup preparation, lineup optimization, and enhance tactical decisions.

This is what I mean by actionable: applying data-driven analysis and decision-making inside the coach’s room and on the ice. All data is from 5v5 situations and is either from the Passing Project or from Corsica.

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Identifying Team Playing Styles with Clustering

Last time, I looked at individual playing styles by clustering players together based on various passing metrics. Today, I’m going to use a similar approach to identify team playing styles and what we can learn about them. I got the idea watching this video on NBA offensive styles (stick tap to @dtmaboutheart for the link). It’s been sitting in my unfinished pile for a while, but I was spurred on to finish it by some comments made about the Washington Capitals and Pittsburgh Penguins series, which I will delve into tomorrow. Today’s piece is to going provide examples of how passing metrics can provide more detailed and actionable scouting reports for a team’s offensive and defensive tendencies.

All data is form 5v5 situations and is either from the Passing Project or Corsica.

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Stop Worrying About Shot Parity

There has been a lot of talk about parity in the NHL lately. Specifically shot attempt parity. The dispersity of corsi for percentage is more centralized this season than any season in the past decade, and people have

This visualization is hard to read, but if we graph the standard deviations of corsi, expected goals (Corsica), and goals, we can get a pretty good idea of the movement towards or away from parity.

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The 2017 NHL GM Report Card – Part 3

It’s been a crazy couple of days writing up this general manager project. If you haven’t already, please read the methodology before checking out our final list of rankings.

When going through the final rankings there were several interesting things that only show up when the data is viewed holistically. Here are some of our big findings that didn’t make it into the rankings piece.

GM Ranking

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The 2017 NHL GM Report Card – Part 2

(This piece was written as a collaboration between Carolyn Wilke and Chris Watkins)

Alright, we’re only a little bit sorry we made you read our methodology post first, because we know what you really want is below. Still, we recommend you understand how we came to our ratings before you continue reading this post.

We’re sure you’ll disagree with us on some points, and that’s fine – despite our best efforts, these are still fairly subjective ranks. Still, try this exercise for yourself, and it’s possible your opinions will change.

Now, without further ado – all 31 GMs, ranked.

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The 2017 NHL GM Report Card – Part 1

(This piece was written as a collaboration between Carolyn Wilke and Chris Watkins)

What makes a good general manager in the NHL?

It’s a hard question, plagued by subjectivity, by bias, and by lack of transparency. It’s complicated by league mandates like the expansion draft and the hard salary cap. It mixes the weight of process, results, and vision into one big stew, where it can be difficult to distinguish the meat from the sauce.

It’s a question, that unlike many others, is difficult to quantify with even the most advanced of stats.

And it’s one that the league has no desire to answer definitively, as that would only hurt the men currently in those roles.

Fortunately for you, Hockey Graphs loves tackling the hard questions.

In the following articles, we will attempt to rank all 31 of the NHL’s GMs, as objectively as possible, according to seven important criteria. They each painstakingly researched trade histories, draft selections, and salary cap management, coming up with a final score for each.

While this process still was subjective, in that these scores are not quantitatively derived, it was an extremely holistic process, and both of us were forced to confront some of our own biases.

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How Indicative are hits in the 2017 Stanley Cup Playoffs: Quarter-finals

As the Stanley Cup Playoffs progress, the intensity rises. This often leads to more physical play, thus an increase of hits. Hockey traditionalists, including players and coaches, have often pointed to increased hits as a part of playoff hockey. Some teams have altered their strategy to embody a more physical style, simply because it is the playoffs.

The impact of hitting has been explored before during the 2014-15 season, the 2015 playoffs (both by Garret Hohl), and the 2016 playoffs (by @yolo_pinyato). However, none found a decisive correlating success to hits.

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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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