On Fourth Lines and Weighing Intangibles

calclutterbucknyi

CalClutterbuckNYI by Lisa Gansky. Licensed under Creative Commons via Commons.

I was listening to this great episode of the PDOCast in which the guys discussed the recently waived Alex Semin and had a couple of additional thoughts on evaluating fourth liners, and on how the mold of the bottom-six forward has evolved over the years.

First of all,  I don’t believe in ever dismissing conventional hockey wisdom. There is wisdom in experience, and guys who have been around hockey for a long time have deep insight that can shed a lot of light on the game. Let’s take fighting for example. A former player might tell you that a good fight can swing momentum, and a quant might (very many have) dismissively wave them aside.

“There is no proof of that. Run along now.”

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The Dangers of Shooting and Save Percentage Graphs

Chart courtesy of @kikkerlaika

Chart courtesy of @kikkerlaika

Regression is a dangerous word.

That’s especially true because with the increase in the popularity of measures like PDO, fans have become prone to yell the term in a (figurative) crowded theatre and then run away. Regression is the beginning of the discussion, not the end. Teams don’t all regress to the same values, or at the same rate. Basically, tread with caution.

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How Do I Know If My Team Is Any Good? Introducing Inceptum

ButlerVolmer1 by

ButlerVolmer1 by Enseeg via WikiMedia Commons.

Corsi has a lot of flaws. First of all, it’s not an accurate measure of possession. Corsi is just shot attempts, so it doesn’t actually measure how often a team has the puck on its stick, or its time in the offensive zone, or any other useful metric like that. Second, all shots aren’t created equal. Corsi treats a feeble wrister from the point with no traffic in front the same as a point-blank one-timer in front. Finally, it doesn’t take into account compete level or chemistry. I’m not sure why people try and use Corsi to evaluate teams.

I have come up with a far superior way to evaluate them. I called it Inceptum. Inceptum is a little difficult to explain, but the important thing is that it does a good job at predicting what will happen for the rest of the season. So if you want to know whether the team you support is as good as (or better than) its record, don’t look at goal differential, don’t look at Corsi — which isn’t even real possession — look at Inceptum, which has been shown to do a better job of predicting the results from the rest of the year than any box-score measures.

Goal differential after 10 games, for example, explains 23% of the variance in end of season goal differential, while Inceptum explains 32%! My metric is certainly not perfect, and one always has to take into account contextual factors and the eye test — luck plays a big role as well — but it’s one of the best evaluative tools we now have.

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Hockey Abstract 2015 and the Skills Needed For Analysis

I was thinking today about the skills that it takes to be able to analyze hockey properly, and it took me back to classroom learning. As somebody who hated memorization, it was always a relief to me when a teacher explained that we didn’t need to know something specific for the test. Providing a periodic table, or a t-table, or allowing us to write our own “cheat sheets” for a test was interpreted as a measure of sympathy by the professor. “I know this stuff is hard as hell; I’ll cut you a break and relieve you of a little studying.”

The truth, though, is that allowing the use of these materials, or going as far as holding open-book tests, has practical legs. In the real world, whether in science or math – really in most fields – it is more important to be able to find and interpret information than to know it offhand. If one needs to perform a chi-squared test, for example, in the internet age I can find that information very easily. The more experience one has in the field, the less one will need to rely on guides to perform such calculations, but until that point there is no need to hold information that can easily be found.

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What Was Wrong With The 2014-2015 Boston Bruins?

Johnny Boychuk 2013-06-07” by Michael Miller. Licensed under Public Domain via Commons.

Joe Haggerty posted a revealing piece last week about what went wrong with the Boston Bruins last season. According to Brad Marchand on the record, as well as a variety of sources off it, the B’s were a divided team, with only part of the group truly on board with the team’s march to the playoffs. Others, he claims, without naming names, didn’t seem particularly bothered to miss out.

It’s a very interesting case, and unfortunately it’s impossible to think of it in terms of an actual case study, since there’s nothing scientific about the way in which players may discuss their perception of the causality involved in failure, and certainly everything a front office says with regards to move it makes has to be taken with a grain of salt. That said, the eye-rolling that some in the analytics community may take part in with regards to this story is a mistake. Dressing room chemistry is important. Leadership is important. And this story does contain some important lessons. Let’s take a closer look.

“In the past years, we were family, but for some reason this past year we were definitely a little bit divided, and had different cliques. It could’ve been because we had a lot of guys coming up in different times from Providence; they felt a lot more together, and it seemed like the older guys didn’t do a good job at integrating other guys.”

There’s no reason to distrust Marchand on this point. I completely believe that the team was divided, and it’s quite possible that guys coming and going from the AHL played a part. Every team deals with those comings and goings to different degrees, but a lack of leadership and communication could play a part in those guys not being well integrated. Just from personal experience, I can tell you that playing on a team when you feel well-liked, or a part of the group, is a lot easier – and often leads to a better performance – that when you feel ostracized or the unity just isn’t there.

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Shootout Game Theory: Should Shooters Want Goalies Guessing?

Jack Han wrote a cool piece the other day about the shootout and game theory. He had a number of different ideas, but I want to address one in particular.

I believe his point was as follows.

“As a shooter in the shootout, if you are unpredictable, the goalie won’t know what is coming and will play you straight up. If, however, you have one prominent move and a lesser-used secondary option, the goalie is likely to know that and cheat, allowing you to score more often on your secondary option, which overall will increase your effectiveness.”

I want to look at this point within the unrealistic context of an NHL goalie having complete information on the shooter’s true shootout talent, ie their base rate, and the percentage of the time in which he uses a primary move relative to a secondary one.

So let’s say you’re a league average shootout performer with two moves (let’s say a backhand deke and a backhand-forehand deke). When the goalie plays reactionary, you score on 33% of your shots. You can, however, decide to adjust this rate by leading the goalie into guessing by using your primary move significantly more than your secondary move. The goalie, as I mentioned above, knows how much you use each move, just not in which cases you will use which.

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A Note on Terminology, Ice Time and Production

The TSN Panel just had a conversation about Matt Beleskey and the kind of production a team can expect from him as a sought-after UFA this summer. Beleskey has been put into the same conversation as David Clarkson in 2013 as a player who will likely be overpaid due to a season in which he scored 22 goals on 15.2% shooting and had a playoffs that raised his profile even more with 0.5 goals per game on 17.8% shooting.

Ferraro made the point that Beleskey will only be a worthwhile signing if he is played in a top line role, with guys like Perry and Getzlaf, and McGuire added that he believes in such a situation the power winger could put up as many as 25 goals. But I think this type of discussion is missing the point. The goal for a general manager, after all, is to maximize team wins and thus team goals (both for and against but in this case we’ll focus on goals for).

Sure, if you put Beleskey in a first line role and give him 18 minutes per game (he averaged 14:29 this year), he’s more likely to put up 20 goals on say 180 shots, which is an 11.1 shooting percentage, something that would seem a lot more “sustainable”. But at what cost?

It makes sense to want to play a net-rushing garbage-goal winger with skilled players to maximize his skill set, but you can’t base your team structure around making a UFA deal you offered look like it paid off. If you find yourself in a position where you HAVE to play a guy in a top line role to make a deal seem worthwhile, you aren’t doing things for the right reasons.

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Two Reasons Why Evaluating a GM Based on His Draft Record is Dangerous and One Solution

I don’t have a ton of time to blog at the moment with finals coming to an end, but just wanted to throw this up quickly with Ray Shero becoming the New Jersey Devils’ new General Manager and the questions about his seemingly poor draft record. Corey Pronman wrote a nice piece a while back about why Shero’s record in particular is underrated, but I wanted to more briefly examine a few more general reasons why I would be weary about being too reliant on such a history or lack of history of success.

1. Small Sample Size.

One of the central themes with regards to analytics in hockey is that we’re trying to maximize sample size in order to get the most accurate possible view of a player or team’s talent. This is no different with regards to drafting. The fact is, a GM can only draft on average seven players per season, meaning that over the course of, say, a five year tenure, that’s only 35 picks. Some may get hurt, some might lose their love for the game, some might develop better than others simply as a result of random variation. It’s very difficult to isolate real success based on 35 or so picks – which is one of the big reasons why drafting also appears to be so random based on studies in just about every sport.

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On Drafting for Skill and Trading for Size

Kyle Dubas had the following quote in Elliotte Friedman’s great 30 thoughts columns this week:

“Here’s the way I look at it,” he said. “Right now, we aren’t good enough to be picky about smaller players. We need as many elite players as we can. If we get into playoffs and are too small, or overwhelmed, it’s easier to trade small for size than draft for size and trade for skill.” (bolding my own)

The quote struck me as interesting because it takes a fundamentally different angle on the size debate than the one I personally ascribe to, and I wonder whether it is simply a matter of semantics, or whether there is actually more to this.

My sense was always that size is not easier to trade for than skill – assuming we mean top 6 size and not grinder size – but that the reason you want to draft for skill was simply that skill players have a higher success rate than big players who don’t score as much. You prefer guys who can score over guys with size because once you accumulate enough of them, you can overpay for the big players that have succeeded, and not bear the risk that they may be busts.

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