Who are the best two-way centers in NHL over past 5 seasons?

Maybe the most important position in hockey is center, certainly more important than wing and probably more important than defense or goaltending. Even more important is having a real good two-way center capable of playing big minutes at both ends of the rink. Think about the recent Stanley Cup winners. In Chicago you have Jonathan Toews. In Los Angeles you have Anze Kopitar. In Boston you have Patrice Bergeron. Going further back you have Datsyuk in Detroit. These four guys are maybe the best two-way centermen in the league but I wanted to take a more analytical approach to answering that

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A look at free agent wingers Williams, Semin and Beleskey

I am sure this post will rattle some feathers in the Hockey Analytics community but hey, it won’t be the first time I have accomplished that. I have been looking through the list of potential free agents looking for players that are possibly under valued, possibly over valued, or otherwise interesting for one reason or another. There has been a fair bit of discussion around the three players that are the focus of this post. Justin Williams has been a favourite of the hockey analytics community posting outstanding Corsi numbers year after year. Alexander Semin, who was bought out by the

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Maple Leaf defensemen and their effect on save percentage

The other day I looked at the effect that Mike Weaver and Bryce Salvador had on their teams save percentage (if you haven’t read it, definitely go give it a read) when they were on the ice versus when they weren’t on the ice. Today I am going to take a look at the Maple Leaf defensemen to see if there are any interesting trends to spot. We’ll start with the new acquisitions. Stephane Robidas (Blue line above orange is good in these charts, opposite is not good) Aside from 2008-09 he has had a negative impact on his team save percentage.

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Face offs and zone starts, is one more important than the other?

I brought this issue up on twitter today because it got me thinking. Many hockey analytics dismiss face off winning % as a skill that has much value but many of the same people also claim that zone starts can have a significant impact on a players statistics. I haven’t really delved into the statistics to investigate this, but here is what I am wondering.  Consider the following two players: Player 1: Team wins 50% of face offs when he is on the ice and he starts in the offensive zone 55% of the time. Player 2: Team wins 55%

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Tips for using Hockey Fancy Stats

I often get asked questions about hockey analytics, hockey fancy stats, how to use them, what they mean, etc. and there are plenty of good places to find definitions of various hockey stats but sometimes what is more important than a definition is some guidelines on how to use them. So, with that said, here are several tips that I have for people using advanced hockey stats. Don’t over value Quality of Competition I don’t know how often I’ll point out one players poor stats or another players good stats and immediately get the response “Yeah, but he always plays

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Regular Season Team Stats and Playoff Success

Yesterday HabsEyesOnThePrize.com had a post on the importance of fenwick come playoff time over the past 5 seasons. It is definitely worth a look so go check it out. In the post they look at FF% in 5v5close situations and see how well it translates into post season success. I wanted to take this a step further and take a look at PDO and GF% in 5v5close situations to see of they translate into post season success as well.  Here is what I found: Group N Avg Playoff Avg Cup Winners Lost Cup Finals Lost Third Round Lost Second Round Lost

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Taking a look at QoC metrics on stats.hockeyanalysis.com

I generally think that the majority of people give too much importance to quality of competition (QoC) and its impact on a players statistics but if we are going to use QoC metrics let’s at least try and use the best ones available. In this post I will take a look at some QoC metrics that are available on stats.hockeyanalysis.com and explain why they might be better than those typically in use. OppGF20, OppGA20, OppGF% These three stats are the average GF20 (on ice goals for per 20 minutes), OppGA20 (on ice goals against per 20 minutes) and GF% (on

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Taking a look at quality of competition

When I updated stats.hockeyanalysis.com this season I added new metrics for Quality of Teammates (QoT) and Quality of Competition (Q0C). The QoC metrics are essentially the average Hockey Analysis Rating (HARO for offense, HARD for defense and HART for overall) of the opponents that the player plays against. What is interesting about these ratings, as compared to those found elsewhere, is that I split the QoC rating up into offensive and defensive metrics. Thus, there is a QoC HARO rating for measuring the offensive quality of competition, a QoC HARD for measuring the defensive quality of competition, and a QoC

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Ilya Kovalchuk’s power play ice time

The last few days I have been looking at the percentage of a teams ice time for a given situation that a particular player is on the ice for.  So for instance, what percentage of the Leafs 5v5 even strength ice time was Joffrey Lupul on the ice in games in which Joffrey Lupul played. When I write a new program to calculate these numbers I need to to some testing to make sure the results are correct.  The first test is always the standard sniff test.  When the program runs I look at the output and ask myself “does

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Player TOI usage charts

One of the challenges in hockey analytics, or any type of data analysis, is how to best visualize data in a way that is exceptionally informative and yet really simple to understand. I have been working on a few things can came up with something that I think might be a useful tool to understand how a player gets utilized by his coach. Let’s start with some background. We can get an idea of how a player is utilized by looking at when the player gets used and how frequently he gets used.  Offensive players get more ice time on

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