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# And the worst Canuck so far is......

Here is an statistical look at which Canuck is just not pulling his weight so far.

I am sure you are skeptical the moment you read the headline. Who am I to tell you who the best Canucks are so far? Well…..besides giving you the internet, I am a guy who is not a huge fan of stats but at the same time I can make use of a spreadsheet and do some analysis. This article start to grow after one of my internet flame wars with ktownfan over who was a better player this year so far, Sbisa or Weber. Ktownfan let it be known that Weber was better than Sbisa in every statistic. So now that Weber has got almost as many games under his belt as Sbisa had before his injury I figured I would do some comparing.

The first question that should be considered when trying to determine the best player is what advanced stat should be used. Trick question, you should never use one when there are dozens available. But to make matters a little less complicated, instead of using a dozen, I used 4 stats. I went to the flame war and used the stat that was discussed GA/60 and added its partner, GA/60. I then added two more stats (CF/60 & CA/60) because of the importance Corsi has been given to show how a player drives play. By using these four stats I not only get to see the chances created for and against while a player is on the ice, but also the result of those chances during a 60 minute span.

The stats are from stats.hockeyanalysis.com, and I used the stats for all situations instead of 5 on 5. I figured it helps even the numbers out for all the players as some do play more PP and some PK. I decided to include all the Canucks instead of just the two defensemen, because who wants to read an article about comparing two Canucks D-men.

So here is how the math works:

1.

Above shows the conversation rate of goals per 60 minutes versus the players Corsi For per 60 minutes.

2.

Above shows the conversation rate of goals per 60 minutes versus the players Corsi Against per 60 minutes.

3. Finally you subtract the Against % from the For %.

***I don’t know if this stat is already out there. If it is please leave me a note in the comments.

The number that a player can receive can be a positive, which is good, or a negative which suggests that when they are on the ice that the other team is converting their chances at a higher rate, which would be a bad thing.

The numbers you see below include all games up to the Columbus game. And I have included all the raw data to show where the CCR came from.

I am sure we can all jump to our own conclusions when we see the results above. Hansen’s 4 point performance sure helped his stats while the stats just confirm that Vrbata is not going to get us that first round pick we all hoped for at the deadline. Some surprises for me were how high Prust and Cracknell are, but the 4th line has really done a great job of not letting other teams score on them this year. Bo Horvat is having a bad start.

As I get back to the original argument or Weber/Sbisa, it is clear that Weber is dominate when it comes to looking at Corsi numbers alone. But more goals are scored when Sbisa was on the ice and less goals have been scored when he’s on as well, even though his Corsi is terrible. Weber is the worst player on the team at this moment.

Now having done at this work I want to point out that while these numbers are individual in nature, I believe they are still team numbers. All of these stats are not created by the individual but by the actions of teammates and opponents. The use of Corsi is defended by saying that, with a big enough sample size, you can see a pattern of what we’re likely to see when a player is on the ice.

I would like to thank ktownfan for giving me the inspiration to go this crap up and making think a little deeper when it comes to comparing players.

I look forward to my Nobel Prize in Math….wait there is no prize for math. Crap. Well then I look forward to telling me that someone else is the worst player on the team.