Are we predicting the future or analysing the past?

So, yesterdays post created an interesting, and mostly positive to my delight, response. Credit goes out to Matt Cane who hit the nail on the head in figuring out the response I was looking for. @hockeyanalysis So I think a key point to note here is that CF% shows a lot more consistency within season. — Matt Cane (@Cane_Matt) February 6, 2016 @hockeyanalysis Split half correlation for CF%: F: 0.74/D: 0.69 Split half correlation for Sh%: F: 0.10/D: 0.05 — Matt Cane (@Cane_Matt) February 6, 2016 @hockeyanalysis Over 3 year periods you’re correct, CF% does have a lot of other

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Observed stats are bad, regressed stats are good

That’s what I have been told. The fact that you are still basing your arguments on observed data means that, even now after at least six years of exhortations from successive generations of people who have tried to work with you and help you, you do not understand what it means to regress your data. I refuse to engage any longer till you learn to do this. —Benjamin Wendorf Under the threat that Benjamin Wendorf will never engage with me again (how could I live with myself if that happened), I decided that I must finally investigate this regression thing

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Hockey Analysis, War on Ice and the future of stats sites

The big news in the online Hockey Analytics community yesterday, aside from the Ottawa Hockey Analytics Conference, was the Andrew Thomas announcement that war-on-ice.com will be shutting down in the upcoming months. Although in many respects War on Ice was a competitor to my site (stats.hockeyanalysis.com and puckalytics.com) it was also a great site that was developed for different reasons and served a different purpose. My sites were developed by myself over time largely as a hobbyist and a blogger. I started hockeyanalysis.com in 2005 after the ‘lost season’ lockout as a way to further enjoy my love of hockey

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What baffles me most in hockey analytics

I am always astonished by how much I get critiqued about shot quality and whether it exists or not and whether it is important or not. I get even more criticism if I ever bring up the idea that defenders can have an ability to boost save percentage. To me this is the most baffling thing I have come across in hockey analytics. Let me explain why. I would hazard a guess that everyone that criticizes me about my belief in shot quality and ability of players to drive shooting or save percentages absolutely believes in score effects. This is why

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Are you adequately accounting for Shot Quality in your Expected Goals model?

The other day on twitter I questioned whether an existing expected goals model by @DTMAboutHeart adequately accounted for shot quality. This tweet it seems prompted a response from the Hockey Graphs crew in which they all take turns downplaying the importance or shot quality. We already know that the impact of shot quality (context + skill) is miniscule in comparison to other factors –pet bugs   The question to ask then is: “why does shot quality have so little relative impact on long-term results?” –rjessop   I didn’t intend to start a debate on shot quality, I was simply expressing my

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Muzzin, Phaneuf, and the impact of roles on possession stats

The other day I wrote about what I hope for from hockey analytics in 2016. It largely focused on more investigation into the impact of coaching and specific roles players play have on individual statistics. For the most part hockey analytics has developed by looking at what team statistics correlate well with team success and then transferring those observations to player evaluation (i.e. Corsi correlates with winning so good players should have good Corsi numbers as well). While transferring what we know from the team level to the player level has merit one problem I have is we have largely treated players

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Schenn, Jones, Weber and Sv%RelTM

I get criticized a bit (I might even say mocked) for suggesting that defensemen have the ability to influence their goalies save percentages. It surprises me some because to me the idea that defenders can’t is just a bit far fetched. A defender that turns the puck over a lot at key moments has to have a negative impact on his goalies save percentage. Conversely one who doesn’t would boost his goalies save percentage. Unfortunately we live in a Corsi world these days and these ideas get criticized, if not mocked. Today though saw the trade of a pair of defensemen

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My hope for Hockey Analytics in 2016

The last couple of years have been eventful for the hockey analytics community and hockey analytics has definitely gone main stream. The past year or so has seen some interesting developments including more investigation into shot quality (Scoring Chances by war on ice, expected goals from @DTMAboutHeart, etc.) , some interesting enhancements in goalie evaluation, and the passing project led by Ryan Stimson has shown some interesting results and could provide many more interesting insights into the game. All that said there is one area where I hope will get more attention from the analytics community in 2016.   Impact of Coaching and

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Forward Usage and Stats by TOI Rank on Team

The other day I posted an article on evaluating defensemen by their ranking on their team. In this article I am going to do the same but for forwards and focus on the offensive side of the game. I grouped forwards in a similar way to how I grouped defensemen. Specifically, this is my methodology: I used 5v5close data to eliminate score effects I used data from the first two thirds of the season (first 820 games of the season) because few trades occurred before that point in the season. Trades can mess up rankings of players and how do

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More on Evaluating Defense by Rank on Team

The other day I wrote a post on evaluating forwards and defensemen based on their rank on their team. The purpose of that post is to show the value in breaking down performance beyond just Corsi but into Corsi For/Against and shooting and save percentages. I wanted to expand on that by looking at past seasons to see if there are trends that emerge. In the previous post I ranked players on their team based on their total ice time because that is what Travis Yost did in his post on TSN.ca. I don’t believe this is the best methodology

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