Archive for the ‘Advanced Stats’ Category

San Jose Sharks v Vancouver Canucks

Whether you’re into advanced stats or not, you should definitely give PDO, a terribly named measure of “luck,” a chance.

Our own Cam Charron (well, he’s everywhere, but you get the point) laid it out for us in plain english here, but in a nutshell, it’s a combination of a player (or team’s) on-ice shooting percentage plus his team’s save percentage while he’s on the ice. Basically, if a guy’s PDO is really high he’s had some good luck, really low and he hasn’t, and the idea is that everyone will regress back to 1.000 (or 1000, however you want to write it), because nobody can shoot 20% all year, and nobody is going to be on the ice while the goalie has a .600 save percentage all year. Those numbers are impossibly unsustainable in the NHL today barring them putting me in net and starting me on the daily with magazines taped to my shins for pads.

Only…a lot of good teams tend to have high PDOs, and a lot of bad teams tend to have low PDOs, and I’m unwilling to say that those teams are good and bad because of their luck. One of my current beefs with the advanced stat community in hockey is an over-attribution of luck to success. Hockey players are taught to create luck, and I think some players and teams are better at doing it than others. I’ll get more into that in a sec, but let’s take a look at the numbers.

Cam Charron does a weekly update over at NHLNumbers.com of team’s PDO’s (and more) and where they sit, which I’ve cribbed below: Read the rest of this entry »

Toronto Maple Leafs v Florida Panthers

I can almost pinpoint the day that I first dove headfirst into advanced hockey analytics. It was at the conclusion of the 2009-10 regular season, and I was set to appear on a radio program as part of a panel to discuss our picks for the NHL Awards. With a couple of days notice before my appearance, and knowing who else would be joining the panel, I did some background work to see who my peers would be picking for individual awards. When it came to the Norris Trophy, I discovered that the two other panelists were leaning toward Mike Green of the Washington Capitals. I was all-in on Duncan Keith, the eventual recipient.

Knowing full well that the Mike Green crowd would be pointing to his superior point and +/- totals as reasoning to go with the Capitals defenseman over Keith, I set out to learn more about the analytics that you can find on www.behindthenet.ca. Armed with a few days of research and a slightly more than fundamental understanding of how stats like Corsi, quality of competition, and zone starts are calculated and weighted, I felt like I effectively argued how Keith was a superior defenseman.

Today, advanced stats have become a major part of my daily player analysis routine. Of course, a balance between watching games and analytics is optimal when formulating an opinion on a specific player or team. I wanted to do something important. Like, the most IMPORTANT thing ever. So, I took it upon myself to apply this same analytical approach to the most barbaric aspect of hockey: PUNCH FIGHTS. Anybody can look up a scrap on hockeyfights.com and cast a vote for a winner, but, much like goals don’t tell you the whole story, a knockdown punch or homerific voting system can’t tell you everything about a fight.

So, I’ve spent far too much time over the last three weeks watching hockey fights and counting punches exchanged in an effort to look at who among the NHL’s fighting major leaders controls the battles.

Enter: Punch Corsi

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blocked shot

You can use numbers to prove pretty much anything.

The NHL publishes five types of RTSS, or “real-time scoring statistics”. These are “hits” “blocks” “missed shots” “giveaways” and “takeaways”. They produce a mountain of data and quite oftentimes, there’s so much numbers available that people are constantly adding and subtracting and dividing these numbers to paint a rational picture of why teams are succeeding as they are.

This tweet from Sault-Ste. Marie Greyhounds head coach Sheldon Keefe caught my eye:

I’ve never met Sheldon, but I have heard that he has “a real affinity for advanced stats”. I’ve seen the RTSS used so many different ways. I saw a blogger two years back mention if Detroit is such a good hockey team, why are they always ranked so low in takeaways and blocked shots? (Her conclusion was that the Red Wings were flawed, not the numbers) I’ve seen people credit Toronto being first in hits for why they’ve improved this season (side note: after 19 games this season, the Leafs have 22 points. After 19 games last season, the Leafs had 22 points). I’ve seen defencemen judged by their “giveaway:takeaway ratio”.

The most egregious over-analysis of these numbers was CBC during last playoffs who added blocked shots and hits together to form some sort of catch-all “grit” rating that had the Rangers ranked very high. PJ Stock alluded to both numbers in the pre-game show for the Leafs game against Ottawa on Saturday. (Don Cherry Saturday mentioned toughness as a reason the Leafs are improved by, again, zero points)

The problem with these numbers is that they lie and that they really mean the exact opposite of what you’d think they mean. In the real world, a “giveaway” is actually preferable to a “takeaway”. In the real world, “blocked shots” correlate so highly with losing you may as well just be counting goals against. In the real world, the importance of “hits” is imaginary.

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Law: only one goaltender for a California team whose name is a synonym for "Speedy" can play well at any given time.

Law: only one goaltender for a California team whose name is a synonym for “Speedy” can play well at any given time.

Google News results for ‘Los Angeles Kings struggles’: 20,400.

Google News results for ‘St. Louis Blues struggles’: 11,300.

Google News results for ‘Los Angeles Kings PDO’: 5.

Google News results for ‘St. Louis Blues PDO’: 2.

I think it’s worth noting that both of the news results that look at ‘PDO’ for the St. Louis Blues, news items from Puck Daddy’s Harrison Mooney and Grantland’s Katie Baker show up as results for both of the latter two list items.

Once I think the whole hockey world and not just our corner of the blogosphere latch onto the concept of ‘PDO’, we can start to expand analysis to assume adjustments to tactical formations or personnel changes. I’m not good with Xs and Os, but I’m sure a few of them better at reading the game than your average beatwriter choking on a ham sandwich while waxing on topics like “adversity” or “complacency” would provide more insight into the causes of the struggles and possible solutions.

Somebody joked on Twitter a while ago that if they were a hockey player, they’d be able to answer about 90% of their questions with the single word “variance”.

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It’s still impressively early in the NHL season, and I don’t want to fall into the trap of looking at six or seven games any squad has played and make a bold proclamation on the rest of the season. Still, it’s fun to scour performance numbers found over at Behind the Net and look at some early season trends.

Trends like this…

Crazy Trend No. 1 – No regular Carolina Hurricane has a negative Corsi rate

Corsi is a basic advanced measure, like plus/minus, but rather than counting only goals and goals against it counts every shot attempt including blocks and misses. It’s not a perfect measure of who the best players in the league are, but it syncs up very well with offensive zone time. Playing at the offensive end of the rink is obviously a bonus.

Proponents of the Corsi measure take some heat because invariably, somebody will come along and suggest, as if they’re the first person to do so in the last six years that “well, Corsi doesn’t take the quality of a shot into consideration”. Bloggers for several teams have spend hours of their lives physically recording “quality shots” (or “scoring chances”) into spreadsheets and notebooks, the only thing separating them from watching a hockey game being the finger grease-stained cheap eyeglasses they wear for near-sightendness or astigmatism. As it turns out, the “quality shots” measure syncs up with Corsi better than it does with plus/minus, so maybe there’s something to this whole puck possession thing.

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This is how Tarasenko was introduced for his first NHL game. It is know my life goal to look this cool when being introduced somewhere. (Mark Buckner, Getty Images)

At first glance, the two players could not have less in common. Cory Conacher is undersized at 5’8″ and went undrafted. Vladimir Tarasenko was a highly-touted first round draft pick. Conacher is Canadian. Tarasenko is Russian. Conacher came up through the NCAA, and played a season in the AHL before earning an NHL contract. Tarasenko has been playing in the KHL since he was 16 and has known for years that he has an NHL future in St. Louis.

But the two now have one thing in common: they’re both early frontrunners for the 2013 Calder Trophy.

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PDO explained

Advanced statistics, fancy stats, underlying numbers—whatever you want to call them, the ones you can find on behindthenet.ca—are becoming more and more prominent. CBC’s first intermission panel for Saturday’s game between Anaheim and Vancouver had a discussion about offensive zone start deployment. TSN’s graphic guys showed a puck possession tracker at times last year with “shots for and against” as measurements. Sportsnet, is, uh, well, their Canucks broadcast team selected Zack Kassian as the first star of their Sunday night tilt against the Oilers after everybody else watched Ales Hemsky skate circles around everybody through the third period and scored a late tying goal and the clincher in the shootout.

Point being, there’s a lot more interest for analytics now than there was two years ago or so when I first started really getting into blogging. So much so that there are a lot of misconceptions about the way these statistics work or what they’re good for. For determining whether a player would be a good fantasy hockey pickup, his Corsi Relative number would be about as useful as his height and weight, yet I get questions all the time like that.

Forget Corsi Relative for now, though. The most important advanced statistic, or the most critical to understand, is the PDO number. I’ve been asked to write about it this morning.

Frequently Asked Question No. 1 – What does PDO stand for?

You’ll have to ask it’s creator, an Edmontonian who commented at old Oilers blogs under the Internet handle “PDO”. A lot of problems people have in understanding the new hockey statistics is that they don’t stand for anything. In baseball you have OBP, ISO and WAR, which stand for on-base percentage, isolated power and wins above replacement. In the same field of hockey, you have Corsi, PDO, and Fenwick, which are all named after the people who are generally credited with inventing the modern concept.

How is it pronounced? Well, whenever I’ve mentioned it in conversation (which is more often than you might imagine) I spell it out. “Pee” “Dee” “Oh”. It’s not pronounced “pedo” or “pidoo”.

Frequently Asked Question No. 2 – What does PDO count?

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