Over the holidays, I created some charts that show the distribution and density of Shots and Scoring Chances for Per 60 for each team. There’s a lot of information in these charts, so I chose a few to inspect further.
First, we’ll look at the league-wide chart for Shots and Scoring Chances For. Keep in mind that each individual dot is a game, and the contour lines show the density of the dots (i.e. how close they are to each other).
Lighting the lamp is the hardest thing to do in all of hockey, and scoring is down yet again in the NHL.
But instead of talking about ways to make goalies worse, by reducing the size of their equipment or by limiting what they can do on the ice, let’s look at a few habits shooters can acquire to shift the odds in their favor.
While the statistics and research behind the information presented here come from the NHL and CIS levels, I’m confident that any player – from Peewee to Beer League – can put them to good use in 2016.
I’ve already written about scoring in the shootout, so here are four scientifically sound tips for scoring more in open play.
Luca Sbisa may be one of the players who best epitomizes the divide between the old-school, eye test view on hockey and the statistics-focussed analysts offering their opinions from their mother’s basements on fan curated sites across the internet. While GM Jim Benning clearly thinks Sbisa is a useful defender, rewarding him with a 3-year, 10.8MM deal, and consistently praising his defensive zone smarts, Canucks fans have been less bullish on the talents of the 25-year-old Swiss pointman. Correctly noting his less than stellar possession numbers, J.D. Burke commented that his first season with Vancouver featured few “extended stretches in which any pairing with Sbisa on it looked passable”. These aren’t just the criticisms of a bitter fan wishful for better years, Burke backed up his arguments with a detailed numerical breakdown of Sbisa’s many failings, and video evidence of some of his less than professional defending from 2014-2015. Burke, and the Canucks’ fanbase in general, seemed to paint a picture of Sbisa that stands in stark contrast to what Vancouver management observed. Where the fans saw a player who frequently found himself out of position at critical junctures when defending his own end, Vancouver’s brain trust viewed Sbisa as the ideal player to disrupt a cycle down low. How could two groups of people who watched the same games with such intense devotion come to such different conclusions?
One of the biggest difficulties with evaluating Sbisa, and defencemen in general, is that what the eye test says is important is often wildly out of sync with what statistics can currently measure. While stats-based analyses focus on a defender’s ability to prevent shot attempts (in other words, their Corsi Against per 60), most of the praise for defensively-minded defencemen tends to focus on hockey IQ, being in the right position, and winning battles in the corner. While ideally these less “quantifiable” skills should lead to favourable statistical results, issues with differences in player deployment and the teammate-dependent nature of defending often mean that what gets praised in post-game interviews isn’t what shows up on the scoresheets, leaving a divide between management’s view and the story told by pure shot attempt numbers.
A few weeks ago, I was playing in a weekly beer league hockey game with some McGill University staff members. At one point, I came down the left wing with the puck, looked off a defender and whipped a wrist shot high, far side.
However, instead of the puck going bar-down as I had (ambitiously) hoped, it caromed off the glass and went all the way around the rink for an odd-man rush against. When I got back to the bench, someone said something to the effect of: “Stop missing high and wide. You’re just helping the other team break out of their zone.”
It was a light-hearted chirp – we weren’t playing for the Stanley Cup, after all. But it got me thinking about coaches who yell up and down the hall when their teams don’t “put the puck on net.” Is it really something that some teams do better than others?
A few days ago, our friend Micah Blake McCurdy did some work in an effort to answer that question. He took a look at the proportion of goals/shots on net/missed shots/blocked shots for each NHL in the past two seasons. Here is what he found: Continue reading
Jacob Voracek is having an especially poor season. After averaging above 1.6 Primary Points per 60 at 5v5 the last few seasons, he’s down to .6 in 2015-16. Voracek’s assist rate is down, and most prominently, he has only scored one goal at 5v5.
What is causing this run of poor form? The most direct route of analysis is to examine his shot metrics. How does Voracek’s 2015-16 season stack up against his previous seasons?
This week, I wanted to illustrate several ways that passing data can be used to more accurately assess players, offer examples of advance scouting and opposition analysis, and identify how and where teams attack and defend. On Monday, I gave you the basics for passing data. Tuesday saw a deeper look at network and linkup data. Wednesday introduce lane Corsi concepts. Yesterday, I combined most of this to illustrate how it could be used to prepare for an opponent.
Today is much lighter. Today is about releasing our data to the community. Enjoy!
This week, I’ve introduced some of our latest and newest data and metrics. On Monday, I posted a refresher on what passing metrics were and why they were important. Tuesday had network and linkup data that really only scratches the surface on what we can do this season. Yesterday, I introduced lane Corsi concepts and how they might reveal a bit more information when examining how teams generate offense and defend against the opposition. Today’s post is rather ambitious in that I will take all the different ways of using this data, synthesize it into an advance analysis of the opposition, and then provide the data of the game in question to see how things matched up.
We’ve had a Toronto focus all week, so that will continue. The opposition will be the New Jersey Devils and we’ll focus on their clash from December 8th. This will be done under the hypothetical assumption Toronto’s opposition analysts have access to this type of information league-wide and how they could make use of it. All data is 5v5 unless otherwise stated. Leafs data is from 13 of their first 26 games in aggregate; Devils data is from their first 21 games.
So far this week I’ve introduced some of our newer metrics using our data on the Toronto Maple Leafs. We’ve looked at general shot contribution and on-ice data as well as network and linkup data. Today ,we’re going to look at something new that may help us understand more about how teams generate offense and where teams fail to defend the opposition.
Understanding where the offense originates while preparing for a specific opponent can provide great value. If I know which lane and zone a player is likely to linkup with another, perhaps I can scheme for such a situation. If a LW-LD combination is getting overrun down their side of the ice, yet the LD has decent left lane numbers apart from that LW and the LW’s terrible numbers persist irrespective of who is behind him, I know there’s either a communication breakdown between those players, or that the LW is more likely being propped up by the LD.
Digging deeper into how the game can be analyzed with new data is the first step in how we’re going to answer some of these questions. All data is 5v5 unless otherwise specified, and is through games completed as of 12/4. This represents 13 of the Leafs first 26 games this season.
Recently, I wrote on some data I’d collected from the 2015 OHL Final between the Erie Otters and the Oshawa Generals. This primarily focused on passing network anaylsis that Steve Burtch introduced at the Rochester Institute of Technology Hockey Analytics Conference. Today, I’d like to use the thirteen games we’ve collected to examine the Leafs network.
I’ll be focusing on the weighted degree measure that weights each degree (pass or shot) that a player has within the network. These weights are assigned based on several factors (scoring chance, shot on goal, one-timer, etc.) so we know which connections were more likely to result in a goal than others. This weighting will be adjusted as we get more data, so it’s quite basic and likely not nuanced enough at this point in time.
I’ve used the weighted degree measure because I think it is the best way to use this type of analysis for this sport. This is for a few reasons, some of which I mentioned in the Erie piece, but the biggest is this: Not all players are on the playing field at the same time, so there are actually several networks withing a single game (first line and first pairing, second line and second pairing, and so on). This may level out of over the course of a season, but we’re going to look at the Leafs as a whole, and the Leafs top line network on its own as well. All data is at 5v5 unless otherwise specified. These types of metrics have a purely offensive-minded focus as well.
Hockey Graphs 
