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.
The G is the opposing goal, so all arrows to that central node are shot attempts. The size and coloring of players is representative of their weighted degree. The size of the edges (arrows) showing the direction of a shot or pass indicates the quality of chances (weight) they either attempted or set up for the recipient at the end of the arrow. Let’s have a look at the defenders weighted degrees.
So, on a per sixty minute basis, Dion Phaneuf had the highest total weighted degree. Weighted In and Out simply refer to whether or not the player was the recipient (In) or passer/shooter (Out). A player with a high Out number indicates they are setting up scoring chances, one-timers, linking in transition, etc. and a high In number indicates a player is being set up with quality chances. The out number also takes into account a player’s shooting contributions.
Phaneuf links well and starts attacks on secondary and tertiary passes that things that get missed. Morgan Reilly and Jake Gardiner do as well, highlighted by the weighted out degrees of all three players. Another aspect of Phaneuf’s game is set up for one-timers quite often. He had ten of these attempts, four were on target, and one resulted in a goal. Considering who his most common forwards are, Reilly’s numbers impress me a bit more than Phaneuf’s. Reilly also had the highest contribution rate for “Danger Zone Chances” (shot attempts preceded by a Royal Road pass or a pass from behind the end line), which was triple the next highest defensemen.
Let’s move to the forwards.
No surprise here as Nazem Kadri again sits on top by a healthy margin. Tyler Bozak puts in a decent showing here. Things mostly fall in line as we would expect, I would think.
Leafs Top Line Network
So, this is how where everyone falls in line when we look at their contributions across these thirteen games. As I mentioned at the beginning of this post, the overall network includes a variety of players with varying minutes with each other. Just to compare, we’re going to take a look at the same type of analysis looking at solely the Leafs top five-man unit of Kadri, James Van Riemsdyk, Leo Komarov, Phaneuf, and Gardiner. This focuses on only shot attempts and passes made while these five were on the ice.
These are the weighted degree totals for the Leafs top line. Kadri still is the best. Then Gardiner and JVR, followed by Phaneuf and Komarov. The totals are relatively close due to the small sample size, but it’s important to see within the top five-man unit for the Leafs, Phaneuf doesn’t stand out like earlier.
What does that tell us? It could tell us that when paired with lesser lines, Phaenuf is relied upon more upon to generate in that role as compared to when he’s behind the top line, they are working to create their own chances among themselves. It also tells us which players will link up more often than others, suggesting how teams work within a system (i.e. is the left or right winger naturally more productive based on how the team wants to operate in the zone?) and also providing an example of chemistry. If two players consistently link up for better chances, perhaps it’s because they have a better feel for when and where to make a pass. Let’s look at the Leafs top trio of Kadri, JVR, and Komarov.
You can click on the image to see it better, but you’ll see a nearly even split in the attempts generated by Komarov and JVR. Kadri seems to prefer, or is simply more able to, setting up chances for JVR over Komarov. This may be a function of how they play, or it may be Kadri and JVR know where the other will be and it is easier for them to generate offense.
You can also see where they linkup by looking at which zone (OZ, NZ, or DZ columns) the pass is attempted. The L, R, C columns indicate which lane on the ice a player links up as well. What’s interesting is that even though Kadri is the center on this line, he generates most of his offense from the wings.
Conclusions/Where to Go Next
As we build our database, we can more accurately evaluate how teams move the puck, generate offense, and which players shoulder most of that burden. Network Analysis and Linkup data offer more tools to evaluate your own team, but also offer new ways to prepare for an upcoming game or playoff series. We’ll cover that later on in the week. Tomorrow we’ll learn more about Lane Corsi.
Have any questions? Hit me up on Twitter @RK_Stimp.
2 thoughts on “Toronto Maple Leafs Passing and Linkup Network”
Where are you getting the data to create the network? I you hand-coding the data from watching, or do you have a data source? Good stuff….
Thanks Brock. The data comes from people tracking several games using guidelines I’ve put together. The data source will be released on Friday. Be sure to come back and check it out!