Buffalo Sabres 4 at Toronto Maple Leafs 3 – 2023-03-15 recap

Well, for me, this is where it ends.

I’d like to thank the hundreds of readers I had in this space (and dozens of subscribers) this season, but these posts have to end.

My issues with Sportsnet Now are not a secret. Since it was impossible to track games live on their application since the rewind function didn’t work, I had to carve out time late nights and early mornings to watch Canucks and Leafs games to provide original content for hockey fans. This took a pretty significant strain on my mental being, since being unable to track games live meant I could easily fall behind by one or two games, and it was very difficult to keep up with.

This morning, I logged in to track last night’s Canucks-Stars game only to find that my workaround method of rewinding by ten seconds on Sportsnet Now was no longer available to me, and after an hour of trying in vain to find an efficient workflow with a VPN for both the new NHLTV and ESPN+, I decided that it’s been too taxing on me, and I’m going to step away from game tracking and regular hockey writing for the time being.

This is pretty sad for me, because I wanted to at least see this project through to the end. In my capacity with the Leafs, I regularly tracked games for upcoming opponents and wrote regular reports. I figured, with a similar setup, I could regularly follow two teams and provide original content. My goal this year was to show to fans the sort of data I worked with regularly behind the scenes with the Leafs, and what you can do with events that go beyond the NHL’s play-by-play. Gabriel Desjardins, a pioneer in hockey analytics, eventually quit the space because he felt there was no new ground to cover.

The things that really interested me was the interplay between zone exits, zone entries, and shot attempts. I loved just sitting down with hundreds of thousands of events and looking for connections between them, with complete datasets. I’m not allowed to discuss any of the conclusions I came to with a giant set of data, but my goal was to create my own individual database so that I could cover some new ground of how this sport works. Why do score effects exist? Is it better to win an offensive zone faceoff or exit the defensive zone with control? More importantly, when the Leafs lose, how did that happen?

Take this game against Buffalo, for instance. Using available data, it tells that the Leafs pretty well controlled the first 25 minutes of play and then let the Sabres back into it:

via Natural Stat Trick

Well, the tracked data shows a different picture. While it’s true that the Leafs were getting the attempts and chances early on, Buffalo was right there in generating offensive zone possessions. Here’s the cumulative zone entry attempt and controlled entry differential by the teams in this game:

Notice that, even as the Leafs were beating the Sabres in terms of shot attempts and scoring chances (at the time of the 2-0 goal, I had the shot attempts 22-12 for the Leafs and the chances 10-4), you could argue that the Sabres were tilting the ice just a little bit more.

At that point in the game, the Sabres hadn’t generated a single scoring chance off any of their 17 controlled entries, and the Leafs had 7 out of their 14. Both those numbers are unsustainable even over the course of a game, and things eventually flipped for the remainder.

So, rather than painting a picture of the Leafs being a team that were great to start and then struggled as Buffalo got their legs together, the closer answer is that the Leafs weren’t that great in the first place. They had two quick strike goals but probably had more scoring chances than they’d earned at the time, while simultaneously taking advantage of the fact that the Sabres were just missing on a lot of their rushes. The distinction is important, since it affects the narrative. The Leafs didn’t blow a lead so much as they just weren’t as prepared as Buffalo were for this game. I believe (and I have not been able to prove it) that zone entry differential matters more than anything else when predicting the rest of the game: shot attempts are a good approximation of how the game has tilted, but a team that’s able to put up a +5 entry differential in a period has multiple stretches where they’ve been able to stifle the opponents’ breakout and not allow any chances.

So, for instance, at the time of the 2-0 goal, the Leafs were exiting the defensive zone with control 54% of the time, which is pretty good, but the Sabres were exiting with control 63% of the time, which is better. The Leafs also had 96 touches in their own defensive zone to the Sabres 60, which meant more potential for things to go wrong. There was no indication that the Leafs really deserved to have that 2-0 lead once you pulled back the veil of the shot attempt numbers.

Those are the things I care about when watching hockey: not ranking players or determining who is good or bad, but rather, what is a team doing or not doing that is going to impact its chance to win? Later on in my career as an analyst, I got heavily involved into researching forechecking after spending the early part focusing on which defenders were avoiding it. I’m still searching for a way to quantify disruptive players in the offensive zone. Unfortunately, the only way to do this is by watching and counting things, and if you’re based in Canada, there’s no easy way you can research NHL hockey by watching and counting things, and that’s a loss for everybody.

Part of my goal when doing this was to turn down the temperature a little bit in both the Toronto and Vancouver markets and try and write about things a bit more sensibly. There is a lot of randomness and variance in this sport and there is a small number of loudmouths that make everything seem way worse than it is. This whole tracking project isn’t intended to be a massive undertaking to protect Justin Holl’s reputation and defend the Leafs’ decision to protect him in the expansion draft over Alex Kerfoot and Jared McCann, but it may as well act as one. I wanted to show that, even though players make lots of visible mistakes, sometimes what they do that positively impacts the play is rarely noticed.

I’ve long believed that tracking is good not only because you get access to a whole bunch of data nobody else has, but because it forces you to pay attention to details and minutiae you would otherwise miss. A lot of hockey analysis on TV is, unfortunately, directed at what immediately happens leading up to a goal, and almost never about what created the conditions that allowed that goal to exist. When Carolina and New Jersey face off in the second round this season, you probably won’t hear many analysts talk about the clash of styles: the Devils are great with the puck and the Hurricanes are great about it. The Devils create and the Canes disrupt. Per Corey’s tracking (Corey has more patience and me and has been doing this for years), the Devils are 2nd in the NHL in carry-in percentage and the Hurricanes are 32nd. Both teams are influenced heavily in all aspects of the organization by two other pioneers in the field, Eric Tulsky with the Hurricanes and Tyler Dellow with the Devils. Calling one approach rooted in analytics and another not is kind of missing the point, but what’s important is the mechanism that makes either team successful. How do Carolina succeed despite playing so little with the puck? Answering that question helps us understand the game much better.

So, I’d like to continue this, and answer questions, and provide an outlet for fans that just want to understand hockey a little bit better, but I don’t think I can continue it. This is the last game recap, but I’m thinking about other things I can do with the space that involves maybe a little more than data. I still love this sport and think I can contribute, but what follows may look and feel a little different.

One thing I’d like to do is look for better ways to organize the data, and hopefully have sortable tables and maybe refine the game pages I have so they read a lit better and are easier to access. WordPress is actually pretty limited in what you can do with tables and such, but I’ll determine what the least-bad option is. I’m probably going to write what I can with the extent of what I have in my database, most likely on this website so that I own whatever I write.

Since I’ve written quite a bit here in this preamble, subscribers will get the numbers from the game against Buffalo, but I won’t write any analysis. The dog needs a walk and my neck hurts, so I’ll post the numbers, thank you for supporting the blog and the project, and I’ll have some Canucks and Leafs updates before the weekend.

And, as a coda, the real annoying thing with this whole thing is that I’m stopping my Canucks tracking just as they’re getting interesting, so the quantifiable change for the Canucks under Rick Tocchet is going to mostly be left to guesswork, without real data to work with. It’s annoying, but blame Sportsnet. They deserve every bit of scorn they receive, both for the quality of their streaming service and the quality of their broadcasts. The NHL will be better off once that national TV deal expires.

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