Toronto Maple Leafs Passing Lane Corsi

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.

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Toronto Maple Leafs Passing Metrics 101

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Nazem Kadri has been the name on the tongues of most Toronto Maple Leafs fans for many reasons. It could be due to his slow start, where at the time of this writing he currently ranks 324th of forwards in points per sixty minutes. However, thanks to the words of Mike Babcock, Kadri’s efforts have not gone unnoticed. In fact, Babcock is quoted as saying, in reference to Kadri, “He is in on all the chances, he generates a ton.” After Babock’s words and this piece on Kadri and his chances, it’s apparent the goals will come and his slump is simply a natural byproduct of this chaotic sport.

Well, in addition to some of our newer data and metrics, let’s take a look at how and where Kadri generates chances for his teammates, shall we? All data is 5v5 unless otherwise specified, and is through games completed as of 12/4.

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The 2015 OHL Final Part Three: Erie Otters and Oshawa Generals Passing Data

The last two pieces of mine focused on passing network analysis for both the Erie Otters and Oshawa Generals from their 2015 OHL Final. The point of this short series was to look at how and why teams are successful over a playoff series. Generally, five-game samples aren’t large enough to give much credence to, and yet a sound game plan and tactical preparation can influence an upcoming playoff series. With the type of data collected and analyzed through passing networks, it provides a baseline of how influential the Otters and Generals players were in their series. From there, naturally, any good analyst will go to the video to find evidence of how these numbers occurred, to augment their conclusions. These first two series served as a opportunity to present new ways of analysis with our data this season, something that, come playoff time, will be given a test run in terms of series predictions.

This final piece in this series will focus on the production for each player and team in a more traditional sense. What does that mean? Lots of numbers and charts. For starters, Erie generated far more passing offense in this series than Oshawa. At 5v5, here were the Otters possession numbers for shot sequences from single, multiple, and scoring chance passes.

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The 2015 OHL Final Part One: Erie Otters Passing Network

At the Rochester Hockey Analytics Conference, Stephen Burtch presented on Network Analysis using our passing data from last season. You can access Stephen’s slides here. It was an intriguing presentation on how we can use the passing data to better understand the on-ice environment of players and teams. If you’re at all familiar with my work, you won’t be surprised to hear me say that what happens prior to a shot being attempted is something that escapes us and is more important than just the final act of shooting. Only in better understanding how things happen, or don’t happen, prior to that, will we be in a better place to properly evaluate players. When Stephen presented at #RITHAC, I was sitting there thinking, “Boy, this would be great to do with the 2015 OHL Final Passing Data” I’d tracked, but hadn’t gotten around to sharing the results.

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