Timothy T. Ludwig-USA TODAY Sports
Truth be told, it took me a while to get on board with the analytical revolution that’s taken the NHL by storm. I remember when I was a kid, TSN’s website used to have a player profile link for every NHL player. It detailed the strength and weaknesses of players, and what their career projection as a player was. Now the thing I didn’t notice back then was how when you read the player profile for depth forwards (say your typical third- or fourth-liner or a fifth or sixth defenseman), they were pretty much all similar. So when TSN stopped running that for NHL players, my idea of valuing a player’s worth was through listening to Don Cherry on the Coach’s Corner segment of Hockey Night In Canada, never noticing his at times misogynistic tendencies or how he just embraced the “Glue Guys who block shots blah blah blah”, and praising players for how much they hit or fought.
When I first found out about such stats like Corsi or Fenwick, I was confused beyond belief. I once had a quick conversation with Cam Charron (a great hockey writer by the way) on Twitter concerning these new things (at least to me), and I didn’t exactly sound like a guy who knew much about shot differential/possession numbers. Without any knowledge, I compared Corsi to PER in the NBA (a summing up of a player’s accomplishments offensively) thinking it was this new way of identifying how much a player contributes. He told me that wasn’t the case and we can still figure out who’s great by the eye test, and using stats like Corsi to back up or to discredit what you see with your eyes.
Over the next couple of months, I slowly warmed up to the idea of the analytic revolution taking place. I figured out what Corsi/Fenwick actually meant. I learned about stuff like Corsi Relative Quality of Competition, Corsi Relative Quality of Teammates, PDO and other stats of that nature that you can find on such sites like Behind the Net and Stats Hockey Analysis. What came of this expedition was a new way of analyzing a team or player’s worth. Gone are the days of me sitting on my couch listening to Don Cherry rave about Player X’s ability to block shots and go to the dirty areas. Gone are the days of me using plus-minus as a way of knowing who’s a great defenseman and who isn’t. The other thing sadly that surfaced was the growing divide between the old school hockey media (a good portion of mainstream media, radio stations, etc…) and the new school (bloggers, certain media members and more). Mikhail Grabovski’s buyout over the summer is maybe the example of all examples for this.
Bringing it back to the point of this post (thanks for the moment of self-indulgence by the way), for the last two weeks, I’ve been curious as to figuring out how we can see who drives the play most often on a roster. As it stands right now, zone entry stats aren’t readily accessible and more importantly, the template on how to track them successfully is even more scarce. With massive help from Eric T. from NHL Numbers and Broad Street Hockey, he gave me access to templates on how to go about this project successfully. I used Game 4 of the Bruins-Leafs series as a preview for what hopefully is a regular feature here on Editor In Leaf after every Leaf game. Below is a chart of the 5-on-5 zone entry stats that were compiled.
Player |
# of entries
Shots generated from player’s entries
Shots per entry
# of controlled entries
Shots generated from player’s controlled entries
Shots per controlled entry
% of entries with control
3
4
1
0.25
0
0
#DIV/0!
0%
4
4
1
0.25
1
1
1.00
25%
11
4
0
0.00
1
0
0.00
25%
16
0
0
#DIV/0!
0
0
#DIV/0!
#DIV/0!
19
4
1
0.25
4
1
0.25
100%
21
5
2
0.40
5
2
0.40
100%
23
1
0
0.00
1
0
0.00
100%
28
5
3
0.60
1
1
1.00
20%
36
3
0
0.00
0
0
#DIV/0!
0%
39
3
2
0.67
0
0
#DIV/0!
0%
41
5
3
0.60
3
3
1.00
60%
42
2
1
0.50
1
1
1.00
50%
43
2
2
1.00
1
0
0.00
50%
45
2
0
0.00
1
0
0.00
50%
47
3
2
0.67
3
2
0.67
100%
51
5
5
1.00
3
5
1.67
60%
81
11
12
1.09
10
12
1.20
91%
84
6
6
1.00
5
6
1.20
83%
Team
69
41
0.59
40
34
0.85
58%
Opp
76
26
0.34
34
19
0.56
45%
The more shots that are generated from a player’s entry, the more play has been driven by said individual. Phil Kessel, Grabovski and Jake Gardiner are the three best play-drivers from this chart above. Kessel in particular is so good at creating shots on the rush it’s almost as if he exists in his own world speed-wise. Obviously these numbers are only from one game, but this project will hopefully have seasonal stats going forward and not just isolate a game to make impressions of a player’s ability to “make plays”.
Hopefully as the season begins, I’ll have a full grasp on using zone entries and the scoring chance app created by Vic Ferrari to move past the individualism that presides in zone entry stats and see how the Leafs for example play with Player X on the ice in each zone. This isn’t the be-all and end-all of assessing players, but it’s another avenue of statistical analysis that can hopefully pop into the semi-mainstream in the same way Corsi and Fenwick have lately.