I am sure many of you are aware that Corey Sznajder (@ShutdownLine) has been working on tracking zone entries and exits for every game from last season. A week and a half ago Corey was nice enough to send me the data for every team for all the games he had tracked so far (I’d estimate approximately 60% of the season) and the past few days I have been looking at it. So, ultimately everything you read from here on is thanks to the time and effort Corey has put in tracking this data.

As I have alluded to on twitter, I have found some interesting and potentially very significant findings but before I get to that let me summarize a bit of what is being tracked with respect to zone entries.

• CarryIn% – Is the percentage of time the team carried the puck over the blue line into the offensive zone.
• FailedCarryIn% – Is the percentage of the time the team failed to carry the puck over the blue line into the offensive zone.
• DumpIn% – is the percentage of the time the team dumped the puck into the offensive zone.

The three of these should sum up to 100% (Corey’s original data treated FailedCarryIn% separately so I made this adjustment) and represent the three different outcomes if a team is attempting to enter the offensive zone – successful carry in, failed carry in, and dumped in.

I gathered all this information for and against for every team and put them in a table. I’ll spare you all the details as to how I arrived at this idea I had but here is what I essentially came up with:

• Treat successful carry ins as a positive
• Treat failed carry in attempts as a negative (probably results in a quality counter attack against)
• Dump ins are considered neutral (ignored)

So, I then came up with NetCarryIn% which is CarryIn% – FailedCarryIn% and I calculated this for each team for and against to get NetCarryIn%For and NetCarryIn%Against for each team.

I then subtracted NetCarryIn%Against from NetCarryIn%For to get NetCarryIn%Diff.

In all one formula we have:

NetCarryIn%Diff = (CarryIn%For – FailedCarryIn%For) – (CarryIn%Against – FailedCarryIn%Against)

Hopefully I haven’t lost you. So, with that we now get the following results.

 Team Playoffs? NetCarryIn%Diff RegWin% Chicago Playoffs 12.2% 61.0% Tampa Playoffs 6.1% 53.0% Anaheim Playoffs 5.9% 64.6% Colorado Playoffs 5.5% 59.1% Detroit Playoffs 4.7% 51.2% Minnesota Playoffs 4.1% 53.0% Pittsburgh Playoffs 4.0% 59.8% Dallas Playoffs 3.8% 51.8% New Jersey . 3.4% 48.2% Los Angeles Playoffs 1.7% 53.7% Boston Playoffs 1.3% 67.1% St. Louis Playoffs 1.2% 60.4% Ottawa . 0.9% 47.6% Columbus Playoffs 0.7% 51.8% Edmonton . 0.7% 35.4% NY Rangers Playoffs -0.1% 54.9% Phoenix . -1.3% 48.8% Montreal Playoffs -1.3% 53.0% Vancouver . -1.7% 43.9% Philadelphia Playoffs -1.8% 53.0% Winnipeg . -1.8% 43.3% San Jose Playoffs -2.3% 59.1% NY Islanders . -3.0% 40.2% Toronto . -4.8% 42.7% Nashville . -6.0% 50.6% Calgary . -6.4% 38.4% Washington . -6.4% 46.3% Florida . -6.7% 35.4% Carolina . -6.8% 47.0% Buffalo . -7.7% 25.6%

‘Playoffs’ indicates a playoff team and RegWin% is their regulation winning percentage (based on W-L-T after regulation time).

What is so amazing about this is we have taken the first ~60% of games and done an excellent job of predicting who will make the playoffs. The top 8 teams (and 11 of top 12) in this stat through 60% of games made the playoffs and all of  the bottom 8 missed the playoffs. That’s pretty impressive as a predictor. What’s more, the r^2 with RegWin% is a solid 0.42, significantly better than the r^2 with 5v5 CF% which is 0.31. Here are what the scatter plots look like.

I think what we are seeing is that if you are more successful at carrying the puck into the offensive zone, but not at the expense of costly turnovers attempting those carry ins, than your opponent you will win the neutral zone and that goes a long way towards winning the game. Recall that I have shown that shots on the rush are of higher quality than shots generated from zone play so an important key to winning is maximizing your shots on the rush and minimizing your opponents shots on the rush. To an extent this may in fact actually be measuring some level of shot quality.

Of course, why stop here. If it is in fact some sort of measure of shot quality, why not combine it with shot quantity? To do this I took NetCarryIn%Diff and add to it the teams Corsi% – 50%. This is what we get.

 Team Playoffs? NetCarryIn%Diff – CF% over 50% Chicago Playoffs 17.7% Los Angeles Playoffs 8.5% New Jersey . 7.8% Tampa Playoffs 7.1% Detroit Playoffs 6.2% Anaheim Playoffs 5.7% Boston Playoffs 5.2% St. Louis Playoffs 4.3% Dallas Playoffs 4.3% Ottawa . 3.3% Minnesota Playoffs 2.7% Pittsburgh Playoffs 2.7% Colorado Playoffs 2.5% NY Rangers Playoffs 2.3% San Jose Playoffs 1.4% Columbus Playoffs 0.6% Vancouver . -0.4% Phoenix . -0.8% Winnipeg . -1.7% Philadelphia Playoffs -1.8% NY Islanders . -3.6% Montreal Playoffs -4.6% Edmonton . -5.0% Florida . -5.7% Carolina . -6.5% Nashville . -7.5% Washington . -8.7% Calgary . -10.1% Toronto . -11.9% Buffalo . -14.7%

New Jersey still messes things up but New Jersey is just a strange team when it comes to these stats. But think about this. If New Jersey and Ottawa made the playoffs over Philadelphia and Montreal it would have a perfect record in predicting the playoff teams. It was perfect in the western conference.

Compared to Regulation Win Percentage we get:

That’s a pretty nice correlation and far better than corsi% itself.

Now, this could all be one massive fluke and none of this is repeatable but I am highly doubtful that will be the case. We may be on to something there. Will be interesting to see what individual players look like with this stat and I’ll also take a look at whether zone exits should somehow get factored in to this equation. I suspect it may not be necessary as it may be measuring something similar to Corsi% (shot quantity over quality).

### 10 Responses to “Team Zone Entry Data and Predicting Standings”

1.

This is really great stuff. Thank you for putting this together.

2.

3.

Great info!

Out of curiosity, what format are you using to report your equation? The slope appears to be where I am used to seeing the intercept and vice versa.

4.

Great stuff, David, I’m amazed at how high the R^2 is for your 2nd model. Just curious, did you run a multiple regression with both NetCarryIn%Diff and 5v5 CF% as the variables? I’d be interested to see how much (if at all) the R^2 increases.

5.

“I think what we are seeing is that if you are more successful at carrying the puck into the offensive zone, but not at the expense of costly turnovers attempting those carry ins, than your opponent you will win the neutral zone and that goes a long way towards winning the game.”

Totally agree with this statement, but I think what we need to do next is to find out which is worse: attempting to force carries (and having both a higher carry rate but also a few more turnovers) or being a bit more conservative and dumping the puck in (fewer turnovers but also lower carry rate). Where is the turning point at which forcing too many carries becomes costly?

•

Where is the turning point at which forcing too many carries becomes costly?

The challenge of course is this probably varies for every player or groups of players on the ice. The risk calculations for Sidney Crosby are likely a lot different than they are for Jay McClement.

I typically believe that you generally coach conservatism and the players natural instinct is to be more on the aggressive side. The Kings dump the puck in a lot, that is probably a coaching driven decision. The Blackhawks don’t which means the coach is probably letting the players be more creative through the neutral zone.

6.

great stuff, especially in August

7.

Very very interesting. Considering the analysis you did on rush shots and just the simple fact that pretty much all the odd man rushes qualify as a carry-in, this isn’t all that surprising. It would be very interesting to see, if the teams with a good carry-in differential have more or less the same carryin%for and carryin%against or if they get to the same goal (high carryindifferential) on separate routes. I’d expect some teams to be really good at carrying the puck in, but average at avoiding carry-ins and the other way round. But maybe the really good teams all have more or less similar carryinfor and carryinagainst. That would be very interesting to see.

8.

New Jersey sucked mostly because of their goaltending (Brodeur was done). Othervise they would have hit the playoffs.

9.

It didn’t help that when Schneider was in net, the Devils weren’t giving him any scoring support.

I’m somewhat surprised to see the Devils that high in NetCarryIn, as they strike me as a team that, even though they produces a high Corsi%, didn’t have much offensive ability. Would it be possible to do a combination of GF% at Corsi%, and divide by 2, and see the correlation for that? (I know, sounds a bit crude, but it would be a way to combine the effects of Corsi, and the shot quality that GF% has).