The lock-out shortened season was chock full of surprises and absurdities, including the Toronto Maple Leafs’ first playoff appearance in nearly a decade. They rode into the playoffs on the massive PDO wave driven by an allegedly unsustainably high shooting percentage and excellent goaltending. But that was supposed to be a fluke; after all, the test cases for regression to the mean in PDO were consistent with the idea that you couldn’t out-“luck” your problems consistently.
And yet here we are, over a month into the full 2013 season and the Leafs are once again riding that big wave of team shooting and save percentages right to the top(-ish) of the East. Most analysts are predicting heavy regression to the mean for the Leafs and a rude awakening for head coach Randy Carlyle & Co.’s conception of “quality possession”.
Sean McIndoe (Down Goes Brown) had an excellent piece in Grantland about what this season’s Leafs performance means for the hockey analytics community. Basically, the idea is that their money is where their mouth is.
But for all this discussion of the statistically impaired Maple Leafs being the poster boy of small sample sized success on the back of unsustainable performance, there is surprisingly little said about what could be another team that’s challenging analytics.
While the Leafs were busy allegedly playing unsustainably well during the lockout season, the New Jersey Devils who were fresh off a Stanley Cup final appearance were doing just the opposite. From reading the stuff available about the Leafs, you’d think regression to the mean only went one way when it’s actually symmetric (meaning that both teams below and above are affected by it). The Devils have strong possession numbers, some of the best in the league in fact and when paired with a brutal PDO it indicates that the fundamentals are there but the Devils simply are getting unlucky (or horrific goaltending performance).
During the 48 game 2012-13 season the Devils had the best 5v5 FF% in the league with 55.7%, just edging out their recently crowned rival the Kings. But the Devils PDO was 976, a very low number and something analysts at the time pointed out would regress upwards to the mean. The Devils sported the 25th best 5v5 sv% in the league at 0.912 and the 28th best sh% at 6.42%.
So the Devils bought out Hedberg and sent their 9th overall pick to Vancouver for Corey Schneider. Surely, with this upgrade between the pipes and the acquisition of forwards Jaromir Jagr, Michael Ryder and Ryane Clowe this new-look New Jersey team would somewhat offset the loses of David Clarkson, Zach Parise and Ilya Kovalchuck; experience some puck luck; and see that sh% and sv% rise on the wave of upwards regression to the mean. But the Devils are 14 games into this post-lockout season and sit 7th in the Metropolitan division. It’s still early and the race in the Metropolitan division is pretty tight (outside of first place) but early indications are not good for the Devils.
The Devils are currently 7th in 5v5 Fenwick For % (which measures the percentage of unblocked shots taken by that team) at 52.3% but sit 30th in the league with a PDO of 957. New Jersey has the third worst 5v5 sv% in the league and sit 26th in the league with a 5v5 sh% of 5.8%.
That’s a lot of numbers, so what exactly is the point here?
The point is not to stomp up and down that hockey analytics are wrong, that the Leafs have figured out a way to consistently snipe goals and that the Devils are a team full of plumbers that can throw the puck on net.
The point is partially that we need to give this season time to play out and give unsustainable patterns are chance to correct themselves.
But the big point is really that if traditionalist and analytics people alike want to cast this season as the proving grounds for some kind of dichotomous dick-measuring competition, they had better look at the allegedly unsustainable play of the New Jersey Devils as well. The Devils, who don’t get much spotlight, deserve to be just as much to be this season’s co-most fascinating team with the Maple Leafs as test cases for the power of hockey analytics.