Luck plays a massive role in determining the outcome of National Hockey League games, mostly due to the parity, introduced by the salary cap, and because of the relatively low number of events per game.
When we talk about luck within the analytics circles it is often assumed we are referring to PDO. This is
normally true, but PDO does not capture all portions of results that are outside of players’ control. So far the Canucks have a fairly normal PDO
but they still have been receiving the benefits of luck other teams have not.
the jump if you’re feeling lucky.
wrote about luck in his 2013 Hockey Abstract.
He has a really nifty tool on his website, the “Hockey Abstract Luck Chart,” which shows how lucky teams have been over varying attributes. These includes: PDO, the summary of shooting
and save percentages which regress to the mean; special-teams index, a “PDO” for
your power-play and penalty kill; teams’ success in 1-goal and over-time games; and a
teams’ CHIP, or Cap Hit of Injured Players.
are a form of luck. Every team should expect to experience some form of injuries within their roster throughout
the season. Teams who experience a large
number of injuries, from a number of players, especially those major injuries to the top end
players are considered “unlucky” while the opposite holds as well.
@LW3H keeps track of all teams’ CHIP numbers and
updates them throughout the year. His most recent report of all teams through the first 20
games was just released today. The idea behind
CHIP is that typically teams pay the better players a higher salary compared to
replacement level players. Comparatively
a team losing a first-line player (like a Dan Hamhuis) is more likely to be severely impacted than if they’ve lost a
fourth-line player (like Tom Sestito). CHIP adds up all the
salaries-per-game for all of the injured players on a team, the higher your
CHIP the more injuries to better players and the harder it becomes for the team
So how have
the Canucks fared so far?
right, the Canucks are right at the bottom of the league in terms of injuries. All of the numbers can be broken down further
simple English, relative to the rest of the league, the Vancouver Canucks have
had almost no injuries to key players which likely has played a role in the Canucks currently sitting at near the top of the Western Conference.
The “worst” injury the Canucks have had to deal with so far is the loss
of Radim Vrbata for a few games.
Now this is
not to downplay the success of the Canucks, because they have played better
than expected; rather, the teams the Canucks are competing against have not
had the benefit of equal luck. Within the Pacific
Division the real race for playoff positions are against: Los Angeles, San Jose
and Anaheim. By Score-Adjusted
Corsi these teams
are all better than the Canucks. When
looking at their PDOs two of these teams have been unluckier, and when we compare their CHIP to the Canucks they have all fared worse off.
We can use
a simple Monte Carlo simulation to try and predict the rest of the season and there’s
no surprise that all three of these teams are likely to vault over the Canucks
over the next 60 or so games. In the end
the most likely standings within the Pacific Division, based on the teams as
they are right now, are: Anaheim, San Jose, Vancouver, Los Angeles, Calgary,
Arizona and Edmonton.
current CHIP report is only through the first twenty games it does not yet
capture the effect on the Canucks of having lost Hamhuis. I imagine by the next CHIP report the Canucks
will fall in the rankings closer to the middle of the group, but it’s unlikely the Canucks will see the
injuries that the Columbus Blue Jackets have faced.
Luck matters in hockey. Using data like PDO we can forecast the
direction a team is likely moving in as regression has enough time to take hold. Injuries are another form of
luck, but it’s nearly impossible to predict how many times
the injury-bug will strike, and for how long players will be out. There is work within academia and other major sports, such as soccer, that uses biometrics to try and predict when over-usage injuries are likely to occur. Finding ways to forecast these will greatly give teams another competitive advantage.