Goals are such a small sample stat that even over a full season you’ll see some raw figures that may not be overly indicative of ability. As a general rule, you can normally expect a player’s goal total to bounce back from a down season if the player is still producing shots on goal but suffered a significant drop in shooting percentage.
Toronto -with its high profile in the media combined with some questionable management- has consistently been the brunt of jokes over blogs, message boards and twitter from other fanbases.
Recently the Toronto Maple Leafs has made a bunch of savvy, low-risk, high-potential steps both in management and player personnel to improve their team. While they are still a distance away from being a contending team, the steps taken are not those that the online hockey community has grown to love about Toronto.
With this knowledge and the offseason nearly in our rearview mirror, it is time for Hockey-Graphs to ask its analytically inclined following:
All teams in poll came from an unofficial nomination survey I conducted on twitter.
When Patrick Kane and Patrick Kane signed matching eight year extensions with $10.5 million annual cap-hits, many wondered out loud if a team can be successful with two players occupying $21 million in cap space together.
So I decided to take a look at the relationship between a team’s success, measured by total regular season and playoff wins, and how much of their total cap outlay is from their top two cap charges.
The more ubiquitous metrics like Corsi and Fenwick become, the stronger their skeptics will argue against them. Though modern analytics have now permeated big-time media and drawn the attention of renowned hockey personalities, they continue to be met with resistance among the more stubborn fans. Somewhere between the polarization of statistics acceptance and complete groupthink is a happy place where opinions may differ but people are knowledgable enough to understand what they’re disagreeing about. I maintain that much of the resistance against advanced statistics is born from a lack of understanding, or a lack of desire to understand. I’ll use Ottawa’s Erik Condra as an example. Condra has been a net relative plus for on-ice possession at even strength for each of his four NHL seasons, yet is seen as expendable by the majority of Senators fans. I’ve heard on multiple occasions that any metric which puts Condra ahead of say, Kyle Turris, must be wrong. What’s getting lost in the shuffle here is that Corsi is not the be-all-end-all stat its doubters perceive it to be. Condra’s CF% REL is telling us he sees a greater share of the 5v5 shot attempts directed at his opponent’s net relative to what occurs when he’s off the ice than Kyle Turris does. Nothing more. This is unequivocal as long as you put trust in the league’s trackers.
There is an axiomatic truth regarding on-ice possession that is seldom spoken albeit intuitive enough not to have to be. Not all possession shares equal worth. The differences that exist between shot rates and shooting percentages while on the ice add or subtract importance to the minutes you play and in turn, the share of shot attempts you generate. At equal CF%, a first-line player’s minutes will hold more value than a fourth-liner’s due to the simple fact more goals are scored in those minutes. It is thus an oversimplification to compare Turris and Condra’s CF% ratings without proper context. A different way to look at possession is to examine the expected goal differential based on shooting percentages we can reasonable expect from the quality of the players on the ice. In other words, how rewarding are a player’s minutes at a set possession share?
For quite some time there has been a debate going on: those who think you should add a defenseman’s effect on save percentage into player evaluations and those who think that adding such information causes more harm than good to the analysis. Note that this does not mean defensemen do not affect save percentage. That is an entirely different stance.
When it comes to evaluating a player statistically, you want the number to account for two things: effect and control. If a statistic does not help quantify how a player improves their team’s chance at winning, it is useless in measuring effect. If a statistic has too much white noise or other contributing factors that it would take too large of a sample to become significant to the player’s contribution, it is useless in measuring a player’s control over the effect.