Recently, I showed how passing data is a better predictor of future player scoring than existing public metrics. In this piece, I’m going to show that by accounting for shot quality via passing metrics we can more accurately predict a team and player’s on-ice goal-scoring rates. I’m going to do this by quantifying the pre-shot movement that occurs when a player is on the ice. Finally, I’ll spend some time discussing certain forwards/teams that caught my eye. All data is from 5v5 situations and special thanks to Dr. McCurdy for pulling the on-ice player data for me. All non-passing project data is from Corsica.
Last time, I showed how passing data is a better predictor of future player scoring than existing public metrics. In this piece, I’m going to spend some time talking about how we can more reliably evaluate offensive and defensive contributions from defensemen, which has been difficult due to a lack of data. Not only due to a lack of data, but from a lack of flexibility regarding the identity of the position. Traditionally thought of as existing to defend and “make a good first pass,” I feel this limits the scope of both how we evaluate the position and its responsibilities.
In order to better evaluate defensemen, we need to identify specific metrics that we can tie to future goals. In looking at entry assists (a pass occurring in the neutral or defensive zones that precedes a shot), both for and against, we can quantify how effective that defensemen is at generating offense in transition, as well as suppressing those chances. The importance of those things at the team level is something I’ve previously discussed (transition here and defensive work here with Matt Cane). Once we identify these metrics as having a strong impact on future scoring and goal-suppression, we naturally then reevaluate what the proper roles are for a defensemen, which in turn forces us to reevaluate how we evaluate them.
Personally, I’d like to see us think of them more as fullbacks or midfielders in soccer (this is part of a larger concept of redefining positions and responsibilities, which will be posted in the next month or so, I hope). There are still going to be various types of players based on their individual skill set and team tactics, but supporting play, overlapping on the attack, and distribution are all pillars of what teams should look for. Let’s get to it.
While I have spent a lot of time over the last several months digging into how we can quantify passages of play and inform better tactical decisions, it’s time to revisit how passing impacts scoring at the player level. We have only been using half of the picture in terms of individual shots and goals for player evaluation. Sure, we have primary and total points, but primary assists aren’t a very useful metric. The rate at which players create shot assists also appeared to have significantly more value than a player’s own shots in some analysis I did last year.
This piece will release individual passing data for the 2014 – 2015, 2015 – 2016, and 2016 – 2017 seasons, the latter of which tracked by Corey Sznajder, the former tracked by myself and many others. However, it is important to provide context and meaning to the numbers rather than simply inundate you with data.
Last week I wrote about a new metric, ZEFR Rate, which measures zone entry success on the power play and is relatively repeatable and predictive of future goal scoring efficiency. The metric was based around the idea that getting into formation efficiently — most frequently a 1-3-1 — is a catalyst for power play success.
But now let’s say you’re a team that has perfected your entry scheme, and you find yourself setting up in formation at a consistent rate. What now? How can one maximize one’s use of possession in formation to score goals at the highest possible rate?
Listen; I get it. Some people are sick and tired of this supposed debate that’s been ongoing for over ten years now. But what really is the actual debate all about? What is it we are arguing on Twitter over? What should we be aware of?