The Metric Lab is a deep dive using the FantasyLabs Tools to analyze the predictiveness of different statistics and proprietary data. The series provides analysis by looking at the dynamics of value, ownership, and consistency by using our massive database of historical trends.
What NHL Metrics Should I Prioritize in my Models and Player Selection?
Many of our subscribers will be dipping their toes into DFS hockey, as we are still about a month away from MLB. So for a short time put down one of Jonathan Bales’ books; there’s plenty of time for that before baseball gets rolling. Think of the Metric Lab as a far less entertaining and carefully researched version for NHL.
Baseline Trend and Power Play Shot Attempts % (Month)
The upside with targeting shots on goal over blocked shots is pretty simple: small events (shots) are exactly what lead to less predictable — but often more significant — events like goals and assists.
As you can probably guess, power play shot attempts are measured only on the man advantage. These “attempts” include not just those that make it on goal but also those that are blocked and/or miss the net entirely. Perhaps how often a skater tees one up on the power play is a positive indicator for someone being featured when it matters most.
Using our Trends tool, we can compare the Plus/Minus values to both Consistency and ownership. For those of you who have no idea what I just said:
Plus/Minus: A player’s actual points minus his expected points in the context of his salary-based expectations. Note that DFS scoring is typically lower for NHL than other sports like NFL or NBA, so the NHL Plus/Minus values we see are likely to be relatively small.
Consistency: The percentage of games in which a player has produced within a standard deviation of his expected points based off of historical scoring and pricing. Can be used to identify high-floor players for cash games.
As always, we should identify a baseline trend before we get too deep. Perhaps a very basic peripheral stat trend, such as power play skaters in the 50th percentile or better in power play shot attempts over the past month would be a good starting point:
If we want to focus on those players being featured on the power play, it would be hard to consider someone in our player pool who doesn’t meet that type of benchmark.
The following charts highlight power play skaters using different percentile buckets for power play shot attempts over the past month. In these charts we can compare Plus/Minus to both Consistency and Ownership:
There’s a lot to unpack here, but outside of just our baseline trend it makes sense to analyze this through the lens of a traditional peripheral stat metric like shots+blocks as well:
The Plus/Minus is much stronger for something like shots+blocks, but the Consistency is very comparable, which suggests that power play shot attempts could be valuable, but shots+blocks offers a bigger edge if we take into account its greater independence from ownership.
Let’s dive deeper into how power play shot attempts affect the value of different positions.
Each position has unique intricacies as to which stats strongly affect value, but the true edge comes in identifying which of these metrics are not typically priced into the salaries. This chart looks at the same percentile buckets as above, but breaks things down by position and has removed samples (counts) of fewer than 25:
Per usual, defensemen in the top-10 percentile or better dominate this metric from a Plus/Minus and Consistency perspective. Metrics like shots+blocks and shot attempts (total) historically provide more value but also at an elevated ownership. What’s exciting is that power play shot attempts could offer similar value while also offering differentiation in large field guaranteed prize pools.
Intuitively, shot attempts from farther away would have a much better shot of being blocked or missing the net. However, by targeting defensemen who attempt more shots on the power play we get a better idea of who is truly being featured at the point. Notably, the Upside Rating is low in comparison to that of other metrics, but that is likely because included in this metric are shot attempts that don’t translate into fantasy points.
Note: Upside figures show the percentage of games in which a player has scored at least one-half standard deviation above his point expectation based on salary. Can be used to identify high-upside players for tournaments.
Targeting mid-ranged salaries for players like Justin Faulk (97th percentile in power play shot attempts over the past month) could be a great way to spend salary. If you’re looking purely for upside, you may want to look to other metrics, but differentiation is key if you can find comparable value.
The distinction seems to be less important for centers and wingers — with only those in the top-one percent presenting consistent relevance — but there are exceptions like Tyler Seguin and Vladimir Tarasenko (99th percentile in power play shot attempts over the past month), who might offer more value than expected.
Photo via Terrence Lee-USA TODAY Sports