Jeff Skinner returned from his three-game suspension for Saturday's game against the Arizona Coyotes. The 30-year-old Buffalo forward scored three points to raise his production to a career best 103-point pace. Skinner has 15 goals and 20 assists (35 points) in 28 games; he's ranked 12th in the league in points per game (ahead of star players such as Steven Stamkos, Jack Hughes, and Auston Matthews).
Skinner is currently rostered in 77% of Yahoo fantasy leagues, but had an ownership rate of just 16% on opening night. His average draft position (ADP) was 174.1 which translates to the middle of the 15th round in standard 12-team leagues. Skinner has returned incredible value for managers who drafted him or grabbed him off the waiver wire early in the season. But the time has come to realize that Skinner's production is about to decline; your window to trade him for a player of value is closing and you must act soon.
The Regression Meter
In deciding whether or not to trade a player on your fantasy hockey roster, you need to determine if the player's production is sustainable. There are a number of useful metrics to apply when making this decision and they can be found on numerous websites. The problem is, these metrics are never found in the same place. Our team set out to fix this problem this past Summer and designed a new tool called the regression meter. On every NHL skater's player profile page at this website, a regression meter is published. These meters provide you with an "at a glance" view of a player's luck metrics so you can quickly determine if a player's performance has been boosted by luck. The meter reveals how a player's current performance compares to his career averages in five luck-influenced metrics: secondary assist rate (2A/60), team shooting percentage at even-strength (tEVSH%), individual shooting percentage (SH%), individual points percentage (IPP), and power play shooting percentage (PPSH%). If the meter is orange (above the midpoint representing his career average), the player has been lucky; if the meter is dark grey (below the midpoint), the player has been unlucky. It's that simple to use! Below is Skinner's meter for the 2022-2023 season:
It's clear from looking at Figure 1 that Skinner's performance has been boosted by luck. In fact, the meter reveals that Skinner has been lucky in all five major luck metrics.
We recommend starting the sustainability analysis with a player's goal scoring. Skinner has 15 goals on 92 shots for a 16.3% shooting percentage. This scoring rate is inconsistent with Skinner's career average of 11.0% (as well as his three-season average of 11.8%). What is driving Skinner's high shooting percentage? An unsustainably high scoring efficiency on the power play. You can see that in Skinner's regression meter or in the graph below which shows Skinner's power play shooting percentage over the course of his career. It turns out that Skinner has scored on 21.7% of the shots he's taken on the power play this season. Compare that with his career power play shooting percentage of 11.9% and you're looking at a value that is nearly double his expected rate. When you adjust for luck, Skinner's most likely goal expectation (at this point in the season) is 11 goals.
How about Skinner's assist total? You could easily jump into some assist-specific data for this analysis, but it's more instructive in the long term to look at the bigger picture. The Buffalo Sabres are converting shots into goals (at even-strength) at a rate of 11.7% with Skinner on the ice. A typical tEVSH% value for players is 8.5%; in fact, Skinner's career average for tEVSH% is 8.1%. Since Skinner's individual shooting percentage (SH%) is high -- but driven by luck on the power play, that leads us to the conclusion that the tEVSH% value is being boosted (mostly) by his teammates. Futhermore, we notice that Skinner's individual points percentage (IPP) is elevated by luck (his IPP is 79% compared to his career average of 73%). A high IPP means that a player is being awarded points on even-strength goals at a rate higher than expected. It's important to pause for a minute and put the concepts of tEVSH% and IPP into a single, tidy package. The high tEVSH% suggests that there is an elevated number of goals available to earn points from; the high IPP suggests that on these goals, Skinner is being awarded points at too high of a rate. This double shot of luck (high tEVSH% coupled with a high IPP) always leads to a heavily inflated rate of assist production.
How inflated are Skinner's assist rates (measured on a per-60 minute basis)? His secondary assist rate sits at 0.67 which is more than double his career average of 0.28; his primary assist rate sits at 1.34 which is more than double his career average of 0.59 (and about 30% higher than his primary assist rate from last season). To put that primary assist rate into perspective, if Skinner were to maintain that level he would finish the season with the 15th highest primary assist rate in NHL history (notes: the NHL only documents these stats as far back as the 2009-2010 season; we used a minimum 70 games played cutoff). If you walk Skinner's assist rates back toward reasonable levels, you'd expect his assist total to come in at 15 assists instead of 20.
Trade Skinner Today
All five major luck metrics (displayed in his regression meter of Figure 1) reveal that Jeff Skinner is likely headed for significant regression. Adjusting for luck has led to the conclusion that Skinner is very likely a point-per-game player masquerading as a 100-point player. It would be wise of you to use this to your advantage. The ideal exchange is to move Skinner for a player with luck-adjusted point production that lands in the 85-100 point range.
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Of course Jeff Skinner scores a goal the next day. All the more reason to trade him!
Skinner finished the season with 82 points in 79 games -- on pace for 85 points over the course of a full 82-game season. From the time this article was published, Skinner generated just 47 points in his final 51 games (0.92 points per game).
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