Framework For Evaluating Players Feat. Mike McLaughlin (Left Wing Lock)

Framework For Evaluating Players Feat. Mike McLaughlin (Left Wing Lock)

Table of Contents

Framework For Evaluating Players Feat. Mike McLaughlin (Left Wing Lock)

In the 40th episode of the Fantasy Hockey Hacks Podcast, the FHH team discusses a framework for evaluating fantasy hockey players. Given that the NHL has essentially been on pause since December 21st; we felt that this was a good opportunity to do something a little different. Mike McLaughlin, our partner over at Left Wing Lock, joins the team for this special episode that takes an analytical approach to evaluating the best players for your fantasy roster. Also, we introduce an exciting new segment that Mike will be covering for us every month – so be sure to tune in for that!  Subscribe to our podcast on all major directories; and stay up to date with the latest in fantasy hockey news and fantasy hockey advice.

  • L.A.P.S. – A framework for evaluating fantasy hockey players
  • Introduction of Mike McLaughlin’s (Left Wing Lock) new special segment (monthly)
  • Week 12 waiver wire picks (blog only)

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Framework For Evaluating Players Feat. Mike McLaughlin

L.A.P.S (Line Mates, Analytics, Production, Strength of Schedule)


One of the first things that I will check when evaluating whether to acquire a player (either through the waiver wire, or by trade) is who are his line mates? Is he playing with those line mates consistently? and the Left Wing Lock app are great tools to quickly check line combinations from a previous game, 3 games, 10 games, or all season long. Check line production, and review possession metrics (SAT%, USAT%), Goals-For Percentage (GF%), and Zone Start Percentage (ZS%).  Natural Stat Trick also has a line tool to evaluate how much time a particular line has spent together on the season, and includes similar possession metrics. 

After establishing who said player is skating with, and whether they have been productive together; I like to review the player’s deployment on the ice. Is he seeing a ton of offensive zone starts? What quality of competition is he facing? How much power-play time is he receiving? Again, our friends over at Left Wing Lock have a useful Player Usage Chart that illustrates quality of competition, player time on-ice, zone starts, and whether or not they are outshooting their opponents.   


When assessing a player’s overall value compared to another, it’s always best to do so objectively by reviewing the underlying numbers. This portion of the framework will provide details on specific metrics that the team at FHH and Left Wing Lock review when evaluating a player’s fantasy value. Similar to the Player Usage Chart mentioned in the previous section, Left Wing Lock also has a Possession & Luck (“Pluck”) Chart available that combines possession and luck data for NHL teams into one easy-to-read chart. Mike will discuss further in this section of the framework. 

Individual Shooting %

What: The percentage of shots by a player that were goals. For example, a player that scored 15 goals on 200 shots would have an individual shooting percentage of 7.5%. 

Why: The league average is ~8.5%, so any number extremely high or low would be expected to regress eventually. This number can also be used to evaluate past performance and assess whether a player is performing within career levels or not. 

Even-Strength Shooting % (EVSH%)

What: The shooting percentage of a player and his teammates when that player is on the ice (even-strength). During the 2020-2021 season, NHL goalies posted an average save percentage of .915, while NHL skaters posted a 8.5% shooting percentage. (Source: LWL Draft Kit)

Why: This is an important metric to identify players that are performing above, or below, expected levels based on league averages.  

Points Per 60 (Pts/60)

What: As ice-time is not equal across all players in the NHL, Points Per 60 is a great metric to assess how individual players are performing with the time on-ice that they are given. Essentially levels the playing field with regards to ice time. 

Why: This metric helps to level the playing field when it comes to time on ice for individual players. 

Individual Points Percentage (IPP)

What: IPP measures how often a player earns a point while his team scores when he is on the ice. The calculation for IPP is rather simple; you take the number of points awarded to Player X and divide it by the number of goals scored by Player X’s team while he was on the ice. Multiply that fraction by 100 and you have your IPP value. Typical values of IPP are 70% for forwards and 30% for defensemen. Of course, elite players will exceed these values.

Why: The example discussed during the show was Matt Duchene, who saw his IPP regress positively from last season. 

Secondary Assist % or Secondary Assist Rate

What: What percentage of a player’s assists are secondary (first of two players to touch the puck prior to the goal scorer) versus primary? 

Why: This is an important metric to note given that secondary assists are much less reliable/volatile. Compare current season rate or percentage to the past three seasons for a specific player to establish a baseline performance. Mike mentioned a few specific examples, including Jonas Donskoi and Troy Terry. The benchmark for secondary assists rate is 1.00 A/60, so any player whose rate is above 1 is likely to regress. 

Individual Scoring Chances For (iSCF) & Individual High-Danger Scoring Chances For (iHDCF)

What: iSCF – Any scoring chance by the player, outside of the shootout. iHDCF – Any high-danger scoring chance by the player, outside of the shootout. A scoring chance being defined as a shot attempt (corsi) taken inside the offensive zone. A value is assigned to shot attempts based on where they were taken inside the zone (perimeter – 1, “home plate” – 2, In-tight/crease – 3)  Any shot attempt from the defensive or neutral zone is excluded. Source:

Why: For me this is a helpful, straightforward metric for how many chances an individual player is creating to score during the course of a game. You can review this game by game, or over the course of a season. Can be helpful when assessing trade values. 

Even-Strength & PP Production

What: What percentage of a player’s total points are collected at even-strength – play where both teams have the same number of players (including goalies) on the ice. Includes 5v5, 4v4, 3v3, as well as when teams have pulled the goalie to turn 5v5 into 6v5, 4v4 into 5v4 or 3v3 into 4v3.

Why: Important metric to note because power play production and deployment can change throughout the season, power play efficiency can fluctuate throughout the season, and the number of power plays for each team drop throughout the course of the season. Mike mentioned in his draft kit that penalty minutes per game are decreasing in the NHL in general. 

Even-Strength Save Percentage (EVSV%)

What: A goaltender’s save percentage at even-strength. 

Why: The league average for NHL goaltender even-strength save percentage is approximately .925%. 

Penalty-Kill Save Percentage (PKSV%)

What: A goaltender’s save percentage on the penalty-kill. Throughout the course of the regular season a goaltender’s penalty-kill save percentage will always regress to the league-average of .865%. 

Why: Knowing that a goalie’s average save percentage on the penalty kill will regress to the mean (up or down)of 86.5%, fantasy managers can better assess a goaltender’s current performance based on underlying numbers. 

Goals Saved Above Average (GSAA)

What: A cumulative metric that shows a goaltender’s performance relative to the league average. For example, Connor Hellebuyck’s GSAA since October 21st is 8.1120, this indicates that Hellebuyck saved 8 more goals than an average goaltender would have. 

Why: A simple metric to assess a goaltender’s performance relative to an “average” NHL goaltender. As Mike mentions during the episode, these metrics are not perfect based on how the data is collected; however, this metric can provide a simple comparison from one goaltender to another. 

“Pluck” (Possession & Luck) Chart

What: Two of the most important statistical ideas that you can use to analyze a team are possession and luck. Possession stats tell us which teams are  consistently outshooting their opponents. Measurement of luck tells us which teams are playing beyond their means.

Why: You can use the charts to analyze hockey teams and you can use the charts to gather intel for use in fantasy hockey. These charts have been used by the team at Left Wing Lock to accurately identify which teams are most likely to make the playoffs each year. Below is an example “Pluck Chart” from Left Wing Lock. 

Pluck Chart


This one is pretty straight forward – I will review the player’s production to date in all relevant categories for my league. Additionally, I review to see if that production is consistent with what the player has done over the past 3 seasons, and throughout his career (more weight is given to results seen in the three most recent seasons). Is the production sustainable? Or is the player simply on a heater? All of the points discussed in “Analytics & Luck” will help to determine the sustainability of a player’s production.  


We have mentioned this in past episodes and on Twitter, but Left Wing Lock has a very useful strength of schedule tool that will help fantasy managers to identify which teams have the strongest schedule in any given week. 

Additionally, I always like to assess whether a player has better results at home or on the road, against a particular opponent, and what does their schedule look like during the playoffs? Obviously volume becomes crucial in the final few weeks of the season, so always keep that in the back of your mind.

Introduction to Fact or Fiction With Mike McLaughlin

As many of you know, the Fantasy Hockey Hacks podcast recently became the official podcast of Left Wing Lock; and we mentioned in a previous episode that a new segment was coming once we finalized the details. I’m excited to say that we have nailed down the format of that new segment, and we’re calling it Fact or Fiction. Mike, since this is essentially your segment, would you care to tell our listeners what it’s all about? 

The idea here is to highlight interesting cases where teams and individual players are performing at levels that may or may not be supported by the data. Each week we will cover three different scenarios involving a team, forward, defenceman, or goalie. Below is one such example that Mike has provided. 

Team Case: Detroit Red Wings 

The Red Wings enter this weekend as a wildcard team in the Eastern Conference. Can you count on their current standings position as you evaluate their goalies for fantasy hockey? 

The Details 

A simple, no-nonsense indicator of a team’s playoff possibilities is their goal differential. Teams with positive goal differentials usually make the playoffs, while teams with negative goal differentials miss. 

{note: From the 2015-2016 season through the 2018-2019 season, only 6 teams violated this rule – this simple rule had a 94% accuracy rate.} 

The Red Wings currently have a -18 goal differential through 28 games, but have somehow won nearly 50% of their games and hold the wildcard spot in the playoffs. We have a contradiction here that we must explore by digging a little deeper. 

  • Are the Red Wings a strong puck possession team? No. They rank 28th in the league and are outshot consistently by their opponents.
  • Do the Red Wings have particularly strong scoring or goaltending at even-strength? No. The Red Wings are at, or slightly below, the league average for both metrics. 
  • Do the Red Wings have strong special teams? No. They rank 25th in PP% and 25% in PK% along with having a 28th ranked penalty differential. It’s safe to say they have one of the league’s worst sets of special teams. 
The Verdict 

Fiction. The results don’t match the process. The Red Wings playoff position is a mirage of luck and they will quickly fall out of playoff contention. Their 50% win rate should not be relied upon for analysis of the team’s future.


If you have a particular case that you would like Mike to assess and discuss on the show, please email us at; or reach out to us on IG, FB, or Twitter.

Edge Work | NHL Week 12 Waiver Wire Targets

Waiver wire targets will be focused on players with ownership ~50% or less. 

Top Waiver Wire Targets

📈 Alex Tuch (LW/RW), BUF | Rostered: 24% (Yahoo) | Strength of Schedule: -18 | GP: 3 (Vs.NJD, @NYI, @BOS) | Line: 1 (w/ Murray & Cozens, PP1) 
  • Tuch was expected to make his season debut against the Columbus Blue Jackets last week; but instead will square off with the New Jersey Devils on December 29th. Tuch showed flashes of brilliance during his time with Vegas; although, largely in a third line role. On-pace last season for 27 goals and a valuable contributor for hits (1.13/GM), Alex Tuch could become Buffalo’s best player with a regular role in the top 6 and top power play – something he never really experienced in Vegas. 
📈 Noah Dobson (D), NYI | Rostered: 18% (Yahoo) | Strength of Schedule: -7 | GP: 3 (Vs.DET, Vs.BUF, Vs.EDM) | Pair: 2 (w/ Chara, PP1) 
  • In his last 8 games played, Noah Dobson has 6 points (3 goals), 20 SOG, 3 PPP, 8 hits, 14 blocks, and is finally being utilized as the power-play quarterback for the Islanders (73% PP Share). With matchups against Buffalo and Detroit, Dobson should provide great fantasy value for the upcoming week. 
📈 James Reimer (G), SJS | Rostered: 62% (Yahoo) | Strength of Schedule: -19 | GP: 4 (@ANA, Vs.ARI, Vs.PHI, @PIT) | Role: Tandem (53% Net Share)
  • The San Jose Sharks have a favourable schedule (-19) for next week, including matchups against Arizona and Philadelphia. Reimer is still available in roughly 40% of leagues and has some very impressive peripherals  on the season – 2.03 GAA, .936 SV%, 11.92 GSAA. The Sharks are 5-5-0 in their last 10 games, and will be without Kevin Labanc; however, Arizona is 2-8-0, and the Flyers are 4-5-1. Anaheim and Pittsburgh present challenges, but Reimer should see at least 2 or 3 games for next week. If you’re league counts saves, save percentage, goals-against average, Reimer could be a great option.  
📈 Ukko-Pekka Luukkonen (G), BUF | Rostered: 20% (Yahoo) | Strength of Schedule: -18 | GP: 4 (Vs.NYI, Vs.NJD, @NYI, @BOS) | Role: Tandem? (17% Net Share)
  • Luukkonen was mentioned in our week 11 waiver wire targets, but give the NHL pause, and Buffalo’s 4-game schedule for next week, he is worth mentioning again. First 5 starts for Buffalo: 2-2-1, 1.96 GAA, .939 SV%, and a 4.43 GSAA.

Also: Review our recommended waiver wire picks for week 11, as much of the information found there will still be applicable to the extended week 11/week 12 schedule. 

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