Midseason Trade Article

Introduction

Through Tuesday night, 600 NHL games have been played -- equivalent to 46% of the season. All but a single team (Tampa Bay Lightning) have appeared in at least 35 games (Tampa Bay falls short by one game due to a weather-related postponement of a game that will be played on January 7). With the halfway mark of the season less than 10 days away, now is a great time to evaluate your fantasy hockey rosters. Below, we'll present a list of players who we believe hold high market value and should be moved soon due to the unsustainable nature of their production. In each case, we'll provide a set of potential trade targets (from the reasonable to the moonshots) for you to seek in return.

The goal of a fantasy trade is not to win the trade for the upcoming week. Or even the next week. You want a full-value return that makes it highly likely that you win the trade for the remainder of the season. Don't be afraid of looking dumb for a week or two if your traded player continues on his hot streak; selling on the way up always provides more return than selling on the way down.

Anze Kopitar - Los Angeles Kings - 83% Yahoo ownership

The 37-year-old Anze Kopitar is on pace (89 points) for the second-highest points total of his career (Kopitar produced 92 points in the 2017-2018 season). He's maintained a point-per-game pace over his last 10 appearances and scored two goals in Sunday night's game against the Philadelphia Flyers. The time could not be more right for you to trade him. But why trade a player nearly matching his career high in points? The answer is best understood by viewing Kopitar's regression meter (seen in Figure 2 below):

The regression meter (a fantasy hockey tool developed by Left Wing Lock) allows you to quickly compare a player's current production to his career levels across five important metrics: secondary assist rate (2A/60), on-ice shooting percentage (tEVSH%), individual shooting percentage (SH%), individual points percentage, and shooting percentage while on the power play (PPSH%). When the meter displays orange color, the player's current production has been inflated by good luck; when the meter displays a dark grey color, the player's current production has been muted by bad luck. In Kopitar's case, all five metrics reveal that his production has been buoyed by luck. Importantly, his tEVSH%, SH%, and PPSH% values display current values that are at least three standard deviations above his career averages.

While Kopitar's regression meter is alarming, it doesn't tell the full story. Not only are Kopitar's SH% and PPSH% double his career averages, his shooting percentage at even strength (EVSH%) is also double (24.3%) his career average. There are two problems here. First, Kopitar is a low-volume shooter who generates just 1.3 shots per game. Low shot volume and high shooting percentage are a dangerous combination. When the luck in shooting (high SH%) runs out, there is no shot volume to stem the bleeding; Kopitar's future cold spell will include many games with no goals. Second, there are just two players in NHL history who have maintained an EVSH% at, or above, Kopitar's current level for a full season (we required 70 games played as the cutoff). By ignoring the likelihood of a regression, you're implictly assuming that Kopitar will match (or set) an NHL record for even strength shooting percentage (at the age of 37). Reminder: none of this is about certainties; instead, we're asking you to take action on your roster that aligns with the most likely outcomes.

Potential targets: because Kopitar is a Top-5 contributor to the face-off wins categories, finding the right targets for a return will be highly league-specific. Let's ignore that category (momentarily) to get a first approximation of Kopitar's true value. To start with, you need to limit your targets to players who take more than 1.3 shots per game (this should be easy -- and you really want a high margin of safety here in the exchange on shot production given that one of the key reasons you're trading Kopitar is due to his low shot volume). Next, you'll want a player with luck-adjusted point production of at least 60 points for the full season (or about 0.75 points per game). This point production level is chosen so that you can match (or exceed) Kopitar's most likely point production in the second half of the season. Consider this level the floor and 89 points (Kopitar's current production pace) to be the ceiling (the type of manager who is willing to trade for Kopitar here is also not going to give up a player with higher production). It's important to remind yourself of the true goal of a fantasy hockey trade: you want to acquire a player who will outperform Kopitar in the second half of the season not the first half of the season.

Now that we set the foundation for the level of player to expect in return, you can add in any other categories that might be critical in your league's format (e.g., face-off wins). Below, we offer up a handful of potential trade targets based on these parameters:

Brandon Hagel - Tampa Bay Lightning - 95% Yahoo ownership

One NHL record that remains untouched for the past 15 years is Henrik Sedin's single-season tEVSH% of 13.8%. The tEVSH% metric measures the team's shooting percentage (at even strength only) with a particular player on the ice. At present, Brandon Hagel's tEVSH% for the 2024-2025 season would dwarf Sedin's number at 14.9%. This high tEVSH% has Hagel on pace for a 99-point season which would outpace Hagel's 2023-2024 (and career high) production by 33%. As with any player performing at higher-than-expected levels, we ask you to consider the most likely outcome: will Hagel set a new NHL record in tEVSH% or will Hagel's tEVSH% regress toward the mean? If breaking NHL records were a likely outcome, they wouldn't last for 15 years. Let's assume that Hagel finishes the season with a tEVSH% of 13.0% (note: this would be a Top-5 value in NHL history). What would this mean for his second-half production? Instead of the Lightning converting shots into goals (with Hagel on the ice) at a 14.9% rate, they would convert at 11.1% instead. This would lead to a 25% drop in available even-strength goals on which Hagel could be awarded points. Put another way, Hagel's even-strength point production could drop by 25% in the second half of the season even with a Top-5 finish in the tEVSH% metric. Hagel currently has 32 even-strength points in 34 games (0.94 per game). A 25% drop would result in a per-game point production of 0.70 even strength points.

Hagel's regression meter (seen below in Figure 3) reveals not only an elevated tEVSH%, but a high IPP as well. The Individual Points Percentage (IPP) is calculated by summing Hagel's even-strength points and dividing by the total number of even-strength goals scored by Tampa Bay with Hagel on the ice. The range is 0% to 100% and forwards typically land in the neighborhood of 70% (Hagel's career IPP sits at 69.5%). His current IPP of 81% suggests that Hagel has been awarded points on Lightning goals at too high a rate. What's interesting here is that both Hagel's IPP and tEVSH% are high. Ignore the numbers for a minute and put this into words. Tampa Bay is scoring goals at an unsustainably high rate with Hagel on the ice (tEVSH%). And on this inflated goal total, Hagel is being awarded points at an inflated rate (IPP). This double shot of luck almost always means a player's assist rates are well above his career averages. Such is the case with Hagel who has produced primary assists at a level 25% above his career average and secondary assists at a level 85% above his career average.

You see that dark grey bar on Hagel's regression meter for PPSH%? This tells us that Hagel's power play production (just five points through 34 games) has been muted by bad luck. If you were to examine Hagel's PPIPP (individual points percentage while on the power play), that number comes in at 25% so far this season (in prior seasons, that number has landed between 40% and 50% for Hagel). It's clear that he's missed out on some power play points due to bad luck and we'll need to account for that when determining Hagel's value.

Potential targets: Hagel's current pace (99 points) serves as a ceiling for the ask in a trade. The floor is computed by determining Hagel's current luck-adjusted point pace. We've pegged this number at 77 points. With no notable peripheral production (low hits, low shot blocks, and low face-off wins), the one key parameter is finding players who exceed Hagel's shot production of 2.6 shots per game (even if your league does not use the SOG category). Targets who meet these requirements are found below (if your league counts hits, Brady Tkachuk is a pipe dream here):

Anthony Cirelli - Tampa Bay Lightning - 52% Yahoo ownership

Given that Anthony Cirelli and Brandon Hagel are each other's most frequent linemate, Cirelli's tEVSH% also sits at record-setting levels: 14.5%. As you're now familiar with, a high tEVSH% is an indication that the team is scoring well above their talent level at even strength. A high tEVSH% (absent any indications that a player has been unlucky in other metrics) always results in inflated point production. As a Cirelli owner, you're going to use this to your advantage to acquire a "better-than-Cirelli" player for the second half of the season.

One important difference between Cirelli and Hagel is that the former is riding the deadly combination of low shot volume (just 1.9 shots per game) and high shooting percentage (21.9% as compared to his career average of 14.1%). As noted in the section above on Anze Kopitar, low shot volume provides no protection against a drop in shooting percentage. And when these drops occur, low shot volume players experience maximum pain in terms of goal production.

We include Cirelli's regression meter below for reference:

Potential targets: we use Cirelli's 80-point pace as our ceiling here and we've calculated a floor of approximately 60 points. There is no point in exchanging a low shot volume player for another low volume player (the risk for goal droughts is too high). So, lean hard into winning the shot production exchange in this trade. Cirelli's hits and blocks are not particularly strong, so the only real category of interest (depending on your league settings) is face-off wins. Below, you'll find a list of players to target in trades:

Brayden Point - Tampa Bay Lightning - 99% Yahoo ownership

At this point, you might be wondering if we've got something against the Tampa Bay Lightning. Hagel, Cirelli, and now Brayden Point? We encourage you to explore the Possession and Luck chart below:

The full explanation of how to read the Possession and Luck chart is found in this legend. The detail most relevant to this discussion is the size of the Tampa Bay bubble found at the approximate coordinates (50,1037). This bubble, the largest on the chart, represents a team-level, even-strength shooting percentage of 11.6%. If the Lightning were to maintain this level of shooting success, they would set a new NHL record for the metric. Remind yourself of the big picture here; what is the more likely outcome? Record setting or regression? It's almost surely the latter.

Putting aside the team-level discussion, the "slap you in the face" data point you should focus on for Point is his individual shooting percentage of 31.9%. With great confidence, we can state that Point's shooting percentage is likely to regress significantly in the second half of the season. For NHL shooters who take at least 196 shots on goal during a season (Point's current shooting pace), the highest shooting percentage on record is 24.5% (set last season by Florida's Sam Reinhart). Prior to that record, it was Point himself who held the NHL record at 21.7%. Again, we pose the important question: record setting or regression? Even if you assume that Point matches Reinhart's NHL record of 24.5%, this would require that Point's shooting percentage drop below 20% for the rest of the season. Those 23 goals he posted in his first 30 games so far? It would take him all 48 remaining games to produce another 23 goals (Point has missed four games so far this season).

Potential targets: Point's (absurd) ceiling is 118 points based on his current pace. A first look at his floor has it at 82 points (or point-per-game pace). But that first look assumes that his 2.4 shots on goal average will sit at the level for the full season. Earlier this season, our team dug deep into Point's shooting troubles (shot production not shot conversion) and learned that an unsually high fraction of his shot attempts were being blocked. We showed that this was a small sample size effect and we correctly predicted a sharp rise in his shot production (it sat at just 1.6 shots per game at the time). Greater shot production in the second half will lead to more opportunities for points (even though the conversion rate will drop significantly). As such, you probably don't want to get too close to that 82-point range with your trade targets. Point owners would probably be best served by initiating a two-for-one trade, but here is one option for a one-for-one trade:

Alex Ovechkin - Washington Capitals - 98% Yahoo ownership

Alex Ovechkin could not have asked for better timing for an inflated shooting percentage as he chases down Wayne Gretzky's record for most career goals in the NHL. He's currently converting shots into goals with a success rate of 22.1%. This value far exceeds his career average of 13.0% and three-year average of 13.7%. Interestingly, this boost is not coming by way of the power play. Instead, Ovechkin's inflated shooting percentage is solely from even-strength play where his EVSH% sits at 25.5% (more than double his career average of 12.1%). Ovechkin's 0.81 goals per game pace is unsustainable; while he may end up passing Gretzky this season, his goal production will drop noticeably in the second half.

In addition to the inflated goals total, Ovechkin's assist rates are coming in hot as well. His primary assist rate is 50% higher than his career average and his secondary assist rate (this rate is more heavily influenced by luck) is 95% higher than his career average. We anticipate a drop in assist production in the second half.

Potential targets: for proper context, over an 82-game season, Ovechkin's current scoring pace translates to 105 points. We'll use that as our ceiling for trade value. Luck-adjusted point totals put Ovechkin's pace at 75 points (over an 82-game season). Reminder: we're not suggesting Ovechkin will finish the season with 75 points; instead, we're saying his production (to date) would have a pace of 75 points if adjusted for luck. Ovechkin is a tricky player to trade. He generates incredible shot volume at 3.7 shots per game and produces a little over two hits per game. Depending on your scoring categories, Ovechkin could elicit very different returns. Let's have a look at some players who share some of these traits and land within this floor-to-ceiling range:

Dylan Strome - Washington Capitals - 80% Yahoo ownership

We went back and forth on whether or not to include Dylan Strome on this list. We were recommending managers trade Strome as early as November 15 (as seen below in this post on X).

At the time of our trade recommendation, Strome was on pace for 126 points. A snarky reply (to the X post above) a few days later would mock us after Strome added another five points of production over the next three games. Since our trade recommendation on November 15, Strome has produced 17 points in 22 games. That's a 63-point pace -- an astounding 50% dropoff in production. Is there more room to fall? Probably (though it won't be as drastic as what has already occurred). The basic idea is that Strome's tEVSH% still sits at 13.7% -- just shy of the NHL record held by Henrik Sedin. Why is Strome's tEVSH% so high? The driving factor here is that he is Ovechkin's most common linemate. Since Ovechkin's even-strength shooting percentage is double his career average, somebody on that line (it's Strome) is seeing a massive boost in assists. Now, you might protest and wonder how Strome's tEVSH% could remain so high with Ovechkin having only played in 21 games. The answer to this is that Strome's other most common linemate (Aliaksei Protas) has an even-strength shooting percentage over 20%. There are approximately 50 lines in the NHL that have been on the ice together for at least 100 shot attempts. The Ovechkin-Strome-Protas line leads them all with a whopping 19.0% shooting percentage.

Potential targets: if you view Strome over the course of his full 37-game sample, he maps out as an 89-point pace player. We've computed his floor to sit at 69 points. Strome is a low-volume shooter (1.7 shots per game) with very little production in hits and blocked shots. If your league uses face-off wins, understand that Strome has 330 on the season and lands in the Top-15 of the league for that statistic.

Matt Duchene - Dallas Stars - 72% Yahoo ownership

The Dallas center is having a solid season with 14 goals, 19 assists, and 33 points through 36 games. He's on pace for 75 points which would rank as the fourth-best finish of his 16-year career. The trade thesis on Matt Duchene is straightforward; he is a low-volume shooter (1.8 shots per game) riding a lucky shooting percentage (21.9% compared to a career average of 13.5% and a three-year average of 15.8%) to inflated goal production.

Potential targets: if we use Duchene's three-year shooting percentage to calculate his floor, we arrive at approximately 66 points. Our window of opportunity is then to find players with luck-adjusted production in the 66-75 points range who produce more shots per game than Duchene. If face-off wins are a consideration in your league, note that Duchene generates nearly five face-off wins per game.

Neal Pionk - Winnipeg Jets - 87% Yahoo ownership

On November 13, Winnipeg's conversion rate on the power play sat at a league-high 42.2%. That number has predictably cooled off to 32.5% as of the New Year. But, more interesting than the overall success of the Jets' power play was that both of their units were scoring at high rates. In fact, Winnipeg is the only team in the NHL with two units ranked in the Top-20 for power play goal scoring (today, those two units rank 2nd and 11th overall). One result of this power play success is that Neal Pionk (the defenseman who runs the second unit) has a PPSH% of 37.5%; compare that with his career average of 7.6% and it's clear that Pionk is scoring at levels far above his talent level.

Pionk's regression meter reveals that he's also been lucky at even strength. His IPP is elevated and the Jets are converting 9.4% of their shots into goals while he is on the ice (that tEVSH% number is not alarmingly high, but it's a fair bit above his career average of 7.8%). The excess points on the power play and at even strength offer up an opportunity for you to move Pionk and replace him with a player who is likely to outscore him in the second half.

Potential targets: Pionk is currently on pace for a 57-point season. A luck-adjusted analysis of his performance so far would put a floor at about 36 points. While Pionk is a low shot volume player (1.6 shots per game), he provides strong numbers in hits (4.07 per 60 minutes) and blocks (4.70 per 60 minutes). The trick in finding acceptable targets here is to not give up too much in the peripherals while chasing down points.

Timing the Fantasy Hockey Trade Market

You cannot time the fantasy hockey trade market. I cannot time the fantasy hockey trade market. The nature of unsustainable production is that neither you nor I can know when the luck runs out. You might be thinking you can hold your unsustainable players just a little while longer to squeeze out more fantasy production in the coming weeks -- but you'd likely be making a big mistake. Fantasy managers have a strong bias toward recent production. You see this every Summer when managers jump way too early in drafts for guys with high shooting percentages from the previous season. This happens during the active fantasy season as well. Consider the statistics most readily available to managers; go to your league page (or favorite fantasy analysis sites) and the emphasis is on sorting players by 30-day, 14-day, and 7-day averages. In most instances, you can't even find the three-year or career averages for production. So what happens when one of your top forwards with 20 goals goes on a five-game cold streak and then you decide to trade him? The opposing manager's valuation of this player will be negatively influenced by his recent cold streak. That peak trade value we wrote about in this article? Gone. Doubt has crept in and managers will no longer pay top returns.

A good example of this from earlier this season involves Martin Necas of the Carolina Hurricanes. On November 18, we published this post stating that we had traded Necas in all of our fantasy leagues. At the time of these trades, Necas had generated 30 points in 17 games played (1.76 points per game) and ranked third overall in NHL scoring. As of the time of this writing, Necas has just a single point in his last seven games and posted just 15 points in the 20 games (0.75 points per game) after we traded him -- a stunning 57% drop in production. Don't get left holding the bag!

Fantasy Hockey Dashboard

Wondering how to find players with unsustainable production? The pros at Left Wing Lock use the one-of-a-kind tool known as the Fantasy Hockey Dashboard. You can identify players on your roster (or your opponents' rosters) who are most likely to see increases or decreases in point production in the future. This unique tool uses a heatmap approach so that you can instantly identify which players you should consider trading (or targeting in a trade). Some of the features of this tool are displayed below: