***warning: I probably should have changed the title to VERY LONG Adventure since this article ended up going on forever. My apologies in advance…hope you can make it all the way to the end. Maybe consider it a challenge? lol***
My summer so far has been untypically hectic to date…..a radical departure from my normal laissez faire lifestyle. As a result, while I did get the opportunity to read both of Mullayo’s articles about his True Value (TV) metric and spend a bit of time doing research & comparisons, I just haven’t found the time to put all of that into an article that might capture people’s attention. Well, my calendar finally hasn’t got any To Do tasks written on it just as I am starting a week of holidays…..perfect. So I decided to put Led Zeppelin IV on the stereo and finally dive into this thing….
Everyone around here is probably well aware of my fondness to use statistics to evaluate the performance’s of the Winnipeg Jets, so the opportunity to add another one to my arsenal truly got me excited. It should be noted that while I did focus on statistics in university, my employment has only required the use of the most basic of these calculations. So I am far from an expert and certainly creating a complex metric like True Value isn’t really in my repertoire. Maybe if I dug through all my memories to get back to the mid-90’s some of that math would come back to me? What I do have going for me is years of watching, playing, and analyzing the sport of hockey.
Over those decades, I have gathered a bunch of freely available statistical categories that I like to depend on to accurately reflect a player’s performance. Just like a builder relies on tools such as hammers, drills, and saws to get their jobs done properly, anyone looking to dive into the work of analyzing a hockey player’s performance will require good metrics to do it well. There are certainly other stats not included that would be helpful, but most of those require a paid subscription somewhere (possession numbers, zone exit & entry data, etc). Since most hockey fans aren’t willing to go to that extent, I have focused on the stats that are easily collected and think they still do a good job of reflecting how a specific player performed in a season.
After Mullayo broke down what his True Value metric is trying to show in his first article at Arctic Ice Hockey, True Value: An Apples vs Apples Scouting Tool, I got to thinking how that information would look along side the statistics I generally rely on. Unfortunately that meant doing the work to add a full 8 columns to the spreadsheet in his article…bringing the total to a rather large 24. When you add that there are 95 players included in our review, that is a lot of information to gather and enter. Unfortunately, despite the great efforts by statisticians over the years….they haven’t come up with just one metric that could accurately reflect how good the overall play of a specific player was. That is because hockey is way too complicated for something like that. Just as a club needs players that can score goals like the TV stat is trying to bring to the forefront, they also need skaters that are adept at stopping goals from getting by their own goalie. It is almost an impossible ask for 1 stat to cover both sides of that coin….so alas, we are left with all these data points for our review:
Since all the metrics above are able to give us a glimpse into a certain aspect of a player’s game and the fact that there are a lot of “aspects” that go into the sport of hockey….it totally makes sense that the more information you include, the more complete the picture you can get. However, you have to draw the line somewhere, so lets get into discussing all the categories I think are noteworthy when trying to analyze the players Mullayo included in his spreadsheet. That spreadsheet included 95 players (all forwards except Zach Werenski) that spanned 1st line to 4th line talent, so it gives us a good array of skaters to look at.
A lot of the categories in the legend above don’t require further explanation, in particular the first column that includes basic stats like points and special team ice times.
The 2nd column include Mullayo’s new metrics and the NHL Equivalent stat he wanted to improve on. The NHLe is not very complicated and when it comes to a NHL player, it basically tells us how many points they would have gotten if they played all 82 regular season games at the average ice time they had that year. Besides saving me a bit of math, the metric is more useful when it comes to a player who was outside of the big league since it attempts to predict how many points they would have gotten if they were in the NHL.
The True Value metrics (including TV, TVA, TVe, & EVe) are our very own number cruncher’s attempt to improve the hockey scouting world by giving them a tool to potentially identify impact players. I certainly suggest you read the actual article linked above for a full explanation, but the aim was twofold – first to find a way to put every player on equal footing to be able to compare them easier, then second to break down the statistics and playing time to give different levels of focus to each area. For example, ultimately the whole point of hockey is to score goals, so players who are good in that area should be important to clubs (therefore G’s are given a higher value than A’s) and the scoring rates vary greatly when a team is at even strength, a power play, or penalty killing so those ice times shouldn’t be treated the same. Makes sense, though I can’t say I truly understand the calculations that go into these adjustments…which does give my logical brain a bit of a problem. Nothing that can’t be overcome though, as I am a huge fan of the Expected Goals metrics and I couldn’t actually calculate those on my own either.
The first True Value stat on the graphics below will be TV, which might be my favourite because it projects a player’s point total if they all played an equal of 10 minutes per game, while using Mullayo’s new goal and assist values. Next up will be the True Value Age Adjusted (TVA) category that which can show what players are still on the rise and who is on or heading for a decline (once again based on 10 min/gm).
The True Value Equivalent (TVe) basically does the same thing as the NHLe, as it projects how the player would have done with a full 82 games based on the average ice time they actually played that season. Next up he used those two stats to create the Differential Equivalent (DIFe) metric with the calculation of NHLe – TVe before giving us his final metric, Even Strength Equivalent (EVe). This stat uses the goals & assist values to tell us how many points they could have scored if they played all of their ice time per game at even strength.
Now we get to the metrics that I have added to the review, which are all limited to Even Strength play. The reason for that is smarter people than I have already done the work to discover that 5 on 5 play is the best factor to use if you are trying to predict future success. In particular, an NHL club’s 5 on 5 Expected and Actual Goals stats for the regular season can provide a good clue as to the likelihood that they end up lifting up Lord Stanley’s Cup in the playoffs. Over the past 11 campaigns the eventual winners finished in the top 6 of Expected Goals 7 times and only once has the team had a negative rating (Washington Capitals – 2017-18 – 46.96%). The worst rating after that was the 52.09% for the Tampa Bay Lightning in 2020-21. When it comes to Actual Goals, no NHL team has lost the scoring battle at 5 on 5 in the regular season and go on to win it all. The worst ratings in the past 11 seasons were 52.4% for the 2024-25 Florida Panthers and the 2017-18 Washington Capitals. When it comes to the playoffs, surprisingly 2 squads have lifted the Cup while losing the 5 on 5 Expected Goals battle in the post-season, the 2016-17 Pittsburgh Penguins with 48.29% and the 2018-19 St. Louis Blues with 48.15%. Yet the worst Actual Goals % was the 2016-17 Penguins again with a 53.19%, so all the winners ended up on top in even strength scoring.
The first one I added is Points Per Minute (PTS/MIN), which basically tells us the rate a player scores at 5 on 5. Helpful to me, yet I’ll admit that it does produce a value that doesn’t immediately tell you much. Somewhat related are the next two stats, Goal Percentage (G%) and Points Percentage (PTS%), which show what part of their point totals came at even strength. We round out with another scoring metric in Individual Point Percentage (IPP), which tells us how often the player is directly involved (get on the scoresheet) in goals their teams score when they are on the ice (5 on 5).
Okay we are almost done, only 4 more categories to go. I have already mentioned Expected Goals Percentage (xG%) and this metric is calculated to tell us if the player or team created more scoring chances than their opposition. They get to this figure using decades of historical data to come up with the likelihood that an average player would score on an average goalie from various places on the ice and under various conditions (passes prior to shot, angle to net, etc). I also talked about Actual Goals Percentage (aG%) and this doesn’t require any projection since it is calculated by taking actual goals scored and allowed to give us a figure. The Goal-Expected Goal Differential Percentage (G-xG Diff%) just shows us whether a player was on for more Actual than Expected Goals.
All of the above metrics are different tools that I can use to compare players and potentially use to see if they are in agreement with who the True Value stats are highlighting. In Mullayo’s article introducing us to the TV metrics he posed the question of who is more valuable…Mark Scheifele or Brandon Hagel, Kyle Connor or Cutter Gauthier? Well, ignoring any age differences and solely focusing on last season’s performance, I thought these 4 players would provide a good vehicle to take my readers through all these different statistics to find out what each of them says. That way, you can determine on your own which stats you will focus on in your own research.
To kick things off, let us take a look at the top 25 players from the 95 in our review according to the TV stat (projected 10 mins of ES play per game).
As you can see, the TV stat doesn’t just mirror what the point totals are telling us, as many players changed positions. Scheifele had the 5th most points in the group, yet sits in 9th. Connor went from the 9th most points to drop all the way to 24th. Hagel was 18th in scoring, yet rose all the way to 6th, while Gauthier was 23rd in points but finished 11th in the TV metric. Because Mullayo’s new stats reward goals more than assists, you might be worried that the players with the most red lights lit this past season would finish at the top of TV…yet that is also not the case. The leading snipers in our group were Nathan MacKinnon (53) and Cole Caufield (51), yet they ended up 3rd & 7th respectively in the TV category. Probably the only surprise on this list would be the inclusion of Anthony Mantha, but he is coming off a truly exceptional campaign so it is likely warranted. We will keep an eye on him as we move through the stats too.
My own simple attempt to put all the players on an equal footing was the PTS/MIN (ES) metric, easily calculated by taking total even strength points and dividing it by total even strength ice time. While the result is a very miniscule figure, I think it still points out which players are very dangerous offensively when playing at 5 on 5. Here is how the Top 25 looks when the spreadsheet is sorted by this stat:
Nikita Kucherov was able to still hold onto the top spot, but fellow superstar Connor McDavid fell from 2nd to 15th when it comes to even strength scoring. Interestingly, the players Mullayo suggested swapping finished very close to each other in this metric as Hagel slightly edged Scheifele in even strength scoring, while Connor had a similar lead on Gauthier. I always knew that Brandon was an even strength stud, but Mark had a fantastic year last season too despite the Winnipeg Jets’ overall woes. And once again, Mantha makes an appearance high up on the list, with the 6th best rating in our group for 5 on 5 scoring pace.
Overall, the numbers actually show just how difficult it is to score goals in the NHL at even strength, since when you do the math you discover that the top 25 range from beating the opposing tenders once every 16.47 to 27.03 minutes. That means that it is very difficult to be on the ice for a 5 on 5 goal every single game because less than 20 players received more than 20 minutes of 5 on 5 ice time per game (a pt/gm at even strength is something that Macklin Celebrini did). You may also have picked up that the TV stat did a good job of picking out good goal scorers at even strength, as only 6 players fell out from its Top 25 and Anton Frondell had only a small sample size to work with (12 gms) so his Pts/Min data could be inflated.
Our next graphic includes the G%, PTS%, and IPP metrics, though I have kept the spreadsheet sorted by the Top 25 TV ratings.
The Goals & Points % stats aren’t hugely valuable on their own, as a very high rating likely just means that you aren’t getting powerplay time. Yet when used in conjunction with other metrics, they can provide an insight into whether a player is using man advantages to pump up their numbers. Not that lighting lamps on the PP is a bad thing….but it could be depending on how you play at other times in the game.
Typically, you will see the really good snipers finish with a G% less than 60% since they tend to take advantage of the extra ice man advantages provide. So it is not surprising that Scheifele, a set up man mainly, has a 69.44% rating….although I was not expecting to see Connor at 64.1%. That shock continued when I discovered that the True North sniper has never really relied on the PP to score a lot of his goals, with the finisher role recently being held by Gabriel Vilardi on Winnipeg’s #1 unit.
When it comes to PTS%, Hagel has the lead with 66.22% at ES (49 pts), slightly edging out Scheifele’s 63.11% (65 pts). Connor (55 pts) & Gauthier (41 pts) were almost tied with 59.78% & 59.42% respectively. And who was it that held that best PTS% rating in the Top 25? That would be Mantha’s very impressive 73.44% (47 pts). Six of the players above scored more than half of their points outside of 5 on 5 play with McDavid’s 42.03% (58 pts) being the lowest, however he was joined by the likes of Mark Stone (45.21% – 33 pts), Jack Hughes (45.46% – 35 pts), Nick Suzuki (48.51% – 49 pts), Seth Jarvis (43.94% – 29 pts), & Sebastian Aho (45% – 36 pts).
Finally we get to the IPP statistic, which tells us what percentage he was involved in of all the goals the player’s team scored when they were on the ice this year at 5 on 5. Again, not hugely valuable on its own, but it can show you who is a play driver, who is a helpful passenger, and who is just not making an impact in the offensive zone. When you look at the top 10 players in this category for the Winnipeg Jets this past season (via Natural Stat Trick), you will see the True Northers’ best play driver (Scheifele) is up near the top.
Cole Koepke led the way with a 84.21% rating and fellow bottom sixer Morgan Barron’s 80% is also up there, potentially suggesting that if they were given more ice time or better linemates, their output might actually increase to help their team even more. IPP is another stat to support that Cole Perfetti is a better player than some of the Jets’ fans give him credit and also shows us that while Connor’s 72.97% is still good….he is not nearly as good at driving play as Scheifele is (82.28%). Surprisingly, Jonathan Toews (82.61%) was up there, getting involved in most of the limited goals his lines managed to score.
Going back to the spreadsheet above the most recent graphic, the Top 25 players in our group include 6 that have large playmaker ratings (above 80%) and they include our own Scheifele, who finished 4th behind the likes of Celebrini (84.93%), Drake Batherson (84.09%), & David Pastrnak (83.08%). Twelve more finished in the 70-79% range, with all of Connor, Hagel (74.24%), & Gauthier (74.55%) in that group. When I sort the entire spreadsheet by the IPP category, Celebrini falls to 3rd place behind Matthew Knies (86.36%) and Calum Ritchie (94.12%).
Up to now, we haven’t really looked at anything other than offensive based statistics. Well our final 4 categories will change that by adding in a little defense to our review. While the Expected (xG%) & Actual Goal Percentage (aG%) stats do look at your club’s scoring chances/actual goals created, they take into consideration the scoring chances/actual goals allowed too. Also included in the graphic below are the G-xG Diff% and REL xG% metrics, which has been expanded to show the top 47 in our group based on the TV stat.
I have highlighted the players with a negative xG% or aG% above to show us which ones are winning the expected & actual goal battles when they are on the ice. Fifteen of the 47 skaters (31.9%) ended up on the wrong end of the xG% metric, while only ten of 47 (21.3%) ended up losing the actual goal race. That decrease from expected to actual is not a surprise though, since the stat is based on average shooters, which most, if not all, of the above players are better than.
In terms of Expected Goals at even strength, Hagel (60.1%) clearly outshines Scheifele (48%) & Connor (48.1%), while Gauthier (52.3%) is in the middle. Out of the full 95 skaters in our review, Mark Jankowski was tied for 1st with 60.1% while players like Mark Stone (59.3%), Jack Eichel (58.3%), Jack Drury & MacKinnon (57.6%) round out the top 6. Former Winnipeg Jet Nik Ehlers is also up there with a 57.4% last season, while both Adam Lowry (55.2%) & Cole Perfetti (52.8%) were solid in 2024-25. Of the players the True Northers may receive from the Buffalo Sabres in any Connor Hellebuyck trade, Josh Norris finished with the best xG% rating at 52.7%, while Noah Ostlund was at 52.1%, Ryan McLeod at 51.2%, and Jack Quinn at 47.6%.
Thirty of the 95 players involved ended up losing the Actual Goal battle at 5 on 5 (31.6%), which includes the Ducks’ Gauthier by the narrowest of margins (49.5%). Hagel was once again the clear leader in the aG% category with a very impressive 64.1%, which put him 7th out of the full 95 players. The top 6 included #1 MacKinnon (70.4%), Martin Necas (69.2%), Anthony Cirelli (67.4%), Norris (67.3%), Suzuki (64.9%), and Kucherov (64.2%). Once again Lowry & Perfetti’s 24-25 campaigns stood out, with their 59.2% to 61.8% aG ratings having them in 12th & 21st respectively. Both Scheifele (52%) & Connor (51%) finished on the right side of the aG stat despite being sub-50% in the xG one, yet that still left them slightly below the midway mark in the group of 95. The other Sabres players put up the following: Ostlund (61.4%), Quinn (56.7%), & McLeod (56.4%).
Neither of the above stats are really comparable to the True Value metrics, since xG% & aG% are talking about both ends of the ice while TV is focusing on who puts pucks into nets. Yet used in conjunction, it should give management a good idea on whether a player may fit a particular opening they have in their line ups. For example, with our Jets and their desperate need to find offense after last year’s miserly goal output. If you are of the idea that Winnipeg needs to play Vilardi with Perfetti on the 2nd line, there is an opening on the RW of the 1st unit since Alex Iafallo clearly isn’t up to those duties. One of the main rumoured players coming back in a potential Helly trade is Quinn, who has shown the ability to score with a 20 goal & 51 point campaign under his belt. Yet, the sub-50% xG numbers he put up with Buffalo suggest he may not be an excellent fit along side Scheifele & Connor, who also struggle to get more scoring chances than they give up. The others like Norris, McLeod, & Ostlund all put up solid figures this past year….and all four of them ended up scoring more actual goals than expected. That is something you want to see when your team needs goals because it suggests that they are an above average shooter or else they should have performed worse than they did (as shown by the xG-aG Diff% stat, a positive suggests sniper & a negative is more likely than not to be attached to a level of stone handedness). For example, Perfetti’s 24-25 season saw him end with a -0.1% and that pretty much aligns with him having a very average shot. Iafallo was worst than that last year with a -4.2% and I’m sure most of us will agree with that bottom six scoring touch rating for him. Though a positive number doesn’t always mean a great scoring touch, as Lowry finished with a +2.4% in 2024-25 that likely can be explained by his being better than average defensively to make up for the lack of mitts.
I know that this article has gotten excessively long at this point, but with one more stat to look at….we might as well plod on. The Relative Expected Goals% (REL xG%) is a handy thing because negative xG% & aG% doesn’t always mean the player is bad. Sometimes it means that they are on a horrible team that has to spend way too much time defending. This is when the REL xG% can come in handy. For example, talented youngster Celebrini ended up with a 49.6% xG rating last season, yet the REL xG% stat tells us that he was 3.1% better than his average Shark teammate. That indicates that as San Jose improves as a team, Maclin’s xG numbers should also go up. If you are looking for solid 2 way players, here is the spreadsheet of the top 47 players in the REL xG% category, which ranges from elite to average defender options:
Probably fitting that Hagel (+9.5%) is sitting at #1 on our final graphic, as it seems like he has been there all article long. Connor (+0.8%) & Scheifele (+0.6%) are hurt by their poor xG% numbers, but they always seem to manage to win the aG battle each season thanks to their better than average finishing skills. Gauthier (+1.4%) finished slightly above our two Jets, while 3 of the potential Buffalo targets (Ostlund, Norris, & McLeod) finished in the +1.5% to +2.9% range.
RECAP
Mullayo’s TV stat made him suggest the whole Mark Scheifele–Brandon Hagel and Kyle Connor–Cutter Gauthier comparison that ran through this article and when I checked that against my favoured metrics, the scales fell on the same side. Of my key 6 categories including TV, Pts/Min, IPP, xG%, aG%, and REL xG, the Lightning’s Hagel ended up winning 5 of them over Scheifele. The other match up was a bit closer, as Connor managed to win 2 of 6 over Gauthier….but both victories were by narrow margins.
Therefore statistically, any proposed swap of either Winnipeg player should be accepted. However, when I take position into it, I just couldn’t really recommend trading the Jets’ only top six center for a winger. Yet I could definitely see the benefit of moving Connor for either of Gauthier or Hagel…though I can’t really see Anaheim moving on from the young talent unless cap space becomes an issue in the future or Cutter’s contract ask becomes unreasonable. Then maybe KFC’s $12M/yr deal might look attractive instead of something much closer to $20M. I really don’t know what Tampa Bay would do if the True North sniper was dangled in a deal to them but they just might be willing to give up Hagel plus to get it done. All very unlikely….but certainly would be worth considering if the opportunity arose.
Overall I am very impressed with Mullayo’s work, despite my constant questioning of things during the development of the TV metrics. The “apples to apples” approach used in the basic TV stat is excellent and while I don’t think it will get me to dump my Pts/Min metric, it would be a welcome companion. The EVe stat also captures my attention which is probably understandable since I’ve already explained why I love metrics that deal with even strength play. Unfortunately the length of this article didn’t allow me to dwell on that TV stat, you can see the data at the bottom of the original article (link near the top of article).
The TVA category provides some promise, as it sort of is trying to predict the future for these players (will they see their production increase or decrease?). Once again, I didn’t really discuss this metric but that is mainly because there is nothing to compare it to. It is talking about next year and we just don’t know how that will play out yet….so this will be put in the Check Back Later stack to see what 2026-27’s TV numbers say.
Too bad that Garret Hohl is no longer a member of AIH because he actually has the background in hockey analytics to provide useful feedback on the TV metrics. I do follow his blog called The Five Hohl which offers a statistical look at the Winnipeg Jets, but only paid members can post comments so I can’t prompt him to check it out. I can’t recall his entire history, but believe he has worked with or collaborated with some of the biggest hockey analytic people in the NHL (like the Canes’ GM Eric Tulsky). But hopefully impressing me and the rest of AIH is enough for now and maybe we can work out a way to get Garret to check out Mullayo’s articles.
Congrats to those that made it to the end. As always, Mullayo & I would love comments, critiques, or questions about anything in this article or about the Winnipeg Jets. Hoping everyone in our hemisphere is enjoying summer…we are only 6 weeks away from the Young Prospects tournament where the True North prospects will play a pair of contests on Sept 13th & 14th. Looking forward to watching Viggo Bjorck and the rest of the kids.
**am still planning on a much shorter article looking at the players TV identified in True Value Targets and Kill Power. So come back for that after you recover from all that reading you just did.**
