The moment you’ve been waiting for: our fantasy hockey cheat sheet is finally here! It’s time to start prepping for your draft.
There are a couple of new things to touch base on, but if you’re a veteran of these rankings and want to skip right to the good stuff, scroll down to the big bold link at the bottom and download the link(s).
For the newbies, here’s what’s up: we have a fancy spreadsheet that creates fully customizable rankings tailored to whatever weird way your league is set up. There’s no real standardized hockey league and the best way to get ahead is having rankings that take into account your league’s specific settings. Your league is not the same league as the next person’s and that’s a very important point when it comes to research and rankings. In fantasy hockey, it’s not one-size-fits-all, and depending on any ranking — no matter how smart the person writing it is — might already be your first mistake.
Other rankings are great for standard leagues or for research purposes, but they may not apply directly to your own league. That’s why this spreadsheet exists, to create an easy way for any fantasy hockey manager to have their own rankings that actually fit their league’s scoring system and format. (And trust me, it pays to have projections for every stat you need, not just points).
You can find download links to the sheets below (In Excel and Numbers/Mac formats). Underneath that you can find instructions on how the sheet works as well as answers to some frequently asked questions. (Note: All patches and updates will be updated at the bottom of this story)
Microsoft Excel download link (Yahoo)
Microsoft Excel download link (Fantrax)
Numbers download link (Yahoo)
Numbers download link (Fantrax)
New in 2024
Quality of teammate adjustments
One of the faults with using a model for projections is that cold, hard calculations don’t leave room for subjective truths. Think Phil Kessel being traded from Pittsburgh to Arizona a few years ago, where anyone could’ve predicted his scoring would drop in a worse environment, but most models not being able to account for that change in context. Two years ago we added the “boom-or-bust” adjustment feature for that reason.
This year we’ve taken that idea a step further by adding a more explicit adjustment for changes in teammate quality.
Using the same methods from this article regarding the model as a whole, we looked at how player point totals changed depending on changes in offensive teammate quality. There was a decently-sized effect, one that we conservatively applied to this year’s projections.
That means more points for Viktor Arvidsson and Jeff Skinner, who likely get a spot next to Leon Draisaitl. More for Connor Bedard getting linemate upgrades. And more for young players like Logan Cooley, JJ Peterka and Jake Neighbours, who likely see more time on their team’s top line. That also goes the other way for player’s going to worse situations like Tyler Toffoli, Warren Foegele and Steven Stamkos. Or Brandon Hagel likely being bumped from Tampa Bay’s top line.
It meant fewer “boom-or-bust” adjustments on our end, but the beauty is that if you don’t agree with the projections, a couple pluses or minuses in the adjustment column can easily fix that.
Color-coded context
Knowing a player’s expected output going into a draft is crucial, but it’s not the only thing worth considering — especially if you’re stuck picking between two equal-ish players. This year we’ve added a few color-coded indicators that can help with a legend on the settings page.
Team: This is a double-whammy with two things to consider: strength-of-schedule and fantasy scheduling itself.
On the first front, we’ve colored teams with the weakest average defensive opponents blue and the strongest in red. Basically, the more games against the Blue Jackets, Sharks or Ducks the better.
On the second front, we also looked into which teams play the most off-days (Monday, Wednesday, Friday, Sunday). Those teams are in bold and are especially good targets lower in the draft as they’ll be much easier to fit into lineups. It’s a way to get more out of your bench as a player sitting there doesn’t do much good.
There’s a big range here, from 44 for Anaheim to 20 for Boston, so it’s worth keeping in mind. Here’s a full table of where each team lands.
Age: The projections are already age-adjusted, but it’s worth being mindful of players who may take bigger jumps, up or down, to age. Blue is anyone who isn’t in their prime yet (95 percent of peak age production) and could breakout. Red is anyone who is likely past their prime.
Salary: If you’re a believer in contract-year hype, we’ve got you covered. Any player with one year left on his deal is highlighted in bold blue.
ADP Difference: This is one we’ve already had for years, but there’s a slight tweak this year. Now, bold means anyone who is a two-round difference or more, while the normal weight (but colored) is anyone between 1-2 rounds off where we have them.
Knowing who to draft is only half the battle. Where to draft them is the other, and this slight tweak should be helpful when it comes to figuring out where to target players.
TOI: One of the foundations of these projections is projecting which players should see an increase and decrease in ice time. Now those changes will be easier to spot as anyone who is expecting to see a one-minute difference from last year, or higher, is highlighted.
PPP: Going hand-in-hand with role is knowing which players will have a coveted job in the top power play. It’s especially true for defensemen, where there’s usually only one spot available. Getting that right — especially for teams where there’s uncertainty — can be the difference between winning and losing. This year we’ve highlighted exactly who we expect to get those roles in blue, with bold names likely to get over 60 percent of the available power-play ice time.
In some cases, we’re making an educated guess, with some names looking like serious sleepers as a result (hello, Adam Boqvist in Florida, maybe). But in a few cases, there’s still some uncertainty leading to a 50-50 split, like in New Jersey with Dougie Hamilton and Luke Hughes.
As things become more clear during training camp, we will provide updates, but the knowledge of who we have in those roles should be helpful — especially if you disagree.
Now that that’s settled, let’s get to the usual: how this bad boy works.
How to use this fantasy hockey cheat sheet
Step 1: Scoring settings
The first thing you need to do is to go to the “Settings” tab to start customizing. On the left side is where you’ll enter your league’s scoring system. If you’re in a points-based league, you only need to worry about the points column. If you’re in a categories or roto league, only worry about the categories column.
For points leagues, simply type in the value next to each stat. If a goal is worth three points, type “3.” That’s it.
For category leagues, I use standard deviations to score players in each category, so simply type “1” next to whatever category your league uses. This weights each category equally. If you would like to add more weight to certain categories (goals, assists, shots) over others (PIM, hits, blocks) based on scarcity, you can make the former categories worth more than one and the latter categories worth less.
Step 2: League settings
Next, you are going to focus on the right side of the page.
At the top, fill in how many players are in the starting lineup. If you’re in a league that doesn’t separate forwards by position, just make sure all three positions add up to the total amount of forward positions. At the bottom under “number of teams” type in how many teams are in your league.
For ADP data type in which fantasy service you use.
For league type, type in “points” or “categories” depending on whichever your league uses. This year there’s only one page for rankings as opposed to separate pages for both so this step is crucial as it makes sure the correct total fantasy point calculation is used.
For forward positions, type in “C/LW/RW” if it’s separated or “forwards” if it’s not. We realize all of this is pretty self-explanatory from looking at the spreadsheet, but for those less fluent we figured we would spell it out for you.
Then there’s “games played projections.” “No” means that every skater is projected to play all 82 games, while “yes” uses my games played estimates. If you’re of the mind that injuries are random, it might be better to go by a player’s projected per-game output. Just make sure you go through the list to mark the players who already have known injuries.
There’s one more setting, but that one requires further explanation.
Step 3: Establishing a baseline
There’s a reason that you’re asked to fill out league size and starting lineup size above, and that’s so the spreadsheet can calculate each player’s value over replacement – the key to the rankings.
Essentially, each player has a projected fantasy point rate, but that number needs the added context of what position he plays and what other players at the position are expected to do. Generally, this is to control for the fact that there’s an abundance of strong scoring centers and not as many capable wingers or defenders.
For example, if Adam Fox is expected to score 400 fantasy points and Sidney Crosby is expected to score 450, it’s the latter that seems more valuable. But it all depends on that baseline, where you can generally get a 400 point center just as easily as a 350 point defender. That makes them equally valuable.
Now, here’s where things get tricky: how do you actually establish the baseline?
Previously, it was an automatic calculation based on league size and starting lineup combined with how players are normally drafted within the top 100. That generally made defenders more valuable, but to some it doesn’t go far enough – that the need for four defensemen in a standard league makes them even more valuable than using that method suggested. To some, the positional requirement is the way to go and that the baseline should be starting line up multiplied by team size – essentially, the true definition of replacement.
Using the draft to dictate the process helps account for the fact that people don’t normally draft defensemen super high (even if they should), but perhaps it’s not cognizant enough of the reality of defensemen needs. With most teams now opting for a 4F1D power play, finding a good defender is more difficult than it’s ever been.
It’s for that reason that there’s an option for how to establish baseline: draft (the usual way that’s based off top 100 selections), position (based off starting lineup and league size) and blend which mixes both together. We made blend the default because we’re not sure which method is better, but the option is now there for you based on your own beliefs. As long as you’re not reaching for targets and drafting along ADP, you should be fine either way.
Step 4: Creating the ranking
All of that work sets up your rankings, but once you go to “The List” page you will see that everything is probably out of order. All you need to do now is sort. In Apple Numbers, just double click the top of the rank column and press “Sort Ascending.” In Excel, click the arrow at the top of the rank column and then click “Sort Ascending.” The rank is based on each player’s VORP, so alternatively you can sort by that as well. If you’re in a league that uses a salary cap, the “/$” column compares VORP to a player’s salary, so alternatively you can also sort by that.
For keeper leagues, use the “keep” column to denote any player who is a keeper. You can then filter them out using the arrows above in Excel or the filter option under “Organize” in the sidebar in Numbers.
Step 5: Projection Customization
The final step is disagreeing with us. We know you probably will and that’s okay! We’ve even made that process easier with a specific column for adjusting called “boom or bust.”
The gist is that we know a model, no matter how good, is not infallible. Human intuition is important and we wanted to include some adjustments based on our own feelings and disagreements with the model’s output. This is especially helpful for younger players who look ready to break out or players who switched teams – two instances the model has historically struggled with.
We went through each projection and debated whether a player deserved to be a little higher or lower. On the actual list page you can see a “+” or “-“ denoting exactly which players we tinkered with, making it easy to see exactly where our subjective judgement came in to play.
This is also where your own disagreements come into play. On the sheet there’s a column labelled “ADJ” that’s already filled with adjustments made by Shayna Goldman and I, but you can change those or add your own. It’s as simple as adding a “+” or “-“ for small adjustments, or “+ +” and “- -“ for bigger adjustments (not recommended for goalies). Don’t be afraid to use your gut instinct.
Step 6: Drafting
The final step is actually drafting your team. Here’s what we usually do: once a player is drafted, we use the “keep” column as a way to denote which players are taken already. If they were drafted, we might type a “Y” in that column, and then you can use the filters above the column to automatically hide players who are selected as it happens.
It’s also worth keeping track of your team while you draft, and that’s where the “Team Comparison” tab comes in. Type in the players you draft as it happens and the table will auto-populate their projections. If you’ve got time, you can also type in the other teams as well for an instant comparison of where your team stands.
FAQ
How do these projections work?
First and foremost, it starts with Evolving Hockey. We don’t know where we would be without that site, but all the data that goes into creating the projections comes from there.
After that, it’s a relatively simple marcel projection that uses the past three years of data for each stat, weighted by recency, regressed to the mean, and age-adjusted. That’s done on a per-minute level and applied to time-on-ice projections based on each player’s ice time last season, adjusted for where they’ll likely fit on the depth chart this year (with the help of the NHL beat writers here).
How accurate are these projections?
This is always a very important question. While the projections themselves look absolute, the reality is that there are margins of error. A player projected to hit 80 points really means “somewhere between 70 and 90 points.” Worst case, he’s on the low end; best case, he’s on the high end – with some exceptions to the rule that end up way higher or lower. I didn’t go through last year’s projections, but the general rule of thumb is points are usually off by 9-10 points, while shots are usually off by 25.
Do you really believe Connor McDavid is going to score exactly 139 points?
Not exactly, and it goes back to the point made above. Think of a projection as a probability and the figure shown as an average of that probability. The likeliest point total for McDavid next year is 139 points in 81 games, but as we saw above, the average error for projections is about nine points in a normal season. So really, that’s saying he’ll likely score between 130 and 148 points in a season, give or take. It’s a ballpark figure where the goal is to be less wrong. A projection model will do that better than just guessing, and a good projection will do better than a bad one.
Once I have customized rankings, should I blindly follow the list that was generated for me?
No, and it comes down to the range discussed above. If Jason Robertson is at 447 fantasy points and Filip Forsberg is at 440 points, both players are well within each other’s error bounds. Robertson is probably the better player, but if you want to argue Forsberg is safer because he performed better last season, that’s an important debate to have while making selections.
That type of context won’t be explicitly included in a projection like this. That’s why it will be helpful to read all the other fantasy hockey draft content coming down the pipeline to become more informed about the players who might underperform or overperform their projections.
In general, it’s best to follow the rankings, but it’s fair to squabble when players are very close to each other. In those cases, you’re usually splitting hairs.
Why is there a mediocre goalie ranked so high?
Fair enough! For points leagues, start volume is the key for goaltenders and often when you see a goaltender ranked higher than expected here, it’s because of that reason alone. In a league that’s veering more and more towards tandem situations, it pays to have a legitimate starter likely to play in 70 percent of their games as those are few and far between.
The downloadable cheatsheets
Microsoft Excel download link (Yahoo)
Microsoft Excel download link (Fantrax)
Numbers download link (Yahoo)
Numbers download link (Fantrax)
(Top photo of Nathan MacKinnon: Isaiah J. Downing-USA TODAY)
