Friday Main Primer
NFL | Week 1 | 2021
Well folks, we’ve made it. It’s been a long winter, an even longer summer, but at last the air is crisp, the leaves are (not quite) turning, and most importantly we’ve got a hefty Sunday slate ahead of us in a mere two days.
If you read our Substack last year, you’ll know the focus is to identify games, teams, and players to stack and try to talk through the tradeoffs with some of the more appealing (and even less appealing) stacks. In our inaugural newsletter last year, we talked through some of the key tradeoffs one must consider when targeting desirable team & game stacks. In short, we must navigate considerations of stack upside, cost, correlation, and ownership.
For this year’s weekly segment (and projections in general), we’ve built out a simulation system that incorporates industry sentiment, our own projections, lineup construction hyperparameters & assumptions, and variance to simulate through both the process of filling out a contest field with an anticipated mix of lineups and the games themselves. With this, we can score lineups, calculate return of different team & player-combination stacks, and get to the bottom of what stacks might give us the best expected ROI come Sunday.
Power Stack to Overweight - Seattle Seahawks
The total (49) is good not great, but definitely has enough juice to support a nice game stack. With David Moore & Greg Olsen gone (enter Gerald Everett), there is reason to believe that the Seattle offense could be a little more condensed. We don’t think Freddie Swain & rookie Dee Eskridge will be total zeroes, but David Moore appeared as a somewhat regular thorn in the side of conventional Seahawks stacks. Typically Lockett + Metcalf are a tough stack, but with good cheap value at receiver and the cost value of the the Indianapolis side, we do think keeping it simple with Wilson + Metcalf + Lockett could be in play. Seattle stacks won’t be super off the board, but with teams like Buffalo, Cincinnati, Arizona, and Kansas City presenting either higher totals or more compelling value, we could see Seattle go a bit underowned relative to their possible ceiling.
On the flip side, Michael Pittman Jr. looks like an awesome value and a prime candidate to bring back. With T.Y. Hilton starting the year on IR, Pittman should be the de facto WR1 against a Seattle secondary that could be picked on last year.
Power Stack to Underweight - Atlanta Falcons
The Falcons might not pop as a top-tier stack, but we think they will be quite popular. Calvin Ridley pops as a great value & should be popular, people are going to be very excited to play Kyle Pitts. But Matt Ryan is a statue quarterback with efficient pricing, even though Julio has departed you could make the case that this Atlanta receiving corp has become more crowded with the addition of Pitts & Mike Davis such that pinning down the primary beneficiary of hypothetical Ryan touchdowns could be difficult.
On top of some challenges in deciding on the Falcons side of an ATL-PHI game stack, figuring out the Eagles side is even harder. The Eagles’ receivers were really tough to figure out last year, as they played as a receiver-by-committee unit. Of course Doug Pederson is gone, but there is still no alpha Philadelphia offensive player who can be trusted as a stable bring back option. Not sure going double-TE with Goeddert & Pitts makes much sense, and DeVonta Smith at WR is an intriguing prospect but it does feel like there are cheaper WR options that have a similar or better projected role.
Sleeper Stack - Indianapolis Colts
We’re going back to the same game here, but it does feel like the opposite side of the Seattle/Indianapolis game could be one to take some shots on. With Wentz in at QB, we should expect the Indianapolis offense to be a little more productive through the air. Wentz also is an underrated rusher (maybe more so in volume than efficiency), so does have some stand-alone equity. Pittman is the obvious partner to Wentz stacks, leaving the two as a single stack could be an option. Adding an Indianapolis tight end could be a good way to increase stack coverage and also gain leverage against more popular tight ends like Pitts or Kelce.
On the Seattle side, Metcalf or Lockett make for fine bring backs. Not sure it make sense to bring back both in a full game stack, if Lockett + Metcalf are able to score a tournament-winning fantasy total, it likely means that you’ll need to have Russ in that lineup to win. But stranger things have happened.
The Best of the Rest
KC doesn’t quite stack up to the value available in Cincinnati, Arizona, and Buffalo stacks, but the ownership should come in a bit lower than these other squads and the explosiveness of this KC offense is a well-known commodity. Hill + Kelce + Mahomes is an expensive stack, our player-level simulations seem to suggest that mixing in Hardman or Pringle with a KC stud could be the preferred approach.
Sam Darnold actually looks like a really good value, but the main draw with Carolina is the weapons. CMC will be popular, probably in the mix with Kamara & Mixon as the most own RBs. Pairing him with Darnold works because of his great pass-game usage, but going off of CMC to Moore + Anderson could be the high-leverage route.
The Jags’ receivers & James Robinson figure to get pretty decent ownership as one-offs, but Lawrence should get ownership around or below 2%, and could make for a fine QB to stack through as a correlated, low-ownership building block. The Texans side could be listless for all of 2021, but JAX doesn’t have a stout defense, and somebody on Houston will have to get some usage. Brandin Cooks figures as a likely candidate for 25%+ target share, we are also relatively high on Jordan Akins who could see an elevated role with guys like Will Fuller, Randall Cobb, Duke Johnson, and David Johnson either no longer on the team or in a decreased role.
We’re still working to fine-tune our player level stack table, but we do intend to provide a list of relevant stacking options, by team, at the player level. When we drill down to the player level, we’re dealing with a higher degree of noise, so average ROIs can appear either exceptionally inflated or deflated based on simulation randomness.