• Smith explodes in Week 3: Buy-low target DeVonta Smith was the WR1 in Week 3 and was on the Milly Maker winning lineup on Draft Kings
  • Air Yards Buy-Low Model proving its value: Nine of Week 3’s 15 buy-low candidates from Week 3 exceeded their consensus projections.
  • Cooks should produce in Week 4: The Houston TexansBrandon Cooks tops the list for Week 4
Estimated reading time: 6 minutes

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Week 3 Review

Week 3 proved to be very good for the model, as it identified the top WR on the week, DeVonta Smith, and three more top 10 PPR wide receiver performances in Zay Jones, Chris Olave and CeeDee Lamb. Smith was also in the Draft Kings Milly Maker lineup (congratulations abedollars). Smith was priced at just $5,200 and was rostered on 5.6% of entries.

Overall, nine of the 15 players who made the list beat their consensus projections. When you consider that the players picked are those who have been performing poorly recently — necessarily limiting the pool of possible candidates to those who are not among the elite and whose floor is something approaching zero — the hit rate is solid.

Nonetheless, this type of analysis is flawed in important ways. The model is about finding players who are not only viewed as longshots to have high-scoring weeks, but those who will vastly outperform their median projection. The WR position’s boom-bust nature (and the TE position for move tight ends) is a real problem for systems that spit out expected values, so judging results against those expected values is a bit of a myopic way to view success or failure. Instead, we’re looking for spike weeks among the dregs of fantasy receivers. It’s difficult, but this week (as well as last), we got them.

Finally, consensus projections are a valuable tool to aid in sifting through the players who are identified by the model each week. A higher projection implies a higher probability of a spike game while a low projection can indicate a very low floor. However, it’s not wise to sort by consensus projections and be done with it. This past week, unless you are playing every player on the list in a lineup (a solid strategy for what it’s worth), you would have missed out on the WR5 on the week. Zay Jones had the third lowest projection among Week 3’s buy-low candidates. Since its inception in 2016, large hits have routinely come from all over the list. 

Here’s the full list of results from Week 3, with players who beat their consensus projection bolded:

Week 4 Air Yards Buy Low Model

Below is the list of buy-lows for Week 4, sorted by consensus projection for Week 4:  


  • Brandon Cooks is back on the list for a second straight week. There are real reasons for concern about his quarterback and the Houston passing offense, but Cooks is dominating the team’s target (28 percent) and air-yard share (34 percent). One sticky wicket is that as of this writing, the market does not seem to be undervaluing his lack of production to date. His consensus projection is currently 14.2 PPR points. He may be more of a simple blowup candidate than a true buy low. 
  • Brandon Aiyuk is also back for another run at a boom week. People seem positive Jimmy Garoppolo is terrible right now despite years of evidence that he can support a decent passing game under Shanahan. He is a terrific buy low.
  • D.J. Moore is causing much gnashing of teeth and rending of garments from analysts across Twitter. The model believes good things are in store, and that now is the time to take advantage of this sentiment.
  • Ashton Dulin is now REDACTED. When the model likes a player I’ve grown to hate, I respect the model enough to keep him on the list. But I will not sully it with his name. The Indy offense is a tire fire despite a surprise win over Kansas City, he has done nothing, and even though I can’t prove it, I’m convinced the math is somehow in error. Vaya con dios, Dulin.

Explaining the Air Yards Buy-Low Model

Buy Low Model quick primer: In fantasy, receivers put up the highest scores each week on average, yet they are the hardest position to predict. The model uses target share and air yards (among other metrics) to estimate a player’s expected production in the passing game, then highlights the players that underperformed relative to expectation. 

The key insight behind the model is that opportunity is sticky and production (in the form of catches and touchdowns) is not. Fantasy scoring is driven by touchdowns, and touchdowns are extremely difficult to forecast. And often the receivers who get lots of opportunity but have dropped a deep ball, had passes broken up, or were tackled at the 1-yard line end up undervalued or deemed “bad” by the fantasy community, making them low-owned in tournaments or available via trade.

In general, pay most attention to the consensus projections, as this will give you insight into a player’s upside and floor. The next piece of information you should weigh is the size of the difference between what the model says a normal game from this player should be given his opportunity, and his actual performance in the recent past. The larger this difference, the greater the chance that the public will be fading the player, making him low-owned in tournaments and giving you a good shot at differentiation. And while we might be tempted to infer that larger differences might lead to a stronger “rubber band” regression effect, it’s typically the case that what dominates is the opportunity. Any player anywhere on the list can hit, and hit big. 

Since it’s Week 4, the model is working with three weeks of data and is now a fully-armed and operational battle station.


Predict = The full-PPR projection the model gives for a player for the rest of the season based upon his opportunity in the previous three games. For this week, it’s based on just two weeks of data.

Actual  = How many full-PPR fantasy points a player scored in the previous week.

Difference = The difference between projection and previous week result in full-PPR fantasy points.

Proj. = The consensus projection for a player from the Tuesday before the week’s games.


By nflbowl

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