• Davante Adams finishes at WR2 in Week 17: Adams was the top player in the Buy-Low model last week.
• Brandon Aiyuk places as WR3 in Week 17: The 49ers wideout tallied nine catches on 12 targets for 101 yards and a score, in addition to a 16-yard rush.
• Ja’Marr Chase tops Buy-Low list in Week 18: He has been underperforming despite his elite talent and opportunities.
Estimated Reading Time: 5 mins
Week 17 Review
Week 17 was excellent for the Buy-Low model. While none of the players were present on a Milly Maker-winning lineup, both Davante Adams and Brandon Aiyuk turned in stellar weeks. Adams’ PPR total of 34.3 was good for WR2, while Aiyuk’s 26.7 was right behind him at WR3.
Adams is a great receiver, but a number of factors led to him being virtually unrostered in tournaments. First, he was coming off three weeks of poor production and no touchdowns; second, a new quarterback was going to be behind center for the Las Vegas Raiders, which is always unsettling; and third, the Raiders were playing a talented 49ers defense. The Buy-Low model saw the first factor as a positive indicator since Adams’ opportunity was still high over that span, it down-weighted the quarterback change and it completely ignored the defensive context. In this case — and more often than not based on my modeling — that is the correct way to view matchups. Ignoring defense, in particular, in fantasy football remains an absolute edge.
Adams was included in just 0.4% of lineups in the main event, and Aiyuk was rostered in 2.7% of lineups. For context, running back Christian McCaffrey was rostered in 17.8% of lineups. And that’s despite the fact that Adams was $500 cheaper than McCaffrey and ultimately turned in a better performance than the San Francisco running back.
Rostering CMC was still a great play — he was on the Milly Maker-winning lineup — but if rostering him meant you took Jared Goff over Tom Brady ($5,600 vs. $6,100), that ended up being a large mistake.
Here are the full results from Week 17:
| Player | Team | Proj. | Actual |
| Davante Adams | LV | 19.1 | 34.3 |
| Brandon Aiyuk | SF | 12.3 | 26.7 |
| Christian Kirk | JAX | 13.8 | 4.1 |
| David Njoku | CLE | 9.9 | 3.1 |
| Christian Watson | GB | 12.1 | 2.1 |
| DeAndre Hopkins | ARI | 14.1 | DNP |
| Greg Dulcich | DEN | 8.6 | DNP |
And here’s the list of Week 18 buy-lows, sorted by consensus projections.
| Player | Team | Predict | Actual | Diff. | Tgt Share | Air Share | WOPR | Proj. |
| Ja’Marr Chase | CIN | 21 | 20.4 | -0.6 | 0.27 | 0.41 | 0.692 | 20.1 |
| Garrett Wilson | NYJ | 9.1 | 8.5 | -0.5 | 0.27 | 0.35 | 0.65 | 14.2 |
| Zay Jones | JAX | 14.5 | 14.2 | -0.3 | 0.17 | 0.27 | 0.444 | 12.7 |
| Christian Watson | GB | 8.3 | 7.2 | -1.1 | 0.21 | 0.3 | 0.525 | 12.7 |
| Marquise Brown | ARI | 9 | 8.9 | -0.1 | 0.2 | 0.29 | 0.503 | 11.2 |
| Tyler Higbee | LA | 15.9 | 15.7 | -0.2 | 0.3 | 0.1 | 0.52 | 9.2 |
| Juwan Johnson | NO | 12.8 | 11.9 | -0.8 | 0.27 | 0.35 | 0.65 | 8 |
| Robert Woods | TEN | 8.8 | 8.4 | -0.4 | 0.21 | 0.19 | 0.448 | 6.3 |
Key Takeaways
• Ja’Marr Chase is a stud who is underperforming despite significant opportunity, much Like Adams last week. These types of situations are solid bets.
• Garrett Wilson and the Jets have little to play for after their playoff hopes were dashed, which may drop his rostership rate. He is getting a truckload of opportunity in the Jets’ offense, and a spike game in Week 18 would be a terrific way to close out an excellent rookie season for the 2022 10th overall pick.
• Christian Watson played last week, but his injury appears to be limiting him. However, he’ll perhaps be feeling better this week and could be a sneaky start who outperforms expectations.
• Robert Woods is coming off a nine-target performance, and Tennessee is playing for their playoff lives. Throw him in a lineup or two.
Explaining the Air Yards Buy-Low Model
In fantasy football, 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 who 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 one-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.
KEY
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 three weeks of data.
Actual = How many full-PPR fantasy points a player scored in the previous week.
Difference = The difference between projection and the previous week’s result in full-PPR fantasy points.
Proj. = The consensus projection for a player from the Tuesday before the week’s games.