Breakout Receiver Model: Week 10

Nov 10, 2021
 Breakout Receiver Model: Week 10

The Breakout Receiver Model returns for Week 10 after a so-so Week 9. With over half the fantasy season behind us, the data reaches near full strength, and there are a ton of intriguing breakout recommendations to dig into this week.

About the Breakout Receiver Model

The 4for4 data science team, in conjunction with our award-winning projections expert John Paulsen, have recalibrated and reconfigured the receiver "buy-low" model from years past in order to better identify under-performing wide receivers and tight ends who might be on the verge of a breakout performance. For the uninitiated, the breakout receiver model utilizes historical data and recent player performance to help determine players who have received opportunity that is typically more valuable than their recent production would indicate. This particular model's features include air yards and routes-run data as a means of determining opportunity. It also utilizes a proprietary efficiency metric that looks back at the past 10 weeks of individual player performance, all to better help predict the likelihood of a bounce-back in a player's future production. The model most heavily-weights the last three weeks of data.

Last Week's Results

Hits

Week 9 Breakout Receiver Model, Hits
Player Team Position XFP - Last 3 Week 9 XFP-Diff.
Travis Kelce KC TE 13.3 15.3 2
Justin Jefferson MIN WR 12.2 15.5 3.3
Darnell Mooney CHI WR 11.5 19.1 7.6
DeVonta Smith PHI WR 9.5 20.1 10.6
Tim Patrick DEN WR 9.4 16.5 7.1
Marquez Callaway NO WR 8.7 10 1.3
Jared Cook LAC TE 8.1 8.8 0.7
Cole Kmet CHI TE 6.9 11.7 4.8

While the model’s hit rate was slightly below the weekly average in terms number of players who scored above expectation, we did see five players produce 15.3 or more half-PPR fantasy points last weekend. DeVonta Smith and the BearsDarnell Mooney had huge games against elite defenses, scoring 20.1 and 19.1 against top-10 defenses this past weekend. Second-year star Justin Jefferson also had a strong game despite seeing five total targets, catching just three. He still managed a top-15 wide receiver performance in Week 9. Marquise Brown was on last week’s breakout model and scored 16.1 fantasy points against the Vikings on Sunday, but is inexplicably missing from this list. You’ll have to read on in order to find out the reason behind his omission.

Misses

Week 9 Breakout Receiver Model, Misses
Player Team Position XFP - Last 3 Week 7 XFP-Diff.
Emmanuel Sanders BUF WR 16.5 8.5 -8
Kyle Pitts ATL TE 16.5 7.7 -8.8
D.J. Moore CAR WR 14.9 6.1 -8.8
Diontae Johnson PIT WR 14.3 9.2 -5.1
Courtland Sutton DEN WR 13.7 3.4 -10.3
Tee Higgins CIN WR 12.5 10.8 -1.7
Mark Andrews BAL TE 11.6 6.9 -4.7
Allen Robinson CHI WR 11 8.8 -2.2
Dallas Goedert PHI TE 10.9 5.8 -5.1
Dalton Schultz DAL TE 10.4 7.4 -3
Nelson Agholor NE WR 10.2 0 -10.2
Jarvis Landry CLE WR 9.9 2.6 -7.3
Robby Anderson CAR WR 9.2 0.7 -8.5
Darius Slayton NYG WR 9 0 -9
Chase Claypool PIT WR 9 5.8 -3.2
Jakobi Meyers NE WR 8.8 1.3 -7.5
Quez Watkins PHI WR 8.1 1.9 -6.2
Mike Williams LAC WR 8 6.8 -1.2
Mo Alie-Cox IND TE 7.4 3.3 -4.1
Durham Smythe MIA TE 6.9 3.7 -3.2

The Bills’ offense disappointed across the board, so it’s no surprise to see Emmanuel Sanders on this list. With Tim Patrick having a huge day for Denver, Teddy Bridgewater ultimately relied much less on Courtland Sutton, as he scored 3.5 fantasy points, his worst performance since his one-reception Week 1 performance. Tee Higgins had a modest 10.8 fantasy points against the Browns last Sunday, far from a complete dud, and he’s now seen 29 targets over his last three games, but he’s still under-performing his expectations from the last three weeks by 13.6%. Despite eeking out a victory on Monday night football, both Chase Claypool, who left briefly with an injury, as well as teammate Diontate Johnson combined to finish 8.3 fantasy points short of expectations. Instead, rookie tight end Pat Freiermuth nabbed two receiving touchdowns against the Bears, saving the Steelers’ day.

Breakout Receiver Model Recommendations

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