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Estimated reading time: 6 minutes
Welcome to the latest installment of “Coach, I Was Open,” my ongoing statistics series, where I build and refine a model to predict targets for every route in every NFL game.
In my first article, we discussed creating the foundational “predicted targets” model. The second article focused on refining this model and introduced powerful derivatives: “share of predicted targets” and “share of predicted air yards” (these derivatives have shown to be significantly more stable and predictive than their counterparts). In the third article, we explored the model's practical implications and how the predicted target probability was developed. In the fourth, we tweaked the model to account for the timing of NFL offenses and defenses.
In Week 9, the “Coach, I Was Open” table successfully highlighted several top-24 performers at their positions, including Amon-Ra St. Brown, Jordan Addison, Jakobi Meyers, Brock Bowers, Jayden Reed and Austin Ekeler.
This table aims to identify players who are likely to see an increase in targets in the upcoming week. Exceptional performances often result from increased target volume, though not always. For example, Keenan Allen saw 10 targets — his highest total since Week 1 — which can be considered a success even though he caught only four passes for 36 yards. Similarly, Jonathan Taylor set a season-high with five targets, and Tucker Kraft received six targets in a rainy game, his second-highest count of the season.
Model Overview for New Readers:
• I built a model using route-level PFF data to predict the probability that a player would be targeted on a given route.
• The model considers route characteristics, PFF grades, route timing and many other factors. It has been trained on hundreds of thousands of routes and targets.
• This approach allows us to assign a target probability to each route run on every play. For instance, if Justin Jefferson runs a 10-yard slant, earns a good PFF grade and is open, we can estimate the likelihood of him being targeted on that route, regardless of whether he actually receives the target.
• We aggregate these probabilities into a game-level metric called “predicted targets.” This can be further distilled into “share of predicted targets,” calculated as [predicted targets/sum of team predicted targets].
• This metric gives us a stable and insightful look at which players are consistently getting open and running high-value routes.
Season Leaders in Share of Predicted Targets As of Week 9
Caleb Williams currently has two receivers, Allen and Moore, in the top 15 for Share of Predicted Targets. Both have excelled at getting open and running high-value routes this year. However, Williams has struggled overall, with a PFF passing grade of just 59.2 — 32nd among quarterbacks with at least 100 dropbacks.
Notably, only two rookies made the list, one of whom is my weekly highlight: Malik Nabers. He's a must-have, especially in dynasty leagues. Meanwhile, with the Chargers embracing a PROE (pass rate over expected) gameplan, Ladd McConkey is also positioned for a strong rest of the season.
I’m convinced Jakobi Meyers has WR1 potential, but the limitations of the Raiders‘ offense may hold him back from reaching that level.
The Ravens’ acquisition of Diontae Johnson has been impactful, and Johnson’s ability to get open makes him a valuable asset. Whether the Ravens will continue to feature him in high-value routes remains to be seen.
WEEK 10 BREAKOUT CANDIDATES
These players recorded a “share of predicted targets” at least 0.05 higher than their actual target share. The idea is that these players have the strongest case to request more targets in Week 10.
Ja’Marr Chase leads this week’s “Coach, I Was Open” table with an impressive 34% “share of predicted targets” over the past three weeks. While Chase received 11 actual targets, he ran 21 routes with a target probability above 30% — the 12th-largest difference this season between actual targets and high-probability routes. He faces a favorable situational matchup against the Ravens, where the Bengals are 6-point underdogs in a game with the highest total on the slate (53).
Brian Thomas Jr. had the largest gap between his “share of predicted targets” and his actual target share. The Jaguars face the Vikings, who lead the NFL in MOFO (middle of the field open) coverage rates, which often suppresses elite WR performances. However, Thomas could see increased targets if the game plan aligns, and he also has an excellent matchup, according to PFF's matchup chart.
Michael Wilson showed a solid discrepancy in Week 9 with a 0.13 “share of predicted targets” compared to a 0.05 actual target share. However, Arizona’s low-volume passing offense may limit his opportunities, even if his target share increases. He does have a favorable single-coverage matchup against the Jets this week.
Players like Tipton and Tolbert are also interesting, as they could see expanded roles due to injuries to starting wide receivers.
One more name worth mentioning is John Metchie III, who saw increased field time after the injury to Stefon Diggs, with a 63% snap share. He recorded 5.6 predicted targets but only saw two actual targets for 0 yards, marking the seventh-largest single-week difference in the 2024 season. The Texans face the Lions, who have allowed the most single-coverage situations in the NFL over the past month. If Nico Collins returns, this point may become less relevant.
The standout names on this week’s list, in my opinion, are Chase and Thomas, both of whom have strong potential for an increased role.
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