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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.
I created this model using route-level PFF data to predict the probability of each route being targeted on every play in the NFL. This model generates interesting metrics such as “share of predicted targets” and “share of predicted air yards.” These metrics are more stable and predictive than their actual counterparts.
The core idea behind creating this model is that a player might be “earning targets” by consistently getting open and running valuable routes but not receiving targets for various reasons—such as quarterback pressure, a misread, or the quarterback forcing the ball elsewhere. After reviewing the film, teams may recognize that certain players were open and adjust their game plan to involve them more in subsequent weeks.
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.
I created this model using route-level PFF data to predict the probability of each route being targeted on every play in the NFL. This model generates interesting metrics such as “share of predicted targets” and “share of predicted air yards.” These metrics are more stable and predictive than their actual counterparts.
The core idea behind creating this model is that a player might be “earning targets” by consistently getting open and running valuable routes but not receiving targets for various reasons—such as quarterback pressure, a misread, or the quarterback forcing the ball elsewhere. After reviewing the film, teams may recognize that certain players were open and adjust their game plan to involve them more in subsequent weeks.
Later in this article, we’ll analyze quarterbacks' decision-making in Week 12, highlighting both optimal and suboptimal choices.
Week 12 recap
Our headliner, Jakobi Meyers, delivered an outstanding performance in Week 12, excelling in both target volume and fantasy production. Meyers continues to solidify his role as a reliable contributor for fantasy managers.
After consistently appearing on the Route Based Heroes model, Jaylen Waddle made his way into the “Coach, I Was Open” category this week. He capitalized on the opportunity with his best performance of the season, showcasing his big-play potential.
Terry McLaurin, Calvin Austin III, and Rashod Bateman each turned in respectable outings, benefiting from decent target shares. While not explosive performances, they proved their ability to provide steady fantasy value when given opportunities.
Romeo Doubs was on track for one of his best target-earning games of the season before an injury unfortunately sidelined him. Doubs could reclaim a significant role moving forward if he can return healthy.
IDENTIFYING BREAKOUT CANDIDATES FOR WEEK 13
This week’s headliner is a tie between D.J. Moore and Marvin Harrison Jr., both positioned for potential breakout performances in Week 13.
Marvin Harrison Jr. might be the most exciting name on the list. Facing a pass-funnel defense in the Vikings, Harrison benefits from a Predicted aDOT of 13.32 over the last three weeks — an excellent indicator of high-value downfield routes. If he sees a bump in targets, it could translate into significant fantasy production, making him a prime breakout candidate.
D.J. Moore and Rome Odunze also make the list this week, likely due to Keenan Allen’s massive target share (16) in Week 12, which draws attention away from other Bears receivers. The Bears face a tough matchup against the Lions, but as 9.5-point underdogs (per DraftKings), they’ll likely need to throw often to stay competitive. This aligns well with the Lions’ tendency to allow one of the highest rates of single coverage, a favorable condition for wide receivers.
Lastly, Xavier Legette stands out as a player to watch. In addition to having the best voice in the NFL, Legette has excelled at getting open and running valuable downfield routes, with a Predicted aDOT of 11+. The Buccaneers are a favorable matchup for wide receivers, as highlighted by Josh Larky, and the Panthers’ status as 5.5-point underdogs suggests a game script that could result in increased passing volume and more opportunities for Legette.
Quarterback Optimal Decisions via the Predicted Targets Model
The Predicted Targets Model allows us to evaluate a quarterback’s performance over a single game, a series of games, or even an entire season. This model analyzes every route on every play, calculating the probability that a given player will be targeted based on factors such as openness, PFF grade, level of separation, and more. By leveraging this route-level data, we can determine whether the quarterback made an optimal decision.
To simplify the analysis, I categorized every decision into three distinct categories:
1. Optimal Decision: The quarterback threw to the player with the highest target probability.
2. Suboptimal Decision: The quarterback threw to a player who did not have the highest target probability.
3. Bad Decision: The quarterback threw to the player with the lowest target probability.
This analysis is focused solely on quarterbacks' decision-making in Week 12. As I clarified in my previous article, the table doesn't necessarily determine whether a quarterback is “good” or “bad.” Instead, it measures how often they targeted the most open or designated receiver—a strong indication of how well a quarterback executes within their offensive scheme for a given week.
One number that stands out is the Bad Decision %, which reflects instances where a QB throws to the lowest-probability target on the field. There are various reasons for this, with aggression being a primary one. Distinguishing between “Good Aggression” (e.g., when trailing by multiple scores) and “Bad Aggression” is a refinement I plan to incorporate into the model in the future.
Even after a highly productive game, Caleb Williams is an example of a player for whom the model suggests room for improvement. This aligns with insights from the Breakout Candidates table earlier, where two Bears players emerged, indicating potential missed opportunities by Williams.
A noteworthy example of how these decisions can impact a game is Justin Herbert. While many might question his lower ranking for Week 12, one specific play stands out. Herbert forced a throw to Quentin Johnston in the end zone, despite Josh Palmer being wide open, a lapse even called out by the announcers. Plays like these can be game-defining and demonstrate the importance of decision-making within a QB's performance evaluation.
If this throw had been to someone like Justin Jefferson or A.J. Brown, it’s probably a touchdown. However, Justin Herbert targeted Quentin Johnston on the play, while Josh Palmer was wide open underneath for what would have been an easy first down. The Chargers ultimately settled for a field goal on this drive. Had Herbert gone for the most optimal target, the outcome likely would have been a touchdown or, at the very least, additional opportunities inside the 10-yard line. This is a prime example of bad aggression, as it was second down in the second quarter, and the team was already up by seven points.
These optimal decision rate numbers are notably volatile week-to-week, showing little to no stability. It is crucial to emphasize that this metric is not predictive. It’s better understood as a lagging indicator of decision-making in a given week. We shouldn’t consider this and conclude, “Justin Herbert will never make optimal decisions.” If anything, reviewing the film could help him adjust and potentially make better decisions in the following weeks.
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