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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.
Later in this article, we’ll analyze quarterbacks' decision-making in Week 13, highlighting both optimal and suboptimal choices.
Week 13 Recap
D.J. Moore was definitely the headliner last week, finishing with a — believe it or not — career-high 16 targets. This is exactly what the model is attempting to identify.
Marvin Harrison Jr. also saw a career-high in targets (12) but did not really produce any meaningful fantasy stat lines.
Xavier Legette (8) and Tucker Kraft (7) finished with their second-most targets ever.
Overall, I would consider these examples a massive success for the model’s efficacy.
IDENTIFYING BREAKOUT CANDIDATES FOR WEEK 13
Last time Ja’Marr Chase was our headliner, he produced 55.40 fantasy points and saw a whopping 17 targets. It was easily our best model play of the entire season. In Week 14, the Bengals land a solid matchup against a Cowboys defense that allows a lot of single-coverage situations. The Bengals are a heavy pass team that does not need to be pushed to pass the ball, so I expect a solid showing from Chase this week.
This might be Calvin Ridley’s first appearance on this list. As the Titans’ WR1, Ridley saw just seven targets in Week 13 but now faces a favorable matchup and a revenge game against the Jaguars. Over the past month, Jacksonville has allowed the highest rate of single-coverage opportunities in the league. Ridley’s target share jumps significantly in these situations, increasing from 23.5% to 39.3%.
Interesting note: Wan’Dale Robinson had the sixth-greatest difference between “predicted targets” (6.3) and “actual targets” (2) on the season.
Check-in on the season leaders for Share of Predicted Targets:
Several players, including Zay Flowers, Garrett Wilson and Keenan Allen, are entering solid regression territory. These receivers have posted strong numbers for “share of predicted targets” but have yet to fully capitalize on those opportunities. Essentially, they are running valuable routes and consistently getting open, but various factors limit their production. If they continue this trend of creating opportunities, a positive spike in performance is likely on the horizon.
Malik Nabers has been the leader for the entire season. He is elite but plays in an offense that is nowhere close to elite. He will continue to struggle as long as Tommy DeVito or Drew Lock is throwing him the ball.
Courtland Sutton is our new leader in “share of predicted air yards,” meaning Denver is sending him downfield more often. This is a great sign for Sutton and Bo Nix, as it means they trust both players with more deep (fantasy-relevant) opportunities.
Quarterback Optimal Decisions Week 13
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. I filtered all of the data only to plays where there were at least two routes on a play so that the QB had to make a decision.
To simplify the analysis, I categorized every decision into three distinct categories:
- Optimal Decision: The quarterback threw to the player with the highest target probability.
- Suboptimal Decision: The quarterback threw to a player who did not have the highest target probability.
- Bad Decision: The quarterback threw to the player with the lowest target probability.
Justin Herbert had another rough outing in Week 13 regarding optimal decision-making, finishing last in the NFL for the second consecutive week. While a lower optimal decision rate isn’t always a bad sign—sometimes indicating a quarterback's willingness to make high-level throws, like “throwing a player open”—Herbert’s 31.2% BAD decision rate is concerning. This means that over 30% of his passes targeted the receiver with the smallest target probability, which raises questions.
Herbert will need to clean this up for the Chargers to make a serious Super Bowl run. It’s worth noting, however, that this metric isn’t necessarily a measure of quarterback performance in the traditional sense—PFF grades are better suited for that. Instead, it provides insight into “how good it could have been” or “how bad it could have been,” making it a useful tool for evaluating decision quality.
Jameis' day out
Jameis Winston delivered one of the most fascinating performances of the season, setting a career-high in passing yards while throwing six total touchdowns—though two, unfortunately, went to the wrong team. For the most part, Winston demonstrated solid decision-making, but either the execution faltered on Cleveland's end or the Broncos' defense stepped up with exceptional plays to capitalize on mistakes.
Looking at Winston’s second pick-six:
This snapshot might not scream “pick-six,” but that’s exactly what happened. Winston releases the ball with solid anticipation, but the throw is slightly off-target, landing on Elijah Moore’s left shoulder—the side closest to the defender, Ja’Quan McMillian. That positioning creates the perfect opportunity for McMillian to make an outstanding play, outjumping and outmuscling Moore to snag the interception. McMillian then takes it all the way back for a touchdown.
In most scenarios, this setup would likely result in a gain of five or more yards, but not this time. It’s worth noting that the model rated this as a good decision, with Moore having a 54% target probability. However, this sequence underscores an important point: even the most optimal decisions can unravel if the execution falters.
Justin Herbert made some interesting decisions last week
An alarming 31.2% of Herbert’s pass attempts (on plays with two or more routes) in Week 13 were classified as “bad.” This means Herbert targeted the player with the lowest target probability on the play nearly a third of the time.
This play starts with a straightforward dropback for the Chargers, devoid of play action. Both Ladd McConkey and Stone Smartt are open early in the play, giving Justin Herbert two viable options depending on his risk tolerance and confidence. However, instead of capitalizing on either target, Herbert hesitates, holds onto the ball, and scrambles to his left, even though the pocket is relatively clean and there’s no immediate pressure.
The frame below captures the moment Herbert finally releases the ball after extending the play unnecessarily by rolling out for several seconds.
By hesitating and rolling out, Herbert allowed the defenders ample time to adjust, culminating in a poor decision. Stone Smartt, targeted on the sideline in a contested situation, had only a 6% probability of being the optimal target on this play. Unsurprisingly, he couldn’t secure the catch.
This serves as a prime example of how an elite receiver like Tee Higgins or George Pickens might elevate a quarterback’s output by making a difficult grab in such scenarios. Unfortunately for Herbert, Smartt isn’t the type of player who can consistently bail out suboptimal decisions like this one.
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