"Coach, I Was Open": Identifying the players who could see more targets in Week 15

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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, we look at why three elite players — A.J. Brown, Deebo Samuel and Tee Higgins — didn't get the ball as much as they should have in Week 14.

Week 14 Recap

Ja’Marr Chase delivered another epic performance, earning a season-high 18 targets and his second-best PPR total of 45.1 points.

While Calvin Ridley and Wan’Dale Robinson didn’t produce big numbers, the model accurately projected strong target volume for both, with Ridley seeing 12 targets and Robinson 11.

Rome Odunze didn’t rack up a huge number of targets, but he made them count, securing two end-zone targets for touchdowns.

Josh Palmer, Ja’Marr Chase, and Christian Watson all finished with season-highs in targets.

IDENTIFYING BREAKOUT CANDIDATES FOR WEEK 14

A.J. Brown was the inspiration for the creation of the “Coach, I Was Open” model, as we explained in my very first article. And as I touched on in my Route-Based Heroes article, A.J. Brown has a solid matchup against the Steelers this week. If the Eagles can manage the Steelers’ pass rush, I expect a big week from him.

Deebo Samuel has been the odd man out lately, with either Jauan Jennings or George Kittle taking the spotlight — or the entire 49ers offense struggling. The Rams have been average defensively overall, but their poor tackling (per PFF’s team tackling grade) presents an opportunity for Samuel. As a proven YAC superstar, he could thrive if the 49ers get him the ball.

Tee Higgins was overshadowed by Ja’Marr Chase’s outstanding performance last week. In Week 15, he faces the Titans, who have used middle-of-the-field-open (MOFO) coverage at the eighth-highest rate since Week 9. Higgins excels in single-coverage situations, but the Titans have allowed the sixth-fewest single-coverage opportunities over that span.

Season Leaders in Share of Predicted Targets

Here’s this week’s updated table, and it’s a notable one — we have our very first tight end on the leaderboard! Trey McBride has been dominating the Cardinals’ Share of Predicted Targets, clearly showing how heavily Arizona’s offense is designed to get the ball into his hands and how much he stands out compared to the other receivers on the team.

Meanwhile, Jalen Hurts is in a strong position, benefiting from two of the best receivers in the NFL at getting open. However, with a PFF passing grade of just 66.5 this season (27th in the NFL, minimum 200 dropbacks), there’s plenty of room for improvement in his performance.

Week 14: Reievers getting open

Brown ran 14 routes with a target probability above 30% but only received four targets (remember, my target data includes every play unless the player was involved in a penalty). This tied for the largest gap of the week between strong route probabilities and actual targets — often an indicator of a potential target increase ahead.

One of these routes is highlighted below. Brown had a 54% chance of being targeted on this play but wasn’t. It came on first-and-10 from midfield, with the Eagles looking to score before halftime.

Hurts held onto the ball on this play, as he has often done this season. He leads the NFL in average time to throw (3.2 seconds) among quarterbacks with at least 100 dropbacks.

The result was a checkdown to Kenneth Gainwell for a two-yard gain. This play easily could have gone for seven or more yards had Hurts targeted Brown — and if Brown broke a tackle, the gain could have been much bigger.

Not All Hurts’ Fault

This play took place later in the third quarter on a critical third-and-2, with the Eagles trailing 16-14. While it’s unclear whether the play was called as-is or adjusted at the line, it was a full-on shot play for a short-yardage situation. The design featured three deep routes, all of which eventually got wide open. However, Cam Jurgens was beaten badly by A’Shawn Robinson, and with no checkdown or short options available, Hurts had nowhere to go and was sacked.

As the sack occurred, both A.J. Brown and DeVonta Smith broke free downfield. Assigning blame here is tricky, given we don’t know why the play relied solely on deep routes in a third-and-short situation while trailing. Regardless, it’s clear that this one can’t be pinned entirely on Hurts.

Quarterback Decision Making – Week 14

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:

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.

Justin Herbert finally started making fantastic decisions, but the Chiefs were sadly too much to overcome.

Will Levis, Patrick Mahomes and Russell Wilson all had bad decision rates above 20% on the week, while Herbert and Sam Darnold had above 80% optimal decision rates with wildly different game outcomes.

For more NFL stats and analysis, follow Joseph on Twitter/X.