Identifying players who could see more targets in Week 14.

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Estimated reading time: 6 minutes

Welcome to the latest installment of my ongoing series, “Coach, I Was Open,” in which I create 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 likelihood of each route being taken in each game in the NFL. This model generates interesting metrics such as “percentage of targets predicted” and “percentage of yards predicted.” These indicators are more stable and predictable than their actual counterparts.

The basic idea behind this model is that a player can “earn targets” by consistently opening and running valuable routes, but not receiving targets for various reasons, such as quarterback pressure, a misread, or the quarterback forcing the ball somewhere. move. After reviewing the film, teams can recognize that some players were open and adjust their game plan to involve more of them in subsequent weeks.

Later in this article, we’ll analyze the quarterbacks’ decision-making in Week 13, highlighting both optimal and suboptimal options.

Week 13 results

DJ Moore was definitely the headliner last week, finishing — believe it or not — a career-high 16 goals. This is exactly what the model is trying to determine.

Marvin Harrison Jr. also saw a career-high in targets (12) but didn’t really produce any meaningful fantasy numbers.

Xavier Legette (8) and Tucker Craft (7) finished with the second-most goals ever.

Overall, I would consider these examples a huge success in terms of model efficiency.

IDENTIFYING BREAKTHROUGH CANDIDATES IN WEEK 13

Last time Ja’Marr Chase was our headliner, he scored 55.40 fantasy points and had a whopping 17 targets. This was our best exemplary performance all season. In Week 14, the Bengals have a solid matchup against the Cowboys’ defense, allowing for plenty of single-coverage situations. The Bengals are a strong passing team that doesn’t need to be pushed to pass the ball, so I expect good results from Chase this week.

It could be Calvin Ridleyfirst appearance on this list. As the Titans’ WR1, Ridley saw just seven targets in Week 13, but now faces a favorable matchup and rematch against the Jaguars. Over the past month, Jacksonville has produced the highest rate of single-coverage opportunities in the league. Ridley’s target share in these situations increases significantly, increasing from 23.5% to 39.3%.

Interesting note: Wan’Dale Robinson had the sixth-largest difference between “predicted goals” (6.3) and “actual goals” (2) of the season.

Check out the season leaders by share of projected goals:

Multiple playersincluding Zai Flowers, Garrett Wilson And Keenan Allenare entering the territory of serious regression. These recipients have posted good numbers on “share of projected targets” but have not yet taken full advantage of these opportunities. Essentially, they use valuable routes and are constantly opening up, but various factors limit their production. If they continue this trend of creating opportunities, a positive surge in productivity is likely on the horizon.

Malik Nabers was the leader throughout the season. He is an elite player, but plays in an offense that is far from elite. He will continue to fight until Tommy DeVito or Drew Lock throws him the ball.

Courtland Sutton is our new leader in “predicted air yards share,” meaning Denver is sending him down the field more often. This is a great sign for Sutton and Bo Nixas it means they trust both players to have deeper (fantasy relevant) options.

Defender “Optimal Solutions”, week 13

The projected targets model allows us to evaluate a quarterback’s performance in a single game, a series of games, or even over the course of an entire season. This model analyzes every route on every play, calculating the likelihood of a particular player being targeted based on factors such as openness, PFF grade, separation rate and more. Using this route-level data, we can determine whether the defender made the optimal decision. I filtered all the data to only games that had at least two routes, so the QB had to make the decision.

To simplify the analysis, I divided each solution into three separate categories:

1. Optimal solution: The quarterback threw to the player with the highest probability of hitting.
2. Suboptimal solution: The quarterback threw to the player who did not have the highest probability of being hit.
3. Bad solution: The quarterback threw to the player with the least likelihood of a hit.

Justin Herbert in Week 13, he had another difficult task regarding optimal decision making, finishing last in the NFL for the second week in a row. While a lower optimal decision rate isn’t always a bad sign (sometimes indicating a quarterback’s willingness to make high-level throws like “open man”), Herbert’s 31.2% BAD decision rate is concerning. This means that more than 30% of his passes hit the least likely receiver, which raises questions.

Herbert will need to get things in order if the Chargers are to make a serious run at the Super Bowl. It’s worth noting, however, that this stat isn’t necessarily a measure of a quarterback’s performance in the traditional sense—PFF grades are better 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 assessing the quality of decisions.

Jameis’ day off

Jameis Winston delivered one of the most exciting performances of the season, setting a career-high in passing yards and throwing six touchdowns, although two unfortunately went to the wrong team. For the most part, Winston showed solid decision making, but either execution fell short on Cleveland’s end or the Broncos defense stepped up with exceptional plays to capitalize on mistakes.

Looking at Winston’s second six:

This photo may not scream “sixth pick,” but that’s exactly what happened. Winston releases the ball with strong anticipation, but the throw goes slightly wide and lands in Elijah Mooreleft shoulder – the side closest to the defender, Ja’Quan McMillian. This positioning gives McMillian the perfect opportunity to make a big play, jumping over and outplaying Moore to catch the interception. McMillian then returns the ball for a touchdown.

In most scenarios, this formation would likely result in a gain of five yards or more, but not this time. It’s worth noting that the model rated this decision as good: Moore had a target probability of 54%. However, this sequence highlights an important point: even the best decisions can be ineffective if their execution fails.

Justin Herbert made some interesting decisions last week.

An alarming 31.2% of Herbert’s pass attempts (in plays with two or more routes) in Week 13 were classified as “poor.” This means Herbert targeted the player least likely to make the play nearly a third of the time.

This play starts with a simple kickback for the Chargers, devoid of play action. Both Ladd McConkey And Stone Smartt open at the beginning of the game, giving Justin Herbert two viable options depending on his risk tolerance and confidence. However, rather than capitalize on any target, Herbert hesitates, holds the ball and scrambles to his left, even though the pocket is relatively clear and there is no immediate pressure.

The shot below captures the moment Herbert finally releases the ball after unnecessarily extending the play by rolling the ball out for a few seconds.

By hesitating and rolling out, Herbert gave the defense plenty of time to adjust, leading to a poor decision. Stone Smarttaimed at the touchline in a controversial situation only had a 6% chance of being the optimal target in that game. No wonder he couldn’t catch the catch.

This serves as a prime example of how an elite receiver like Tee Higgins or George Pickens can improve a quarterback’s performance by making it more difficult to tackle in such scenarios. Unfortunately for Herbert, Smartt is not the type of player who can consistently avoid suboptimal decisions like this one.