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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. Based on this model, we can generate interesting metrics such as “proportion of targets predicted” and “proportion 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 look at how quarterbacks make optimal and suboptimal decisions compared to the “predicted targets” model!
Week 11 results
Garrett Wilson there was a fantasy disappointment, but he still led the team with eight goals.
Cooper Coup had a fantastic fantasy performance, leading the Rams with 10 goals.
DeVonta Smith saw six targets, but could not exceed expectations.
Ladd McConkey And Xavier Worthy both had some of their best performances of the season, each leading or leading their respective teams in goals.
Finally, Austin, thank you led his team to goals and showed an excellent game.
IDENTIFYING BREAKTHROUGH CANDIDATES IN WEEK 12
Jacob Meyers this week’s headliner (according to the three-week OWN). The Raiders face a tough Broncos defense that has deployed the seventh-most MOFC coverage (middle of the field closed) over the past month, presenting a favorable matchup for receivers.
Meyers and Brock Bowers They have the same number of goals in MOFC coverage this season, although Meyers has missed a few games. However, not all signs point to good results, since Gardner Minshew ranks 28th out of 33 quarterbacks in PFF passing grade compared to MOFC coverage.
Romeo Dubs appears in both the “Coach, I was open” table and the Heroes based on the route model for the 12th week. The Dubs are also benefiting from a strong matchup this week against the 49ers.
Calvin Austin III also appears in both the “Coach, I was open” table and the Heroes based on the route model for the 12th week. The Steelers face the Browns on Thursday Night Football, and Cleveland has the third-worst PFF team coverage grade over the past month, while MOFC has the fifth-worst coverage over that period. These factors suggest that Austin is ready for the big game.
Optimal defender decisions using a predictive target model
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 given player being targeted based on factors such as openness, PFF grade, separation grade and more. Using this route-level data, we can determine whether the defender made the optimal decision.
To simplify the analysis, I divided each solution into three separate categories:
1. Optimal solution: The defender threw to the player with the highest chance of hitting.
2. Suboptimal solution: The quarterback threw to a player who did not have the highest probability of being hit.
3. Bad decision: The defender threw to the player with the least chance of hitting.
It is important to note that these classifications are based on model metrics and may not always perfectly match actual results. For example, Sam Darnold can throw a bouncing ball 25 yards Justin Jefferson. While the model might call this a suboptimal decision based on Jefferson’s openness, the context—such as Jefferson’s talent—might make it a good choice.
Game example
Let’s look at a hypothetical game in which four players plotted routes with the following target probabilities:
If Sam Darnold throws to each of these players, the corresponding decision category is shown above. I applied this process to every route in every game, combining the data into the table below for a broader understanding.
QUARTERBACK DECISIONS IN WEEK 11
Kyler Murray was the most effective decision maker in the NFL, making the best decision on 72.8% of his attempts – leading the league!
Among the newcomers Drake Maye although it takes first place Bo Nix keeps up.
It was amazing to see Daniel Jones And Gardner Minshew 10th and 11th places respectively. However, it is important to remember that throwing someone like Malik Nabers– when he is the only player creating separation – is still considered the optimal solution, even if it seems simple.
No less surprising was how low Brock Purdy And Joe Burrow in the ranking. This may reflect the quality of their systems and casting, where there are often several good solutions to a single play. However, both the 49ers and Bengals are performing worse than preseason expectations, and decision-making could be a contributing factor.
Aaron Rodgers leads the NFL in “Bad Decision Percentage”, throwing to the least likely player on 13.1% of attempts.
This is the first time such metrics have been created using such detailed data, so it is worth noting that conclusions may change as the approach improves. For example, Purdy’s ranking last in optimal decisions is not inherently bad; making the best decision becomes more challenging when multiple players are constantly open.
In future articles, I will discuss how these numbers change in different situations, such as the first read or the third and long script. For now, this metric gives a reasonable idea of how well a quarterback executes his particular offense, although it doesn’t fully account for the complexity or quality of that offense.
For more NFL statistics and analysis, Follow Joseph on Twitter/X.
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