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Estimated studying time: 6 minutes
Welcome to the most recent installment of “Coach, I Was Open,” my ongoing statistics collection, the place I construct and refine a mannequin to foretell targets for each route in each NFL sport.
In my first article, we mentioned creating the foundational “predicted targets” mannequin. The second article centered on refining this mannequin and launched highly effective derivatives: “share of predicted targets” and “share of predicted air yards” (these derivatives have proven to be considerably extra secure and predictive than their counterparts). In the third article, we explored the mannequin’s sensible implications and the way the anticipated goal likelihood was developed. Last week, we tweaked the mannequin to account for the timing of NFL offenses and defenses.
Let’s evaluate the mannequin’s efficiency in Week 8 and determine some takeaways for Week 9.
My mannequin accurately recognized a number of Week 8 breakout video games:
- Zay Flowers: 7 Catches, 115 yards, 0 TD
- Darius Slayton: 4 Catches, 108 yards, 0 TD
- Ladd McConkey: 6 Catches, 111 yards, 2 TD
- Courtland Sutton: 8 Catches, 101 yards, 0 TD
- DeVonta Smith: 6 Catches, 85 yards, 1 TD
- Noah Brown: 3 Catches, 73 yards, 1 TD
- Sam LaPorta: 6 Catches, 48 yards, 1 TD
The idea behind this mannequin facilities on the truth that as much as 5 gamers can run a route on any given play, however just one participant can obtain the goal. And generally, no move is tried in any respect.
However, this doesn’t imply different gamers didn’t “earn” a goal. My mannequin is designed to foretell whether or not a participant ought to have been focused. This strategy permits us to create extremely predictive metrics, similar to “share of predicted targets” and “share of predicted air yards,” each of that are considerably extra secure and predictive than their “precise” counterparts.
The concept is that after reviewing the movie every week, groups determine which gamers had been open however didn’t get the ball after which alter their sport plan to contain these gamers extra within the following week.
Season Leaders in “Share of Predicted Targets”
Keenan Allen nonetheless has loads of juice left. Notably, he is now incomes a big share of “predicted air yards,” which is uncommon for him. Allen can be a route-based hero this week. I absolutely anticipate an explosive efficiency from him quickly, as I did with Ladd McConkey final week.
Jakobi Meyers finds himself in elite territory for “share of predicted targets” and boasts a excessive “share of predicted air yards,” highlighting the worth of his routes.
The Bears now have two top-15 receivers in “share of predicted targets,” which bodes properly for Caleb Williams and the workforce’s future.
As I discussed final week, Malik Nabers is in really particular territory. This is probably going the weakest offense he’ll ever play in, so anticipate his potential to shine even brighter.
Week 8 “Coach, I Was Open” Table
This desk highlights gamers who excelled at getting open in Week 8 however didn’t obtain as many targets as anticipated. The “Week 8 TS Diff” column reveals the distinction between their precise goal share and their “share of predicted targets.” The bigger the destructive worth, the higher the discrepancy.
Bateman, Addison and Taylor every posted double-digit variations of their Week 8 Target Share Diff. Normally, Bateman can be primed for a spike in targets subsequent week, however with the arrival of Diontae Johnson, his outlook is much less sure. Bateman ran 15 routes with over a 30% probability of being focused however solely noticed 3–5 precise targets, relying on how they’re counted.
Addison had a goal likelihood above 30% on 13 events in Week 8 however acquired solely three targets. He additionally runs the deepest route tree of the group, with a Predicted aDoT of 13.67, making his targets particularly precious once they come his approach.
Taylor persistently received open, and with Anthony Richardson benched and the Colts dealing with the Vikings in Week 9 (who run two-high security appears to be like on the league’s highest fee), he’s prone to see a rise in targets.
Overall, any participant on this checklist is prone to present up in movie evaluate with a powerful case for extra precise targets in Week 9.
Interesting be aware: In Week 7, Sutton had an excessive scenario with 13 routes boasting a 30%-plus goal likelihood, but he acquired just one goal (which didn’t depend). In Week 8, the participant with essentially the most “excessive” scenario by way of missed targets was Jonathan Taylor, who ran 4 routes with a 30%-plus goal likelihood however garnered just one goal. This highlights simply how uncommon Sutton’s Week 7 goal efficiency was.
Week 8 Review: (Reminder: precise targets in my mannequin can embody penalties, so goal counts could not match different datasets.)
TE Brock Bowers
- Predicted Targets: 7.7
- Actual Targets: 5
Bowers had 5 cases with a goal likelihood above 30% the place he didn’t obtain the ball, his highest depend all season.
On one play, Bowers had a 42% goal likelihood whereas QB Gardner Minshew was rolling out in his route. If Minshew had trusted himself and Bowers in that scenario, Bowers would have had a very good probability of scoring a landing if he broke a deal with. Instead, Minshew scrambled and ended up with no achieve. DJ Turner scored on the next play, however this was finally a suboptimal resolution by Minshew.
WR Jordan Addison
- Predicted Targets: 6.7
- Actual Targets: 3
This time, I’ve included two screenshots for extra context. On Addison’s highest target-probability play (almost 70%), he beat his defender and ended up wide-open.
Sam Darnold was pressured on the play. In my opinion, Darnold may and may have thrown the ball as quickly as Addison was even along with his defender earlier than the strain reached him. Instead, Darnold takes a 14-yard sack. This play illustrates how quarterback selections can affect a receiver’s precise goal alternatives in additional nuanced methods. Additionally, Aaron Jones was vast open for a checkdown with a 20% goal likelihood.
For extra NFL stats and evaluation, observe Joseph on Twitter/X.