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A while back, we covered the Next Gen Stats for running backs, including rushing yards over expected and the percentage of plays with eight or more defenders in the box. Check out the running back review of the Next Gen Stats, as it adds a layer to player analysis. We love the advanced stats for fantasy football, and some matter more or less. Any stat and metric needs context because we shouldn't use one by itself to determine a player's value.
One of the areas we hear evaluators discuss for prospects and receivers in the NFL involves their ability to separate. Can a receiver separate against man coverage based on their route running, quickness, and athleticism? Sometimes, the highlight clips for players like Jerry Jeudy or other athletic receivers draw us into it, translating to earning targets and producing fantasy points.
We'll examine two main metrics: average yards of separation and expected yards after the catch per reception. It's important to add in the context of a team's air yards share and air yards per target, which Next Gen Stats calculates slightly differently than other stat providers. After defining the metrics, we'll look at the leaders and players toward the bottom while providing context for each stat.
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Average Separation
Next Gen Stats defines average separation as the yards between a wide receiver or tight end and the closest defender at the catch or incompletion. So, it's the yardage distance between the pass-catcher and defender when the player attempts to make the reception.
They also have average cushion yards, meaning the number of yards between the receiver and tight end when lined up before the snap. However, the average separation might give us more actionable data than cushion since some defenders may not play press coverage as often. NFL teams ran man coverage on average 23.8 percent of the time, with 15 running it higher than the league norm.
Like any metric, there's some noise with average yards of separation because a few plays could cause the mean to move in either direction. At first glance, the leaders in separation contain a mixture of slot receivers and tight ends. Tight ends typically have to win against zone defenses, which could also play into the average yards of separation.
We'll provide additional context because the percentage of targeted air yards (TAY%) finished on the lower end for several receivers and tight ends. Travis Kelce, Zay Flowers, and Elijah Moore had the highest percentages of their team air yards per Next Gen Stats while ranking highly in separation yards.
It's seemingly more impressive to have a higher air yards share with more yards of separation, though some receivers have the speed to blow past defenders. However, the context we don't have involves potential blown coverage or a potential explosive play with few defenders nearby that would impact the average yards of separation.
With the players toward the bottom having the lowest average separation yards, it doesn't mean they can't separate. In the visual below, sorted by the lowest average yards of separation, we find a few polarizing and high-end receivers, including Drake London, Amari Cooper, Tee Higgins, Calvin Ridley, George Pickens, A.J. Brown, and Mike Evans.
Cooper, DeAndre Hopkins, Ridley, Pickens, Brown, and Evans garnered over 35 percent of their team's air yards with a low average yards of separation. Farther down the list, tied for 2.5 average separation yards, we have DK Metcalf and Garrett Wilson meeting the criteria of 35 percent or more of their respective team's air yards.
These receivers likely draw more attention while earning air yards, which leads to more challenging pass attempts for the quarterback. We typically chase volume and want efficient receivers, so a combination of finding receivers with high air yards and low average yards of separation might be one way of identifying near-elite options.
Expected Yards After the Catch (xYAC)
Like the Rushing Yards Over Expected metric, Next Gen Stats has Expected Yards After the Catch (xYAC). They base the metric on tracking data that indicates whether the receiver is open, the speed of their movement, and the number of defenders or blockers in the area. The NFL Next Gen Stats may calculate other metrics and variables into xYAC, but they won't reveal the secret sauce.
In fantasy baseball, expected stats have become the norm when examining players. However, there seem to be fewer metrics in fantasy football besides expected points (EP/G) and fantasy points over expectation per game (FPOE/G). Part of the expected points formula involves a player's opportunity and usage. The leaders in EP/G tend to be some of the best fantasy options.
We provide that context in evaluating xYAC because certain variables lead to the expected yards after the catch. Since we can't discuss xYAC without the actual yards, we'll weave the xYAC conversation under the next section.
Yards After the Catch Above Expectation
This one is somewhat clear as it compares the receiver's actual yards after the catch to the xYAC. Though it might be counterintuitive, a player with a negative xYAC per reception indicates the receiver could have created more yards after the catch. Meanwhile, a positive number in yards after the catch above expectation indicates the player produced more than expected.
When looking at expected stats, we want to know the difference between the expectation and the actual. It's similar to expected points or touchdowns when a player hasn't scored in the red zone or goal-to-go situations. Think about Jamaal Williams and Jalen Hurts stealing touchdowns from D'Andre Swift.
A player like Williams in 2022 seemed due to regress, but the question of when became more unlikely throughout the season. We knew the usage and actual output seemed unsustainable compared to the expected, yet the touchdown production continued.
Deebo Samuel, Pickens, George Kittle, Nico Collins, Higgins, Puka Nacua, Metcalf, and Brandon Aiyuk highlight players with the highest xYAC above expectation. Three 49ers players creating more yards after the catch than expected suggests they run an efficient offense. That's unsurprising when considering Brock Purdy ranked first in adjusted yards per attempt and first in Expected Points Added Per Dropback (EPA/DB).
When we toss in the variable of a team's air yards share, Pickens, Nacua, Metcalf, Aiyuk, Brown, D.J. Moore, and Cooper boasted a 30 percent share of the team air yards. It's more challenging to create yards after the catch when a player garners targets downfield. These receivers do that, meaning they generate big plays while earning high-value targets.
Meanwhile, Rashee Rice and David Njoku fall into the bucket of prototypical receivers garnering targets in the short area of the field to gain yards after the catch. Thankfully, Rice converts his short-area targets, leading to the highest Receiver Air Yard Conversion Ratio (RACR) at 1.91. That's ahead of Samuel in second at 1.51 RACR among qualified receivers with a minimum of three targets per game.
We included Weighted Opportunity Rating (WOPR) on the chart above because it accounts for a player's target and air yards share. We want the crossover of volume and efficiency for our players since that typically means they're scoring tons of fantasy points. That list will show us which receivers produced efficiently based on converting their air yards and overall volume.
Here's the list of concerning players above who produced fewer yards after the catch than expected. On one hand, we could say these players could regress next season or in the coming weeks if we ran the data during the season. However, it could mean inefficient players losing their ability to make explosive plays and evade tacklers in the open field.
Adam Thielen and JuJu Smith-Schuster might fit that list of players who might be inefficient at this stage of their careers. Besides 2022 (-0.6 FPOE/G), Thielen had the worst FPOE/G at 0.2 (No. 83) since 2015. Meanwhile, Smith-Schuster ranked 194th in FPOE/G after his second-lowest target total (47) since 2021, when he played in five games.
Interestingly, another Panthers player, Jonathan Mingo, averaged more xYAC/Rec than actual. Considering the Panthers' team context, it might be a situation where the offense lacked consistency and juice, so their pass-catchers couldn't create more yards after the catch.
When we add the air yards and targets downfield, Justin Watson, Rashid Shaheed, Rashod Bateman, Ridley, and Olave stood out. Theoretically, converting targets downfield is more challenging, meaning a less probable outcome. So, a play with a lower probability would mean fewer yards after the catch than expected. Shaheed, Bateman, Ridley, and Olave might be due for regression in 2024 if the luck factors shift their way based on their skills and roles.
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