We’re coming to a close on the 2020 Season and entering what is likely your leagues championship week. That’s means that many of you, like me, are picking up the pieces of your shattered dreams and beginning to look towards a brighter future. During the offseason, all sorts of stats will be thrown at you. Air Yards, target share, average point of first contact, depth of target. The list goes on forever and then on top of that, you’ll have just as many conflicting opinions based on how applicable those stats are. What I’m going to do now, is add some more confusion with more stats that you’ve likely not used or applied to any strategies. You’re welcome.
Expectations are a strange concept, especially in Fantasy Football. They drive whether or not we consider a player a bust, or can increase his value if we see fit. What’s tricky about expectations is that they are made up. We construct our own expectations. What I expect out of a team, player, or even play could be VASTLY different than what you, or somebody else may expect. Cue NFL Next Gen Stats What NFL Next Gen Stats do in some of their stats is provide expectations. What we can then do is compare the outcome, to what was expected. What you’ll notice when we start to dig in to some of these is that they tend to highlight players that you would “expect” to see (pun intended) as well as players you maybe didn’t. The perfect mix of a stat: A little proof that it isn’t completely off the wall and a little spice to make you tilt your head.
Passing
The first expectation vs reality that we’ll dive into is in the passing game. What we’ll be looking at here specifically is Completion Percentage (Comp%) vs Expected Completion Percentage (xComp%), and the net difference (+/-). The pass game is pretty cut and dry. Meaning that the names you expect to see at the top, are at the top and vice versa. The xComp% is based on NFL Next Gen Stats’ likelihood that a pass is completed. The names that you would expect to see at the top of this list are QBs that tend to throw a lot of checkdowns and/or short passes. We’ll take a look at some of the highest rated QB’s and some of the lowest rated QB’s and compare +/- of the Comp% vs xComp%.
| Name | Rate | Comp% | xComp% | +/- |
|---|---|---|---|---|
| Aaron Rodgers | 118 | 69.6 | 65.8 | 3.8 |
| Deshaun Watson | 110.6 | 69.9 | 65.7 | 4.2 |
| Patrick Mahomes | 110.6 | 67.3 | 67.6 | -0.4 |
| Ryan Tannehill | 110.4 | 66.5 | 63.6 | 2.9 |
| Drew Brees | 107.4 | 70.5 | 70 | 0.5 |
| Daniel Jones | 78.2 | 62.6 | 62.9 | -0.3 |
| Dwayne Haskins | 77.7 | 62.9 | 68.1 | -5.2 |
| Drew Lock | 75.4 | 57.7 | 61.6 | -3.9 |
| Carson Wentz | 72.8 | 57.4 | 61.5 | -4.1 |
| Sam Darnold | 70.7 | 59.7 | 63.2 | -3.5 |
What can be said about this table is that the best (highest rated QBs) not only complete and are expected to complete passes at a higher rate, but also exceed expectations. What is more telling is that the bottom rated QBs are not only below expectation, but rather significantly lower. The best way to map this stat to real life scenarios would be to apply it to the following when considering how useful it is: Throwing receivers open vs Off Target passes, High number of Short throws vs More dangerous/difficult throws, etc. Obviously everything needs context, but the expectations provide a rather good correlation to how good a QB is playing.
Rushing
Trying to find trend lines that everybody can agree on for running backs can be difficult. So much of a running back’s success is affected by factors outside of his control (How well his offensive line playing, playing from behind, etc.) In terms of rushing the ball, the “expected” metric is applied to the Rush Yard per attempt and most of those variables are factored in to the calculation. Another stat is the Rush Percent over Expected (ROE%) which tells us how many of a backs carries gain more yards than expected. Let’s put some names to the stats and then go over how this can be applied.
| Name | YPC | RYOE/Att | ROE% |
|---|---|---|---|
| Nick Chubb | 5.6 | 1.71 | 41.4 |
| Ronald Jones | 5 | 1.13 | 43.9 |
| Gus Edwards | 4.9 | 1.11 | 50.9 |
| Derrick Henry | 5.2 | 0.97 | 45 |
| Dalvin Cook | 5 | 0.79 | 45.2 |
| Giovani Bernard | 3.3 | -0.59 | 35.4 |
| Peyton Barber | 2.7 | -0.63 | 31.5 |
| Le’Veon Bell | 4 | -0.63 | 33.8 |
| Adrian Peterson | 3.7 | -0.76 | 25.2 |
| Duke Johnson | 3.1 | -0.86 | 26 |
As you probably noticed, there are a few extremely talented backs at the top of the list and the bottom is comprised of older and less efficient backs. This means that based on all of the variables on any particular play, if the expectation is that Nick Chubb is going to lose a yard he will instead, gain almost a full yard on average. NFL Next Gen Stats provides a little insight on which backs are not only performing well but exceeding expectations, and again giving us a very good correlation to traditional stats to help us project.
Receiving
If given the option between types of receivers, do you tend to lean toward the prototypical alpha, like Kenny Golladay? Or do you prefer the speedy, breakaway guy like Tyreek Hill? Maybe you prefer a route running tactician, like Keenan Allen. Any of these options are great, both “IRL” and in the fantasy world. However, like Running backs there are factors beyond the scope of running the route and making the catch that will affect production. For example, tighter coverage, bad throws, etc. What we can see if what they do AFTER the catch is made. We’ll be taking a look at the yards gained after the catch (YAC) against the expected yards gained after the catch (xYAC) to see who is making things happen, even when stats tell us they shouldn’t be.
| Name | Catch % | YAC/R | xYAC/R | +/- |
|---|---|---|---|---|
| Deebo Samuel | 75 | 12.3 | 7.8 | 4.4 |
| AJ Brown | 64.4 | 6.8 | 3.8 | 3 |
| DJ Moore | 57.7 | 6.9 | 4.5 | 2.5 |
| George Kittle | 75.5 | 6.4 | 4.4 | 2 |
| Terry McLaurin | 63.5 | 6.3 | 4.4 | 1.9 |
| Tyler Lockett | 75.2 | 3.4 | 4 | -0.6 |
| Anthony Miller | 63.4 | 3.2 | 3.9 | -0.7 |
| Damiere Byrd | 63.4 | 3.4 | 4.2 | -0.8 |
| Austin Hooper | 70 | 4.4 | 5.5 | -1 |
| Robert Tonyan | 89.1 | 4.1 | 5.5 | -1.4 |
The expectation vs reality difference for the receiving stats is probably less useful for fantasy purposes, and more so for real life football. A receiver having the ability to make things happen after the catch doesn’t seem to overcome the other factors that would drive his ability to produce in a fantasy setting. For example, if a running back is continuously breaking tackles and exceeding expectations it’s significantly easier to just simply hand him the ball more. For a WR, he’ll need likely to get open, have a relatively on target throw, and then still secure the catch before he is ever even given the chance to extend the play. However, if you’re a big believer in the “talent prevails” mindset, what proves talent more than the ability to exceed your projected output?
I hope you enjoyed this article and gained an extra tool to put in to your research bag. As always you can find me on Twitter @FantasyAverage, so feel free to shoot me a DM!
Featured Image: CBS Sports
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