When 11-0, Oregon comes into the final weeks of the regular college football season ranked alongside a mid-major team with a 12-0 record that holds a considerably weaker schedule, who’s going to get the nod for the higher ranking? The answer is clear, and the scenario gives us a good example of why you can’t simply look at the base, or surface, of production to give you a true perspective. Similarly, why would C.J. Anderson, averaging 11.43 PPG (but playing the 22nd ranked “RB Points Against” team on average, or PAA), be ranked above Eddie Lacy, averaging 7.20PPG (but playing the 8th ranked “RB Points Against” team on average)? The answer is, we’ve clearly never investigated fantasy output by player, adjusted to their strength of schedule. Thus, following up on last week’s article and playing hand-in-hand with the consistency ratings, FantasyPros has aggregated this data and offers the only venue to find adjusted fantasy football output from the 2016 season. Why is this important? Do you consider past season(s) productivity to help predict this year’s output? If so, wouldn’t you too want to have a deeper analysis of how well a player performed? All “rhetoricals” aside, we’ll start with players that endured the toughest schedule (based on Points Against Average, or PAA, minimum 7.5 PPG) from 2016. You can also see the full dataset/use for your purposes by clicking here.

Combine rankings from 100+ experts into one consensus cheat sheet

*All rankings are only of players with 6+ 2016 games.

Toughest Schedules in 2016 (6 game min., 5 P/G min.)

 
Who had the easiest schedule

 
As you can see in the charts above certain players last year were either very lucky, or very unlucky, based on schedules (Points Against Average, or PAA).

After looking at the toughest and easiest schedules, what you can find below are positional rankings based on “Adjusted Average” (PPG/PAA). Again, this gives us a more in-depth look at true production, since we are essentially holding schedule constant.

Let’s first take a look at Quarterbacks:

 
Many of the usual suspects at the top, yet Jameis Winston (19.06 PPG, 19.06 PAA) and Carson Palmer (19.33 PPG, 18.13 PAA) are absent.

Running Backs

 
A lot of what you would expect, yet absent from the list: C.J. Anderson (11.43 PPG, 22 PAA), Jonathon Stewart (10.08 PPG, 21 PAA) and DeAngelo Williams (9.63 PPG, 19.86 PAA).

Wide Receivers

 
Some notable top 24 WRs: Rishard Matthews, Mike Wallace, and Michael Crabtree. Some intriguing absentees: late-season stud, Taylor Gabriel (8.17/23), and the twin Vikings, Stefon Diggs (7.93/21.77)/Adam Thielen (8.07/21.33).

Tight Ends

 
Notable top (standard scoring) performers missing: Jordan Reed (8.25/21.92), who just missed the cut, was still the top scoring TE in most leagues last year, Zach Ertz (6.53/23.14), and Dennis Pita (4.81/21.5).

This is not only interesting data but can also help fantasy players choose their 2017 team. You can use this data in two ways (together and apart). First, it’s fair to assume there will be some regression toward the mean for players with high/low PAA in 2017. That is, players who performed well (poorly) despite their tough (easy) schedule, are bound to have an easier (tougher) route this season, which you can use to marginally adjust your pre-season rankings. Second, you can make more precise adjustments to predicted 2017 outputs given last year’s productivity relative to this year’s strength of schedule. A mouth full, I know, but this just means you can use this data to your advantage when comparing a player’s true output (holding schedule constant) relative to the 2017 strength of schedule. Additionally, what this data ultimately will tell us is how significantly schedule impacts a player’s output, or what I call sensitivity to the PAA (how correlated is a player’s PPG to schedule difficulty). That data set will come next week.

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Ryan Newman is a featured writer at FantasyPros. For more from Ryan, check out his archive and follow him @Ryannewman20.