Back in the pre-internet days, there was limited data to use when making your fantasy football player selections. Typically, the only information afforded to fantasy managers was a player’s points/game average (PPG). Of course, most managers would do some digging, and use other qualitative information to make their player selections, but at the end of the day, a player’s average usually dictated the decision.

Shortly after fantasy football exploded, some of the more sophisticated owners wanted an answer to the question to make a more informed decision: “How strong is the team my player is about to face?” Hence, the fantasy football internet gods gave us “Points Against Average” (PAA), or how many fantasy points per game the opposing defense is giving up on average to that position. Since then, there has been a myriad of additional “advanced” metrics to quantitatively predict outcomes on particular players, mainly fueled by the daily fantasy explosion.

However, to this day, the two most widely-used metrics to help understand a player’s value/expected outcomes are still PG, and PAA (just look at any fantasy football roster when you’re submitting your weekly lineup). Fundamentally speaking, these two scores should give you a general idea of what each player’s expected output should be.

For example, you are looking at an RB who averages 14 PPG and is playing a defense that gives up only 10 PPG to RBs. It’s fair to assume (which is what most predictive algorithms use to determine expected points) the expected output for that player will be 12 points.

However, how accurate is that sentiment to each given player? Are there specific players more vulnerable to difficult matchups? Are there players more susceptible to a favorable opponent? And if so, who?

Building off of the last two segments in this series, we have gathered this information and ran a correlation matrix between opponent (PAA) and actual weekly output for each player last season. I’m calling this “Point Elasticity” (individual output relative to strength of schedule/PAA), and have created it to help players get a better understanding of a player’s true productivity last season, and help managers make better decisions next year.

Simply put, when a player has a (relatively) high elasticity, it means his score went up when he played bad teams and dropped when he played good teams. On the other hand, inelastic scorers would keep a consistent output, regardless if they played a difficult opponent or not.

This new metric is likely to have the most benefit from week-to-week, but there’s plenty of value in the information come draft time as well. If a particular player’s elasticity is (relatively) high (his output goes up with ease of schedule), and his upcoming “Points Against” average is favorable, it’s fair to assume he could have an uptick in production compared to 2016. Either that or it would be at least higher than what most are expecting.

Additionally, you can use the same thinking when a player with a high elasticity has to endure a tougher schedule in 2017, he’s likely to see a dip in production. Conversely, players that are inelastic (their scores were not sensitive to PAA/SOS) you can select them with confidence, despite (a tough) schedule.

Below I have gathered the positional and global breakdowns of the most/least elastic scorers from 2016. You can also find the full findings here.

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Most elastic QB scores (players whose scores are relatively strongly tied to their schedule or average points against).

*Only fantasy relevant Players included.

 
Least elastic QB scores (players not susceptible to schedule, that is, their scoring output is relatively less likely to be affected by an opposing team’s points against)

*Note: Rated by absolute value since it’s a correlation.

 
QBs as a whole turned out to be the most resilient position against SOS, but it was fitting to see the “elastic QBs” littered with young guns. Equally as interesting were players like Russell Wilson and Jay Cutler (with a small sample set) who (somehow) performed better with harder competition.

Most elastic RB scores

 
Least elastic RB scores

 
The RB group had the second highest elasticity, with some interesting players leading the way, who could have significant jumps in point totals come 2017.

Most elastic WR scores

 
Least elastic WR scores

 
For the group with the most elasticity, which makes sense given the individualistic nature of their matchups (a strong CB can have a higher impact on a WR than a defense as a whole vs. say QBs), the inelastic players stood out the most, for example, Julio Jones and Brandon Cooks.

Most elastic TE scores

 
Least elastic TE scores

 
Jordan Reed also made an appearance on the “Real TE Rank” last week, so no surprise here, but still validates his relative decrease in TRUE value. On the flip side, there were some top TEs that seemed very resilient to strength of schedule. Lastly, below you can find the overall rankings of players from 2016 most and least sensitive to their strength of schedule.

Most

 
Least

 
There are some intriguing findings from this study. As I mentioned before, putting this information in action will take using this year’s upcoming SOS to determine players likely to breakout/not be affected by changes in their schedule. Lucky for you, we are putting together that information for you as well. Keep an eye out next week for a bonus piece on this series “2017 Breakout Players,” based on their elasticity vs. 2017 PAA.

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