Targets can give us additional insight on current productivity, as well as help in predicting outcomes. Looking back at last year there is some interesting data on who got the most targets (relative to how well they performed and vice versa), but it also gives us context on potential changes in this upcoming season. That is, who is in line to gain more opportunities and who is less likely to get as many targets. We investigated, and you can find our analysis below:

Targets are important, but to paint a more thorough picture, let’s see how 2016 shaped up in regards to who did the most with their opportunities (you can find the full breakdown/data set by clicking here):

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*All Charts: Minimum 3 points/game and 30 total targets. All are PPR format.

2016 WR/TE Top PT/G Performers (w/ Points per Target and Rank)
This tells us how well the top performers fared when teasing out the quantity of opportunities. (Ranked by most PT/G)

 
2016 WR/TE Top Points/Target Performances
This tells us who did the most with their opportunities. (Ranked by most PT/TAR)

 
It’s always interesting to look at these two different charts. It helps tell the tale of true production. That is, who’s productivity has more to do with the volume of opportunities vs. what each player does with those opportunities. Most importantly, it gives us some insight to better predict the upcoming season, which we’ll jump into at the end of the article.

2016 Target Consistency

For those that have been following my recent posts specifically Fantasy Football Consistency Rankings, you’ve likely noticed a theme. I’m a sucker for consistency, believing that predicting fantasy performance and having an in-depth breakdown of week-to-week variance by an individual player go hand-in-hand. With that being said, when analyzing targets I find it just as important to know a player’s weekly target standard deviation and coefficient of variation (CV) just as much as targets/game. Personally, if I was on a desert island, tasked with picking a fantasy lineup with one piece of datum, it would be CV, or average/standard deviation. CV, which can be applied to any pertinent statistic, tells us how consistently well a player is performing. Mathematically, CV is simply the average/standard deviation. It’s one number that combines how well a player is performing overall AND how consistent they put up that average. Here’s how last year’s target consistency panned out:

 
Similar to any piece of data, without proper context, they’re just numbers. Thus, let’s take a look at some predictive analysis on this data. For starters, we can look at targets essentially as opportunities. So first, let’s look at which teams have targets “left on the field,” or opportunities for targets that are now available (in addition, we accounted for OCs changing teams and likely bringing/taking their opportunities to their new/old teams). After that we’ll look at it individually, meaning which players are landing in situations that they mathematically will have more/fewer opportunities vs. this past year.

2017 Opportunity/Target Landscape
Where will the opportunities go (*Note: “non-critical targets” were not included, i.e. menial contributors were not included)

 
Baltimore, Buffalo, Clevland, and New York (Jets) lead the way among target opportunities waiting to be snagged. Again, these teams have either offensive coordinators coming in that throw the ball more and/or players leaving that represented a large volume of those targets. Metrics like these are why I love Mike Wallace, Sammy Watkins, and Quincy Enunwa this season. Even a guy like Zay Jones, my No. 1 rated rookie WR, with Watkins’ 82% chance of injury according to SportsInjuryPredictor, is a great ‘breakout WR’ candidate for 2017. Like any other piece of information, there are plenty more variables that may impact this outcome, but it still helps us predict where players going up/down may lie in 2017.

2017 Free Agent Winners
Players that are coming into the most “fertile” environments for increasing their individual targets.

 
This is my favorite data point. Remember, there are other factors that will come into play, but mathematically these players above have a very good chance of upping their point total from the year before. Essentially, we took what each player produced per target last year, and then applied that to the expected (given the offseason’s changes) opportunities they can see with a min/max (assuming a conservative (1/5 of those additional targets) or aggressive (1/3 of targets) prediction to give us a better idea of what they can expect in 2017. We’re using 1/3 and 1/5 as estimates and since these players will still be sharing those additional targets with other receivers. It’s important to note the “1/3 and 1/5” share assumptions will come with some variance, but with all the new teams, coaches and OCs it’s difficult to simply use 2016 target share percentages. The players at the top, like Pierre Garcon, Justin Hunter, and Ted Ginn, seem like they can be intriguing late-round value picks.

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