Now that the draft is over I’m excited to report that I’ve finished my air yards model for NFL receivers.

Well-validated models are the best and most unbiased forecast of NFL receiver production there is.¹ However there are things that they cannot capture that are important to consider.

Changes to team composition, injuries, coaching changes, and more can affect the accuracy of a pre-season model that is based purely on numbers. Finally, many models, including mine, do not give more than an expected value forecast for a player. Point estimates are great, but it’s also crucial to understand the range of outcomes.

In this series, I’m counting down the model’s top-36 wide receivers and publishing forecasts, along with a discussion of the other important factors the model doesn’t know about. Full projections will be released next month.

Pierre Garçon

Air Yards Model Projection

Projected PPR Points per Game: 15.1

Projected WR Rank: 27

Actual ADP²: 36

Model Difference: + 9

Team Personnel Changes

The 2017 Forty Niners will look almost nothing like the 2016 version. That is almost surely a good thing, but it also makes projecting Garcon difficult.

In the 2017 NFL draft, San Francisco added four new offensive players. Starting in the third round, the 49ers selected quarterback C.J. Beathard. They followed with running back Joe Williams in the fourth and wrapped things up with tight end George Kittle and WR Trent Taylor in the fifth. Of the four, Williams and Kittle have the best chances of seeing snaps in 2017, but neither should adversely impact Garcon’s opportunity.

In addition to signing Garcon, the Niners added a slew of offensive players during free agency. Perennially underrated QB Brian Hoyer will be throwing to Garcon, and fullback Kyle Juszczyk was added to do…things. The offensive line was helped by the addition of C Jeremy Zuttah and RT Garry Gilliam. WR Marquise Goodwin was signed from the Bills and could end up lining up opposite Garcon. Meanwhile, WR Aldrick Robinson, QB Matt Barkley, RB Tim Hightower, TE Logan Paulsen and RB Kapri Bibbs were all signed for roster depth.

Garcon looks to be the clear WR1 for San Francisco, and the air yards model is bullish on him. What does the move to the Forty Niners do to change the projection?

Projected Opportunity

Every Kyle Shanahan-coached team starting in Houston in 2008, running through Washington and Cleveland, and finally to Atlanta in 2015-16, has been either at or above the league average in plays per game.

This would be a departure for the Forty Niners. Since 2013, the year prior to Jim Harbaugh’s dismissal, San Francisco has failed to generate anything approaching a league average pace on offense.

Plays per game can be a crucial gauge of effectiveness for an offense if the plays aren’t generated through gimmicks. More plays indicate longer, sustained drives and more opportunity for skill position players to make explosive contributions.

Shanahan has preferred passing to running over his career as an OC, but that doesn’t necessarily mean his previous preferences will translate to the 2017 Forty Niners. It’s been 12 years since San Francisco has beaten the league average in passes per game, and seven since they so much as matched it. They are seemingly a team built to pass 30 times a game and no more. Expecting a huge departure from historical norms is probably foolish, even when we take into account the wholesale changes on offense and on the coaching staff.

In 2016 the Forty Niners attempted just 491 passes. The ARIMA model forecasts a dip, and the expected pass attempts for 2017 is a dreadful 483. I think this is far too low, and even if we discount Shanahan’s ability to make the Niners a league-average passing team, he will surely get them close. Here are the ranges offered by the ARIMA model:

Floor: 429 attempts

Ceiling: 538 attempts

Expected: 483 attempts

The average NFL team attempts roughly 575 passes a season. We’ll adjust our forecast, put our faith in Shanahan, and put the Niners expected passing attempts at 550, well over the ARIMA projection’s most optimistic estimate for San Francisco.

Bovada Sports Book has the win total for the Niners at just 4.5. This is not a good team. But it’s difficult to say what type of losers the Niners will be. Will they try to pass early and often to increase their variance? Or will they be a plodding, methodical team, running even late into games to try and lose more slowly?

My hunch – for this season at least – is that Shanahan and his staff will want to give the impression to fans and ownership that they are making progress. Weekly blowouts are not conducive to building that type of image. Assuming the Niners are indeed a 4-5 win team, I anticipate that we will see a fairly similar form of offense that we have come to expect from San Francisco. The Niners will probably run late into the game even when behind, in order to keep games close and be nominally competitive.

As for Garcon, last season he earned a target share of 19 percent in a crowded and talented Washington receiving corps. During Shanahan’s years in Washington, Garcon enjoyed 27 percent and 30 percent target share seasons. He is the undisputed WR1 in San Francisco, but let’s temper our expectations slightly and pencil him in for 25 percent. Here are his projected totals, with a very low floor thrown in from the ARIMA model to help keep us grounded.

Floor: 107 targets

Ceiling: 143 targets

Expected: 137 targets

Career Efficiency

If you combine catch rate and YAC into one metric you get something I call RACR. Over his career, Garcon has been good at converting a yard thrown at him into a receiving yard.

His catch rate is slightly above average at most depths, and like most receivers in this tier, he profiles as a guy who creates separation and is able to catch his share of contested targets at the shallow and intermediate depths.

Garcon’s YAC curve reminds me of Dez Bryant’s. Both Garcon and Bryant create after the catch in a fairly narrow window from 5-20 yards from the line of scrimmage. Unlike Dez, Garcon isn’t a deep threat, and he won’t overpower a defender with his size. His strength is in getting open and presenting himself to the QB. Crucially though, there really isn’t anything elite about his profile.

RACR

Garcon’s career aDOT is 10.7, and his career RACR is 0.71. Let’s calculate his receiving yards.

Floor: 107 targets  * 10.7 aDOT => 1145 air yards * 0.71 RACR => 813 receiving yards

Ceiling: 143 targets * 10.7 aDOT => 1530 air yards * 0.71 RACR => 1086 receiving yards

Expected: 137 targets * 10.7 aDOT => 1466 air yards * 0.71 RACR => 1041 receiving yards.71

Catch Rate

Garcon’s overall career catch rate is a solid 61 percent. Let’s calculate receptions.

Floor: 107 targets  * 0.61 Catch Rate =>  65 receptions

Ceiling: 143 targets * 0.61 Catch Rate => 87  receptions

Expected: 137 targets * 0.61 Catch Rate =>  83 receptions

TD Rate

Finally, let’s calculate TDs. Garcon’s career TD rate is 4 percent.

Floor: 107 targets  * 0.04 TD Rate =>  4.3 TDs

Ceiling: 143 targets * 0.04 TD Rate =>  5.7 TDs

Expected: 137 targets * 0.04 TD Rate =>  5.5 TDs

Final Projection

Garcon’s final back-of-the-napkin projection is:

Floor: 10.8

Ceiling: 14.4

Expected: 13.8

Let’s compare it to the Sim App:

The Sim Score App, which uses historical comps to derive its projections, is in general agreement with our back-of-the-envelope forecast. The air yards model is influenced significantly by the efficiency of the QB with whom the WR is paired. The efficiency metric I used, depth adjusted yards and completions over expectation, captures the QB’s accuracy and ability to hit his targets on time – areas where Cousins shines. While San Francisco has a very serviceable QB in Hoyer, Cousins is a cut above. This is a large reason for the relatively bullish air yards model projection of 15.1 ppg.

Conclusion

Garcon is an intriguing pick this year. There are scenarios where he could potentially be a league winner. Those scenarios, however, are very unlikely in my estimation. They entail San Francisco being efficient on offense by extending drives and creating more plays and opportunities per game; they entail Garcon adding at least seven percentage points to his target share average of the previous three seasons, and they assume Kyle Shanahan will play each game to win rather than to keep the score close. Because he is so volume dependent, if any of these assumptions are unrealized, Garcon will turn back into a pumpkin. Moreover, Garcon is not getting cheaper. Since late April his ADP has shot up 25 slots.

At Garcon’s current ADP of WR 36, I think he is probably fairly priced. I do not think there is upside significant enough to warrant taking him too much higher. He is a fine WR4 for your fantasy team, but I struggle to see a scenario where he is a consistent week winner.

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1. The air yards model combined four separate machine learning models into one ensemble model. The models used included a Bayesian neural net, a nearest neighbors algorithm, a random forest algorithm and a tuned generalized linear model. Preprocessing steps included data normalization (centered and scaled), the elimination of near zero variance predictors, and principal component analysis transformation. The model data used were 838 observations of 35 predictors, including age, weight, previous season player volume and efficiency data, 3 year weighted player efficiency data, and previous season team passing volume and efficiency data. The dependent variable was fantasy points per game. The data were split 80/20 into training and test sets. 10 fold cross validation was performed. The measured R^2 on the held out data was 0.66.  (back)
2. as of mid-June 2017  (back)