# How to Project Receiving Yards in 2015

A method to come up with a starting point for your wide receiver projections in 2015.

During the last two preseasons, I evaluated the best way to come up with the starting point for your wide receiver projections.  While many start their projections by using last year's rankings, I don't believe that such a method is the best approach.  That data is part noise, part signal: a receiver's ability may be pretty constant from year-to-year, but other factors are much less reliable. If we can isolate the most relevant data, we'll be in a better position to predict what will happen in 2015.

This analysis looks at three variables:

• The number of pass attempts by each receiver's team;
• The percentage of his team's passes that go to him; and
• The receiver's average gain on passes that go to him.

Instead of starting with receiving yards, let's break down receiving yards into the following components:

Receiving yards = (Receiving Yards/Target) x (Targets/Team_Pass_Att)  x  Team_Pass_Att.

Last year, T.Y. Hilton's Colts led the NFL in pass attempts with 660; Demaryius Thomas led the league in targets at 184, and Thomas also was number one in percentage of team targets at 30.3% (although Andre Johnson, with just 146 targets, was only a hair behind Thomas at 30.2%). Meanwhile, DeSean Jackson finished first in yards per target at 12.3. In some ways, deciding among Hilton, Thomas, and Jackson turns into a question as to which variable is most likely to repeat itself in 2015. Now, each player has different risks and circumstances entering this year, but the larger question holds true: do you want the guy on the pass-happy offense, the target hog, or the most "efficienct" receiver per target?

Alternatively, regression to the mean principles could have you high on a receiver who had an unusually low number in one of these three metrics. The Seahawks were last in pass attempts last year; an increase could be good news for Doug Baldwin or Jermaine Kearse (or, relative to expectations, Jimmy Graham). Minnesota's Greg Jennings saw the fewest targets of any number one wide receiver last year; that means there's opportunity for growth for Vikings receivers, whether it's Mike Wallance, Charles Johnson, or even someone else. Finally, Andre Johnson and Keenan Allen finished last among receivers in yards per target (minimum 700 receiving yards). Knowing which of those three statistics - pass attempts, percentage of the pie, and yards per target - is most likely to regress to the mean is very helpful when it comes to making fantasy projections.

## Team Pass Attempts

How sticky are team pass attempts from year to year? I previously broke "Pass Attempts" down into two variables: Offensive Plays and Pass Attempts per Offensive Play (excluding sacks from the numerator but including them in the denominator). We can use those two variables to predict future pass attempts. The best fit formula to predict team pass attempts based on historical data is:

Future Pass Attempts = 36 + (450 x Pass_Attempts/Play) + (0.255 x Offensive Plays)

What does that mean for 2015? I took last year's stats and applied that formula, which gives us a best-guess projection for 2014 in terms of number of pass attempts. Here's how to read the table: In 2014, the Colts ranked 1st in pass attempts, and threw a pass on 62.4% of all offensive plays. Indianapolis ran a whopping 1,105 plays and threw 661 passes, but in 2015, the Colts are projected for "only" 599 passes, a decline of 62 attempts.

RkTeam2014 PA/P2014 Off Plays2014 Pass2015 Proj PassDiff
1 Indianapolis Colts 62.4% 1105 661 599 -62
2 New Orleans Saints 62.9% 1095 659 598 -61
3 Atlanta Falcons 64.1% 1035 632 588 -44
4 Oakland Raiders 66.1% 994 629 587 -42
5 Philadelphia Eagles 57.9% 1127 621 584 -37
6 Pittsburgh Steelers 60.4% 1068 612 580 -32
7 New England Patriots 59.2% 1073 609 576 -33
7 Chicago Bears 64.7% 1005 609 583 -26
9 Denver Broncos 58.5% 1067 607 571 -36
9 New York Giants 58.7% 1086 607 577 -30
11 Detroit Lions 62.1% 1045 604 582 -22
12 Miami Dolphins 61.6% 1040 595 579 -16
13 Buffalo Bills 60.6% 1020 579 569 -10
14 San Diego Chargers 60.6% 1009 574 566 -8
15 Arizona Cardinals 60% 993 568 559 -9
16 Jacksonville Jaguars 63.6% 988 557 574 17
17 Baltimore Ravens 56.1% 1021 554 549 -5
18 Washington Redskins 60.1% 1006 547 563 16
19 Carolina Panthers 55.4% 1060 545 555 10
20 Green Bay Packers 56.5% 1001 536 546 10
21 Tampa Bay Buccaneers 62.3% 936 531 555 24
22 Minnesota Vikings 57.9% 981 517 547 30
23 St. Louis Rams 58.7% 957 515 544 29
24 Tennessee Titans 61.3% 919 513 546 33
25 Cincinnati Bengals 51.7% 1018 503 528 25
26 Cleveland Browns 52.8% 1010 502 531 29
27 New York Jets 51.8% 1052 498 537 39
28 Kansas City Chiefs 56.3% 962 493 535 42
29 San Francisco 49ers 53.4% 1009 487 534 47
30 Houston Texans 48.1% 1062 485 523 38
31 Dallas Cowboys 49.9% 1014 476 519 43
32 Seattle Seahawks 48.6% 1021 454 515 61

The takeaway here is that regression to the mean is real: in 2013, Cleveland led the way with 681 pass attempts. Last year, this formula said to downgrade that total significantly, to 595 pass attempts.  As it turns out, that wasn't enough: the Browns shot right passed the mean and finished with barely over 500 pass attempts.  This formula doesn't change the order of things very much, but it does shrink the distribution. While teams that were extremely run or pass-heavy in 2014 could repeat that fact in 2015, there's a great deal of uncertainty in predicting that to happen, and that uncertainty should be baked into your projections.

## TARGETS PER TEAM PASS ATTEMPT

How "sticky" is the percent of targets each receiver sees from year to year? The best-fit formula for WRs who play in at least 14 games and see 100+ targets is:

Future Percentage of Targets = 6.2% + 71.3% x Past Percentage of Targets

What does that mean? There is some regression to the mean, but not too much: for the most part, the piece of the pie that a receiver sees is pretty consistent from year to year. There were 30 wide receivers who played in at least 14 games and saw 100+ targets in 2014, and are back with the same team in 2015. The table below shows the percentage of team targets this formula would tell us to project for those players for 2014.  Here's how to read it: Demaryius Thomas led this group of players by seeing a target on 30.3% of his team's passes; based on the above formula, we only project a slight downgrade of 2.5%, to 27.8%, in 2015.

RkWide ReceiverTeam2014 Targets2014 Team Pass Att2014 Tar/TPA2015 ProjDiff
1 Demaryius Thomas DEN 184 607 30.3% 27.8% -2.5%
2 Antonio Brown PIT 181 610 29.7% 27.4% -2.3%
3 Dez Bryant DAL 136 476 28.6% 26.6% -2%
4 Jordy Nelson GB 151 536 28.2% 26.3% -1.9%
5 Anquan Boldin SF 131 486 27% 25.4% -1.5%
6 Vincent Jackson TB 142 531 26.7% 25.3% -1.5%
7 Kelvin Benjamin CAR 145 545 26.6% 25.2% -1.4%
8 DeAndre Hopkins HOU 127 484 26.2% 24.9% -1.3%
9 Julio Jones ATL 163 632 25.8% 24.6% -1.2%
10 Steve Smith BAL 134 555 24.1% 23.4% -0.7%
11 Alshon Jeffery CHI 145 609 23.8% 23.2% -0.6%
12 Golden Tate DET 143 602 23.8% 23.1% -0.6%
13 Randall Cobb GB 127 536 23.7% 23.1% -0.6%
14 Emmanuel Sanders DEN 141 607 23.2% 22.8% -0.5%
15 Mike Evans TB 123 531 23.2% 22.7% -0.4%
16 Eric Decker NYJ 114 497 22.9% 22.6% -0.4%
17 Andrew Hawkins CLE 112 503 22.3% 22.1% -0.2%
18 Sammy Watkins BUF 128 579 22.1% 22% -0.1%
19 Julian Edelman NE 134 610 22% 21.9% -0.1%
20 Keenan Allen SD 121 573 21.1% 21.3% 0.1%
21 Rueben Randle NYG 127 607 20.9% 21.1% 0.2%
22 T.Y. Hilton IND 131 660 19.8% 20.4% 0.5%
23 Roddy White ATL 124 632 19.6% 20.2% 0.6%
24 Brandon LaFell NE 119 610 19.5% 20.1% 0.6%
25 Pierre Garcon WAS 105 547 19.2% 19.9% 0.7%
26 Jarvis Landry MIA 112 595 18.8% 19.6% 0.8%
27 Larry Fitzgerald ARI 103 568 18.1% 19.1% 1%
28 John Brown ARI 103 568 18.1% 19.1% 1%
29 Robert Woods BUF 104 579 18% 19% 1%
30 Jordan Matthews PHI 103 622 16.6% 18% 1.4%

As you can see, we shouldn't project too much variation from last year's target ratios absent other information. Of course, for all players, we need to incorporate new information.  Jordan Matthews is at the bottom of this list, but he was a rookie last year, and the Eagles no longer have Jeremy Maclin around. That means Matthews is likely to see an increase in targets this year.

## YArds per TARGET

Compared to "percentage of team targets", yards per target isn't a very "sticky" metric. And that makes sense. Targets are a good thing and are indicators of talent and ability. Punishing a player for a "failed" target doesn't make a lot of sense, and it doesn't help you predict the future. By putting targets in the denominator, you're implying that a target that yields no yards is a bad thing, but in fact, the opposite is often the case. On a given incomplete pass, the targeted receiver may have done the best job of the players on the field, and that makes him more likely to see targets in the future.

The best-fit formula to project future Yards/Target from past Yards/Target:

Future Yards/Target = 5.5 + 0.29 x Past Yards/Targets

This formula tells us that receivers retain only 29% of their Yards/Target value, whether positive or negative. For each receiver, we're better off taking a league baseline number -- here, 5.5 -- and then adding 29% of the actual Yards/Target from the receiver last year. Here's what this means for our 30 receivers in 2015:

RkWide ReceiverTeam2014 Targets2014 Yds2014 Yards/Target2015 ProjDiff
1 T.Y. Hilton IND 131 1345 10.3 8.5 -1.8
2 Randall Cobb GB 127 1287 10.1 8.4 -1.7
3 Jordy Nelson GB 151 1519 10.1 8.4 -1.6
4 Emmanuel Sanders DEN 141 1404 10 8.4 -1.6
5 Julio Jones ATL 163 1593 9.8 8.3 -1.4
6 Dez Bryant DAL 136 1320 9.7 8.3 -1.4
7 DeAndre Hopkins HOU 127 1210 9.5 8.3 -1.3
8 Antonio Brown PIT 181 1698 9.4 8.2 -1.2
9 Golden Tate DET 143 1331 9.3 8.2 -1.1
10 Demaryius Thomas DEN 184 1619 8.8 8.1 -0.7
11 Mike Evans TB 123 1051 8.5 8 -0.6
12 Jordan Matthews PHI 103 872 8.5 8 -0.5
13 Eric Decker NYJ 114 962 8.4 7.9 -0.5
14 Anquan Boldin SF 131 1062 8.1 7.9 -0.3
15 Brandon LaFell NE 119 953 8 7.8 -0.2
16 Steve Smith BAL 134 1065 7.9 7.8 -0.1
17 Alshon Jeffery CHI 145 1133 7.8 7.8 0
18 Sammy Watkins BUF 128 982 7.7 7.7 0.1
19 Larry Fitzgerald ARI 103 784 7.6 7.7 0.1
20 Roddy White ATL 124 921 7.4 7.7 0.2
21 Rueben Randle NYG 127 938 7.4 7.6 0.3
22 Andrew Hawkins CLE 112 825 7.4 7.6 0.3
23 Julian Edelman NE 134 972 7.3 7.6 0.3
24 Pierre Garcon WAS 105 752 7.2 7.6 0.4
25 Vincent Jackson TB 142 1002 7.1 7.5 0.5
26 Kelvin Benjamin CAR 145 1008 7 7.5 0.6
27 Jarvis Landry MIA 112 758 6.8 7.5 0.7
28 John Brown ARI 103 696 6.8 7.5 0.7
29 Robert Woods BUF 104 699 6.7 7.4 0.7
30 Keenan Allen SD 121 783 6.5 7.4 0.9

T.Y. Hilton led this group of receivers in yards per target last year, which means he comes with more risk than you might think.  DeSean Jackson, who played in only 13 games last year, is projected to see his Yards/Target ratio drop from 12.3 to 9.1 using this formula.  Keenan Allen, who is at the bottom of this table, is a good example of the variability of this metric: Allen averaged 10.1 yards per target in 2013.

## CONCLUSION

Now, what if we put it all together? Remember, we can define Receiving Yards as Team Pass Attempts multiplied by (Targets/Team Pass Attempt) multiplied by (Receiving Yards/Target). Since we now have projections for each of these 30 receivers in each of these three categories, we can come up with a baseline projection of 2015 receiving yards.  An obvious candidate to fall in 2015 is Hilton, who played for the most pass-happy team in the NFL last year and who relied on a huge yards/target average.  He ranked 6th in receiving yards in 2014, but the projections here drop him to 14th among the 30 receivers who (1) saw at least 100 targets, (2) played in at least 14 games last year, and (3) are remaining with the same team in 2015.

RkWide ReceiverTeam2014 Yds2015 Proj TPA2015 Proj Targ/TPA2015 Proj Yd/Tar2015 Proj Rec YdsDiff
1 Antonio Brown PIT 1698 580 27.4% 8.2 1304 -394
2 Demaryius Thomas DEN 1619 571 27.8% 8.1 1279 -340
3 Jordy Nelson GB 1519 546 26.3% 8.4 1208 -311
4 Julio Jones ATL 1593 588 24.6% 8.3 1205 -388
5 Dez Bryant DAL 1320 519 26.6% 8.3 1147 -173
6 Golden Tate DET 1331 582 23.1% 8.2 1104 -227
7 Emmanuel Sanders DEN 1404 571 22.8% 8.4 1090 -314
8 DeAndre Hopkins HOU 1210 523 24.9% 8.3 1076 -134
9 Anquan Boldin SF 1062 534 25.4% 7.9 1066 4
10 Randall Cobb GB 1287 546 23.1% 8.4 1064 -223
11 Vincent Jackson TB 1002 555 25.3% 7.5 1058 56
12 Kelvin Benjamin CAR 1008 555 25.2% 7.5 1050 42
13 Alshon Jeffery CHI 1133 583 23.2% 7.8 1049 -84
14 T.Y. Hilton IND 1345 599 20.4% 8.5 1033 -312
15 Mike Evans TB 1051 555 22.7% 8 1006 -45
16 Steve Smith BAL 1065 549 23.4% 7.8 1003 -62
17 Sammy Watkins BUF 982 569 22% 7.7 965 -17
18 Eric Decker NYJ 962 537 22.6% 7.9 963 1
19 Julian Edelman NE 972 576 21.9% 7.6 958 -14
20 Rueben Randle NYG 938 577 21.1% 7.6 931 -7
21 Roddy White ATL 921 588 20.2% 7.7 909 -12
22 Brandon LaFell NE 953 576 20.1% 7.8 906 -47
23 Andrew Hawkins CLE 825 531 22.1% 7.6 895 70
24 Keenan Allen SD 783 566 21.3% 7.4 887 104
25 Pierre Garcon WAS 752 563 19.9% 7.6 848 96
26 Jarvis Landry MIA 758 579 19.6% 7.5 848 90
27 Jordan Matthews PHI 872 584 18% 8 837 -35
28 Larry Fitzgerald ARI 784 559 19.1% 7.7 824 40
29 Robert Woods BUF 699 569 19% 7.4 806 107
30 John Brown ARI 696 559 19.1% 7.5 798 102

What stands out?

• Hilton is definitely on the Buyer Beware list, although his ADP is not out of control (right now, it's only WR12). Because he has never been a big touchdown guy, the fantasy community isn't quite as bullish on him as you might think for the #1 wide receiver in a offense that's expected to lead the league in passing yards. Having Andrew Luck helps, but adding a targets hog in Andre Johnson is yet another reason to be concerned about Hilton. The counter here is that a lot of skepticism seems to be baked into Hilton's ADP, and it's very easy to envision Andrew Luck having another monster year (and Hilton reaping those rewards).
• The top four players on the above list are projected to have big declines, but that's just normal regression-to-the-mean principles at work. More interesting, I think, is someone like Dez Bryant, who jumps to 5th because he played on such a run-heavy team last year. Then again, because of his scoring prowess, Dez isn't being underrated by anyone these days.
• Someone who might be underrated is Anquan Boldin. The veteran was responsible for a whopping 27% of all 49ers targets last year, and San Francisco is expected to pass more in 2015 as a result of both regression to the mean and a weaker defense. If the 49ers throw 550 passes, and Boldin continues to see a huge chunk of targets, he's going to be a fantasy steal (current ADP of WR44).
• DeAndre Hopkins is another interesting player to watch.  The Texans were absurdly run-heavy last year, and that's going to bounce back in 2015. Add in the departure of Andre Johnson, and Hopkins could see a significant uptick in targets in 2015.
• Another veteran to keep an eye on is Vincent Jackson.  He was a target hound last year but fared poorly in terms of yards per target: of these 30 wide receivers, he ranked 6th in the former category and 6th from the bottom in the latter. Adding a new quarterback in Jameis Winston could be all Jackson needs to solve his yards per target issues. With an ADP of WR29, the fantasy community is not exactly betting on a rebound for Jackson.  Kelvin Benjamin is in a similar boat: he ranked 7th in percentage of team targets and 5th from the bottom in yards per target. Expect more out of the Panther in his second season.