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.
| Rk | Team | 2014 PA/P | 2014 Off Plays | 2014 Pass | 2015 Proj Pass | Diff |
|---|---|---|---|---|---|---|
| 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.
| Rk | Wide Receiver | Team | 2014 Targets | 2014 Team Pass Att | 2014 Tar/TPA | 2015 Proj | Diff |
|---|---|---|---|---|---|---|---|
| 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:
| Rk | Wide Receiver | Team | 2014 Targets | 2014 Yds | 2014 Yards/Target | 2015 Proj | Diff |
|---|---|---|---|---|---|---|---|
| 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.
| Rk | Wide Receiver | Team | 2014 Yds | 2015 Proj TPA | 2015 Proj Targ/TPA | 2015 Proj Yd/Tar | 2015 Proj Rec Yds | Diff |
|---|---|---|---|---|---|---|---|---|
| 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.
