Let me start with the prevailing few: targets are important, and if two receivers have the same production on a different number of targets, the one who produced on fewer targets is better/more valuable. Similarly, if all else is equal, the receiver with a higher catch rate — calculated as catches/target — is the better/more valuable one.
There are some problems with the prevailing view. By placing targets in the denominator of a formula, we’re implying that targets are a bad thing, or at a minimum, an opportunity wasted. But targets aren’t like pass attempts. Pro Football Focus has a stat called yards per pass route run, and that actually is the receiver version of yards per pass attempt.1
But targets don’t help identify the player who deserves blame: on a random incomplete pass, assume three receivers are running routes, and one of them is targeted. Absent a drop, I have a hard time saying that of the three wide receivers, the targeted one did the worst of the three. If we grade a receiver by his yards per route run, each receiver is equally penalized with one route run on the play; if we grade a receiver by yards per target, the two wide receivers that did not get open are not penalized, while the one that was targeted is penalized. That seems fundamentally wrong to me.
Here’s another problem: In a broad sense, the player with more targets (or percentage of his team’s targets) is in a very real sense a bigger part of his team’s offense. Either he’s open more often, or the quarterback is throwing in his direction even when he’s not open (whether because the coaches call more plays for him or because he’s earned the quarterback’s trust). In any event, the target itself is an indicator of quality, and penalizing a player — which is what you do when you place targets in the denominator — for an event that is highly correlated with quality is not something I’m comfortable doing.
I ran a regression to predict the number of catches a receiver would record in a season based on his number of receptions and targets the prior season. I looked at all receivers who:
- From 2000 to 2011, had at least 500 yards and played in 8 games; and
- Played for the same team in the next season and played in at least 8 games
There were 726 receivers who met those criteria. I then pro-rated all player seasons to 16 games. Running a regression to predict Year N+1 receptions, the best-fit formula (with an R^2 of 0.37) was:
13.3 + 0.484*Rec_YrN + 0.164*Targ_YrN
That indicates that targets are a decent predictor of future receptions, and would imply that penalizing receivers for failed targets may not be wise. Let’s use Larry Fitzgerald as an example, because he represents an extreme example of high number of targets/low number of receptions. Fitzgerald had 156 targets but only 71 receptions. This formula would predict him to have 73 receptions next year. If Fitzgerald had instead caught his 71 passes on a more typical number of targets — say 120 — he would be projected to have 67 receptions in 2013. On the other hand, Andre Johnson, who had 112 catches on 162 targets, is projected for 94 catches. If Johnson had caught his 112 passes on 200 targets, he’d be projected for an even 100 catches next year.
What if we look at yards instead of receptions? Using the same sample of receivers, the best-fit formula (also with an R^2 of 0.37) was:
124.0 + 0.577*RecYd_YrN + 1.690*Targ_YrN
As above, the p-value on the target variable was statistically significant at the p = 0.01 level. Let’s use Indianapolis’ Donnie Avery as an example. Avery had 781 receiving yards on 123 targets, for a poor 6.3 yards/target average. This formula projects him to have 783 yards next year. Had Avery instead averaged 9.0 yards per target in 2011, he would be projected for only 721 yards in 2013.
What about Denver’s Demaryius Thomas? He had 1,438 receiving yards on only 141 targets; yes, it sure helps to have Peyton Manning throwing you the ball (Thomas averaged 7.9 yards per target in 2011 with Tim Tebow). Because he averaged over 10 yards per target, this formula doesn’t love his 2013 prospects: he’s projected to have only 1,192 yards next year. Much of that is due to general regression, but had he been targeted 170 times instead of 141 times, and gained the same number of yards, he’d be projected for 1,241 yards next year. Not a significant difference, but a small effect.
So what do we make of this? Let’s look at Tampa Bay’s Mike Williams. In each of his first three seasons (despite wildly different years from Josh Freeman), Williams has finished the year with a roughly 50% catch rate. I don’t think this means that Williams is a bad receiver. A low catch rate could be a sign that a player is being targeted frequently because he’s doing his job better than the other receivers, and he’s being targeted even when he’s not open. Other times a low catch rate is simply a reflection of how he’s used in the offense. Or the side effect of being saddled with an inaccurate passer.
Vincent Jackson had a 49% catch rate last year; among the other Buccaneers with over 200 yards, Doug Martin had a 70% catch rate, Dallas Clark was at 63%, and Tiquan Underwood was at 51%. The obvious takeaway is that the Bucs tried to use their receivers to make big plays, but used the running back and tight ends on safer plays — both Clark and Martin averaged fewer than ten yards per completion. Mike Williams averaged fewer yards per target (7.9) than Vincent Jackson (9.4), and saw fewer targets as well. In that case, it’s safe to say he’s not as good or as valuable as Jackson. But trying to extrapolate Williams’ catch beyond lands you in risky territory.
- Unfortunately, yards per pass route run is not going to help us if we want to grade receivers on a historical basis. [↩]