Totally VIP Fantasy Adds: Week Twelve

It goes without saying that the typical reader of Advanced NFL Stats is what's frequently referred to as "VIP material." Power meetings abound, followed by erotic liaisons on private yachts, followed by ever more powerful meetings, followed by even more erotic liaisons on even larger private yachts. It's tiring business, indeed.

Against this backdrop of fast living, the reader attempts to maintain an elite fantasy football team. Yet, it's not always possible to make the necessary mid-week waiver-wire moves. So the reader finds himself at week's end -- perhaps even on Sunday morning -- with an injured quarterback here, an ineffective wide receiver there, and neither the time nor inclination to research all the available players duly.

This is where Advanced NFL Stats can help: below are a couple of options at each of the typical fantasy-football positions that are likely to perform better than what we'd expect from a typical a freely available player. Each player named below is owned in fewer than 50% of Yahoo leagues, making it quite possible that at least one of them is available in the reader's league.

What sort of performance might the reader expect from the following players? Obviously, it's impossible to answer this question with any certainty; however, in the interest of full transparency, I've included a list of all the picks from the first three weeks of this experiment at the bottom of this post.

Here's a summary of the average points by position over that same three-week span (using Yahoo's default scoring, minus the last four categories, which are largely random):


And here are this week's picks:

Fantasy Adds for Week Eleven

For the third week in a row, we offer this modest attempt at fantasy analysis.

This week offers two games, Kansas City at New England and Tampa Bay at Green Bay, with promising fantasy possibilities, as the favored team in both cases (the Patriots and Packers) also has a poor defense (26th and 28th, respectively, per GWP). As such, when the Chiefs and Bucs turn to the pass in low-WP situations, they're likely to be more efficient than we might otherwise expect.

I'd also like to make a note on the timing of this piece: a number of commenters have noted that Saturday might be too late in the week to take advantage of fantasy analysis. The results from a poll I ran last Sunday show that about 20% of readers need to make waiver-wire adds before Saturday. If possible, I'll attempt to run this closer to mid-week in the future; however, for the remaining 80% of readers, there's also some advantage to having all the information that a week of practice and reporting can provide.

Finally, don't hesitate to make comments below, or take time to point and laugh at last week's picks.

Quarterback
The weekly game probabilities suggest that Kansas City has only an 8% chance of beating New England -- nor does that even account for the fact that starting quarterback Matt Cassel will miss the game after undergoing hand surgery on Monday. However, with New England likely to take an early lead, Cassel's replacement Tyler Palko (5% owned) is likely to compile a pretty substantial number of pass attempts. The fact also remains that New England has the seventh-worst defensive GWP and allows the fifth-most net yards per pass attempt (7.3) in the league. Another option at QB is Philadelphia's Vince Young (6% owned), who'll be replacing the injured Michael Vick against the Giants. Young has the capacity to gain a fair number of rushing yards, which are generally worth more than the passing kind in fantasy football.

Poll: What's the Last Day for Adds in Your Fantasy League?

A couple of commenters -- in response to the fantasy pieces that have appeared here each of the past two Saturdays -- have noted that their waiver-wire deadlines occur before Saturday, greatly diminishing the relevance of the analysis therein.

So, a question for our readers-cum-fantasy-owners: what's generally the last day of the week on which you're allowed to make waiver-wire pick-ups? Feel free to add any relevant notes in the comments section.

Fantasy Adds for Week Ten

Last Saturday, we took our first (entirely modest) step towards providing fantasy advice that's also in line with the defining principles of the work here at Advanced NFL Stats.

Some early comments on that Saturday piece led to research in the area of pass/run distribution by Win Probability (WP), with the discovery that a team's play-calling is pretty seriously informed by their in-game WP -- potentially valuable information for the fantasy owner.

Further work in this area last Tuesday by Chase Stuart of the NY Times' Fifth Down blog gives us a portait of every team's "platonic" run/pass mix -- that is, run/pass mix stripped of game state.

In what follows, I've attempted to use these concepts towards the end of a More Informed Fantasy Analysis. Below, I've listed (at least) one player at each of the main fantasy positions who's (a) owned in fewer than 50% of Yahoo leagues and (b) likely to have some fantasy value this weekend. As per usual, don't hesitate either to ask questions or simply harass me in the comments

Quarterback
Matt Hasselbeck is actually available in 55% of leagues (i.e. slightly above the 50% threshold), but the next-best alternative, John Beck (8%), appears to have only a tenuous grasp on the starting job in Washington. Hasselbeck has actually been pretty efficient, having posted a 104 NY/A+ this season. With the collapse of the Titan running game (Tennessee has a league-low 30.9% run success rate), the team has skewed pass heavy -- and will likely be passing late against a favored (0.62 PROB) Carolina team.

Run/Pass Distribution by Win Probability

One question that came about as a result of Saturday's very modest attempt at fantasy football analysis -- a question particularly in the wheelhouse of the present site -- concerned the relationship between run/pass distribution and game situation. Because we know that a team with a lead will try to use the game clock to its advantage -- and because, with few exceptions, the run is the best way to maximize time of possession -- it follows that a team with a lead will call more run plays. This idea is nothing new.

What I haven't seen before, however -- and it could very well be from a lack of looking -- is an illustration of the precise relationship between run/pass distribution and in-game win probability (WP).

Thanks to Mr. Brian Burke, that thing now exists, right here, in table form (numbers from 2006 though Week Eight of 2011):

And here, once again, in graph form:

Five Fantasy Adds for Week Nine

This week's edition of The Weekly League isn't The Weekly League, at all, but rather an experiment in nerd-centric fantasy analysis.

Fantasy Notes for Week Nine
Introduction
There are a lot of sites with a lot of fantasy football experts on them -- and, likely, many of those experts are good at what they do. What follows merely represents an attempt to apply some of the concepts central to Advanced NFL Stats -- especially, for example, our emphasis on separating repeatable skills from those acts which are more the product of variance -- to the world of fantasy football.

Below I've listed one player at each of the main fantasy positions who's (a) owned in fewer than 50% of Yahoo leagues and (b) likely to have some fantasy value this weekend. Don't hesitate either to ask questions or simply harass me in the comments.

Quarterback
Indy's Curtis Painter (11% owned) and Kansas City's Matt Cassel (44%) both play at home against teams (Atlanta and Miami, respectively) with poor defensive ratings per GWP -- and the pair have identical net yards per pass averages, too (at 6.0). If Painter has an edge it's that there's a non-zero possibility that he could add production via the rush. Last week he ran for 79 yards on seven attempts. That's merely one data point, yes, but rushing yards are more valuable in most fantasy formats, which makes it a data point of interest.

A Fantasy League Minus The Randomness?

Carson Cistulli, contributor-extraordinaire at my favorite baseball site Fangraphs, wrote me to ask an interesting question. He wanted to know how someone would set up a fantasy football league using commonly available scoring options that would remove most of the luck and leave mostly the results of skill. Personally I think the randomness of sports, including fantasy sports, is part of what makes them so compelling. But draining a lot of the randomness from a fantasy league would be an interesting experiment. Here's what I suggested:

"First, turnovers are extremely random. The correlate weakly from week to week and even less from season to season. Especially on the defensive side of the ball. I would exclude turnovers from your scoring (including TDs directly occurring on turnover plays).

2010 Koko Fantasy Rankings - WRs

Next up in the 2010 Koko fantasy rankings are the WRs. These projections are intended to establish the baseline minimum accuracy as the most reasonably naive predictions. The general explanation of the system along  can be found in the 2009 QB post. Details on how WRs are projected can be found in the 2009 WR post.

One thing to note--This year I included an estimate of receptions per game for those whose scoring system credits each catch. I did not add them into the total projected points column, but it's easy enough to do on your own. The TE rankings did the same thing.

2010 Koko Fantasy Rankings - TEs

The next installment of the 2010 Koko fantasy rankings are the TEs. These projections are intended to establish the baseline minimum accuracy as the most reasonably naive predictions. The general explanation of the system along  can be found in the 2009 QB post. Details on how TEs are projected can be found in the 2009 TE post.

2010 Koko Fantasy Rankings - RBs

The next installment of the 2010 Koko fantasy rankings are the RBs. These projections are intended to establish the baseline minimum accuracy as the most reasonably naive predictions. The general explanation of the system along  can be found in the 2009 QB post. Details on how RBs are projected can be found in the 2009 RB post.

I tried to remove guys who are injured for the year or who have retired, but I may have missed someone. If you find one, just remember the classic line from Major League when Coach Lou Brown was given the list of players from the management. "This guy here is dead." "Well scratch him off then!"

2010 Koko Fantasy Rankings - QBs

It's that time of year. I suppose some of you have been running mock drafts for weeks already, but for the rest of us it's just now time to start thinking about our 2010 fantasy roster. The first installment of the Koko rankings is for QBs.

These projections are intended to establish the baseline minimum accuracy as the most reasonably naive predictions. The general explanation of the system along with details of the regression plots can be found in the 2010 QB post.

Koko's 2009 Fantasy Report Card

Last August Advanced NFL Stats ventured into the fantasy world with its own player projections. But these projections weren’t what you might expect. Instead of trying to develop a complex and advanced projection system, the Koko system projected fantasy performance using the simplest rules reasonably possible.

The Koko projections, named for George Costanza’s simian nickname from his tenure at Kruger Industrial Smoothing, are what a monkey might guess given the typical regression rates from one season to the next. Koko is a (bad) rip-off of the Marcel baseball projections created by Tom Tango. Instead of competing with other projections, the intention is to establish a minimum baseline of predictive power against which other projections can be measured. Further, Koko tests whether other systems are really worth all the additional analysis, subjective and objective, that goes into them.

Koko Fantasy Rankings - Defense

Last but not least are Koko the Monkey's team defense rankings. In case you aren't familiar with the Koko rankings, they are the simplest projections possible based on previous-year performance. These are intended to serve as the baseline for the bare-minimum accuracy we should expect from all other fantasy projections. A full explanation can be found in the write-up for the QB rankings.

Defense point totals can vary widely depending on your league's scoring rules. For these rankings, I used turnovers, sacks, defensive TDs, and points allowed. Year-to-year correlations for turnovers and defensive TDs are extremely weak, but sacks and points allowed can be projected relatively well. I ignored special teams-based scoring, which should not have any effect on the rankings, as special teams stats are notoriously random and unpredictable.

Here are how sacks and points allowed regress in case anyone is curious.



Here are the final rankings. Regarding fantasy scoring for points allowed, every league is different. I didn't do the math required to estimate how many times a team with a given point average would fall into the various scoring "bins." Instead, I looked at my own league and saw they (very) roughly assign half a point for every point allowed below league-average, which is about 20 pts. So that's what I did here.

Rank	Defense	Sks/G	Ints/G	Fum/G	Pts Allwd/G	TDs/G	Pts/G	Total
1	PIT	2.4	1.0	0.7	18.7	0.16	7.4	118.8
2	TEN	2.3	1.0	0.7	19.0	0.16	7.2	115.3
3	BAL	2.2	1.1	0.7	19.2	0.16	7.0	112.4
4	PHI	2.4	1.0	0.7	20.1	0.16	6.6	105.7
5	NYG	2.3	1.0	0.7	20.3	0.16	6.5	103.9
6	IND	2.1	1.0	0.7	20.3	0.16	6.3	100.0
7	MIA	2.3	1.0	0.7	20.8	0.16	6.2	99.6
8	WAS	2.0	1.0	0.7	20.3	0.16	6.2	98.6
9	NE	2.1	1.0	0.7	20.6	0.16	6.1	98.2
10	MIN	2.3	1.0	0.7	21.1	0.16	6.1	96.8
11	TB	2.1	1.0	0.7	20.9	0.16	6.1	96.8
12	CAR	2.2	1.0	0.7	21.0	0.16	6.0	95.7
13	ATL	2.2	1.0	0.7	20.9	0.16	6.0	95.3
14	DAL	2.5	1.0	0.7	21.8	0.16	5.9	93.7
15	NYJ	2.3	1.0	0.7	21.6	0.16	5.8	92.3
16	CHI	2.1	1.0	0.7	21.5	0.16	5.7	91.9
17	SD	2.1	1.0	0.7	21.4	0.16	5.7	91.1
18	BUF	2.0	1.0	0.7	21.3	0.16	5.6	90.1
19	CLE	1.9	1.0	0.7	21.5	0.16	5.6	89.6
20	JAX	2.1	1.0	0.7	21.8	0.16	5.5	87.5
21	GB	2.1	1.0	0.7	22.1	0.16	5.4	86.5
22	OAK	2.1	1.0	0.7	22.3	0.16	5.3	85.1
23	SF	2.1	1.0	0.7	22.1	0.16	5.3	85.1
24	CIN	1.9	1.0	0.7	21.8	0.16	5.3	85.1
25	SEA	2.2	1.0	0.7	22.4	0.16	5.2	83.8
26	NO	2.1	1.0	0.7	22.4	0.16	5.2	83.1
27	HOU	2.0	1.0	0.7	22.4	0.16	5.1	81.7
28	ARI	2.1	1.0	0.7	23.1	0.16	4.9	77.7
29	DEN	2.1	0.9	0.7	23.6	0.16	4.5	71.4
30	STL	2.1	1.0	0.7	24.0	0.16	4.4	70.6
31	KC	1.8	1.0	0.7	23.4	0.16	4.4	70.5
32	DET	2.1	0.9	0.7	25.1	0.16	3.8	60.0

Koko Fantasy Rankings - Kickers

The rankings everyone's been waiting for--kickers. Koko the fantasy football monkey is making his way through each position. In case you aren't familiar with the Koko rankings, they are the simplest projections possible based on previous-year performance. The rankings are intended to serve as the baseline for the bare-minimum accuracy we should expect from all other fantasy projections. A full explanation can be found in the write-up for the QB rankings.

Kickers are highly unpredictable, both in real terms and in fantasy terms. In fact, there is no year-to-year correlation in the fantasy points gained by field goal kicking. But there is some consistency in extra points, which has nothing to do with kicking skill and everything to do with the rest of the kicker's team.




With that in mind, Koko has ranked his kickers based on XPs alone. The bottom line is that kickers are generally interchangeable. Treat the kicker as a position you'll want to swap out later in the year once you get an idea of who's kicking a lot of FGs. For example, look at which teams have good defenses but can't put the ball in the end zone.

One result of relying only on XPs is that we're only interested in team TDs. Therefore, if a kicker is replaced on a team, Koko's projection remains for new kicker on that team. For example, Steve Houshka is replacing Matt Stover in Baltimore. His projection is what Stover's would have been had he remained on the team. To be honest, I'm not closely watching kicker news, so if there are other examples, or if there are guys on here who will be on the IR or out of a job, let me know.

Player	XP/G	FG Pts/G	Pts/G	Proj Pts
John Kasay	2.5	5.2	7.7	115.1
Mason Crosby	2.5	5.2	7.7	115.1
Nate Kaeding	2.5	5.2	7.7	115.1
David Akers	2.5	5.2	7.7	114.8
Neil Rackers	2.4	5.2	7.6	114.5
Adam Vinatieri	2.4	5.2	7.6	114.2
Jason Elam	2.4	5.2	7.6	113.9
Nick Folk	2.4	5.2	7.6	113.9
Jay Feely	2.4	5.2	7.6	113.7
Steven Houshka	2.4	5.2	7.6	113.5
Robbie Gould	2.4	5.2	7.6	113.5
Garrett Hartley	2.4	5.2	7.6	113.4
Dan Carpenter	2.3	5.2	7.5	113.2
Rob Bironas	2.3	5.2	7.5	113.2
Ryan Longwell	2.3	5.2	7.5	113.2
Stephen Gostkowski	2.3	5.2	7.5	113.2
Matt Prater	2.3	5.2	7.5	112.9
Kris Brown	2.3	5.2	7.5	112.3
Jeff Reed	2.3	5.2	7.5	112.0
Matt Bryant	2.2	5.2	7.4	111.6
Joe Nedney	2.2	5.2	7.4	111.3
Rian Lindell	2.2	5.2	7.4	111.3
Josh Scobee	2.2	5.2	7.4	111.0
Olindo Mare	2.1	5.2	7.3	110.0
Jason Hanson	2.0	5.2	7.2	108.5
Sebastian Janikowski	2.0	5.2	7.2	108.5
Shaun Suisham	2.0	5.2	7.2	108.5
Josh Brown	1.9	5.2	7.1	106.5
Phil Dawson	1.9	5.2	7.1	106.2
Shayne Graham	1.9	5.2	7.1	106.0

Koko Fantasy Rankings - Tight Ends

Tight ends are relatively easy to project. In addition to their steadily increasing prominence, the notable thing about TEs is how steep the fall-off is among the top several players. In case you aren't familiar with the Koko rankings, they are the simplest projections possible based on previous-year performance. The rankings are intended to serve as the baseline for the bare-minimum accuracy we should expect from all other fantasy projections. A full explanation can be found in the write-up for the QB rankings.

I'll cut right to the chase. Here are how tight end TDs per game and receiving yards per game regress. As you'd expect, yards are more consistent and predictable than TDs.



Fumbles are unpredictable, and hover at 0.2 fumbles per game. Top TEs tend to be relatively durable, and will play 14.5 games per year. Based on standard scoring of 6 points per TD, 1 point for 20 receiving yards, and -2 points for each fumble, here is how the projections stack up:

Rank	Player	TD/G	Yds/G	Fum/G	Pts/G	Total Pts
1	Tony Gonzalez	0.40	57.6	0.2	4.9	70.7
2	Dallas Clark	0.30	50.5	0.2	3.9	57.0
3	Antonio Gates	0.34	41.2	0.2	3.7	54.1
4	Jason Witten	0.24	52.7	0.2	3.6	52.9
5	Visanthe Shiancoe	0.32	36.2	0.2	3.3	48.1
6	Tony Scheffler	0.23	45.4	0.2	3.2	46.8
7	Kellen Winslow	0.26	40.3	0.2	3.2	45.8
8	Owen Daniels	0.18	48.6	0.2	3.1	45.1
9	John Carlson	0.26	37.6	0.2	3.1	44.3
10	Anthony Fasano	0.32	29.6	0.2	3.0	43.3
11	Greg Olsen	0.26	35.2	0.2	2.9	42.5
12	Chris Cooley	0.15	48.0	0.2	2.9	42.3
13	Kevin Boss	0.30	27.5	0.2	2.8	40.3
14	Zach Miller	0.15	44.6	0.2	2.7	39.8
15	Heath Miller	0.22	35.8	0.2	2.7	39.2
16	Dustin Keller	0.21	33.3	0.2	2.5	36.4
17	Bo Scaife	0.18	34.6	0.2	2.4	34.9
18	Billy Miller	0.15	37.2	0.2	2.4	34.6
19	Daniel Graham	0.24	26.6	0.2	2.3	33.9
20	David Martin	0.21	29.4	0.2	2.3	33.6
21	Donald Lee	0.26	22.5	0.2	2.3	33.4
22	Jeremy Shockey	0.13	38.4	0.2	2.3	33.0
23	Marcedes Lewis	0.18	31.2	0.2	2.2	32.5
24	L.J. Smith	0.23	25.5	0.2	2.2	32.4
25	Jerramy Stevens	0.19	29.6	0.2	2.2	32.0
26	Todd Heap	0.21	27.2	0.2	2.2	32.0
27	Martellus Bennett	0.24	21.7	0.2	2.1	30.3
28	Alex Smith	0.22	21.8	0.2	2.0	29.1
29	Vernon Davis	0.18	25.1	0.2	1.9	28.1
30	Robert Royal	0.15	25.9	0.2	1.8	26.4
31	Desmond Clark	0.15	25.5	0.2	1.8	26.0
32	Benjamin Watson	0.19	19.6	0.2	1.7	24.7

Koko Fantasy Rankings - Running Backs

Next up in Koko the monkey's fantasy rankings are the running backs. In case you aren't familiar with the Koko rankings, they are the simplest projections possible based on previous-year performance. The rankings are intended to serve as the baseline for the bare-minimum accuracy we should expect from all other fantasy projections. A full explanation can be found in the write-up for the QB rankings.

RBs are notoriously difficult to predict. While a starting QB can be expected to have the vast majority of his team's snaps, starting RBs often share snaps with backups. The dreaded RB-by-committee makes it difficult for a simple quantitative system to account for the varying expected number of carries each RB can get.

Still, the regression plots show us we can still project RB per-game production fairly well. I tried the projections two different ways. One way was to estimate production per run--yds per rush and TDs per rush--and then project the number of rushing attempts for each RB. The second method simply projected per game production--yds per game and TDs per game--without regard for the total number of carries. The two methods were equally accurate, so I chose the second, simpler method.

Koko WR Fantasy Projections

Here is the next installment of the Koko The Monkey's fantasy football projections. Wide receivers are ranked based on a regression from last year's stats.

These projections are intended to be establish the baseline minimum accuracy of projections as the most reasonably naive predictions. The general explanation of the system can be found in the post ranking quarterbacks.

WR fantasy performance is far simpler than QB fantasy performance. Receiving yards and touchdowns are the only driving factors. Rushing or kick returning yards are ignored in these WR projections. Receiving yards and TDs appear to regress at similar rates from year to year, so these projected rankings will likely be a straight regurgitation of last year's end-of-year rankings.

I included WRs with at least 20 receptions in a season in the analysis. Depending on how high the cutoff, or which stat you use (yards, games, receptions), the regression rate is different. In the end however, the overall rankings aren't significantly affected, and the projected points are only slightly different.



Wide Receivers don't appear to have any consistency in terms of fumbles or injuries. They average 0.05 fumbles per game, with guys who get a lot of receptions obviously having more opportunities. WRs play in an average of 14.3 games in a season regardless of how many they played the year before.

I've removed Burress and Harrison, but I didn't spend much time looking through the list to find guys who may have retired or who may already be on the IR.

Rank	Name	TDs	Yds	Fum	Pts/G	Proj Pts
1	Anquan Boldin	0.54	71.1	0.07	6.6	95.0
2	Larry Fitzgerald	0.47	72.8	0.05	6.4	91.3
3	Calvin Johnson	0.47	69.2	0.06	6.2	88.5
4	Andre Johnson	0.38	78.1	0.05	6.1	87.0
5	Steve Smith CAR	0.35	79.9	0.05	6.0	85.6
6	Greg Jennings	0.40	67.8	0.05	5.7	81.7
7	Roddy White	0.36	71.1	0.05	5.6	80.0
8	Randy Moss	0.45	57.4	0.06	5.5	78.1
9	Brandon Marshall	0.34	69.8	0.06	5.4	77.5
10	Terrell Owens	0.43	59.0	0.05	5.4	77.4
11	Antonio Bryant	0.36	66.2	0.05	5.3	76.5
12	Lance Moore	0.43	54.5	0.05	5.2	74.3
13	Marques Colston	0.36	61.0	0.05	5.1	73.3
14	Vincent Jackson	0.36	60.7	0.05	5.1	72.6
15	Reggie Wayne	0.33	62.4	0.05	5.0	71.9
16	Hines Ward	0.36	58.7	0.05	5.0	71.1
17	Dwayne Bowe	0.36	57.9	0.05	4.9	70.7
18	Kevin Walter	0.38	53.4	0.05	4.9	69.5
19	Bernard Berrian	0.36	55.8	0.05	4.8	69.2
20	Santana Moss	0.33	58.7	0.05	4.8	69.2
21	Justin Gage	0.38	52.3	0.05	4.8	68.7
22	Eddie Royal	0.32	58.7	0.05	4.7	67.8
23	Deion Branch	0.38	50.7	0.05	4.7	67.5
24	Derrick Mason	0.31	58.5	0.05	4.7	66.8
25	Donald Driver	0.31	57.6	0.05	4.6	66.4
26	Wes Welker	0.26	63.1	0.05	4.6	66.2
27	Laveranues Coles	0.36	51.6	0.05	4.6	66.1
28	Isaac Bruce	0.36	51.1	0.05	4.6	65.8
29	Muhsin Muhammad	0.31	54.3	0.05	4.5	63.9
30	T.J. Houshmandzadeh	0.29	55.8	0.05	4.5	63.7
31	Santonio Holmes	0.32	52.6	0.05	4.4	63.3
32	Lee Evans	0.26	57.8	0.05	4.4	62.3
33	Steve Breaston	0.26	57.3	0.05	4.4	62.2
34	Jerricho Cotchery	0.31	51.9	0.05	4.3	62.1
35	Matt Jones	0.25	57.6	0.05	4.3	61.6
36	Greg Camarillo	0.26	53.1	0.05	4.1	59.0
37	Braylon Edwards	0.26	52.5	0.05	4.1	58.8
38	DeSean Jackson	0.24	53.9	0.05	4.0	57.6
39	Chad Ochocinco	0.31	44.8	0.05	4.0	57.2
40	Torry Holt	0.26	49.7	0.05	4.0	56.7
41	Devery Henderson	0.26	49.6	0.05	4.0	56.5
42	Michael Jenkins	0.26	49.0	0.05	3.9	56.2
43	Anthony Gonzalez	0.29	44.8	0.05	3.9	55.3
44	Chris Chambers	0.33	39.9	0.05	3.9	55.2
45	Kevin Curtis	0.28	45.9	0.05	3.9	55.2
46	Donnie Avery	0.27	46.8	0.05	3.9	55.1
47	Malcom Floyd	0.31	41.5	0.05	3.8	54.8
48	Devin Hester	0.27	46.5	0.05	3.8	54.7
49	Ted Ginn Jr.	0.24	49.5	0.06	3.8	54.2
50	Mark Clayton	0.26	46.0	0.05	3.8	54.1
51	Antwaan Randle El	0.29	42.3	0.05	3.7	53.5
52	Amani Toomer	0.29	41.8	0.05	3.7	53.0
53	Nate Washington	0.26	43.6	0.05	3.7	52.4
54	Patrick Crayton	0.29	40.7	0.05	3.7	52.2
55	Josh Reed	0.22	47.4	0.05	3.6	51.6
56	Mark Bradley	0.29	39.1	0.05	3.6	51.2
57	Brandon Stokley	0.27	41.2	0.05	3.6	51.1
58	Dennis Northcutt	0.25	43.3	0.05	3.5	50.6
59	Bobby Wade	0.24	44.1	0.05	3.5	50.5
60	Bryant Johnson	0.26	40.5	0.05	3.5	50.2
61	Jerheme Urban	0.29	37.0	0.05	3.5	49.7
62	Brandon Lloyd	0.26	39.9	0.05	3.5	49.6
63	Domenik Hixon	0.24	42.4	0.05	3.5	49.5
64	Koren Robinson	0.25	40.1	0.05	3.4	49.2
65	Josh Morgan	0.29	36.1	0.05	3.4	49.1
66	Ike Hilliard	0.29	36.1	0.05	3.4	48.9
67	Hank Baskett	0.27	37.7	0.05	3.4	48.5
68	Roy Williams	0.27	37.4	0.05	3.4	48.3
69	Johnnie Lee Higgins	0.29	34.0	0.05	3.3	47.5
70	Steve Smith NYG	0.22	41.3	0.05	3.3	47.0
71	Davone Bess	0.22	40.8	0.05	3.2	46.4
72	Chansi Stuckey	0.27	34.6	0.05	3.2	46.3
73	Jabar Gaffney	0.24	37.7	0.05	3.2	46.2
74	Rashied Davis	0.24	36.8	0.05	3.2	45.5
75	Reggie Williams	0.26	33.9	0.05	3.2	45.5
76	Bobby Engram	0.19	42.6	0.05	3.2	45.5
77	Michael Clayton	0.22	39.5	0.05	3.2	45.4
78	Jason Avant	0.24	35.3	0.05	3.1	44.7
79	Arnaz Battle	0.19	41.2	0.05	3.1	44.6
80	James Jones	0.23	36.6	0.05	3.1	44.6
81	Shaun McDonald	0.22	36.8	0.06	3.1	43.8
82	Brandon Jones	0.22	37.0	0.05	3.1	43.7
83	Jordy Nelson	0.24	34.0	0.05	3.0	43.5
84	Dane Looker	0.25	32.7	0.06	3.0	43.3
85	Jason Hill	0.24	32.2	0.05	2.9	42.1
86	Justin McCareins	0.19	37.8	0.06	2.9	41.9
87	Harry Douglas	0.22	32.3	0.05	2.8	40.3
88	Roscoe Parrish	0.22	31.0	0.05	2.8	39.8
89	Antonio Chatman	0.19	31.9	0.05	2.6	37.8
90	Brian Finneran	0.22	26.8	0.05	2.5	36.2