Wide Receiver Class Statistical Projections

[AlNFL19]

I've been working on some college-to-pro statistical projection models around draft season, starting with quarterbacks, non-rush linebackers, and now wide receivers. These haven't gotten much traffic, but I think their results are interesting. I decided to try my hand at wide receivers this time around because of the supposed statistical unreliability of college receivers going pro, and it came out okay.

Things of note about the model:

• The projection is in terms of average Approximate Value in Years 3-4. Approximate Value (AV) is ProFootballReference's sort-of-all-in-one stat that you can see explained here: https://www.pro-football-reference.com/blog/index37a8.html
• The model also projects a % chance that the player reaches the "Bust" and "Pro Bowl" thresholds, which I have loosely defined as <5.0 AV and 10.0 AV respectively.
• The model is built on data from, and built for, only players drafted in Rounds 1-3 because of the even greater statistical unreliability of Day 3 draft picks (and their low likelihood of success).
• The WR model is built only on college statistics, draft position, and height and weight. No combine measurements were significant enough to warrant inclusion.
• The correlation coefficient between projected AV and real AV in the model is 0.45. This might not seem significant, but it's a step in the right direction from NFL scouting (draft position is correlated to AV at -0.31, showing that loosely the higher-drafted WRs perform better).

The model was built based on data from 2006 to 2015 (the last year where players have reached Year 4) and has had decent results compared to reality:

Probabilities of Hitting Thresholds (2006-2015)
Projection: >5.00 AV
Threshold	Reality	Projected Avg.
Pro Bowl	26.32%	16.05%
Hit	78.95%	71.42%
Bust	21.05%	28.58%
Projection: <5.00 AV
Threshold	Reality	Projected Avg.
Pro Bowl	4.90%	6.82%
Hit	33.33%	34.73%
Bust	66.67%	65.27%

Without further ado, the 2019 class rankings, with a bit of a surprise in first (and last) place:

1. ANDY ISABELLA, MASSACHUSETTS

A. Isabella, WR, Massachusetts
Statistic	Figure
Projected AV	5.33
Bust Chance	32.43%
PB Chance	15.08%

My model is a fan of the little guy this year. Isabella's projection, if put in the 2006-2015 data set, would edge out Jordan Matthews' projection for 10th-best. Being projected above the 5.0 bust threshold is in itself an achievement - less than 16% of WRs drafted in Rounds 1-3 from 2006-2015 hit a projection of 5.0 or greater. For those wondering, no, Isabella's projection isn't helped by his crazy 4.32 speed - no combine events were deemed significant enough to warrant including in the model. What Isabella did to have such a high projection was score very highly in both efficiency-based statistics, like receptions per team attempt, and volume statistics, like receiving yards. Including both leaves Isabella with a high projection as he heads to Kliff Kingsbury's Air Raid with Kyler Murray.

2. MARQUISE BROWN, OKLAHOMA

M. Brown, WR, Oklahoma
Statistic	Figure
Projected AV	4.85
Bust Chance	42.60%
PB Chance	12.52%

Brown may not have hit the 5.0 AV threshold, but he's still projected to be a hit in the majority of cases (57.40%). Brown was helped by good efficiency and volume statistics and the highest draft position in the class, but not by a body type that hasn't proven as successful in the pros (5'9", 166 lbs). Still, taking the "gamble" on Brown might be worthwhile for the Ravens if he can hit like his % projection suggests. Brown's projection is sandwiched between that of Kevin White and Jeremy Maclin, so it could go either way. Still, Brown's projection is above that of big names like Odell Beckham Jr. and Julio Jones.

3. J.J. ARCEGA-WHITESIDE, STANFORD

J.J. Arcega-Whiteside, Stanford
Statistic	Figure
Projected AV	4.44
Bust Chance	51.30%
PB Chance	10.33%

The Eagles' new wideout ties Mike Sims-Walker and Paul Richardson with a 4.44 projection, but barely misses the 50% hit mark. Arcega-Whiteside's statistics weren't mind-boggling, but his 14 touchdowns in his final year helped to raise his projection. On the (rightfully) conservative side with his Pro Bowl projections, the model gives Arcega-Whiteside just a 10% chance to be a Pro Bowl-threshold player. 

4. N'KEAL HARRY, ARIZONA STATE

N. Harry, WR, Arizona State
Statistic	Figure
Projected AV	4.43
Bust Chance	51.51%
PB Chance	10.28%

Jim Cobern of CommonManFootball analytics on YouTube ranked Harry as the top receiver in this year's class. My model isn't as high on Harry, but it still gives him a good coin-flip chance of success. If he hits with the Patriots, it doesn't mean anything good for the rest of the NFL. Harry's projection is just below Arcega-Whiteside's, though he did reel in 10 more receptions for a team that threw the ball less than Stanford. Harry put up numbers throughout his career at ASU, and the model gives him a 48.49% chance to keep doing so as a New England Patriot.

5. PARRIS CAMPBELL, OHIO STATE

P. Campbell, WR, Ohio State
Statistic	Figure
Projected AV	4.26
Bust Chance	55.11%
PB Chance	9.37%

Ohio State's speedster is the top-ranked wideout in this class to fall under the 10% Pro Bowl chance threshold. The new Colt built a reputation for himself with his speed, but he produced solid volume stats at OSU. Campbell's 90 catches in 2018 ranked second in the rankings to the aforementioned Isabella. Campbell was solid overall, but not special in many regards.

6. A.J. BROWN, OLE MISS

A.J. Brown, WR, Ole Miss
Statistic	Figure
Projected AV	4.11
Bust Chance	58.29%
PB Chance	8.57%

Metcalf may have received all the pre-draft buzz, but it was Brown who was off the board first, and rightfully so, per the model. With Metcalf out much of the year, Brown was the real target-hog for the Rebels' offense, catching 85 balls. Brown's 6-2, 226 frame is also relatively ideal for the NFL. Brown's efficiency stats, however, were unremarkable, and they, along with his just 6 touchdown receptions in 2018, dragged down his projection. Brandon LaFell (4.14) and Robert Woods (4.09) are the two closest to Brown's projection.

7. DEEBO SAMUEL, SOUTH CAROLINA

D. Samuel, WR, South Carolina
Statistic	Figure
Projected AV	4.02
Bust Chance	60.20%
PB Chance	8.09%

Drafted third among wideouts by San Francisco, Samuel had unremarkable overall statistics at South Carolina that led to a mediocre projection. Steve Smith (the Giants one) is the only other WR to ever notch a 4.02, though Smith recorded better volume statistics. As the middleman of the class rankings, Samuel's 4.02 is just below the average of the 2006-2015 sample (4.05).

8. MILES BOYKIN, NOTRE DAME

M. Boykin, WR, Notre Dame
Statistic	Figure
Projected AV	3.49
Bust Chance	71.44%
PB Chance	5.26%

Boykin, the lowest-drafted of the rankings, kicks off the lower group of players, not even hitting 3.50 AV. Boykin's statistics were nothing special at all, hitting just 872 yards for a decently pass-heavy Notre Dame team. Jaguars (arguably) WR1 Marqise Lee hit 3.47 a few years ago, the closest to Boykin's projection. Boykin was a workout warrior, consistently testing in the 80th percentile and above (https://www.mockdraftable.com/player/miles-boykin), but unfortunately for him, my model is unimpressed with the Underwear Olympics.

9. DIONTAE JOHNSON, TOLEDO

D. Johnson, WR, Toledo
Statistic	Figure
Projected AV	3.40
Bust Chance	73.35%
PB Chance	4.78%

Johnson heads to the Steelers in an effort to usurp James Washington as the team's No. 2 behind JuJu Smith-Schuster. Johnson's 49 receptions for 761 yards and 8 touchdowns is a little low for what the model wants to see, as is Johnson's 183-pound frame. With a Pro Bowl chance below 5%, the Steelers might not have found a diamond in the rough, but they could have found a contributor. Time will tell for Johnson, whose 3.40 barely edges out DeSean Jackson's 3.38. 

10. JALEN HURD, BAYLOR

J. Hurd, WR, Baylor
Statistic	Figure
Projected AV	3.28
Bust Chance	75.89%
PB Chance	4.14%

Hurd's career arc has been an intriguing one, as he went from splitting carries with Alvin Kamara at Tennessee to being Baylor's No. 1 receiver. If my model is to be believed, Hurd's journey could end quickly in the NFL. His below-25% chance of success isn't a great one, and he only compiled okay statistics (4 touchdowns, etc.) for a Baylor team that threw the ball over 37 times per game. The one saving grace for Hurd could be that he's still getting there: as a player just shifting positions now, Hurd might have room to grow into something for the 49ers, who doubled up on wideouts early.

11. MECOLE HARDMAN, GEORGIA

M. Hardman, WR, Georgia
Statistic	Figure
Projected AV	3.15
Bust Chance	78.65%
PB Chance	3.45%

Hardman might not have been looked at as a top-tier wideout prospect, but the Chiefs saw enough to trade up for him to presumably replace Tyreek Hill. Hardman only caught 35 balls in his last year at Georgia, and only compiled 543 yards, neither of which on its own projects to above 3.0 Approximate Value. Georgia didn't throw as much as other CFB teams (only 25.5 attempts per game), but Hardman still didn't impress from an efficiency and market share perspective. Overall, it might be a surprise to see Hardman adequately replace Hill. On the other hand, with Patrick Mahomes manning the Chiefs, I wouldn't be too quick to count him out.

12. TERRY MCLAURIN, OHIO STATE

T. McLaurin, WR, Ohio State
Statistic	Figure
Projected AV	3.02
Bust Chance	81.40%
PB Chance	2.75%

After gaining a bit of steam as potential Round 1 guy late in the process due to a good combine, McLaurin fell to the third round, where he was scooped up by a Washington team that drafted his quarterback, Dwayne Haskins. Again, my model ignores McLaurin's combine performance for the event's statistical unreliability, but it does acknowledge his college performance. McLaurin scored 11 times in his last season, on almost a third of his catches. However, his projection suffers greatly from poor efficiency statistics - McLaurin hit just 35 receptions for 701 yards on an Ohio State squad that threw the ball forty times per game. The result spells a bad projection for Terry.

13. D.K. METCALF, OLE MISS

D.K. Metcalf, WR, Ole Miss
Statistic	Figure
Projected AV	2.45
Bust Chance	93.49%
PB Chance	<1.00%

No, I didn't forget about Metcalf. Count the model firmly not among his supporters. Make no mistake, 2.45 AV is a terrible projection. It's "good" for 10th-worst against the data set from 2006 to 2015. Now, there are things to acknowledge with Metcalf, and a Pro Bowl chance that low is incredibly conservative for this athletic freak. However, there is concern about Metcalf's neck injury, and it limited his statistics. As a result of his injury, DeKaylin only caught 26 balls all of the 2018 season. It might not be his fault, but it's certainly troubling. Compounding that with a team that threw the ball quite a bit (much of it to A.J. Brown), and you have a bad projection. However, if any team can unlock that 6% hit chance for Metcalf, it might be the Seahawks. They certainly have a good deep ball game that will only get better with D.K., as Brett Kollman breaks down here: 

I'd highly recommend it. It's worth a watch if you have the time.

TL;DR -

2019 Wide Receiver Class Projections
Name	Projected AV	Bust Chance	PB Chance
A. Isabella	5.33	32.43%	15.08%
M. Brown	4.85	42.60%	12.52%
J.A. Whiteside	4.44	51.30%	10.33%
N. Harry	4.43	51.51%	10.28%
P. Campbell	4.26	55.11%	9.37%
A.J. Brown	4.11	58.29%	8.57%
D. Samuel	4.02	60.20%	8.09%
M. Boykin	3.49	71.44%	5.26%
D. Johnson	3.4	73.35%	4.78%
J. Hurd	3.28	75.89%	4.14%
M. Hardman	3.15	78.65%	3.45%
T. McLaurin	3.02	81.40%	2.75%
D.K. Metcalf	2.45	93.49%	<1.00%

If you feel like taking a look at more similar statistical projections, check these out if you feel like it:

 

 

 

[AlNFL19]

Here's the historical data showing how the model has performed, comparing NFL results (AV) to projection (bust chance and Pro Bowl chance), and separated into risk-factor groups. Remember, "Bust" is defined as <5.0 AV, and "Pro Bowl" is defined as 10+ in this model. There's definitely a pretty decent track record here:

2006-2015 WR Model Results
GROUP: Moderate Risk I (Projected Bust Chance: <50%)
Results: 26.83% (11/41) Busts
Year	Name	College	NFL AV	Bust Chance	PB Chance
2010	D. Bryant	Oklahoma State	11.5	6.56%	21.58%
2010	D. Thomas	Georgia Tech	13.5	6.77%	21.53%
2015	A. Cooper	Alabama	4	8.04%	21.21%
2012	K. Wright	Baylor	4	21.61%	17.80%
2013	S. Bailey	West Virginia	0.5	22.67%	17.53%
2007	C. Johnson	Georgia Tech	8.5	24.79%	17.00%
2014	D. Adams	Fresno State	9	25.85%	16.73%
2012	J. Blackmon	Oklahoma State	0	29.25%	15.88%
2014	B. Cooks	Oregon State	12	31.79%	15.24%
2014	J. Matthews	Vanderbilt	5	32.43%	15.08%
2009	H. Nicks	North Carolina	9	33.70%	14.76%
2013	D. Hopkins	Clemson	9	34.12%	14.65%
2015	T. Lockett	Kansas State	8	35.18%	14.38%
2014	S. Watkins	Clemson	5	37.09%	13.90%
2015	N. Agholor	USC	7	37.52%	13.80%
2013	T. Williams	Baylor	5.5	38.15%	13.64%
2008	J. Nelson	Kansas State	10	38.79%	13.48%
2010	G. Tate	Notre Dame	8.5	39.21%	13.37%
2006	G. Jennings	Western Michigan	11.5	39.42%	13.32%
2014	J. Landry	LSU	8.5	39.85%	13.21%
2012	A.J. Jenkins	Illinois	0.5	40.06%	13.16%
2010	J. Shipley	Texas	1	40.27%	13.10%
2013	T. Austin	West Virginia	5.5	41.12%	12.89%
2008	J. Hardy	Indiana	0	42.39%	12.57%
2015	K. White	West Virginia	0.5	42.39%	12.57%
2009	J. Maclin	Missouri	6.5	43.45%	12.30%
2009	M. Crabtree	Texas Tech	9.5	44.51%	12.04%
2007	J. Jones	San Jose State	5	45.36%	11.82%
2007	D. Bowe	LSU	8	46.21%	11.61%
2011	J. Jones	Alabama	9.5	46.21%	11.61%
2007	R. Meachem	Tennessee	7	46.42%	11.56%
2012	M. Floyd	Notre Dame	7	47.06%	11.40%
2009	P. Harvin	Florida	10.5	47.06%	11.40%
2009	K. Britt	Rutgers	3	47.48%	11.29%
2008	D. Avery	Houston	0	47.69%	11.24%
2014	O. Beckham Jr.	LSU	6	47.90%	11.18%
2014	M. Evans	Texas A&M	9	48.12%	11.13%
2008	D. Thomas	Michigan State	0	48.33%	11.08%
2014	K. Benjamin	Florida State	6.5	48.96%	10.92%
2006	S. Holmes	Ohio State	9	49.18%	10.86%
2012	R. Randle	LSU	7.5	49.81%	10.70%
GROUP: Moderate Risk II (Projected Bust Chance: 50-60%)
Results: 60.87% (14/23) Busts
2014	A. Robinson	Penn State	3	50.87%	10.44%
2012	S. Hill	Georgia Tech	0	51.08%	10.38%
2011	T. Smith	Maryland	7.5	51.08%	10.38%
2007	M. Sims-Walker	UCF	7	51.30%	10.33%
2014	P. Richardson	Colorado	4	51.30%	10.33%
2007	S. Rice	South Carolina	8	51.51%	10.28%
2012	M. Sanu	Rutgers	6.5	51.93%	10.17%
2006	C. Jackson	Florida	0	52.57%	10.01%
2011	L. Hankerson	Miami	1	53.42%	9.80%
2015	D. Green-Beckham	Missouri	0	54.05%	9.64%
2007	J. Lee Higgins	UTEP	1.5	54.26%	9.58%
2013	M. Wheaton	Oregon State	3	55.33%	9.32%
2008	M. Manningham	Michigan	6	55.54%	9.26%
2007	T. Ginn Jr.	Ohio State	4	55.54%	9.26%
2007	D. Jarrett	USC	1	55.54%	9.26%
2011	T. Young	Boise State	0	55.75%	9.21%
2014	J. Huff	Oregon	1	55.75%	9.21%
2011	V. Brown	San Diego State	3	55.96%	9.16%
2011	A.J. Green	Georgia	11	55.96%	9.16%
2010	E. Sanders	SMU	6	56.17%	9.10%
2015	J. Strong	Arizona State	0	56.39%	9.05%
2010	B. LaFell	LSU	6	57.66%	8.73%
2013	R. Woods	USC	5.5	58.72%	8.46%
GROUP: High Risk (Projected Bust Chance: 60-80%)
Results: 76.32% (29/38) Busts
2007	S. Smith	USC	7.5	60.20%	8.09%
2015	D. Smith	Ohio State	0	60.84%	7.93%
2011	A. Pettis	Boise State	2.5	61.26%	7.82%
2012	A. Jeffery	South Carolina	8	62.32%	7.56%
2015	B. Perriman	UCF	1.5	62.53%	7.50%
2012	R. Broyles	Oklahoma	0	62.96%	7.40%
2009	B. Robiskie	Ohio State	0	63.17%	7.34%
2006	B. Williams	Wisconsin	0	63.17%	7.34%
2013	J. Hunter	Tennessee	2	63.38%	7.29%
2006	M. Stovall	Notre Dame	1	63.38%	7.29%
2009	J. Iglesias	Oklahoma	0	63.59%	7.24%
2012	T.Y. Hilton	FIU	10	63.81%	7.18%
2014	C. Latimer	Indiana	1.5	64.23%	7.08%
2010	D. Williams	USC	2	64.23%	7.08%
2007	A. Gonzalez	Ohio State	0.5	64.23%	7.08%
2009	M. Massaquoi	Georgia	2	65.08%	6.86%
2008	M. Kelly	Oklahoma	0	65.50%	6.76%
2008	E. Bennett	Vanderbilt	4	66.99%	6.38%
2006	D. Hagan	Arizona State	0.5	67.20%	6.33%
2015	P. Dorsett	Miami	2.5	67.83%	6.17%
2011	J. Baldwin	Pittsburgh	0	68.47%	6.01%
2015	D. Funchess	Michigan	6.5	69.53%	5.74%
2007	C. Davis	LSU	2	70.59%	5.48%
2009	P. Turner	USC	0.5	70.59%	5.48%
2014	M. Lee	USC	6.5	71.86%	5.16%
2008	H. Douglas	Louisville	4	72.50%	5.00%
2008	A. Caldwell	Florida	3	72.50%	5.00%
2008	D. Jackson	California	9.5	73.77%	4.68%
2015	C. Conley	Georgia	3	74.20%	4.57%
2011	J. Jernigan	Troy	1	74.62%	4.46%
2009	M. Wallace	Mississippi	8.5	75.68%	4.19%
2015	D. Parker	Louisville	3.5	75.89%	4.14%
2010	T. Price	Ohio State	0	76.53%	3.98%
2009	D. Butler	Penn State	0	76.95%	3.87%
2009	D. Heyward-Bey	Maryland	6	78.01%	3.61%
2014	D. Moncrief	Mississippi	3	78.44%	3.50%
2010	E. Decker	Minnesota	11.5	79.71%	3.18%
2006	S. Moss	Miami	1	79.92%	3.13%
GROUP: Very High Risk (Projected Bust Chance: 80-99.99%)
Results: 94.74% (18/19) Busts
2013	C. Patterson	Tennessee	3.5	81.83%	2.65%
2008	E. Doucet	LSU	3.5	82.46%	2.49%
2013	A. Dobson	Marshall	0.5	83.95%	2.11%
2015	S. Coates	Auburn	0	84.37%	2.01%
2007	J. Hill	Washington State	2	85.86%	1.63%
2011	R. Cobb	Kentucky	10	86.28%	1.53%
2008	E. Royal	Virginia Tech	3.5	87.13%	1.31%
2010	A. Benn	Illinois	0	88.61%	<1.00%
2009	D. Williams	Penn State	0	89.25%	<1.00%
2012	T.J. Graham	N.C. State	0.5	90.73%	<1.00%
2006	W. Reid	Florida State	0	97.73%	<1.00%
2007	Y. Figurs	Kansas State	0	99.64%	<1.00%
2013	M. Goodwin	Texas	2	>99.90%	<1.00%
2008	L. Sweed	Texas	0	>99.90%	<1.00%
2006	T. Wilson	Oklahoma	0	>99.90%	<1.00%
2011	G. Little	North Carolina	2	>99.90%	<1.00%
2012	D. Posey	Ohio State	0	>99.90%	<1.00%
2007	P. Williams	Fresno State	0	>99.90%	<1.00%
2009	B. Tate	North Carolina	1.5	>99.90%	<1.00%

[Danger]

In what world is Tavon Austin NOT a bust? LMAO.
In what world are PERCY HARVIN, HAKEEM NICKS, MICHAEL CRABTREE, and RANDALL COBB better than CALVIN JOHNSON.
Amari Cooper isn't a bust...
When you consider where he was drafted, Sammy Watkins is in fact a bust.

This AV stat is far from reliable/accurate. There are far more players on the list that could be argued for/against.

[AlNFL19]

8 hours ago, Danger said:

In what world is Tavon Austin NOT a bust? LMAO.
In what world are PERCY HARVIN, HAKEEM NICKS, MICHAEL CRABTREE, and RANDALL COBB better than CALVIN JOHNSON.
Amari Cooper isn't a bust...
When you consider where he was drafted, Sammy Watkins is in fact a bust.

This AV stat is far from reliable/accurate. There are far more players on the list that could be argued for/against.

AV does a good enough job for what it's supposed to do, which is in this case give a close estimate for a large sample that can be used to find results for a smaller sample. 

The Amari Cooper one though, was a mistake. His AV was 7, I put 4 for whatever reason. 

I also decided to use the average of their years 3-4 AV because it's still on their first contract, so it represents value the team got out of the draft pick itself. If you take Megatron's literally-almost-any-other-years, he's off the charts. But like I said, it's not perfect, and it doesn't have to be. It's called "Approximate" for a reason.

If you don't like AV, take it up with ProFootballReference. If you don't like my designation of what number is a bust, cool. 

[AlNFL19]

I made a mistake in the original model with a single player's data (Amari Cooper), but it was enough to throw off the entire data set. I had to redo the model for this slight correlation, so here are the new projections:

2019 WR Class Projections
Player	Projected AV	Bust Chance	PB Chance
A. Isabella	5.26	32.63%	14.23%
M. Brown	4.72	43.36%	11.77%
J.J. Whiteside	4.48	52.98%	9.57%
N. Harry	4.48	52.98%	9.57%
P. Campbell	4.31	56.78%	8.70%
A.J. Brown	4.27	60.13%	7.93%
D. Samuel	4.11	62.37%	7.42%
M. Boykin	3.57	74.22%	4.70%
D. Johnson	3.42	76.24%	4.24%
J. Hurd	3.44	78.70%	3.68%
M. Hardman	3.19	81.83%	2.96%
T. McLaurin	3.16	84.73%	2.30%
D.K. Metcalf	2.69	97.48%	<1.00%

Things look even bleaker for D.K. Metcalf (or for this model, if he succeeds).

Edited July 3, 2019 by AlNFL19

[Awsi Dooger]

Great work. Wide receiver has been giving the analytics guys fits. I'm impressed you came up with something so quickly.

And I wish I had a suggestion of where to post this stuff so it would receive the proper spotlight and praise. Inevitably with this type of thing the subjective types are instantly looking to pitpick and attack. 

Many of them feel threatened by any type of numerical analysis. That's silly and I wish it were not the case, but it holds up. The bust rate in this business is so high using subjectivity as guide for 100 years that I have no idea how a different approach is not prized. Certainly there is room for both, given 12 month attention span these days.

[Broncofan]

That's very impressive work, even if the formula is unclear on if how good of a predictive model it will be going forward, it's always good to see the methodology.

2 Q's and 2 comments:

1.  How was the tiering decided on?  I'm asking only because the range is so wide in the 1st tier, and then compresses significanly in moderate/high.  

2.  Specific Q - where is Michael Thomas in this rank?  I don't see him listed, and he was a 2nd rounder.   Apologies if I missed him.  EDIT:  Realize it's 2006-15, why he doesn't show up now.  Do you have his profile regardless?  I have a hypothesis on this model and him, so just curious.

The 2 comments:

1.  The use of college stats alone emphasizes the point that those who fail to produce in college rarely end up as stars in the NFL.  The notable exceptions are those in systems that are less stat-friendly, or those sidelined with injuries that don't carry chronic risk.   In that respect, it's the one flaw I see in trying to use this metric - while OBJ/JJ rank in the 1st tier, their composite score is so much on the low-end because of the systems they were in really limited their college production.   Stuff like that factors in to the weighing IMO.

2.   The game becoming more speed and less size-dependent, I wonder how well the model will hold up as we move to more spread O's.   Not a criticism of the work, more a Q on the emphasis of size as 2 of the variables in the formula.   I think the Holy Grail is the guy with skills/speed & size - but the size requirements we used to see as a necessity to survive in the NFL seem to be fading.  

Either way, though, excellent work.  No system is going to be perfect, and showing your work and being transparent is the key step to developing a sustainable methodology for success.  

 

 

Edited May 23, 2019 by Broncofan

[UKChief]

On 19/05/2019 at 12:10 AM, AlNFL19 said:

Here's the historical data showing how the model has performed, comparing NFL results (AV) to projection (bust chance and Pro Bowl chance), and separated into risk-factor groups. Remember, "Bust" is defined as <5.0 AV, and "Pro Bowl" is defined as 10+ in this model. There's definitely a pretty decent track record here:

 

2006-2015 WR Model Results
GROUP: Moderate Risk I (Projected Bust Chance: <50%)
Results: 26.83% (11/41) Busts
Year	Name	College	NFL AV	Bust Chance	PB Chance
2010	D. Bryant	Oklahoma State	11.5	6.56%	21.58%
2010	D. Thomas	Georgia Tech	13.5	6.77%	21.53%
2015	A. Cooper	Alabama	4	8.04%	21.21%
2012	K. Wright	Baylor	4	21.61%	17.80%
2013	S. Bailey	West Virginia	0.5	22.67%	17.53%
2007	C. Johnson	Georgia Tech	8.5	24.79%	17.00%
2014	D. Adams	Fresno State	9	25.85%	16.73%
2012	J. Blackmon	Oklahoma State	0	29.25%	15.88%
2014	B. Cooks	Oregon State	12	31.79%	15.24%
2014	J. Matthews	Vanderbilt	5	32.43%	15.08%
2009	H. Nicks	North Carolina	9	33.70%	14.76%
2013	D. Hopkins	Clemson	9	34.12%	14.65%
2015	T. Lockett	Kansas State	8	35.18%	14.38%
2014	S. Watkins	Clemson	5	37.09%	13.90%
2015	N. Agholor	USC	7	37.52%	13.80%
2013	T. Williams	Baylor	5.5	38.15%	13.64%
2008	J. Nelson	Kansas State	10	38.79%	13.48%
2010	G. Tate	Notre Dame	8.5	39.21%	13.37%
2006	G. Jennings	Western Michigan	11.5	39.42%	13.32%
2014	J. Landry	LSU	8.5	39.85%	13.21%
2012	A.J. Jenkins	Illinois	0.5	40.06%	13.16%
2010	J. Shipley	Texas	1	40.27%	13.10%
2013	T. Austin	West Virginia	5.5	41.12%	12.89%
2008	J. Hardy	Indiana	0	42.39%	12.57%
2015	K. White	West Virginia	0.5	42.39%	12.57%
2009	J. Maclin	Missouri	6.5	43.45%	12.30%
2009	M. Crabtree	Texas Tech	9.5	44.51%	12.04%
2007	J. Jones	San Jose State	5	45.36%	11.82%
2007	D. Bowe	LSU	8	46.21%	11.61%
2011	J. Jones	Alabama	9.5	46.21%	11.61%
2007	R. Meachem	Tennessee	7	46.42%	11.56%
2012	M. Floyd	Notre Dame	7	47.06%	11.40%
2009	P. Harvin	Florida	10.5	47.06%	11.40%
2009	K. Britt	Rutgers	3	47.48%	11.29%
2008	D. Avery	Houston	0	47.69%	11.24%
2014	O. Beckham Jr.	LSU	6	47.90%	11.18%
2014	M. Evans	Texas A&M	9	48.12%	11.13%
2008	D. Thomas	Michigan State	0	48.33%	11.08%
2014	K. Benjamin	Florida State	6.5	48.96%	10.92%
2006	S. Holmes	Ohio State	9	49.18%	10.86%
2012	R. Randle	LSU	7.5	49.81%	10.70%
GROUP: Moderate Risk II (Projected Bust Chance: 50-60%)
Results: 60.87% (14/23) Busts
2014	A. Robinson	Penn State	3	50.87%	10.44%
2012	S. Hill	Georgia Tech	0	51.08%	10.38%
2011	T. Smith	Maryland	7.5	51.08%	10.38%
2007	M. Sims-Walker	UCF	7	51.30%	10.33%
2014	P. Richardson	Colorado	4	51.30%	10.33%
2007	S. Rice	South Carolina	8	51.51%	10.28%
2012	M. Sanu	Rutgers	6.5	51.93%	10.17%
2006	C. Jackson	Florida	0	52.57%	10.01%
2011	L. Hankerson	Miami	1	53.42%	9.80%
2015	D. Green-Beckham	Missouri	0	54.05%	9.64%
2007	J. Lee Higgins	UTEP	1.5	54.26%	9.58%
2013	M. Wheaton	Oregon State	3	55.33%	9.32%
2008	M. Manningham	Michigan	6	55.54%	9.26%
2007	T. Ginn Jr.	Ohio State	4	55.54%	9.26%
2007	D. Jarrett	USC	1	55.54%	9.26%
2011	T. Young	Boise State	0	55.75%	9.21%
2014	J. Huff	Oregon	1	55.75%	9.21%
2011	V. Brown	San Diego State	3	55.96%	9.16%
2011	A.J. Green	Georgia	11	55.96%	9.16%
2010	E. Sanders	SMU	6	56.17%	9.10%
2015	J. Strong	Arizona State	0	56.39%	9.05%
2010	B. LaFell	LSU	6	57.66%	8.73%
2013	R. Woods	USC	5.5	58.72%	8.46%
GROUP: High Risk (Projected Bust Chance: 60-80%)
Results: 76.32% (29/38) Busts
2007	S. Smith	USC	7.5	60.20%	8.09%
2015	D. Smith	Ohio State	0	60.84%	7.93%
2011	A. Pettis	Boise State	2.5	61.26%	7.82%
2012	A. Jeffery	South Carolina	8	62.32%	7.56%
2015	B. Perriman	UCF	1.5	62.53%	7.50%
2012	R. Broyles	Oklahoma	0	62.96%	7.40%
2009	B. Robiskie	Ohio State	0	63.17%	7.34%
2006	B. Williams	Wisconsin	0	63.17%	7.34%
2013	J. Hunter	Tennessee	2	63.38%	7.29%
2006	M. Stovall	Notre Dame	1	63.38%	7.29%
2009	J. Iglesias	Oklahoma	0	63.59%	7.24%
2012	T.Y. Hilton	FIU	10	63.81%	7.18%
2014	C. Latimer	Indiana	1.5	64.23%	7.08%
2010	D. Williams	USC	2	64.23%	7.08%
2007	A. Gonzalez	Ohio State	0.5	64.23%	7.08%
2009	M. Massaquoi	Georgia	2	65.08%	6.86%
2008	M. Kelly	Oklahoma	0	65.50%	6.76%
2008	E. Bennett	Vanderbilt	4	66.99%	6.38%
2006	D. Hagan	Arizona State	0.5	67.20%	6.33%
2015	P. Dorsett	Miami	2.5	67.83%	6.17%
2011	J. Baldwin	Pittsburgh	0	68.47%	6.01%
2015	D. Funchess	Michigan	6.5	69.53%	5.74%
2007	C. Davis	LSU	2	70.59%	5.48%
2009	P. Turner	USC	0.5	70.59%	5.48%
2014	M. Lee	USC	6.5	71.86%	5.16%
2008	H. Douglas	Louisville	4	72.50%	5.00%
2008	A. Caldwell	Florida	3	72.50%	5.00%
2008	D. Jackson	California	9.5	73.77%	4.68%
2015	C. Conley	Georgia	3	74.20%	4.57%
2011	J. Jernigan	Troy	1	74.62%	4.46%
2009	M. Wallace	Mississippi	8.5	75.68%	4.19%
2015	D. Parker	Louisville	3.5	75.89%	4.14%
2010	T. Price	Ohio State	0	76.53%	3.98%
2009	D. Butler	Penn State	0	76.95%	3.87%
2009	D. Heyward-Bey	Maryland	6	78.01%	3.61%
2014	D. Moncrief	Mississippi	3	78.44%	3.50%
2010	E. Decker	Minnesota	11.5	79.71%	3.18%
2006	S. Moss	Miami	1	79.92%	3.13%
GROUP: Very High Risk (Projected Bust Chance: 80-99.99%)
Results: 94.74% (18/19) Busts
2013	C. Patterson	Tennessee	3.5	81.83%	2.65%
2008	E. Doucet	LSU	3.5	82.46%	2.49%
2013	A. Dobson	Marshall	0.5	83.95%	2.11%
2015	S. Coates	Auburn	0	84.37%	2.01%
2007	J. Hill	Washington State	2	85.86%	1.63%
2011	R. Cobb	Kentucky	10	86.28%	1.53%
2008	E. Royal	Virginia Tech	3.5	87.13%	1.31%
2010	A. Benn	Illinois	0	88.61%	<1.00%
2009	D. Williams	Penn State	0	89.25%	<1.00%
2012	T.J. Graham	N.C. State	0.5	90.73%	<1.00%
2006	W. Reid	Florida State	0	97.73%	<1.00%
2007	Y. Figurs	Kansas State	0	99.64%	<1.00%
2013	M. Goodwin	Texas	2	>99.90%	<1.00%
2008	L. Sweed	Texas	0	>99.90%	<1.00%
2006	T. Wilson	Oklahoma	0	>99.90%	<1.00%
2011	G. Little	North Carolina	2	>99.90%	<1.00%
2012	D. Posey	Ohio State	0	>99.90%	<1.00%
2007	P. Williams	Fresno State	0	>99.90%	<1.00%
2009	B. Tate	North Carolina	1.5	>99.90%	<1.00%

What was tyreeks stats out of interest ?

[AlNFL19]

6 hours ago, UKChief said:

What was tyreeks stats out of interest ?

He’s technically not eligible for the model as a late round pick, but also I can’t really run the numbers because he finished up his collegiate career in Division II. 

[UKChief]

5 hours ago, AlNFL19 said:

He’s technically not eligible for the model as a late round pick, but also I can’t really run the numbers because he finished up his collegiate career in Division II. 

Thanks, I just wanted to see how he would compare to Hardman but makes sense

[AlNFL19]

46 minutes ago, UKChief said:

Thanks, I just wanted to see how he would compare to Hardman but makes sense

Sorry

Though as a D2 late-round pick Hill would, I’m sure, have a terrible projection were he eligible (D2 and FCS stats obviously don’t capture the same things as FBS stats). 

[AlNFL19]

On 5/23/2019 at 8:54 AM, Broncofan said:

2.  Specific Q - where is Michael Thomas in this rank?  I don't see him listed, and he was a 2nd rounder.   Apologies if I missed him.  EDIT:  Realize it's 2006-15, why he doesn't show up now.  Do you have his profile regardless?  I have a hypothesis on this model and him, so just curious.

Sorry I totally forgot to address this until just now. Only a little late.

Thomas' projection was rather unremarkable, falling in the 46th percentile of the data set, but it wasn't terrible:

M. Thomas, WR, Ohio State
Statistic	Figure
Projected AV	4.04
Bust Chance	59.91%
PB Chance	7.98%

Thomas had middling efficiency stats and volume stats and was a second-round pick, so his projection is very middling itself.

[Broncofan]

10 hours ago, AlNFL19 said:

Sorry I totally forgot to address this until just now. Only a little late.

Thomas' projection was rather unremarkable, falling in the 46th percentile of the data set, but it wasn't terrible:

 

M. Thomas, WR, Ohio State
Statistic	Figure
Projected AV	4.04
Bust Chance	59.91%
PB Chance	7.98%

Thomas had middling efficiency stats and volume stats and was a second-round pick, so his projection is very middling itself.

The volume stats are what I suspect is a weakness in the system - as with Julio / Beckham, the OSU system (and QB) he played with really limited his output.   If I read that correctly, Thomas ends up somewhere at the bottom of the Tier II (Moderate risk) portfolio.  Now his neck injury factors in there too, so it's not all formula-based, but that's the limit of going solely data/metrics-based.   The nuance of the competition / system / QB really does influence the rank IMO.   It's still a very useful tool if it can separate, just I don't think it can be the end-all and be-all (frankly that's where we are nowadays with any single form of evaluation).

[AlNFL19]

10 hours ago, Broncofan said:

The volume stats are what I suspect is a weakness in the system - as with Julio / Beckham, the OSU system (and QB) he played with really limited his output.   If I read that correctly, Thomas ends up somewhere at the bottom of the Tier II (Moderate risk) portfolio.  Now his neck injury factors in there too, so it's not all formula-based, but that's the limit of going solely data/metrics-based.   The nuance of the competition / system / QB really does influence the rank IMO.   It's still a very useful tool if it can separate, just I don't think it can be the end-all and be-all (frankly that's where we are nowadays with any single form of evaluation).

Yeah. I think you can say a lot more with statistics and context than you can with one or the other, so this is just the one side of it. Still, it’s reasonable I think. 

[AlNFL19]

Using my AV projections, I've calculated stat line projections for each wide receiver in this year's class. These are somewhat conservative estimates because a large portion of the sample size of NFL draftees accomplished little in the NFL, so there's always the concern that some of these guys will have the same happen to them.

For context, Dez Bryant's all-time best projected stat-line: 6.67 AV, 52 receptions on 89 targets for 731 yards (8.21 per target) and 5 touchdowns.

 

1. Andy Isabella, Arizona Cardinals: 5.26 AV, 43 receptions on 75 targets for 588 yards (7.85 per target) and 4 touchdowns

2. Marquise Brown, Baltimore Ravens: 4.78 AV, 40 receptions on 70 targets for 540 yards (7.68 per target) and 3 touchdowns

T-3. J.J. Arcega-Whiteside, Philadelphia Eagles: 4.35 AV, 37 receptions on 66 targets for 496 yards (7.52 per target) and 3 touchdowns

T-3. N'Keal Harry, New England Patriots: 4.35 AV, 37 receptions on 66 targets for 496 yards (7.52 per target) and 3 touchdowns

5. Parris Campbell, Indianapolis Colts: 4.18 AV, 36 receptions on 64 targets for 479 yards (7.44 per target) and 3 touchdowns

6. A.J. Brown, Tennessee Titans: 4.03 AV, 35 receptions on 63 targets for 464 yards (7.38 per target) and 3 touchdowns

7. Deebo Samuel, San Francisco 49ers: 3.93 AV, 35 receptions on 62 targets for 454 yards (7.33 per target) and 3 touchdowns

8. Miles Boykin, Baltimore Ravens: 3.40 AV, 31 receptions on 57 targets for 400 yards (7.06 per target) and 2 touchdowns

9. Diontae Johnson, Pittsburgh Steelers: 3.31 AV, 31 receptions on 56 targets for 391 yards (7.01 per target) and 2 touchdowns

10. Jalen Hurd, San Francisco 49ers: 3.20 AV, 30 receptions on 55 targets for 380 yards (6.95 per target) and 2 touchdowns

11. Mecole Hardman, Kansas City Chiefs: 3.06 AV, 29 receptions on 53 targets for 366 yards (6.86 per target) and 2 touchdowns

12. Terry McLaurin, Washington: 2.93 AV, 28 receptions on 52 targets for 353 yards (6.77 per target) and 2 touchdowns

13. D.K. Metcalf, Seattle Seahawks: 2.36 AV, 25 receptions on 46 targets for 295 yards (6.35 per target) and 2 touchdowns