Trades on day 1 of the 2018 Draft. AV analysis.

Bill traded up to pick 7 to get QB Josh Allen.

Josh Allen Trade
Buffalo Bills		Buccaneers		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
7	32	12	35
		53	22
		56	19
Total	32	Total	76
				44	2.38

~~~
The Cards moved up to pick 10 to draft Josh Rosen
~~~

Josh Rosen Trade
Cardinals		Raiders		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
10	41	15	28
		79	18
		152	9
Total	41	Total	55
				14	1.34

~~~
Saints move up to get Marcus Davenport, DE
~~~

Marcus Davenport Trade
Saints		Packers		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
14	29	27	25
		147	8
		(25)	24
Total	29	Total	57
				28	1.97

~~~
Bills go up to get Tremaine Edmunds
~~~

Tremaine Edmunds Trade
Bills		Ravens		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
16	32	22	27
154	12	65	21
Total	44	Total	48
				4	1.09

~~~
Packers trade again to get Jaire Alexander
~~~

Jaire Alexander Trade
Packers		Seahawks		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
18	29	27	25
248	5	76	17
		188	5
Total	34	Total	47
				13	1.38

~~~
Titans trade up for Rashaan Evans
~~~

Jaire Alexander Trade
Packers		Seahawks		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
18	29	27	25
248	5	76	17
		188	5
Total	34	Total	47
				13	1.38

~~~
Ravens trade 2019 assets to get their QB
~~~

Lamar Jackson Trade
Ravens		Eagles		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
32	23	52	22
132	11	125	15
		(48)	25
Total	34	Total	62
				28	1.82

 

My computer draft simulator revisited (with a mock draft).

The first version of my Open Source Draft Simulator I wrote in time to analyze the draft of 2001, and it was based on C++. Later on, in 2007, while trying to get a job, I rewrote the simulator in Ruby because I was trying to impress people that I could learn the language. I didn’t get the job. The Ruby simulator isn’t as statistically versatile, but it works on multiple sports.

I pulled out that ten year old code, in part to see if it still works, and part to see if I could make use of the data I had received from Ourlads. Ourlads does a 32 team needs list, which in general is the hardest part of setting up a draft simulator.

The ruby code, as downloaded, has a dependence on the module ‘rdoc/usage’. It is not essential, and I recommend you comment out or delete the line that says ‘require ‘rdoc/usage”. At that point you’ll have a working program. If all the warnings at the beginning bother you, remove the -w flag from the hash bang (first) line.

On Linux create all the files and then get rid of the ^Ms at the end of the lines. I had originally developed this sim on Windows. You can use perl to remove the ^M characters with something like perl -pre ‘s/\r//g’.

Data sources? Sports Illustrated has a top 100 list that works well. The top 100 list from NFL Draft Scout also yields useful results. I used Ourlads as my ‘serious’ set of needs, but Lance Zierlein has a set, as do other sites.

A typical rule file in my current setup is:

#
# rule file for Cleveland Browns.
#
rule need
#
needlist QB RB OL DB DE
#
cond QB max 1 high 
#
cond RB max 1

To note, with the SI top player set, if you don’t set QB to a “high” need, you’ll end up drafting Saquon Barkley number one. That’s one of the things I like about my own code. Slight changes in the needs of a single team can cause ripple effects throughout the draft.

A typical mock draft using this setup is:

ruby rubysim.rb -y 2018 -s football

This mock draft was made by rubysim.rb on 2018-04-16


Round 1.

1. Cleveland Browns select Sam Darnold, QB.
2. New York Giants select Bradley Chubb, DE.
3. New York Jets select Baker Mayfield, QB.
4. Cleveland Browns select Saquon Barkley, RB.
5. Denver Broncos select Josh Allen, QB.
6. Indianapolis Colts select Quenton Nelson, G.
7. Tampa Bay Buccaneers select Minkah Fitzpatrick, S.
8. Chicago Bears select Roquan Smith, LB.
9. San Francisco 49ers select Calvin Ridley, WR.
10. Oakland Raiders select Denzel Ward, CB.
11. Miami Dolphins select Vita Vea, DT.
12. Buffalo Bills select Josh Rosen, QB.
13. Washington Redskins select Josh Jackson, CB.
14. Green Bay Packers select Derwin James, S.
15. Arizona Cardinals select Connor Williams, OT.
16. Baltimore Ravens select Mike McGlinchey, OT.
17. Los Angeles Chargers select Tremaine Edmunds, LB.
18. Seattle Seahawks select Marcus Davenport, DE.
19. Dallas Cowboys select Da'Ron Payne, DT.
20. Detroit Lions select Harold Landry, DE.
21. Cincinnati Bengals select Leighton Vander Esch, LB.
22. Buffalo Bills select Courtland Sutton, WR.
23. New England Patriots select Derrius Guice, RB.
24. Carolina Panthers select Isaiah Oliver, CB.
25. Tennessee Titans select Maurice Hurst, DT.
26. Atlanta Falcons select Taven Bryan, DL.
27. New Orleans Saints select Christian Kirk, WR.
28. Pittsburgh Steelers select Rashaan Evans, LB.
29. Jacksonville Jaguars select Kolton Miller, OT.
30. Minnesota Vikings select Arden Key, DE.
31. New England Patriots select Isaiah Wynn, G.
32. Philadelphia Eagles select Justin Reid, S.

Round 2.

33. Cleveland Browns select James Daniels, C.
34. New York Giants select Lamar Jackson, QB.
35. Cleveland Browns select Mike Hughes, CB.
36. Indianapolis Colts select Jaire Alexander, CB.
37. Indianapolis Colts select Ronnie Harrison, S.
38. Tampa Bay Buccaneers select Carlton Davis, CB.
39. Chicago Bears select D.J. Moore, WR.
40. Denver Broncos select Hayden Hurst, TE.
41. Oakland Raiders select Donte Jackson, CB.
42. Miami Dolphins select Ronald Jones II, RB.
43. New England Patriots select Mike Gesicki, TE.
44. Washington Redskins select Will Hernandez, G.
45. Green Bay Packers select Orlando Brown, OT.
46. Cincinnati Bengals select Billy Price, C.
47. Arizona Cardinals select Chukwuma Okorafor, OT.
48. Los Angeles Chargers select Rasheem Green, DT.
49. Indianapolis Colts select Sam Hubbard, DE.
50. Dallas Cowboys select James Washington, WR.
51. Detroit Lions select Brian O'Neill, OT.
52. Baltimore Ravens select Jessie Bates, S.
53. Buffalo Bills select Deon Cain, WR.
54. Kansas City Chiefs select Tim Settle, DT.
55. Carolina Panthers select Lorenzo Carter, DE.
56. Buffalo Bills select Martinas Rankin, OT.
57. Tennessee Titans select Armani Watts, S.
58. Atlanta Falcons select Harrison Phillips, DT.
59. San Francisco 49ers select Uchenna Nwosu, LB.
60. Pittsburgh Steelers select Dallas Goedert, TE.
61. Jacksonville Jaguars select Anthony Averett, CB.
62. Minnesota Vikings select DeShon Elliott, S.
63. New England Patriots select Tyrell Crosby, OT.
64. Cleveland Browns select Ogbonnia Okoronkwo, DE.

Round 3.

65. Buffalo Bills select Darius Leonard, LB.
66. New York Giants select Sony Michel, RB.
67. Indianapolis Colts select Desmond Harrison, OT.
68. Houston Texans select Mark Andrews, TE.
69. New York Giants select Mason Rudolph, QB.
70. San Francisco 49ers select Dante Pettis, WR.
71. Denver Broncos select Kerryon Johnson, RB.
72. New York Jets select Nick Chubb, RB.
73. Miami Dolphins select Jerome Baker, LB.
74. San Francisco 49ers select Equanimeous St. Brown, WR.
75. Oakland Raiders select Malik Jefferson, LB.
76. Green Bay Packers select Michael Gallup, WR.
77. Cincinnati Bengals select Ian Thomas, TE.
78. Washington Redskins select Frank Ragnow, C.
79. Arizona Cardinals select Geron Christian, OT.
80. Houston Texans select Kyzir White, S.
81. Dallas Cowboys select Jamarco Jones, OT.
82. Detroit Lions select Jeff Holland, DE.
83. Baltimore Colts select Josh Sweat, DE.
84. Los Angeles Chargers select Trenton Thompson, DT.
85. Carolina Panthers select D.J. Chark, WR.
86. Kansas City Chiefs select Braden Smith, G.
87. Los Angeles Rams select Kemoko Turay, DE.
88. Carolina Panthers select Dorance Armstrong Jr., DE.
89. Tennessee Titans select Tarvarus McFadden, CB.
90. Atlanta Falcons select Chad Thomas, DE.
91. New Orleans Saints select Jordan Lasley, WR.
92. Pittsburgh Steelers select Shaquem Griffin, OLB.
93. Jacksonville Jaguars select Rashaan Gaulden, CB.
94. Minnesota Vikings select Tre'Quan Smith, WR.
95. New England Patriots select Anthony Miller, WR.
96. Buffalo Bills select Simmie Cobbs Jr., WR.
97. Arizona Cardinals select Joseph Noteboom, OT.
98. Houston Texans select Nick Nelson, CB.
99. Denver Broncos select Rashaad Penny, RB.
100. Cincinnati Bengals select Jaylen Samuels, RB.

 

The Jets – Colts trade is one of the riskiest trades in recent history.

There are a number of ways to analyze a draft trade. You can do it by comparing the actual players selected (though that takes time), you can do it by trade value, as measured by a trade chart, or you can do it by using the Pro Football Reference statistic approximate value. There are charts of approximate value per draft choice and those charts can be used to calculate trade values and risk immediately.

The recent blockbuster trade by the Jets involves substantially more risk than the last five major trade ups in the NFL (here, here and here). To make these calculations I assume the Jet’s pick next year will be the 10th pick in the second round, hence the 42nd pick.

Trade for 3rd Pick
Jets		Colts		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
3	45	6	39
		37	28
		49	20
		42	25
Total	45	Total	112
				67	2.49

 

With a risk ratio of 2.7 2,5, the risk incurred by the Jets is a bit less to what Washington put up with in the RGIII trade. It’s also comparable to the Earl Campbell trade. The last two trades were high risk – never paid back kinds of trades (though with Earl Campbell, the team’s competitiveness during his peak years may have been enough emotionally for the Oilers).

Update: recalculated risk, which now stands at 2.5 instead of 2.7.

 

Some Python draft related code. simulations.

I’m doing a brief review of Python again, as it relates to things that draft fans might like, and note that the random and statistics modules all seem pretty useful.

So, the design goal here is: can we make a good enough simulation to tell us something about draft strategy. Can we learn something about BPA versus need by using Python code? Right now I don’t have an answer, but I can show you some of the approach so far.

One thing I’ve found if you’re moving from another language into Python, that you can eliminate a lot of scope issues if you’ll do certain substantial bits of work in a Python class. The scope of self variables is easy to measure and then you’re not wondering whether the common variable in Python has exactly the same scope, as say, a lexical in Perl.

So for now, we present the Playa class, a “draftable” object.

import random
from statistics import mean
from pprint import pprint

random.seed()

class Playa:
    def __init__(self, oldid=0):
        self.value = random.randrange(1,101)
        self.pos = self.getposition()
        self.id = oldid + 1
        self.drafted = False
        self.meanshift = -1000.0

    def __repr__(self):
        return "Playa id:{0:3d} pos:{1:s} val:{2:3d}".format( self.id, self.pos, self.value )

    def out(self):
        return "id:{0:3d} pos:{1:s} val:{2:3d}".format( self.id, self.pos, self.value )

    def getposition(self):
        poslist = ["QB","RB","WR","FL","SR","TE","LT","LG","RT","RG","OC"]
        return poslist[random.randrange(0,11)]

    def draft(self):
        self.drafted = True

This object will allow us to generate players and then associate them with teams. Players can be identified by their id, a draft value can be derived from their real value (1-100), and a logical variable shows whether they are drafted or not.

I’m only using offensive positions in this simulation. And since more and more teams use a slot receiver as opposed to a fullback, we have “SR” in our position charts.

If with 32 teams, you generate 320 players per draft, then the values of 1 to 100 break nicely, as real value of 91 to 100 are first round talent, 81 to 90 are second round talent, and so on.

 

The break even point of the three major 2017 QB draft trades

This would have been done earlier, but Pro Football Reference dropped its very handy chart of draft position versus AV. I started missing it more and more, and using the Wayback Machine I found it here.

The three major QB trades of 2017 were the trade for Mitch Trubisky, Patrick Mahomes, and Deshaun Watson. We will analyze them in sequence.

Mitchell Trubisky Trade
Chicago Bears		49ers		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
2	46	3	45
		67	19
		111	12
		(71)	21
Total	46	Total	97
				51	2.11

 

The Bears have a trade risk comparable to a typical trade for a #1 draft choice and a quarterback at that. The trade has less fundamental risk than Goff or Wentz. The comparable that comes to mind is Eli Manning. By contrast, the delta AV of the other two trades are substantially less.

Patrick Mahomes Trade
Chiefs		Bills		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
10	41	27	25
		91	17
		(25)	24
Total	41	Total	66
				25	1.61

 
Mahomes merely has to give six seven good years, and the trade ends up warranted. The issue in the case of Deshaun Watson is keeping him upright. A fistful of whole years almost as good as his freshman year in the NFL and he would end up bordering on Hall of Fame numbers.

Deshaun Watson Trade
Texans		Browns		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
12	35	25	24
		4	44
Total	35	Total	68
				33	1.94

 

So here is wishing Deshaun Watson a healthy career from now on.

 

The AV cost of the 2016 Eagles-Browns trade.

I didn’t expect another trade of this magnitude, and so quickly. But let’s crunch the numbers on this trade, and compare them to the 2016 Titans-Rams trade.

The Browns received from the Eagles, the #8, #77 and #100 picks in this draft. In 2017 they receive the Eagles first round pick. In 2018 they receive the Eagles 2nd round pick. The Eagles have received the #2 pick in this draft, and the Browns 4th round pick in 2017.

For the purposes of this calculation, we assume the Eagles will pick 20th in 2017 and 2018, and that the Brown in 2017 will rise from 2nd to 10th.

 

The AV costs of the 2016 Eagles Browns trade.
Eagles		Browns		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
2	46	8	40
(138)	8	77	12
		100	17
		(20)	29
		(52)	22
Total	54	Total	120
				66	2.22

 

The Delta AV for both trades are the same, but since the Eagles received a lot less AV, the relative ratio of AV given to AV received is higher. The trade cost is the same, but the purchase is more highly leveraged.

 

The AV cost of the 2016 Rams-Titans trade

Determining how to assess draft trades in the NFL is not hard (see here, here, and here). Ever since Pro Football Reference went through the trouble of determining what average AV can be assigned to a draft slot, it’s merely a matter of counting. The technique has some variance, as the draft slot of a future pick is not known. Even so, with a bit of conservative extrapolation, you can still get a feel for the overall cost of a trade.

 

First, the numbers:

 

The AV costs of the 2016 Rams Titans trade.
Rams		Titans		Results
Pick	Average AV	Pick	Average AV	Delta AV	Risk Ratio
1	51	15	28
113	14	43	24
177	5	45	25
		76	17
		(20)	29
		(84)	13
Total	70	Total	136
				66	1.94

 

In the data above, we assume that the Rams will improve 5 slots in draft placement, so that the first and third they sent to the Titans would be picks 20 and 84. If the Titans end up 18th or 23rd, it’s notable that the difference in value at this point is less than the point-to-point deviation, so that kind of change won’t affect the calculation much. Pro Football Reference’s raw data are moderately noisy.

The Rams total investment is 136 AV, roughly equal to the career value of John Elway. That’s not entirely accurate, as the Rams actually received three picks in return, and if the other two return 19, then the player they pick at #1, to return the value of the investment, only has to yield 117 AV.Now, 117 points is about mid in between Phillip Rivers and Aaron Rogers in value.

Update: Johnny Unitas, at 114, is a closer comparable.

In terms of risk, the trade is riskier than the Eli Manning trade, and less risky than the RG III trade or the Earl Campbell trade. For 9 more AV than the RG III trade, they received 24 more AV in return.

Best of luck to the Rams. I hope their picks work out well for them.

 

It isn’t about you, draft pundits: thoughts on the NFL draft.

The NFL draft is a kind of auction, with auction-like dynamics. It’s also akin to a marriage. It only takes one, not a crowd, to get married and the opinion of the one outweigh the many. When analyzing the draft, I’ve been known to say things like between three players of the same true value, the one that gets drafted is the one whose value is most overestimated (1). I’ve also said things like one scouting opinion isn’t important, but the envelope of opinions is. The distribution of those opinions is crucial to knowing when a player can be drafted (2).

The distribution of player rankings can affect the possible draft positions of a player. Hand drawn curves on a brand new pen tablet, so they’re not perfect curves. Imagine the purple curve with more extensive tails.

In the diagram above, there are three distributions, with different peaks, means and spreads. Player A, in black, has a tight distribution of values and barring any issues with uniqueness of position, there is a consensus where he will be drafted. Player B, in red, has a broader distribution, but is unlikely to suffer more than half a round of variance in draft position. Player C, in purple, has an envelope encompassing two whole rounds of the draft. It’s the player C types that create a lot of controversy.

Travis Frederick and ZZ Top: Separated at birth?

The player the Dallas Cowboys drafted in the first round, Travis Frederick, is exactly one of those types. He was highly ranked before the combine, but suffered because of his bad 40 time. People like Gil Brandt, who had him ranked 27th best at the time, dropped him because of his 40 time. Perusing various links, such as this one, you see rankings ranging from 31 (Gary Horton) into the 90s. Now please note that draft pundits really don’t count, NFL teams do. But for the sake of argument, we’re using media scouts as an estimator of the envelope of NFL opinions. And that envelope of values encompass two whole rounds of variance.

So, what happens when you must have a player whose valuation envelope is a broad distribution? This player must be taken pretty far from the mean, in the tails of the “high value” side, or else you risk losing him (3). What is guaranteed though, is that the pundits on the other side of the mean from you will undoubtedly scream bloody murder. That’s because a draft pundit’s opinion is his life’s blood, and they make their money validating and defending that opinion, usually in print, and sometimes on television. That it’s one of many doesn’t matter if that’s how you make your living. So of course pundits will scream.

2013 was a draft with few good players. If estimates are valid that there were only 16 or so players truly worth a first round pick, then by default you’re overdrafting your quality by a half round by the middle of the first round. If the span of Frederick’s valuations really ran from, say, mid second to the beginning of the fourth, then the so-called overdraft is not, it’s entirely the function of three things: first, the perceived need for the player and second, such a broad valuation envelope that Dallas had to draft him in the tails of the distribution. Third factor, the lack of talent overall in the draft that led to overdrafting in general.

~~~
Footnotes

1. Jonathan Bales, before he became a New York Times contributor, favored this comment (common sense, IMO) and used it to help validate a pet drafting theory of his. I never saw enough rigor in his theory to separate it from the notions of BPA or need, as it was more a collective efficiency concept. IMO the notion hardly led to the invalidation of BPA or needs based drafting.

2. In the early 2000s, I wrote a Monte Carlo simulator of the draft, which explicitly used those distributions to estimate where players would be drafted. More discussion of that code, released as a Sourceforge project, is here.

3. Let me note that in “must have” situations, teams whose draft record no one complains about .. New England say .. will draft players above their worth. Belichick’s rationale, given in the link, is instructive. An excerpt is:

Now, the question is always, “How much do they like him and where are they willing to buy?’ I’m sure for some teams it was the fourth round. For some teams it was the third round. But we just said, ‘Look we really want this guy. This is too high to pick him, but if we wait we might not get him, so we’re going to step up and take him.’

PS – tskyler, a Cowboys Zone forum contributor, has a very nuanced fan analysis of the Frederick draft here, one worth reading.

 

How good a NFL player do you get when you trade up in the first round?

This has been part of an ongoing conversation among Dallas fans, and perhaps among any of the 9 teams, from the Redskins to Patriots to the Vikings, that traded up in the first round of the 2012 NFL draft. There are some new tools for the analyst and the fan, and these include: (1) Pro Football Reference’s average AV per draft choice list, (2)  Pro Sports Transactions’ NFL draft trade charts, and (3) The Jonathan Bales’ article on Dallas Cowboys.com where he analyzes a series of first round trades up from 2000 to 2010. He concludes that in general, the trade up does not return as much value as it gives.

I suspect that Jonathan’s conclusion is also evident in the fantasydouche.com plot we reposted here. The classic trade chart of Jimmy Johnson really does overvalue the high end draft choices. You’re not paying for proven value, but rather potential when you trade up. I suspect by the break even metric we chose, comparing relative average AVs, that many draft trades never pay off, in part because people pay too much for the  value they receive. This is most evident in trading a current second or third and a future first for a current first round draft choice. These trades tend almost to be failures by design, and smack ultimately of desperation, true even when the player obtained (e.g. Jason Campbell) actually has some skills.

That said, how many of these players exceed the average abilities of the slot in which they were drafted? Now that we have the PFR chart, this is another question that can be asked of the first round players. Note that Jonathan Bales’ study doesn’t really answer the question of how good the player becomes, in part because the time frame chosen doesn’t allow the player adequate development. I started in the year 2000 1995, ended in the year 2007. I identified 67 players in that time frame, and I compared the AV for each player as given by the weighted average on the PFR player page. I’ll note that the player page and the annual draft pages do not agree on players’ weighted career accumulated value, so I assumed the personal pages were more accurate.

As far as a scale, we’re using the following:

AV relative to average	Ranking
-25 AV or more	Bust
-24 to -15 AV	Poor
-14 to -5 AV	Disappointing
-4 to +4 AV	Satisfactory
+5 to +14 AV	Good
+15 to +24 AV	Very Good
+25 AV and up	Excellent

 
Note there are some issues with the scale. Plenty of players from 1995 through 2007 are still playing, and their rankings are almost certainly going to change. In particular, Eli Manning at +24 and Jay Cutler at +23 have a great chance to end up scored as Excellent before the next season is over. Jason Campbell is at +19, and if he starts for a team for one season, he will end up with a ranking of Excellent. Santonio Holmes (+19) also has a shot at the Excellent category.

Players in the years 2006 and 2007 in lower categories (Manny Lawson at +7, Joe Staley at +4, Anthony Spencer at 0 ) could end up as Very Good, perhaps even Excellent if their careers continue.

The scoring ended up as

Scale	Number	Percent as Good	Percent as Bad
Excellent	14	20.9	100.0
Very Good	9	34.3	79.1
Good	13	53.7	65.7
Satisfactory	10	68.7	46.3
Disappointing	7	79.1	31.3
Poor	5	86.6	20.9
Bust	9	100.0	13.4

 
Data came from the sources above. A PDF of these raw data is here:

NFL Trade Ups

Update: Increased the dates of players considered from 2000-2007 to 1995-2007. Moved Ricky Williams back to 1999.

 

Assessing the break even point of the two Ricky Williams trades

There were, of course, two substantial trades of Ricky Williams. The first netted the Washington Redskins the whole of the Saints 1999 draft, plus the Saint’s first and third round picks of 2000. Three years later, Ricky was traded to the Miami Dolphins for a pair of first rounders, plus change. The first was obviously not paid off. How did the Miami Dolphins fare in their trade, using our new risk metrics?

Risk Ratio no longer makes sense as a term when you’re talking about someone already drafted. The important term becomes the net risk term, 52 AV. That’s 1 more AV than the typical #1 draft choice, and that’s the amount of AV Ricky had to generate in order for this trade to break even. And note, these calculations are derived from weighted career AV, not raw AV. So any raw AV we apply to these numbers is a rough approximation (A typical career summing to, say, 95 AV, might end up around 76 or so WCAV).

That said, Ricky Williams had a great first season with the Dolphins, generating 19 AV in that season alone. His total ended up somewhere around 57 AV. I’d suggest the second trade approximately broke even.

End notes: I’ve seen a lot of discussion around  this set of data, discussing the quality of draft picks on a per pick basis, posted in of all places, a Cav’s board. If this board isn’t the original source of these graphs, please let me know. An excellent resource for high quality NFL draft trade information is here. And finally, a reader named Frank Dupont writes:

I wrote a book about decision making in the NFL.  It’s sort of a pop science book because it seeks to make what happens in the NFL understandable via some work that people like David Romer, Richard Thaler, and Daniel Kahneman have done.  But because all pop science books make their point through narrative, I spend a lot of time looking at why football coaches are so old, but other game players like chess players and poker players are so young (Tom Coughlin is 65 and yet the #1 ranked chess player in the world is 21, the world’s best poker players are 25-ish).

The link for the book is here, if this topic sounds interesting to you. I’ll only note in passing  that while physics prodigies are common, biologists seem to hit their stride in their 60s.  Some areas of knowledge do not easily lend themselves to the teen aged super genius.