As Dallas Cowboys fans, the inability of the team to get anywhere in the playoffs for the last three decades hangs like a dark cloud over our collective fandom. And for many of us, this often diminishes or even invalidates some of the positive things the Cowboys do or have done.
Dak an MVP candidate? No es posible, because playoffs. Jerry Jones wins NFL Executive of the Year in 2014? Ridonculous, because Super Bowl drought. Three consecutive 12-5 seasons? All hat, no cattle without postseason success.
So, as we embark on our Consensus Board exercise today, it’s important to understand that the Cowboys draft very well overall. You may not like every pick, you may quibble with some decisions, and yes, the draft success has not translated into the postseason success we’re all looking for, but the data here is very clear.
Over the last decade, the Cowboys rank No. 2 overall in terms of Weighted Approximate Value (wAV) for the players they drafted. wAV comes courtesy of Pro-Football-Reference.com and is their attempt to put a single number on the seasonal value of a player at any position from any year. Here’s the full list of all teams over the last decade.
First-round AV points by team, 2016-2025
Rank
Team
Picks
wAV
Rank
Team
Picks
wAV
Rank
Team
Picks
wAV
1
BAL
93
1,368
12
CIN
86
989
23
MIA
70
924
2
DAL
87
1,189
13
CLE
84
983
T24
PIT
74
907
3
BUF
77
1,167
14
NOR
62
972
T24
MIN
93
866
4
IND
90
1,135
15
CHI
74
970
26
ATL
67
863
5
GNB
97
1,096
16
SEA
90
961
27
WAS
82
857
6
SFO
88
1,078
17
PHI
76
960
28
HOU
74
833
7
KAN
70
1,061
18
DEN
80
955
29
CAR
71
805
8
JAX
87
1,059
19
TAM
72
943
30
NYJ
75
792
9
DET
79
1,048
20
NYG
75
943
31
LVR
82
714
10
LAR
89
1,021
21
TEN
74
934
32
ARI
79
711
11
LAC
77
1,001
22
NWE
91
928
But what about Taco Charlton, Mazi Smith, or Trysten Hill, the inquiring mind wants to know? No team has a 100% hit rate, every team misses on draft picks. The Cowboys have done well despite those misses.
The Cowboys have also been fortunate in that they’ve had more draft picks than the average team, in part because they (like Baltimore) have done very well in collecting compensatory draft picks. Their 87 picks rank ninth in the league.
And the Cowboys have done well despite a relative lack of draft capital which comes in part from being a Top 10 team in regular season wins and in part from trading away a first-round pick for Amari Cooper. They rank just 27th in draft capital available over the last decade.
If we look beyond just Total wAV and divide it by the number of picks and draft capital, the Cowboys are still a Top 5 team. Here’s a breakdown of the Top 5 teams for each metric.
Total wAV
wAV/No. of Picks
wAV/Draft Capital
Team
Rank
Team
Rank
Team
Ramk
BAL
#1
NOR
#1
LAR
#1
DAL
#2
KAN
#2
KAN
#2
BUF
#3
BUF
#3
BAL
#3
IND
#4
BAL
#4
BUF
#4
GNB
#5
DAL
#5
DAL
#5
Any way you look at it, the Cowboys have drafted well. But that doesn’t mean we can just “trust the process”, sit back, and relax. Far from it.
Case in point: The Cowboys’ draft success swings wildly by round. We saw that the Cowboys ranked N0. 2 overall by wAV, but here’s how they rank by round:
1st round: 12th2nd round: 23rd3rd round: 5th4th-7th round: 6th
That second-round dip does not look good, but before we rush to judgement, let’s look at how the Top 5 teams by total wAV rank across draft.
wAV by round, 2016-2025
Team
Total Draft
1st
2nd
3rd
DAY 3
BAL
1st
1st
31st
1st
3rd
DAL
2nd
12th
23rd
5th
6th
BUF
3rd
5th
5th
10th
11th
IND
4th
25th
2nd
19th
7th
GNB
5th
17th
14th
29th
1st
The first thing that catches the eye is that four out of the Top 5 teams have one round in which they rank in the bottom third of the league. So the Cowboys’ second-round dip is not unique, very few teams are consistent from round to round. And Baltimore, kings of the first round, are beggars in the second round.
The Cowboys are pretty consistent in rounds 3-7, but for a team ranked second overall in wAV, their first-round rank (12th) is also a little surprising. This is largely explained by the relative lack of first-round picks (trading away a first for Amari Cooper is a negative here) and low relative draft capital. Here’s what the Top 5 teams look like when we divide Total wAV by the number of picks and draft capital.
wAV / No. of picks
Team
Total Draft
1st
2nd
3rd
DAY 3
NO
1st
22nd
5th
1st
20th
KC
2nd
13th
7th
17th
2nd
BUF
3rd
2nd
9th
2nd
11th
BAL
4th
4th
29th
10th
3rd
DAL
5th
6th
27th
5th
5th
wAV / Draft Capital
Team
Total Draft
1st
2nd
3rd
DAY 3
LAR
1st
8th
24th
5th
4th
KC
2nd
3rd
1st
11th
6th
BAL
3rd
1st
27th
7th
12th
BUF
4th
2nd
8th
3rd
5th
DAL
5th
4th
21st
6th
3rd
By accounting for the number of picks and the available draft capital, the Cowboys rank sixth and fourth respectively in the first round, but they still have that dip in the second round. And while that dip is not unique to the Cowboys, the reasons for that dip may be unique to the Cowboys.
My hypothesis going in, and one that we’ve looked at repeatedly here on BloggingTheBoys, is that some of the issues come from the Cowboys deviating from a Consensus Big Board too much. So I went and compiled all the Cowboys round 1-3 draft picks over the last 10 drafts (2016-2025), looked at each pick’s wAV, and compared that to the average wAV of the Best 5 Players left on the Consensus Big Board at the time of the pick. Here are two examples of what that looks like:
2022 – Consensus Best 5 Players available at #24
Round
Pick
Consensus Rank
Tm
Player
Pos
Age
wAV
1
26
11
NYJ
Jermaine Johnson
LB
23
19
1
27
20
JAX
Devin Lloyd
LB
23
35
1
30
21
KAN
George Karlaftis
DE
21
27
1
28
25
GNB
Devonte Wyatt
DT
24
10
2
42
26
MIN
Andrew Booth
CB
21
3
1
24
35
DAL
Tyler Smith
OL
21
33
In 2022, Tyler Smith was ranked 35th on the Consensus Big Board (via MockDraftDatabase.com). He was picked at #24 overall, and at the time of the pick, the Consensus Best 5 Players were the five players listed above. Had the Cowboys followed the consensus board, one of those five guys likely would have been the pick. Those Best 5 Players combined for an average wAV of 18.8, which means that by picking Tyler Smith (33 wAV), the Cowboys created a wAV Surplus of +14.2 points versus that basket of players.
The next example is Trevon Diggs. At the time he was drafted, three players ranked above him on the Consensus Board. Add the two players directly below Diggs on the Consensus Board and you’ve got the Best 5 Players averaging 22.6 wAV points, giving Diggs a wAV Surplus of +10.4 points.
2022 – Consensus Best 5 Players available at #24
Round
Pick
Consensus Rank
Tm
Player
Pos
Age
wAV
2
61
24
TEN
Kristian Fulton
CB
22
18
2
54
27
BUF
A.J. Epenesa
DE
21
18
2
59
29
NYJ
Denzel Mims
WR
22
5
2
51
30
DAL
Trevon Diggs
CB
21
33
2
58
36
MIN
Ezra Cleveland
T
22
18
3
74
38
NOR
Zack Baun
LB
23
18
Two small caveats: I removed all quarterbacks from the calculation; their wAV can be quite wonky and can drive wAV Surplus significantly in both directions. Also, I tweaked Leighton Vander Esch’s number to show only the AV until 2023 for him and his Best 5 players.
Once I had the surplus wAV for each player, I added additional metrics that I would use to analyze their impact on driving surplus wAV:
Age when draftedRelative Athletic Score (RAS) as a marker for traits/athleticismKnown pre-draft injury flags/character concernsPower Five SchoolLevel of reach/steal vs Consensus Big Board
That left me with the following unwieldy table:
Cowboys Picks
Year
Player
Age
RAS Score
Flag
Power Five
Round
Pick
Consensus Rank
Reach/Steal
wAV
Top 5 Consensus wAV
wAV Surplus
2016
Ezekiel Elliott
20
8.65
yes
1
4
9
-5
68
65.2
2.8
2016
Jaylon Smith
20
—
Yes
yes
2
34
47
-13
37
37.0
0.0
2016
Maliek Collins
20
7.78
yes
3
67
79
-12
51
16.6
34.4
2017
Taco Charlton
22
8.17
yes
1
28
23
5
9
35.0
-26.0
2017
Chidobe Awuzie
21
9.64
yes
2
60
41
19
28
17.2
10.8
2017
Jourdan Lewis
21
5.02
yes
3
92
84
8
26
12.0
14.0
2018
Leighton Vander Esch*
21
9.98
no
1
19
22
-3
39
36.6
2.4
2018
Connor Williams
20
9.52
yes
2
50
33
17
34
23.0
11.0
2018
Michael Gallup
22
5.87
no
3
81
95
-14
30
37.2
-7.2
2019
Trysten Hill
21
9.53
no
2
58
106
-48
5
17.4
-12.4
2019
Connor McGovern
22
9.77
yes
3
90
130
-40
37
11.2
25.8
2020
CeeDee Lamb
21
7.44
yes
1
17
13
4
65
41.4
23.6
2020
Trevon Diggs
22
—
yes
2
51
30
21
33
22.6
10.4
2020
Neville Gallimore
23
7.1
yes
3
82
50
32
14
9.8
4.2
2021
Micah Parsons
21
9.59
yes
1
12
13
-1
67
25.2
41.8
2021
Kelvin Joseph
21
9.01
Yes
yes
2
44
61
-17
2
17.2
-15.2
2021
Osa Odighizuwa
22
7.64
yes
3
75
109
-34
31
6.8
24.2
2021
Chauncey Golston
23
7.6
yes
3
84
180
-96
12
12.2
-0.2
2021
Nahshon Wright
22
2.44
yes
3
99
348
-249
11
20.4
-9.4
2022
Tyler Smith
21
9.62
no
1
24
35
-11
33
18.8
14.2
2022
Sam Williams
23
9.72
yes
2
56
89
-33
7
11.8
-4.8
2022
Jalen Tolbert
23
8.62
no
3
88
68
20
9
15.0
-6.0
2023
Mazi Smith
21
9.99
yes
1
26
35
-9
9
11.2
-2.2
2023
Luke Schoonmaker
24
9.86
Yes
yes
2
58
100
-42
4
9.0
-5.0
2023
DeMarvion Overshown
22
8.18
yes
3
90
107
-17
8
6.0
2.0
2024
Tyler Guyton
22
9.73
yes
1
29
29
0
9
9.6
-0.6
2024
Marshawn Kneeland
22
9.08
no
2
56
47
9
2
5.2
-3.2
2024
Cooper Beebe
22
9.29
yes
3
73
55
18
10
4.0
6.0
2024
Marist Liufau
23
5.63
yes
3
87
159
-72
6
4.2
1.8
2025
Tyler Booker
21
3.68
yes
1
12
28
-16
7
4.4
2.6
2025
Donovan Ezeiruaku
21
8.28
yes
2
44
29
15
3
2.4
0.6
2025
Shavon Revel
24
—
Yes
no
3
76
42
34
1
2.6
-1.6
I understand that beyond “some cells are green, some are red” it’s very hard to read anything from this table, which is why we’ll break down the data set here into more digestible and hopefully meaningful insights.
If you want to be successful in the draft, you’ll want to maximize your surplus wAV, meaning more green cells and less red cells in the table above. One way to measure what is driving surplus wAV is to analyze the correlation between surplus wAV and any one of the metric above.
The value we get from a correlation analysis defines the strength of the relationship between two variables: r = 0 means there is no correlation. r = 1 means there is a perfect positive correlation. r = -1 means there is a perfect negative correlation.
1. Consensus Board: The Round Correlation
If we look separately at each round, the correlation between Consensus Rank and wAV Surplus is quite dramatic:
Round 1 Correlation: r=−0.37Round 2 Correlation: r=−0.70Round 3 Correlation: r=-0.28
What this means, at least for the Cowboys, is that in Round 1, adhering to the consensus board is important (r=−0.37). In Round 2, adhering to the board is mandatory (r=−0.70), and in Round 3 it’s best ignored
(r=-0.28). Importantly, these correlations are for the Consensus Bord only. If we look at where each player was actually picked (as a proxy for the Cowboys’ Big Board), the numbers change quite considerably:
Cowboys Round 1 Correlation: r=−0.46Cowboys Round 2 Correlation: r=+0.03Cowboys Round 3 Correlation: r=-0.43
The Cowboys are actually outperforming the Consensus Board in rounds 1 & 3, but in round 2, their picks have close to zero correlation with surplus wAV. For the most part, they would have done much better in the second round by following the consensus draft board than by following whatever decision-making process they use in the second round.
2. The Cowboys’ ability to “beat the board” depends on the tier of talent they are picking in.
The Elite Floor: When picking early in the first round (up until around pick 24), the Cowboys show a surplus wAV on every single pick. Micah Parsons (+41.8 wAV Surplus), CeeDee Lamb (+23.6), and Tyler Smith (+14.2) are the obvious standouts, but Ezekiel Elliott, Leighton Vander Esch, and Tyler Booker all have a positive surplus. However, as they move into the late first round, the Cowboys’ ability to identify better players than the consensus board drops significantly.
The Second Tier Struggle: When picking late in the first and all the way to the end of the second round, the Cowboys often seem to reach for specific traits (RAS) and end up with neutral or negative value. Mazi Smith (-9 reach versus consensus board), Sam Williams (-33 reach), Trysten Hill (-48 reach), and Luke Schoonmaker (-42 reach) were all reaches with elite athletic traits (RAS > 9.5) and all delivered negative surplus value. Internally, the Cowboys likely justified these picks with the players’ elite athletic traits, but the data shows this to be a high-risk and net negative drafting strategy when it ignores the consensus board rank, and the Cowboys have repeatedly run into this “Trait Trap”.
The single strongest driver of value in Round 2 is Reach/Steal (r=0.78), or in simpler terms: Players picked later than consensus rank (“Steal”) combined for a wAV Surplus of 29.6 points, while players picked higher than their consensus rank (“Reach”) combined for a wAV of -37.4 points. That’s quite a swing.
Reaching for a player in the second round is the primary cause of value collapse for the Cowboys, while getting a steal is the primary generator of surplus.
The Third Round Rebound: In the third round, the Consensus Board is no longer a reliable predictor of success for Dallas. Round 3 is where the Cowboys’ internal scouting shines and the Trait Trap is less relevant. The Cowboys have found massive surplus value by reaching for players they specifically liked who were ignored by the consensus. Connor McGovern (reached by 40 spots) resulted in a +25.8 wAV Surplus; Osa Odighizuwa (reached by 34 spots) resulted in +24.2; Maliek Collins (reached by 12 spots) delivered a massive +34.4 surplus. In contrast, taking a “consensus steal” in Round 3 has not reliably led to overperformance.
The reason for this is because third-round performance in this model is driven by Athleticism (RAS) (r=0.42), Age (r=−0.64), and Power Five status (r=0.66) much more than by consensus board ranking (-0.28) or even Reach/Steal (-0.24). The Cowboys win in Round 3 by identifying elite athletes from major programs who are still young.
In short, by the third round, the Consensus Big Board loses its warning power. This is the round where the Cowboys’ internal conviction picks actually pay off. While the consensus is great at identifying Round 1 and 2 talent, the Cowboys’ scouts have proven more adept at finding Round 3 contributors than the consensus board.
3. Young, athletic, and undervalued? Come to Dallas!
The following table summarizes the correlation of key variables with wAV Surplus:
Cowboys Picks
Variable
Overall Correlation
Summary
Age when drafted
−0.32
Negative. Younger players consistently provide higher long-term surplus, especially in Round 3 (−0.64).
RAS Score
+0.09
Positive. Overall a moderate driver, but becomes critical in Round 3 (+0.49), where athleticism separates hits from misses.
Injury/Character Flag
−0.26
Negative. Flags are “value traps.” They are most damaging in Round 2 (−0.39), where high-risk picks often fail to return value.
Power Five
+0.24
Positive. Offers a stability floor. Power 5 players provide more reliable surplus value than non-Power 5 players in the mid-rounds.
Reach/Steal
+0.19
Positive. In this dataset, a higher “Steal” value (picking after consensus) correlates with higher surplus, particularly in Round 2 (+0.78).
4. RAS is not a “Reach License”
We saw above that RAS can be a driver of surplus wAV – in the right circumstances.
But there is a very specific subset of picks, Elite Athletes (RAS >9.5) picked between 25 and 64, that – depending on your view – show a fascinating and/or worrying “Jekyll and Hyde” dynamic in the Cowboys’ drafting strategy: There is a near-perfect correlation (+0.98) between Reach/Steal and wAV Surplus within this group.
When the Cowboys wait for an athlete to fall to them (e.g., Connor Williams, Chidobe Awuzie), they generate massive surplus value.When the Cowboys chase an athlete by “reaching” ahead of the consensus board (e.g., Trysten Hill, Luke Schoonmaker), the elite athleticism almost never overcomes the technical or developmental deficiencies that caused the consensus board to rank them lower.
The data suggests the Cowboys often fall in love with athletic traits in the late first and second round and ignore the consensus warning. Specifically in the second round for the Cowboys, elite athleticism is a multiplier, not a substitute. It multiplies the value of a good football player (consensus high rank) into a potential star, but it cannot turn a project (consensus low rank/reach) into a surplus producer quickly enough to justify the draft capital.
In the 25–64 range, the Cowboys are at their best when they are patient. The data suggests they must resist the urge to use draft capital in that range on internal favorites who are ranked low by the consensus. In this range, the Cowboys should use the consensus as a boundary or sanity check to improve the quality of their own board.
The data also gives you six different player clusters, or rather: five clusters and Taco Charlton. I’m adding these because they essentially tell the same story of the Cowboys draft performance over the first three rounds, but in a simpler, more linear narrative
1. Blue Chip / Top 25 Assets (+87.4)
High-round, high-consensus players picked in the Top 25. In this range, the Cowboys board and the consensus board pretty much converge, and the Cowboys have consistently drafted surplus players in this range.
Micah Parsons (Pick 12) — Overperformed (+41.8)CeeDee Lamb (Pick 17) — Overperformed (+23.6)Tyler Smith (Pick 24) — Overperformed (+14.2)Ezekiel Elliott (Pick 4) — Overperformed (+2.8)Tyler Booker (Pick 12) — Overperformed (+2.6)Leighton Vander Esch (Pick 19) — Overperformed (+2.4)
2. The Athletic Reaches (-20.0)
Pick 24-64 players drafted earlier than or at consensus (Reach) where elite athleticism (RAS >9.5) was probably the key driver of the selection.
Trysten Hill (RAS: 9.49 / Non-P5) — Underperformed (-12.4)Mazi Smith (RAS: 9.61) — Underperformed (-2.2)Sam Williams (RAS: 9.72) — Underperformed (-4.8)Tyler Guyton (RAS: 9.73) — Underperformed (-0.6)
3. The Consensus Value Steals (+47.4)
Second or third round players who fell past their consensus rank and could be considered steals. This cluster has a very high success rate for surplus value, even with two underperformers in this cluster.
Connor Williams (Round 2) — Overperformed (+11.0)Trevon Diggs (Round 2) — Overperformed (+10.4)Chidobe Awuzie (Round 2) — Overperformed (+10.8)Jourdan Lewis (Round 3) — Overperformed (+14.0)Neville Gallimore (Round 3) — Overperformed (+4.2)Cooper Beebe (Round 3) — Overperformed (+6.0)Donovan Ezeiruaku (Round 2) — Overperformed (+0.6)Jalen Tolbert (Round 3 / Non-P5) — Underperformed (-6.0)Marshawn Kneeland (Round 2 / Non-P5) — Underperformed (-3.2)
4. Red Flag Gambles (-21.8)
Players marked with an injury or character flag. This group has a 100% failure rate versus wAV surplus.
Kelvin Joseph (Reach/Red Flag) — Underperformed (-15.2)Luke Schoonmaker (Reach/Medical) — Underperformed (-5.0)Jaylon Smith (Reach/Medical) — Underperformed (0.0)Shavon Revel (Steal/Medical/Non-P5) — Underperformed (-1.6)
5. The Late Reaches (+72.2)
Players reached for in Round 3 that the Cowboys identified correctly despite consensus being lower. Strong player cluster with a high surplus value, even if not every reach here is an automatic hit.
Maliek Collins (Reach) — Overperformed (+34.4)Connor McGovern (Reach) — Overperformed (+25.8)Osa Odighizuwa (Reach) — Overperformed (+24.2)DeMarvion Overshown (Reach) — Overperformed (+2.8)Marist Liufau (Reach) — Overperformed (+1.8)Nahshon Wright (Reach) — Underperformed (-9.4)Michael Gallup (Reach / Non-P5) — Underperformed (-7.2)Chauncey Golston (Reach) — Underperformed (-0.2)
Taco Charlton doesn’t fit any of the player clusters. He wasn’t athletic enough to join the Athletic Reach group, he’s not really a value steal, no flags, etc. In many ways, Taco Charlton was a unicorn, but not the type of unicorn you want to draft.
Taco Charlton (RAS: 7.64) — Underperformed (-26.0)
The table below summarizes the different correlations we’ve been looking at and splits them by round.
Correlation Matrix
1st Round
2nd Round
3rd Round
Rounds 1-3
Age when Drafted
-0.40
-0.23
-0.64
-0.32
RAS Score
+0.10
+0.10
+0.42
+0.09
Injury/Character Flag
—
-0.44
-0.18
-0.26
Power Five Status
-0.05
+0.40
+0.48
+0.24
Reach/Steal Value
-0.04
+0.78
+0.24
+0.18
Consensus Rank
-0.37
-0.70
-0.28
-0.17
The data presented here paints a very clear overall picture of the strengths and weaknesses of the Cowboys draft process. And, yes, there are limitations to this approach, starting with sample size overall, but also with trying to infer meaning from ever smaller clusters, and also with the broad sweeping generalizations made about the Cowboys draft process.
But at the end of the day, my question to you is this: Does what you’ve read here match what your gut has been telling you?