History shows Cowboys have significant blind spot in NFL draft

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.

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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.

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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: 12th
• 2nd round: 23rd
• 3rd round: 5th
• 4th-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.

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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.

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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:

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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.

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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 drafted
• Relative Athletic Score (RAS) as a marker for traits/athleticism
• Known pre-draft injury flags/character concerns
• Power Five School
• Level of reach/steal vs Consensus Big Board

That left me with the following unwieldy table:

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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=−0.37
• Round 2 Correlation: r=−0.70
• Round 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=−0.46
• Cowboys Round 2 Correlation: r=+0.03
• Cowboys 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:

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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.

5. Player clusters

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)

6. Others (-26.0)

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)

6. Correlation Matrix

The table below summarizes the different correlations we’ve been looking at and splits them by round.

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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?