With quarterback seeming to be off the agenda in the first round of the upcoming draft (or is it?), discussions on Hogs Haven have been focusing on what elite play makers at other positions Washington might consider targeting with the 11th overall pick. One of these discussions got me thinking about the concept of position value, specifically whether it might be counterproductive to rule out certain positions at #11. This prompted a broader exploration of where elite players are taken in the draft, and whether some teams are better at finding them than others.
In my last article, I showed that there is weak correlation between teams’ ability to find elite talents in the draft and long-term success on the field. At first, I didn’t know if I was more surprised by how weak that correlation was or by the fact that it was even visible at a statistical level. On further reflection, it became clear that it was the latter. There is obviously a lot more to team building than finding a few star players. In the words of former Redskins’ GM, and draft savant, Scot McLoughan:
“Blues are like perennial Pro Bowl players,” he said. “Reds are like really good football players.”
“If you’re lucky, you have four or five blues and hopefully one of those is your quarterback. If you have another 30 who are reds, starters, solid backups, core special teams guys, then you have a chance. That’s how you build your roster. That’s how you build a team.”
If McLovin is right, and how could he not be, my previous attempt to examine the effect of draft performance on team success might have come up short because I only included Blues and neglected the larger number of Reds who are essential for their success. Perhaps picking up a couple of Reds each draft is more important to long-term team success than finding four to six Blue players each decade.
Statistical Definition of Reds and Blues
In this analysis, I expanded my definition of draft success to include selecting Reds as well as Blues.
In the previous articles, I defined elite (A.K.A. Blue) draftees as players whose Weighted Approximate Value (wAV) at the time of analysis was greater than two standard deviations above the mean wAV for their draft class. This threshold essentially filters the handful of outliers, or freaks, within each draft class.
To add the contribution of Reds, whom McCloughan defines as “really good football players,” I simply dropped the threshold to one standard deviation above the mean. This is essentially a statistical definition of “really good to outstanding.”
To put this is real terms, here are the 10 players who just made the cut as Reds in the 10 drafts being analyzed:
2010 – WR Brandon LaFell, Carolina Panthers, 78th pick
2011 – QB Tyrod Taylor, Baltimore Ravens, 180th pick, 1 Pro Bowl
2012 – CB Josh Norman, Carolina Panthers, 143rd pick, 1 All Pro, 1 Pro Bowl
2013 – DE John Simon, Baltimore Ravens, 129th pick (Former Teamster LB Jon Bostic was the 3rd player above the cutoff)
2014 – RB James White, New England Patriots, 130th pick
2015 – DT Tyeler Davison, New Orleans Saints, 154th pick (WR Jamison Crowder was the 2nd player above cutoff, WR Nelson Agholor and CB Ronald Darby were the first two players below cutoff)
2016 – C Graham Glasgow, Detroit Lions, 95th pick
2017 – LB Zach Cunningham, Houston Texans, 57th pick
2018 – G Wyatt Teller, Buffalo Bills, 166th pick, 2x AP2, 1 Pro Bowl
2019 – G Max Scharping, Houston Texans, 55th pick
One limitation of wAV is that it takes a while to build up and get a solid read on players. Ratings for recently drafted players, such as Wyatt Teller and Max Scharping are a bit more shaky. A lot of promising players in the 2019 draft were excluded, which is the right kind of error. It is better to set the threshold high enough to exclude some good players than set it low enough to include average to below average players by mistake.
Another limitation is that, since it is cumulative, it tends to reward longevity. I minimized that effect by only using wAV to compare players within a draft class. Nevertheless, an above average player from one of the earlier draft classes, like Ryan Tannehill, can achieve a higher wAV than a star, like Andrew Luck, by virtue of playing longer.
Despite those limitations, wAV is still the best available metric for this type of bulk analysis, so we just have to live with its quirks and weaknesses. The errors introduced by these limitations would be expected to weaken the correlations I am looking for and thereby work against finding support for the hypothesis that drafting well drives team success.
Which Teams Are Best at Drafting Reds and Blues?
In the previous article, that just looked at Blues, the worst drafting teams (Chicago, Jacksonville) only selected one elite player in 10 years, whereas the best performers picked seven (Kansas City, Rams). Expanding the analysis to Reds and Blues reveals a much larger total difference between the best and worst teams.
The average NFL team drafted 12 Red and Blue players from 2010 to 2019, giving an average hit rate of 1.2 very good to elite additions per draft. The best drafting team, Seattle, hit on six above average and the worst, Jets, hit on six below average:
The addition of Red players shuffles the order of teams a fair bit. In this view, Seattle overtakes Kansas City and the Rams as the draft leader. The Chiefs don’t fall that far behind the leader, but the Rams drop to the middle pack with 12 Blues and Reds. The Jets drop from the middle pack to last place. Baltimore, Green Bay, Carolina and Chicago do better when Reds are taken into account, while Arizona, Indianapolis and the Jets look worse. Washington retains its status as a league-average drafting team, regardless of whether we look at Blues alone or Reds and Blues.
As you might expect, Reds could be found over a wider draft range than Blues, who are mainly drafted in the first two-and-a-half rounds:
Red players continue to be taken well into the fifth round, and a few were even drafted late in the seventh round.
Is draft success correlated with team success?
To determine whether these differences in draft performance might translate into differences in performance on the field, I examined the correlation between the numbers of Reds and Blues that different NFL franchises drafted and three different measures of sustained success over the decade from 2010 to 2019:
Numbers of Winning Seasons –This provides a better measure of sustained regular season success than total Win-Loss record which is subject to bias when teams have a few really good seasons interspersed with long losing streaks or a few really bad seasons within long winning stretches.
Numbers of Seasons with a Playoff Win –This provides a good measure of being a long-term playoff contender, as opposed to metrics based on overall playoff performance, which can be tilted by a single Super Bowl year within a long playoff drought (e.g. 2011 New York Giants).
Point Differential – The difference between points for and against provides a strong predictor of game outcomes within a season, and is a good metric for separating teams that are worse than their records from true contenders. For this analysis, I calculated regular season point differential across the 10 seasons from 2010 to 2019.
The first figure shows the relationship between draft success and number of winning seasons.
There is a clear trend for teams that draft well to have better season records over the decade. The effect is fairly moderate, with draft success explaining approximately 18.5% of the variance in numbers of winning seasons. This is a major improvement over Blue players on their own, which only explained 8.7% of the variance in numbers of winning seasons.
Adding Reds to the mix also increases the strength of correlation between draft success and numbers of seasons with a playoff win:
As we saw with winning seasons, adding Reds increases the strength of correlation between draft success and sustained playoff success. In this case, the addition of Reds increased the amount of variance explained from 5.5% to a little over 20%.
Last of all, the strongest correlation was with Point Differential.
The most remarkable thing to me in this graph is how the Patriots completely separate themselves from the rest of the league. There is a clear trend for better drafting teams to have higher point differentials and worse drafting teams to have lower ones. The trend would be even stronger, if not for a group of teams, including Washington, Cleveland, Miami, Jacksonville, Detroit, Tampa, and the Raiders, which had poor records despite drafting around league average. Even so, draft performance explains around 21.6% of the variance in point differential.
Let me start by saying that these results do not actually prove anything. Correlations show a relationship between two variables, but they do not prove causality.
The results do show reasonably strong relationships between draft success and three measures of team performance over the decade from 2010 to 2019. These results are consistent with the idea that building through the draft is a key determinant of long-term success on the field. However, it is also possible that what we are really seeing here is that players drafted by better teams tend to do better in the NFL as a result of better coaching, supporting casts and team cultures. I’m not sure how to tease those two possibilities apart. I have a strong suspicion, though, that draft success drives team success rather than the other way around.
The difference between the results obtained in the present analysis and my previous article on elite draftees is striking. Adding Red players to the mix resulted in a substantial increase in the strength of correlation between draft success and long-term team success, compared to the previous analysis of the impact of drafting Blue players only. This suggests to me that the infusion of talent throughout the roster via the draft makes a bigger overall contribution to team success than the relatively rare elite “difference makers” that we tend to focus on in draft discussions.
Perhaps the really good players that allow the elite players to shine are the real difference makers. The best drafting teams manage to find these guys well into day three of the draft.
Acknowledgement: Thanks once again to James Dorsett for making a difference to this article
Which prospect with Red upside would you target after pick #11 in the 2022 draft?
This poll is closed
WR George Pickens, Georgia
LB Quay Walker, Georgia
DT Phidarian Mathis, Alabama
TE Isaiah Likely, Coastal Carolina
OT Tyler Smith, Tulsa
CB/KR Marcus Jones, Houston
IOL Cade Mays, Tennessee
WR Wan’Dale Robinson, Kentucky
RB Hassan Haskins, Michigan
Someone else - tell us in the comments