During a recent podcast of the Steelers Preview, Bryan Anthony Davis laid down the challenge for me of determining a method for judging how well a team has drafted over the years. Of course, it’s difficult to back down from a challenge of this nature, so I did my best to use available resources to calculate which team has performed the best throughout the NFL draft.
First of all, I had to set the parameters of how far back I would go. The year 2000 seemed like a wonderful starting point because not only was it the beginning of a new millennium, it also was the start of the Kevin Colbert era for the Pittsburgh Steelers. So rather than going back a nice round 20 years, I encompassed the last 21 NFL drafts.
The next, and definitely the most difficult, step was to determine a metric for measuring a franchises ability to draft. How do you judge more recent draft as it takes a few years to fully determine if players are going to succeed in the NFL or not?
In order to get this process going, I decided on a method to help make this more possible: Let somebody else do all the hard work. Therefore, I decided to use the Approximate Value rankings determined at Pro Football Reference. Specifically, I was looking at Career Weighted Approximate Value which is slightly different.
Here is an explanation of Approximate Value from pro-football-reference.com:
Created by PFR founder Doug Drinen, the Approximate Value (AV) method is an attempt to put a single number on the seasonal value of a player at any position from any year (since 1950).
Doug’s Brief AV Explanation:
“AV is not meant to be a be-all end-all metric. Football stat lines just do not come close to capturing all the contributions of a player the way they do in baseball and basketball. If one player is a 16 and another is a 14, we can’t be very confident that the 16AV player actually had a better season than the 14AV player. But I am pretty confident that the collection of all players with 16AV played better, as an entire group, than the collection of all players with 14AV.”
“Essentially, AV is a substitute for --- and a significant improvement upon, in my opinion --- metrics like ‘number of seasons as a starter’ or ‘number of times making the pro bowl’ or the like. You should think of it as being essentially like those two metrics, but with interpolation in between. That is, ‘number of seasons as a starter’ is a reasonable starting point if you’re trying to measure, say, how good a particular draft class is, or what kind of player you can expect to get with the #13 pick in the draft. But obviously some starters are better than others. Starters on good teams are, as a group, better than starters on bad teams. Starting WRs who had lots of receiving yards are, as a group, better than starting WRs who did not have many receiving yards. Starters who made the pro bowl are, as a group, better than starters who didn’t, and so on. And non-starters aren’t worthless, so they get some points too.”
Additionally, here is an explanation of Career Weighted Approximate Value from PFR:
Weighted Career Approximate Value
At the top of every player’s PFR page, you will see “Weighted Career AV” and a ranking since 1950. This is Doug’s way of balancing peak production against raw career totals; for each player, he computes the following weighted sum of seasonal AV scores:
100% of the player’s best season, plus 95% of his 2nd-best season, plus 90% of his 3rd-best season, plus 85% of his 4th-best season, ....
So now that I have a metric that quantitatively measures how well a player performs in given years throughout their career, it is a starting point for measuring how well a team drafts. I decided I was going to look at this in a two-fold manner: Drafting top of the line players (Superstars), and drafting every game players (Key Contributors).
When looking at what I called Superstars, I was looking for players with a Career Weighted Approximate Value (now called AV from this point on) with a score higher than 50. In order for a player to reach this level, they would have had to have played successfully for several years or have a very long NFL career which is not the easiest thing to do. When looking at the Key Contributors aspect, I was looking for players with an AV of 20 or above, which would obviously include the first category.
Although I looked at these numbers, I did not implement them in the same way in order to draw my conclusion.
To put these numbers into perspective, T.J. Watt as a career AV (weighted) of 47. He is on the cusp of being in the Superstar category after only four seasons and should get there next season barring the unspeakable. This obviously puts him solidly in the Key Contributor group. Players such as Cameron Heyward, David DeCastro, and Maurkice Pouncey are all well over the 50 AV score with 77, 68, and 78 respectively.
For players with an AV of 50 or above, I used it as a raw number per franchise. I didn’t simply rank these number of players, because that would not be fair. After considering it more, teams who are constantly drafting in the top 10 of the NFL draft should have more of these players. So my ultimate equation came to this:
(# of drafted players with an AV of 50+) — (# of top 10 draft picks since 2000) = (Raw score)
Before I go any further, I want to explain that I did not specify which team any draftee played for in order to earn the score. Whichever team drafted a player, their entire career counted towards the score. I did this, first of all, because I could not easily separate it. Additionally, if a team drafts a great player who leaves after four seasons for a big payday and continues to have a great career, it doesn’t mean that said franchise did not do a great job in evaluating the talent, they simply could not keep it (the New York Jets are actually a good example of this). Therefore, these are simply players career score regardless of which team they actually played for. As an example, Drew Brees counts as the San Diego/Los Angles Chargers, as does Eli Manning.
For this comparison, I simply looked at the raw score with the highest being the best. The one team who was at a slight disadvantage was the Houston Texans as they did not join the NFL until the 2002 NFL draft. Here was the breakdown for the Steelers in the AFC North:
Next, here are the top seven scores in the AFC:
Just for fun, here were all the zero and negative scores in the AFC:
Now let’s look at the NFC. There were two teams stood out above the rest and only four which matched the score of the AFC’s top seven:
And for consistency sake, here were the NFC teams with a negative score:
Now that we’ve seen which teams are the best at drafting the Superstars category, I looked at how many players each franchise have drafted with a score of 20 or more in career weighted AV. Rather than look at this as a pure number, I divided it by the total number of draft picks the team has made since 2000 and looked at it merely as a percentage.
What is interesting is how there are a few teams who did well in both categories while some of the teams that had low or negative numbers in the superstar category still managed to do well in the Key Contributors.
Here was how the AFC North played out:
Next, here are the top five teams in the AFC:
As much as the AFC ruled drafting Superstars, the NFC blew them away in drafting Key Contributors. Here is the top 10 in the NFC:
In case you didn’t notice, even though the Steelers were the top team in drafting Key Contributors in the AFC, they would have ranked ninth just ahead of the Packers in the NFC. When looking at all the numbers, it’s the New Orleans Saints which came out on top of the entire NFL in both categories.
Of the three teams which were tied behind the Saints in the Superstars category, they all finished extremely close in the percentage of Key Contributors drafted. Ultimately, it went Pittsburgh, then Green Bay, then New England, but we’re only separate it by 0.12%
So the final results are as follows:
1.) NO: 18; 32.59%
2.) PIT: 15; 27.54%
3.) GB: 15; 27.51%
4.) NE: 15, 27.42%
The results or so close from two to four, they are all basically tied. The reason teams like Atlanta and Carolina, both of which were above 30% in drafting Key Contributors, is because their Superstars numbers were low at four and six respectively.
So how did I do in determining my methodology? Does it at least make sense numerically in determining the best drafting NFL teams since 2000? Feel free to tell me everything I did wrong in the comments below.