Apr 26, 2012; New York, NY, USA; NFL commissioner Roger Goodell introduces Andrew Luck (Stanford) as the number one overall pick to the Indianapolis Colts in the 2012 NFL Draft at Radio City Music Hall. Mandatory Credit: Jerry Lai-US PRESSWIRE
Step inside for the double-secret formula that can tell you how YOUR team fared in the first round. SCIENCE!!! (WARNING: This will be the dorkiest thing you have ever seen).
Thursday night’s festivities had barely ended – in fact, they were still in progress – when the blogosphere, Twitterverse and all other Footballcosms broke out in discussions around who ‘won’ or ‘lost’ the first round. Now, it doesn’t take a great deal of logic to figure out that these kinds of snap judgments are largely valueless since no one REALLY has a clue how 2012’s first rounders will pan out. Of course, it also takes very little logic to know that if you write about football and have a whole day to kill between Round One and Round Two, then you’re damn well going to engage in some kind of evaluative exercise no matter how silly it is. So onward with the silliness!
I figured that as long as I was going to do something silly like this, I might as well take something that could be disguised as a logical approach to figuring out how teams fared in the first round. My thought process was this:
1) Figure out which factors are most important in helping a team reach, and succeed in, the playoffs.
2) Estimate the degree to which a team’s selections in the first round helped them improve in those factors, relative to the team they had going into Thursday night.
3) Account for the ‘going in’ value of the teams’ first-round picks, as well as any choices in subsequent rounds that they gained or lost through trades
4) ????
5) PROFIT!!!!!
First off, let’s decide what’s most important to a team’s fortunes. There are a lot of ways to break down an NFL squad, but let’s stick to five main categories of outcomes:
Passing Offense
Rushing Offense
Passing Defense
Rushing Defense
Special Teams
Things like luck and officiating obviously factor in to all of those elements to some degree, but by and large anything a team does on the field fits into one of those categories.
Now, how do we tell how good a team is at any one of these factors? Probably my favorite evaluative measure is FootballOutsiders’ DVOA (Defense-Adjusted Value Over Average). In a nutshell, DVOA purports to tell you how much better a team has performed relative to an ‘average’ team each time they do something – run the ball, throw a pass, etc. (For those not familiar with DVOA, there’s an extensive explanation here.) Basically, if a team has a passing offense DVOA of 25%, they performed 25% better than a league-average passing offense across all the situations where they threw the ball in a given season. The defense-adjusted part adjusts for opponents, so in 2011 it was more impressive to move the ball through the air against the Ravens’ defense than it was against the Panthers. The way the calculate DVOA, you want your offensive stats to be positive and your defensive stats to be negative.
So, how did teams stack up in 2011? Here’s a chart that shows the teams’ DVOA figures for each of the five main categories, along with their 2011 regular season record:
| Team | Pass Offense | Pass Defense | Rush Offense | Rush Defense | Special Teams | 2011 Record |
| GB | 73% | 16% | 11% | 8% | 5% | 15-1 |
| NE | 61% | 24% | 17% | 9% | 4% | 13-3 |
| NO | 56% | 23% | 22% | 3% | -1% | 13-3 |
| SF | 23% | -1% | -4% | -25% | 6% | 13-3 |
| PIT | 31% | -7% | 10% | -4% | 3% | 12-4 |
| BAL | 20% | -16% | 8% | -11% | -4% | 12-4 |
| DET | 26% | -6% | 0% | -1% | -7% | 10-6 |
| HOU | 27% | -1% | 8% | -11% | 0% | 10-6 |
| ATL | 29% | 2% | -4% | -17% | 1% | 10-6 |
| CIN | 25% | 12% | -6% | -3% | 3% | 9-7 |
| NYG | 36% | 14% | -1% | -2% | 2% | 9-7 |
| TEN | 22% | 14% | -10% | -7% | 7% | 9-7 |
| DAL | 36% | 14% | -7% | -8% | 0% | 8-8 |
| NYJ | 7% | -11% | -1% | -13% | 4% | 8-8 |
| PHI | 19% | 2% | 22% | -2% | 1% | 8-8 |
| SD | 34% | 28% | 6% | 4% | -1% | 8-8 |
| OAK | 18% | 15% | 7% | 12% | -1% | 8-8 |
| CHI | -15% | -2% | -3% | -22% | 10% | 8-8 |
| ARI | -16% | 8% | 0% | 5% | 3% | 8-8 |
| DEN | -11% | 16% | 4% | -4% | 4% | 8-8 |
| SEA | 8% | -1% | -1% | -6% | 3% | 7-9 |
| KC | -8% | 5% | -9% | -3% | 2% | 7-9 |
| MIA | 8% | 5% | -6% | -6% | 3% | 6-10 |
| CAR | 20% | 25% | 36% | 15% | -5% | 6-10 |
| BUF | 8% | 16% | 9% | 8% | -2% | 6-10 |
| WAS | 4% | 9% | -1% | -4% | -1% | 5-11 |
| JAC | -39% | -3% | -1% | -11% | -3% | 5-11 |
| TB | -7% | 27% | 2% | 11% | -1% | 4-12 |
| CLE | 3% | 10% | -9% | 7% | -1% | 4-12 |
| MIN | -15% | 29% | 16% | -7% | -4% | 3-13 |
| STL | -23% | 8% | -7% | 10% | -3% | 2-14 |
| IND | -11% | 24% | -4% | 6% | -4% | 2-14 |
OK, so what? Kind of noisy when it’s laid out like that. Let’s try a hypothesis to distill this down into something more meaningful. The hypothesis I’m going with is that passing, and stopping the pass, is far more important to a team’s success than running or stopping the run. The idea of the passer rating differential – basically the difference between your offense’s passer rating and your defense’s passer rating allowed – first came to prominence in this article from SI and made a pretty compelling case. This differential seemed to be the single most important factor in determining which teams won the championship year after year. Since we’re using DVOA efficiency ratings here, let’s go ahead and call it PED – Passer Efficiency Differential. If we look at 2011’s teams in order of their PED (with the defensive DVOA subtracted from the offensive DVOA since negative is good on defense), things look like this (with 2011 playoff teams highlighted in green):
| Team | Pass Offense | Pass Defense | TOTAL | Playoff Seed/Comment |
| GB | 73% | 16% | 58% | #1 Seed - NFC |
| PIT | 31% | -7% | 38% | #5 Seed - AFC |
| BAL | 20% | -16% | 37% | #2 Seed - AFC |
| NE | 61% | 24% | 37% | #1 Seed - AFC (Super Bowl Participant) |
| NO | 56% | 23% | 33% | #3 Seed - NFC |
| DET | 26% | -6% | 32% | #6 Seed - NFC |
| HOU | 27% | -1% | 28% | #3 Seed - AFC |
| ATL | 29% | 2% | 27% | #5 Seed - NFC |
| SF | 23% | -1% | 24% | #2 Seed - NFC |
| NYG | 36% | 14% | 22% | #4 Seed - NFC (Super Bowl Winner) |
| DAL | 36% | 14% | 22% | Newman!!!! |
| NYJ | 7% | -11% | 19% | Sanchize'd!!!!! |
| PHI | 19% | 2% | 16% | Castillo'd!!! |
| CIN | 25% | 12% | 13% | #6 Seed - AFC |
| SEA | 8% | -1% | 9% | |
| TEN | 22% | 14% | 8% | |
| SD | 34% | 28% | 7% | |
| MIA | 8% | 5% | 3% | |
| OAK | 18% | 15% | 3% | |
| CAR | 20% | 25% | -6% | |
| WAS | 4% | 9% | -6% | |
| CLE | 3% | 10% | -7% | |
| BUF | 8% | 16% | -8% | |
| KC | -8% | 5% | -13% | |
| CHI | -15% | -2% | -13% | |
| ARI | -16% | 8% | -24% | |
| DEN | -11% | 16% | -26% | #4 Seed - AFC (Most anomalous team possibly ever) |
| STL | -23% | 8% | -31% | |
| TB | -7% | 27% | -33% | |
| IND | -11% | 24% | -35% | |
| JAC | -39% | -3% | -36% | |
| MIN | -15% | 29% | -44% |
Pretty interesting, eh? Of the 12 slots in the 2011 playoffs, ten were filled by the top 10 teams in PED for a ratio of 83%. In both 2009 and 2010, nine of the top 12 PED teams made the playoffs for a three-year aggregate ratio of 78%
Looks like we’re on to something with this whole ‘importance of passing’ thing. Now, how can we use this info to figure out who came out of Thursday’s first round smelling like a rose? I think that comes down to two parts – figuring out how much a team’s selection(s) will help them improve in one of the key factors (ignoring special teams, as it’s the rare first-round pick who will be deployed too much in that area), and how much weight to give each factor.
To address the second part first, I assigned the following weights to each of the four factors:
Passing Offense: 4.0x
Passing Defense: 3.0x
Rushing Offense: 1.25x
Rushing Defense: 1.0x
The degree of improvement in each factor is obviously where the guesswork comes in, and I’ve tried to give an assessment of how much a guy will impact the team’s DVOA relative to where they stood just before the draft. This factors in not only a guy’s level of play, but also how much of an improvement he’ll be over what the team had at that position as of Wednesday.
The final piece of the puzzle in evaluating a team’s drafting performance is figuring out what value to assign each pick in the draft. Jimmy Johnson’s famous ‘value chart’ that he used to help rape the Vikings in the Herschel Walker trade was probably the genesis of this concept, but that chart is probably outdated now. I’ve decided to use the slot values from Mel Kiper’s PlayTheDraft.com website to assign a ‘going-in’ value to each pick – the #1 pick is worth 3800 ‘points’, the #15 is worth 1900, the #45 pick in the second round is worth 845 and so on. Obviously it’s easier for a team to add value to its squad if it started the draft with several high selections, so I’ve factored in the ‘points’ for each team’s first round pick(s) as well as the net of points that they added or subtracted for the rest of the draft by trading later-round picks during Thursday’s action.
The chart below shows my ranking of each of the 28 teams who made a selection in the first round. As a quick recap, the chart shows:
- The starting ‘points’ a team had based on where its pick(s) fell in the first round
- Who they selected
- My estimation of the improvement that their chosen player(s) will generate across the four major factors over the next few seasons holding the rest of their roster from Wednesday constant – basically, how much will Melvin Ingram improve the Chargers’ defense assuming he starts over Travis LaBoy at outside linebacker? If a team had two first-round selections, the improvements are for both players in aggregate
- The residual value for subsequent rounds based on their trades – if a team traded down and added more second-day picks the number is positive, and if they gave up picks the number is negative
- The total ‘net impact’ of the selection using the weight of each of the four factors (with passing offense being 4x as important as rushing defense)
- The Value per Point the team created based on their first-round trades and selections – basically that Net Impact figure divided by the team’s net pick-based point values and then grossed up by a factor of 10,000 to bring the numbers to a tens digit and a couple of decimals
For the brave three of you who slogged through all of that explanation, here’s the chart:
| Team | Starting Picks | Total Starting Points | Selection(s) | Pass Offense Upgrade (DVOA) | Pass Defense Upgrade (DVOA) | Rush Offense Upgrade (DVOA) | Rush Defense Upgrade (DVOA) | Residual Value of Picks Traded | Total Net Impact | Value/ Point |
| Indianapolis Colts | #1 | 3,800 | Andrew Luck | 25% | 0% | 5% | 0% | - | 106% | 2.80 |
| Washington Redskins | #2 | 3,500 | RGIII | 18% | 0% | 12% | 0% | - | 87% | 2.49 |
| Pittsburgh Steelers | #24 | 1,575 | David DeCastro | 5% | 0% | 7% | 0% | - | 29% | 1.83 |
| Arizona Cardinals | #13 | 2,000 | Michael Floyd | 8% | 0% | 3% | 0% | - | 36% | 1.79 |
| Minnesota Vikings | #3 | 3,250 | Matt Kalil | 8% | 0% | 7% | 0% | 900 | 41% | 1.73 |
| San Diego Chargers | #18 | 1,725 | Melvin Ingram | 0% | 9% | 0% | 2% | - | 29% | 1.68 |
| Detroit Lions | #23 | 1,600 | Riley Reiff | 5% | 0% | 5% | 0% | - | 26% | 1.64 |
| Cincinnati Bengals | #17, #21 | 3,400 | Dre Kirkpatrick, Kevin Zeitler | 3% | 7% | 5% | 2% | 556 | 41% | 1.45 |
| St. Louis Rams | #6 | 2,750 | Michael Brockers | 0% | 6% | 0% | 8% | 845 | 26% | 1.36 |
| Jacksonville Jaguars | #7 | 2,650 | Justin Blackmon | 9% | 0% | 3% | 0% | (325) | 40% | 1.34 |
| New York Jets | #16 | 1,800 | Quinton Coples | 0% | 7% | 0% | 3% | - | 24% | 1.33 |
| Green Bay Packers | #28 | 1,475 | Nick Perry | 0% | 6% | 0% | 1% | - | 19% | 1.29 |
| Tennessee Titans | #20 | 1,675 | Kendall Wright | 5% | 0% | 1% | 0% | - | 21% | 1.27 |
| Philadelphia Eagles | #15 | 1,900 | Fletcher Cox | 0% | 9% | 0% | 5% | (675) | 32% | 1.24 |
| New York Giants | #32 | 1,375 | David Wilson | 3% | 0% | 4% | 0% | - | 17% | 1.24 |
| Dallas Cowboys | #14 | 1,950 | Morris Claiborne | 0% | 11% | 0% | 1% | (845) | 34% | 1.22 |
| Tampa Bay Buccaneers | #5 | 3,000 | Mark Barron, Doug Martin | 4% | 8% | 4% | 5% | (1,130) | 50% | 1.21 |
| San Francisco 49ers | #30 | 1,425 | AJ Jenkins | 4% | 0% | 1% | 0% | - | 17% | 1.19 |
| Chicago Bears | #19 | 1,700 | Shea McClellin | 0% | 6% | 0% | 2% | - | 20% | 1.18 |
| Buffalo Bills | #10 | 2,250 | Stephon Gilmore | 0% | 8% | 0% | 2% | - | 26% | 1.16 |
| Houston Texans | #26 | 1,525 | Whitney Mercilus | 0% | 5% | 0% | 2% | - | 17% | 1.11 |
| New England Patriots | #27, #31 | 2,900 | Chandler Jones, Donte Hightower | 0% | 12% | 0% | 7% | (981) | 43% | 1.11 |
| Miami Dolphins | #8 | 2,500 | Ryan Tannehill | 6% | 0% | 3% | 0% | - | 28% | 1.11 |
| Baltimore Ravens | #29 | 1,450 | Harrison Smith | 0% | 5% | 0% | 1% | - | 16% | 1.10 |
| Seattle Seahawks | #12 | 2,050 | Bruce Irvin | 0% | 5% | 0% | 0% | 675 | 15% | 1.09 |
| Cleveland Browns | #4, #22 | 4,775 | Trent Richardson, Brandon Weeden | 10% | 0% | 14% | 0% | (900) | 58% | 1.01 |
| Kansas City Chiefs | #11 | 2,100 | Dontari Poe | 0% | 4% | 0% | 8% | - | 20% | 0.95 |
| Carolina Panthers | #9 | 2,350 | Luke Kuechly | 0% | 5% | 0% | 7% | - | 22% | 0.94 |
And there you have it! The winners and losers of the 2012 first round, presented in gorgeous, gorgeous science. Should anyone be up for it, this can also be our open thread for Rounds 2-3 tonight.


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