Who gets paid? I mean really paid in football.
QBs, CBs, WRs, Left Tackles, and pass rushing specialists.
In the NFL QB's seem to almost be a set % of the cap, and it's more than 10%. Today they are approaching 25M per year on their new contracts. But it's not just the QBs. WRs are worth more than TEs, cue Jimmy Graham (and probably others) trying to get his franchise tag appliued as a WR. DEs are worth more than LBs and DTs, cue Terrell Suggs trying to get his franchise tag as a DE.
SS isn't paid as much as FS, and you see this year Kameron Chancellor holding out, despite being in the top 2 or 3 for strong safeties. He adds FSs to the market he thinks he's worth and Bam Bam, we have a disagreement.
The passing game premium makes even pass rushing specialists that weigh 250 lbs earn 4-7 million while starting linebackers struggle to get that from their teams unless they are also a pass rushing specialist on third down. The market identifies value pretty well.
Who doesn't get paid?
OGs, FBs, run support LBs, run support DTs, TEs
I used most of my examples, but these positions are far more likely to be shuffled than paid. A top run-stuffing DE or DT will get 6-8M per year. If they bring a high-level interior rush, you can add maybe even 10M onto that salary. A top pass rushing DE will go north of 15M even if they can't stuff a blocker in the hole.
Since I said I was doing GZL data analysis (again, beginners work) I decided to illustrate this using GZL salary data. This time around, data gathering was a bitch. I trolled around the site to see if there were any league-wide tables that included players and their salary, but of course I knew there was not.
Went page by page to the Team Page -> Roster -> Roster View and pulled the team rosters into a spreadsheet...32 times. I got the Falcons twice (and that's the only error I caught) so it gave me the chance to get dirty data. Beginner says it's good, so I massaged it for the following numbers.
Methodology: I took the top 20 for single positions: QB, FS, SS, HB, K, P
Top 30 for 1.5 on the field: DT, and the TE/FB combined
Top 40 for 2 on the field: CB, WR, DE, OT,
Top 60 for 3 on the field: OG/C, LB
That's still not perfect, but close enough to be kind of fair. The results were predictable. Passing and stopping the pass earns the most money. These salary numbers are for the whole league, not just draft contracts. These numbers are most effected by FA values, which of course bump up the renegotiating numbers for our players during restructures and re-signs.
GZL Earnings by position
|POS $$ (in M)
I decided I wanted to know something else. Add draft data. Since the salary data took so long to deal with (removing the "$" and "M" or "K," turning the K into the correct decimal while leaving along the other contract numbers) I went for easy.
Top ten picks, cream of the crop. And when I got to 2012 I started getting to the missed picks so I made that the last time I moved into the past. That gave me 8 drafts, 80 players, enough of a sample size to pretend it's valuable information.
When you look at this addition, keep in mind the following. Teams start (and therefore need):
1 QB, 2 CB, 2 OT, 2-3 WRs, 2 DE, 1-2 DTs, 1 HB, 3-4 LBs, 1.5 TE/FB, 3 OG/C, 1 FS, 1 SS
GZL Value, & Top 10 picks (by position)
|POS $$ (in M) Top 10 selections
QB 11.8 11
CB 7.3 21
OT 7.0 8
WR 6.9 12
DE 6.3 7
DT 6.3 4
HB 5.5 8
LB 5.4 7
TE/FB 4.6 -
OG/C 4.2 2
FS 4.0 -
SS 3.3 -
K 2.6 -
P 2.0 -
Pass Specialist positions (QB, CB, OT, WR, DE)
Average Salary: 7.9M
Top 10 selections: 59(/9.5 slots)
Run Specialist positions (DT, HB, LB, TE/FB)
Average Salary: 5.5M
Top 10 selections: 19(/7.5 slots)
Dominant, difference-makers in the passing game are more valuable to the market. Not just in the real world, but here in the GZL. We pay them more, draft them higher, and in the next article, I hope to explain how they boost the winningest teams here. With numbers, cause that's harder.
Data Visualization is certainly an important part of data sciences. At this point, I'm trying to pick numbers that make sense, rather than creating some charts and graphs, since I'm learning this piece by piece. Hopefully on one of these article I'll get the chance to spend time doing something that displays better than a series of numbers.