Win Values for the NFL

Jimmy Graham's contract values him at about 0.9 wins per season. Here's how I came to that estimate.

In 2013 the combined 32 NFL teams chased 256 regular season wins and spent $3.92 billion on player salary along the way. In simple terms, that would make the value of a win about $15 million. Unfortunately, things aren't so simple. To estimate the true relationship between salary and winning, we need to focus on wins above replacement.

Think of replacement level as the "intercept" term or constant in a regression. As a simple example think of the relationship between Celsius and Fahrenheit. There is a perfectly linear relationship between the two scales. To convert from deg C to deg F, multiply the Celsius temperature by 9/5. That's the slope or coefficient of the relationship. But because the zero point on the Celsius scale is 32 on the Fahrenheit scale, we need to add 32 when converting. That's the intercept. 32 degrees F is like the replacement level temperature.

No matter how teams spend their available salary, they need to have 53 guys on their roster. At a bare minimum, they need to spend 53 * $min salary just to open the season. We can consider that amount analogous to the 32-degrees of Fahrenheit. For 2013, the minimum salaries ranged from $420k for rookies to $940k for 10-year veterans. To field a purely replacement level squad, a franchise could enlist nothing but rookies. But to add a bit of realism, let's throw in a good number of 1, 2, and 3-year veterans in the mix for a weighted average min salary of $500k per year. The league-wide total of potential replacement salary comes to:

The Pay-Performance Linear Model

A couple months ago I posed an apparent paradox. Aaron Rodgers' new $21M/yr contract was either a solid bargain or a disastrous ripoff depending on how we analyze the data. By only flipping the x and y axes of a scatterplot, we can come to completely opposite conclusions about the value of a QB relative to what we'd expect for a given salary or for a given level of performance. Much of this post is derived from the many insightful comments in the original. Please take the time to read them, especially those from Peter, X, Phil and Steve.

By regressing salary on performance (adjusted salary cap hit on the vertical (y) axis and Expected Points Added per Game (EPA/G) on the horizontal (x) axis), Rodgers' deal is insanely expensive by conventional standards. But by regressing performance on salary, his new contract is a bargain.

Which one is correct? That depends on several considerations. First, there are generally two types of analyses. The one I do most often is normative analysis--what should a team do? The second type is descriptive analysis--what do teams actually do? The right analytic tool can depend on which question we are trying to answer.

The reason that we saw two different results by swapping the axes is that Ordinary Least Squares (OLS) regression chooses a best-fit line by minimizing the square of the errors between the estimate and the actual data of the y variable. OLS therefore produces an estimate that naturally has a shallow slope with respect to the x axis. When we swap axes, the OLS algorithm is not symmetrical because of that shallowness.

Point / Counterpoint on Rodgers' Extension

Today we're going to try a new format here at ANS--a debate between me and myself on the market value of Aaron Rodgers' recent contract extension. Rodgers recently signed a deal adding 5 years to his current contract. This will pay him roughly $21M per season over the next 3 years. See if you can figure out which Brian has the right idea and why they get different results.

Brian 1: Rodgers' new deal is a fantastic bargain. He's one of the truly elite QBs in the league today, and guys like that don't grow on trees. But more scientifically, just look at this super scatterplot I made of all veteran/free-agent QBs. The chart plots Expected Points Added (EPA) per Game versus adjusted salary cap hit. Both measures are averaged over the veteran periods of each player's contracts. I added an Ordinary Least Squares (OLS) best-fit regression line to illustrate my point (r=0.46, p=0.002).

Rodgers' production, measured by his career average Expected Points Added (EPA) per game is far higher than the trend line says would be worth his $21M/yr cost. The vertical distance between his new contract numbers, $21M/yr and about 11 EPA/G illustrates the surplus performance the Packers will likely get from Rodgers.

(This plot includes for all free-agent or veteran extensions since 2006. Cap figures are averaged for each player's career and, to account for cap inflation, are adjusted for overall league cap ceiling by season. Only seasons with 7 or more starts were included.)

How Much Money is a Sack Worth?

I realize it's draft season but I'm still working on building a salary database and combining it with performance statistics. Most of the work in this type of analysis is building the data, but it's harder than it might seem at first. Salary data might use one kind of player identifier while performance statistics use another. Merging them and creating a flat data file to play with is more than half the battle. There's still more I need to do. First up will be segregating free agent years from rookie contract years. But for now, here's some trivia.

Offenses get all the attention, so I thought I'd toy around with what I have so far on defense. Sacks are one of the most visible and tangible defensive statistics. I took the primary sack makers, defined as DTs, DEs or LBs who have averaged over one sack per season in their career, and plotted their career average sack rate against their average cap hit.

A few notes about the data: Only those years with cap hits greater than $1M were included as a crude way to focus on every-down starters. Additionally, only seasons where the player had 7 or more game appearances were included. The data ranges back as far as 2006 for whoever I had salary data for. Cap hit was adjusted for salary cap inflation--All cap hits are in $2012 cap dollars. Lastly, 'sacks per season' is extrapolated for each player to full 16-game seasons.

EPA Production and Cap Value, Skill Positions

I've been playing around with the connection between player value and production, and I thought I'd post some interesting observations.

As a measure of production I used Expected Points Added (EPA)--actually EPA per game to account for injury shortened seasons. For the measure of player value, I used cap hit. Cap value is useful because it boils down the complexity of many NFL contracts into one number. It can be tricky, though, as many contracts can be quite uneven from year to year in terms of cap value. For cap management and player-incentive purposes a 4-yr/$40M will often diverge far from a steady $10M per year cap hit. To account for this, I averaged each players' per year cap hit for the full period ('06-'12) and plotted against each player's EPA/G. The purpose of doing this is to see what level of production teams expect per $1M of salary. Is there a solid connection between true production and salary?

There are assumptions and limitations inherent in this analysis. Player production is dependent on the abilities of their teammates. WRs and TEs rely on their QBs to get them the ball, and some will have better passers and some will have worse, and vice versa. RBs are dependent on their line and scheme. But over the league as a whole, these considerations would (ideally) balance out. Put simply, for every Larry Fitzgerald being victimized by the offense around him, there's a Brandon Lloyd benefitting from being on a great offense.

The chart below plots offense skill position production by annual average cap hit. Each position is color coded.

What is Ed Reed's Going Value?

Last year I looked at the salaries of free agent safeties to estimate the going rate based on production stats.  FA safety salary correlated fairly well with production, specifically +EPA (positive Expected Points Added). With Ed Reed, one of my favorite safeties, on the FA market at moment, I thought I'd take a look at what would be a fair market price for his services in 2013.

Here, price is defined by salary cap hit. NFL salaries are notoriously complex with bonuses and guarantees. But these are boiled down into a single cap number, which is the cost to his team's overall salary cap, the most precious resource it has in acquiring and keeping players.

The analysis here produced the chart below. In short, top FA safeties are valued at about $2.0 million for every +EPA per game, minus $170k.

For example, for a guy who produces 2.1 EPA/G, his going rate would be about $4M per year. So where would Ed Reed stand in this context?

Reed is a future Hall of Famer, but he is now 34 and headed into his twelfth season. Here is how his total production and per-game production have tracked over the course of his career. (Note that all stats  are for regular seasons only. +EPA stats can be found here.)

How Much Is Joe Flacco Worth?

Joe Flacco is finishing his 5th year in the league. It's a contract year for a QB who remains frustratingly difficult to assess. He shows flashes of brilliance in one game only to be followed by apparent ineptitude in the next. He has the size, the strength, the arm, the accuracy, but there's something missing.

Statistically, he's just as hard to figure out. Among all QBs since 2008, when Flacco entered the league and began his uninterrupted starting streak, his WPA has exceeded his EPA. Of the 30 QBs with the most attempts since 2008, Flacco has the 3rd highest ratio of WPA to EPA. Most observers would call him "clutch."

Based on the recent historical relationship between QB performance and salary, we can estimate Flacco's market value. But depending on how we value his production, we get very different salary values.

Flacco is remarkably consistent. Receivers come and go, and lineman come and go, but since his rookie year Flacco is a rock solid 70 EPA per year guy. Adjusting for team cap inflation, that makes Flacco worth approximately:

Offensive Line Salary and Performance

Last season I took a cursory look at the importance of depth at offensive line. I highlighted the thinness of the Redskins' line by pointing out that although their total salary spent on their line was in line with most other teams, a large part of their cap space was allocated to a single player, left tackle Trent Williams. Their median salary was less than half of that of the division leading teams at the time, suggesting that the Redskins annual mid-season swoon was due to a lack of replacement talent following inevitable injuries to starting linemen.

This post will go far more in depth and look at correlations between team offensive line (OL) salary and performance. Using salary data from USA Today's database for the 2000 through 2009 seasons, I calculated correlations between OL salary and various advanced offensive performance statistics. (USA Today's database ends at 2009. Additionally, the 2005 season was excluded because USA Today's database appeared incomplete that season, listing half as many players than the other seasons.)

As in my other salary analyses, I relied on cap hit as the truest measure of a player's cost to a team. NFL salary structures are notoriously complex, with base pay, bonuses, guarantees, and other factors. But cap hit comprises most if not all of those considerations, and it represents the cost to most team's most precious resource--its cap space. For each season, I adjusted all salary numbers according to the league's cap for that year to account for salary inflation.

Paying Free Agent RBs

Ray Rice, Matt Forte, Payton Hillis, Marshawn Lynch, and Arian Foster were all due to become free agents this off-season. Rice and Forte have already been franchise-tagged, and Foster is a restricted free agent. Still, the question of what they're worth in terms of salary and cap space remains.

A couple months ago, I took a look at the safety position and how free agents are paid. And more recently I took a look at how QBs are paid with the intent to eventually establish a rational framework for how they should be paid according to their actual production in terms of wins and points. Like the prior analyses, my primary goal for now is to get a feel for the market.

As in all individual player analysis, it's worth stating up front that football is always a team sport. Like all other positions, a RB's stats are not his alone, as they depend heavily on offensive lines, opponents, scheme, situational variables, passing game strength, and other considerations. We can account for the situational variables with the EPA and WPA models, but we must rely on the tendency for factors beyond the control of the individual player to even out in the long run. They may not even out for any individual RB, but in aggregate, the idea is that some RBs will have above average lines, some will have poor lines, and collectively we can roughly estimate the financial value that a RB brings his team based on production.

First, let's take a look at how RB salaries are actually determined. There are two graphs below. The first plots career total rushing yards vs. career total cap hit. The second plots career Yards Per Carry (YPC) vs. career cap hit. The data include years 2000-2009 and is limited to RB seasons with greater than 6 game appearances and greater than 50 carries. Cap hit is adjusted for NFL inflation. Only players who had a cap hit greater than $1M in year 2000 dollars were considered. This limitation is so that we sample only high-priced RBs, and is necessary to see any relationship at all between performance and pay.

How Much Does A Win Cost?

How many wins does a million dollars of quarterback salary buy? How many points does a million bucks buy? How much should you pay a QB assuming a certain level of expected performance?

I looked at salary data from 2000 through 2009 courtesy of USA Today and compared it with stats like Win Probability Added (WPA) and Expected Points Added (EPA). For those not familiar, WPA measures the impact a player has on his team's fortunes in terms of wins. EPA measure his impact in terms of net point differential. I looked at other stats too, but EPA and WPA fit very nicely with salary, at least for QBs.

Specifically, I wanted to discover what teams are willing to pay in exchange for expected levels of performance. To find out, I plotted the salary cap values of QBs against their performance. First, I made an adjustment for year. Between 2000 and 2009, QB salaries have steadily increased. By 2009, they were about double what they were to start the decade. This fits nicely with overall team cap numbers, which approximately doubled between 2000 and 2009.

QB performance has inflated too, but I haven't yet added a correction. QB performance has increased a total of about 10% since 2000, which pales in comparison to salary. In the future I'll add a correction and it might sweeten up the results a bit.

NFL contracts are notoriously complex, with signing bonuses, guarantees for both performance and injury, roster bonuses, and performance incentives. But none of that matters to me. I just want to know how much of a team's payroll under the cap--its most precious resource--is it willing to spend. How much are they spending, and how much are they getting back? For now, I'm looking at all big-money players, whether they're draft picks or free agents.

Here is how the relationship between salary and performance shakes out. The top graph plots EPA vs. cap hit, and the second graph plots WPA vs. cap hit broken out by season. The plot filters out QBs that earned less than approximately $2M in adjusted salary.

Football Freakonomics: Incentives

I recently helped the Freakonomics folks and NFL Network with an installment of 'Football Freakonomics', the series of mini-documentaries on interesting wrinkles in fabric of football. This particular feature is on incentives, particularly big contracts and Super Bowl championships. They air periodically on NFL Network, I believe on GameDay Morning on Sundays.

Here is the feature.
 
After watching it, ANS readers will quickly say 'Wait! What? That's it? What about concepts like regression to the mean or the winner's curse? Wouldn't they explain what we see in the data? We did cover those considerations in detail on camera, but they just didn't make the cut. In defense of the producers, they only have a few minutes to tell an interesting story, and it has to appeal to a broad audience. It's just the nature of the medium.

For those curious, here's a brief discussion on why we see performance decline after big contracts:

LaRon Landry's Value

This week's post at the Washington Post's Redskins Insider site uses the recent research on the contracts of top safeties to value LaRon Landry's value as an impending free agent.

How Much to Pay a Free Agent?

Let's say you have a star safety finishing his final year with your team. What kind of contract offer is he worth? What does the market for such a player look like? This post will begin to answer those questions using salary data and advanced player statistics. Think of this as a 'demonstration of concept', which begins to establish a framework for a quantitative estimate of player contract value.

I'll first air the assumptions and limitations. This analysis is based on contract/salary data provided by Spotrac.com. I've limited the years of analysis to only 2010 and '11 because that was the sample of data made available. More years of data could always be added to refine the results. +Expected Points Added per Game (regular season only) is used as the basis of performance because although it's far from perfect, it captures the impact of individual contribution to game outcome without sensitivity to situational variables beyond the control of the player. It also happens that +EPA/G fits best with the salary data, so it is the measure player performance for the purpose of this analysis. Lastly, due to the complexity of contract incentives, bonuses, guarantees, and duration, I have chosen annual 'cap hit' as the measure of contract cost.

That's a lot of assumptions, I realize. But the real world is messy, and the purpose here is to establish the general market--a starting point for further adjustments. As you'll see, the relationship between +EPA/G and cap hit is fairly strong, suggesting the assumptions aren't unreasonable. We should also recognize the distinction between observing what players tend to be paid and what they ideally should be paid.

WP: Rebuilding with Moneyball for Football

The Redskins need to restock the cupboard with talent. Here's how a team can build and sustain a winning roster.

Playing Moneyball in the NFL is about jettisoning expensive and under-producing veterans, rejecting the big-splash free agent, and stockpiling draft picks. There are two ways of generating those picks. First, you can trade away soon-to-be free agents to other teams in return for picks or allow restricted free agents to sign elsewhere in return for compensatory picks. For too long, the Redskins have been on the wrong end of those transactions.

The second way is to trade picks for more picks. Overconfidence and urgency run rife in personnel departments around the league, and smart teams can take advantage of this. There are always teams willing to overpay for a pick that they are so certain will immediately turn their team into a Super Bowl winner.  A team can sell its first-round pick for a second-round pick this year, plus a first-round pick next year.  In the next draft, that team will have an additional first-round pick that could be sold for another second-rounder, plus another future first rounder. Presuming there are enough buyers, a team could generate an additional second-round pick in perpetuity by foregoing its first-round pick in only one year.

There's one team in the league that understands this, and they've been phenomenally successful doing it:

Don't Pay Fortay!

Well, actually, do pay him, just don't pay him the kind of guaranteed money he's looking for. On one hand, Forte is having a career season as the epicenter of the Bears offense. On the other hand, he’ll be a 26-year old running back next season.

CJ2k, or CJ0.7k as he might be better known after this season, has highlighted the folly of handing over 10% of a team’s cap space to single RB. That’s just too many eggs in one basket for a position that simply does not drive wins and losses except in rare instances.

Forte is 2nd so far this year in WPA/G, 6th in EPA per play, and 18th in SR. He’s averaging 5.4 YPC, which ties for 6th in the league for qualifying RBs. We all agree that’s really good. The question is whether the Bears are better off locking him up for 6 years and $20 million guaranteed or spending that money elsewhere.

We don’t need to look at Chris Johnson or DeAngelo Williams to see the danger of giving huge guaranteed contracts to RBs. We only have to look at Forte himself. As we’ve seen with several RBs, they can go from thermonuclear hot one year to cold as a fish the next, even without injuries or switching teams, and Forte is no exception.

WP: Median Cap Dollars Spent on O-Lines

What do Bill Gates and Redmond, Washington have to do with Trent Williams and Ashburn, Virginia? And what can Barry Bonds and papayas tell us about offensive lines? 

This week's post at the Washington Post's Insider provides the answers. It's a look at how much the Redskins spend on depth on their offensive line compared with the current division leaders. Last season I wrote that their off-season needs were (in order): offensive line, offensive line, followed by offensive line. They didn't listen.

You might want to check this one out, even if you're not a Redskins fan.

Player Salaries and Economic Rent

A couple months ago, I wrote about rookie salaries--whether or not they're "too high," and how the NFL's next labor agreement is certain to reduce them. With all the recent attention on NBA free agents, some are wondering why a backup point guard on the Orlando Magic is paid more money than NFL superstar Tom Brady. The issue of player salaries is an emotional one because it touches on our human instincts for fairness.

First, let's look at some facts. From USAToday's salary databases, here are the 2009 salaries for the three major professional sports leagues, plus something I'm told is called "hockey."

Are Rookies Overpaid?

I recently looked at what might explain why the top draft picks are paid disproportionately to their expected performance compared to later picks. But that doesn't address the larger issue--are rookies overpaid compared to their veteran counterparts?

A 2005 research paper called The Loser's Curse by economists Cade Massey and Richard Thaler tackled that question. In a nutshell, the paper compares rookie pay to the pay of a 6th-year veteran who could be expected to deliver the same performance as a rookie from each slot in the draft. (Performance is defined by a mix of measures including: being on a team roster, starts, and Pro Bowls.)

The conclusion of the paper is that team executives and scouts overpay for the top picks in the draft relative to the later picks, likely due to overconfidence in their ability to identify the best players. But what might surprise some readers is that rookies at every level of the draft are bargains compared to equivalently performing veterans.

This graph from the paper is the study's bottom line. The red 'compensation' line is the average annual pay for each draft pick. The blue 'performance' line is the salary a team would have to pay a 6-year veteran free agent for the same expected performance. The green 'surplus' line is the difference between the two pay levels.


The surplus performance peaks shallowly at the bottom of the first round and through the second round. That's where teams get the biggest bang for the buck. But still, the surplus is strongly positive throughout the entire draft. According to Massey and Thaler, rookies are a bargain compared to veterans.

There's a good explanation why rookies would be underpaid. Veterans are known quantities while there is a tremendous amount of uncertainty with draft picks. Think of it this way--Peyton Manning has been to nine Pro Bowls and Ryan Leaf to zero, for an average of 4.5 between the two players. Four Pro Bowls--that's not bad. But would a GM pay more for a guaranteed 4.5 Pro-Bowl-type player or for a 50/50 shot between a total bust and Hall of Famer? Just about every modern economic and psychological theory tells us that people will pay a premium for the sure average.

Unfortunately, that's not an option in the draft. Peyton Leaf just doesn't exist. But 6-year veterans do, and GMs will be willing to pay a premium for the reduced uncertainty in performance.

One note of caution on the paper. The draft years studied were 2000-2002, and rookie salaries have increased substantially since then. But veteran salaries have too. The question is whether rookie pay increases have outpaced veteran pay increases since then. However, rookie pay would needed to have increased over 15-20% faster than veteran pay to change the conclusions of the paper.

Draft Picks: Bricklayers or Gladiators?

With the draft upon us, there is a lot of chatter about ballooning rookie salaries for top picks. The consensus seems to be that top picks are not worth the cost and salaries should be capped. But there’s a good reason why the top players’ salaries are so high, and the explanation can be found in economic ‘tournament theory.’ A short example by economics professor Robert Schenk explains it nicely:

Say you’re a contractor and your company builds brick walls. Most of your bricklayers lay about 3 bricks per minute and make about $8 per hour. (You can think of this as the replacement level.) But along comes a guy who lays bricks twice as fast--6 bricks per minute. How much would you be willing to pay him? Simple fairness suggests $16 per hour. Certainly no more than that because you could just replace him by hiring two replacement-level guys and get the same production. So in this example rewards are based on absolute differences in productivity. Large differences in productivity result in large differences in rewards. Similarly, small differences in bricklaying ability would result in small differences in hourly pay.

Now consider two ancient gladiators entertaining the emperor in combat. Even if one gladiator is only slightly better than the other, he’ll very likely win, and the differences in rewards could be extreme. Here, in a winner-take-all system, absolute differences in ability do not matter, only relative differences.

What about sports like football? First, in many ways the NFL is a winner-take-all system. Whoever wins the game earns 100% of the win while the loser eats all of the loss, and there is only one champion left standing at the end of the season.

Second, football players are not like bricklayers. You cannot replace an All-Pro QB by sending two average QBs out on the field and expect the same productivity. When there is a constraint on the number of people that can be employed at one time, the value of the most productive people rapidly increases.

And when there is a constraint on the number of contributors combined with a winner-take-all reward structure, the value of the top performers will skyrocket. This is why the top NFL draft picks make so much more than the lower picks. Even if the abilities of the top picks are only marginally better than those of the picks in later rounds, there will be very large differences in pay.

It’s not much different than CEO compensation. If a company is in competition with other companies for market share, the shareholders should want the best CEO they can get--especially because a competitor with a slightly more visionary CEO will likely steal market share, even if your guy is still top-notch. And since you can’t replace a single chief executive with two average guys or a whole mob of slackers, the CEO’s pay is going to end up being wildly disproportionate to his actual ability. There can be only one guy at the top, only one winner of the tournament.

Note that I’m not claming that rookie salaries should be this high, just trying to understand why they’re so high. And I’m not comparing rookie pay to veteran pay. That’s another topic for another day.

Blindsided?

Michael Lewis, author of the best-selling baseball book Moneyball, recently followed up with a book on innovation in football. The Blind Side follows the story of the left tackle, the player whose job of protecting the more vulnerable side of right-handed quarterbacks has become increasing important in the NFL ‘arms race’ of the pass rush vs. passing offense.

The entirety of Lewis’ premise is based on the relative pay of LTs compared to other positions. Lewis cites the fact that LT has become the second highest paid position, behind only the all-important QB. Unfortunately, the comparison of LT salaries with those of other positions is a false comparison, and a fairer comparison reveals a different story.

I was intrigued by Phil Birnbaum’s response to a write-up of Blind Side at the Freakonomics blog. Phil questioned the justifications for the extremely high salaries for LTs. And although I believe there are sound economic reasons based on the scarcity of qualified players and the contribution of the position, my main concern questioned the premise that LT salaries are truly any higher than other positions.

Like many other positions, offensive tackles are largely ’swappable’ in that they can go from left to right pretty easily. Most backups don’t even have a defined side and are available to fill in on either side to spell a starter or replace him in case of injury.

Due to the 'blind side' consideration, the LT is almost always the better of the two starting tackles on each NFL team. And he’s very likely to make a lot more money than the lesser player who is assigned RT. Starting LTs are basically a group of the #1 offensive tackles from each of the 32 teams.

So when we compare average salaries of LTs to those of say, left corner back or all starting wide receivers, the comparison is not fair. Those positions do not place the better player on a certain side, or they are not defined as left/right positions to begin with. And if a player does always line up on one side, it’s not always the same side for every team.

If we compared the average salaries of LTs to the average salaries of all the best WRs from each team, we might expect to see drastically different results.

A much fairer comparison of position salaries is to compare the average salary of the 32 top paid offensive tackles, whether left or right, with the top 32 salaries of players at WR, CB, or various other positions. So that’s what I did.

I looked at the average of the top 32 salaries of 2007 at OT, QB, WR, CB, and RB. Because a player’s salary is a convoluted mix of regular salary, signing bonuses and other bonuses, I favor salary cap charges as the best measure of salary. A cap charge is basically a player’s base salary plus an amortized amount of bonus salary. I think it’s the best measure because it most realistically reflects the value of the player to the team. Total salary and base salary, the only other plausible measures, can be highly irregular based on the particular timing of bonuses. However, I’ll include all three types of salary below the graph, and you can judge for yourself.

The graph and table below list the salaries in $millions for the 32 highest paid players at various positions.

Average Salary of Top 32 Players by Position ($ million)

	QB	OT	WR	CB	RB
Base Salary	2.9	2.3	3.5	3.3	1.8
Total Salary	5.8	4.9	5.7	5.7	4.9
Cap Charge	5.6	4.5	5.2	5.4	3.8

The 32 highest paid offensive tackles, whether left or right, rank only 4th out of 5 in all three measures of salary. I haven’t looked at other positions yet, so there may be others that are higher paid than OT. Further, only 33 of the 100 top paid offensive linemen were tackles, left or right.

While I agree LT is a critically important position and should be highly paid, the comparison of salaries against other left/right positions, or non-“sided” positions is severely biased. A fairer comparison reveals that the top players at other positions are paid even higher salaries.