Introducing Expected Contract Value – Part 3

Introducing Expected Contract Value

Part 3:  Contract Comparison

Part 1:  Justification, Theory, & “Contract Analytics”

Part 2:  Inputs & Outputs

In order to expand upon Part 1 and Part 2, and demonstrate how Expected Contract Value can be helpful in comparing contracts, let’s take a look at an article from this website in June 2014 in which Jason compared the recent large contracts of centers Maurkice Pouncey, Alex Mack, and Ryan Kalil. This article is the archetype of the type of insightful subjective analysis I identified in Part 1 that could be enhanced by Expected Contract Value.

Jason first correctly points out that the face values of contracts can be misleading, and that they should not be determinative in the comparison at hand. He then goes on to refer to “dead money protections” for the various deals. This is a concept addressed by Expected Contract Value through the inputs Save:Cap and Save:Avg. Jason goes on to refer to Kalil’s three-year payout as “virtually guaranteed.” While that may be true, Expected Contract Value allows us to assign a numerical value to the descriptive term “virtually.”

Throughout the article, the reader gets a sense of Jason’s thought process as he analyzes all of the various characteristics of the three contracts.  One can imagine that a team executive would go through a very similar thought process when initially negotiating a contract, and then again each year as the player and his contract are evaluated.  From our perspective, the key quote comes in the 5th paragraph:

“Between Pouncey and Kalil the results are a bit muddled.”

Exactly!  Comparing contracts is very difficult, as there are many variables that must be simultaneously considered without giving a single variable disproportionate importance.  The human mind is not particularly well suited to performing this task, as there will be a tendency to focus on certain variables to the detriment of others.  A formula, on the other hand, is perfectly suited for weighing numerous variables with the appropriate degree of importance given to each.  Let’s take a look at the Expected Contract Value outputs for each of the three contracts:


Ryan Kalil
Face Value: $49.116 million
Guaranteed: $19 million
Three-Year Payout:   $30,750,000
YearSalaryExpected OutcomeExpected ValueAdjustment
Total ECV$39,577,000
Expected APY$6,596,167
Age at Conclusion32


Alex Mack
Face Value: $42 million
Guaranteed: $18 million
Three-Year Payout:   $26 million
YearSalaryExpected OutcomeExpected ValueAdjustment
Total ECV$28,008,000
Expected APY$5,601,600
Age at Conclusion34


Maurkice Pouncey
Face Value: $47 million
Guaranteed: $13 million
Three-Year Payout:   $26,500,000
YearSalaryExpected OutcomeExpected ValueAdjustment
Total ECV$37,270,000
Expected APY$6,211,666
Age at Conclusion31

As you can see, Mack’s contract is inferior from an Expected Contract Value standpoint in comparison to Pouncey’s and Kalil’s. Despite having more guaranteed money and an almost identical three-year payout as Pouncey, Mack can expect to earn $9 million less over the course of his contract than Pouncey can.

Despite having $6 million more guaranteed money and $4 million more three-year payout, Kalil can only expect to earn about $2.3 million more than Pouncey over the course of their respective contracts.   One of the major reasons for this was that Pouncey saw much more money in year two of his contract than Kalil, a year in which Expected Contract Value places a very high likelihood that both players will remain under contract.   Pouncey’s higher salaries than Kalil in year four and year five also outweigh the lower likelihood that he has of remaining under contract for those seasons.

While Mack trails by a considerable margin in Expected Contract Value, his deficit in Expected APY is not as significant due to the fact that his contract only spans five seasons, rather than six as in the case of Pouncy and Kalil. If Mack was younger than the other two, and would therefore have a greater likelihood of signing another significant contract, then sacrificing Expected Contract Value in favor of Expected APY would arguably be justifiable. However, Mack will actually be older than Pouncey or Kalil at the conclusion of each of their respective contracts, and at age 34 he will be unlikely to sign a large contract. Conversely, Pouncey has a colorable argument that his contract is superior to Kalil’s, despite trailing in both Expected Contract Value and Expected APY, as he can plausibly expect to sign a relatively sizeable contract at the age of 31.

The results of this analysis aren’t particularly different than when Jason conducted it, but the useful thing about Expected Contract Value is that it allows us to place a numerical value on the conclusions that we might otherwise draw from subjective analysis.  We are also able to ensure that we have placed the appropriate amount of importance on each variable that we may consider important in the analysis. Furthermore, analysis such as this can be instantaneously performed on a mass scale with spreadsheets.

In Part 4, we will demonstrate how Expected Contract Value can be extended one more step to aid teams in budgeting their salary cap payrolls. In Part 5, we will address feedback received throughout the week.

Expected Contract Value was created by Bryce Johnston and Nicholas Barton.

Bryce Johnston earned his Juris Doctor, magna cum laude, from Georgetown University Law Center in May 2014, and currently works as a corporate associate in the New York City office of an AmLaw 50 law firm.  Before becoming a contributor to, Bryce operated for 10 NFL offseasons, appearing multiple times on 610 WIP Sports Radio in Philadelphia as an NFL salary cap expert. Bryce can be contacted via e-mail at or via Twitter @eaglessalarycap.

Nicholas Barton is a sophomore at Georgetown University. He intends on double majoring in Operations and Information Management and Finance as well as pursuing a minor in Economics. Currently one of the leaders of the Georgetown Sports Analysis, Business, and Research Group, Nick consults for Dynamic Sports Solutions, an innovative sports start-up that uses mathematical and computational methods to evaluate players. He also writes for the Hoya, Georgetown’s school newspaper, and his own blog, Life of a Football Fan. Nick can be contacted via e-mail at

  • Drew Jordan

    This is some super interesting stuff. I do wonder, however, if this analysis can be improved at all using the idea of the Time Value of Money.