Expected Contract Value Weekly Roundup

As the contract details from the recent signings are reported, I will use Expected Contract Value to analyze the deals.  Doing so will allow for meaningful valuation of the contracts, enabling useful comparison between, for example, Maclin v. Cobb, Maxwell v. Jackson, Suh v. Watt, etc.  However, the numbers for most of the newly-signed contracts have not yet been reported with sufficient detail to calculate Expected Contract Value.

One contract that has, though, is the extension given to LeSean McCoy by the Buffalo Bills.  First, let’s examine the Expected Contract Value of the three remaining contract seasons as of the moment before the Eagles traded him:

LeSean McCoy: Old Contract
Year Salary Expected Outcome Expected Value Adjustment
2015 $10,250,000 76.3% $7,820,750
2016 $7,150,000 65.9% $4,711,850
2017 $7,850,000 37.7% $2,959,450
Subtotal $15,492,050
Total $15,492,050

This was a strong contract to begin with, as a 65.9% Expected Outcome in the fourth year of a contract is abnormally high, and a 37.7% Expected Outcome in the fifth year of a contract is extremely high.  Part of the reason for this is that McCoy is younger than the typical player on a second contract is, as most players are several years older than 29 at the time of the fifth season of a second contract.  But another important factor for this contract was that the cap numbers decrease from the third season through the fifth season, rather than spike up in a back-loaded fashion.  As this demonstrates, it is possible, although perhaps counterintuitive, that a player can expect to earn more money on a contract by including smaller salaries.

However, Buffalo, despite having a player on a declining contract that was stripped of all dead money (other than $1 million in guaranteed 2015 base salary), and thus carrying zero risk for 2016 and 2017, deemed it wise to “reset the risk clock” by giving McCoy an extension with a significant signing bonus.  In detailing the McCoy contract, Pro Football Talk notes that “the salaries don’t spike on the back end.  This increases the likelihood of McCoy finishing the full five years.”  That observation certainly comports with the theory of Expected Contract Value, but due to ECV we don’t have to settle for a vague statement such as “increases the likelihood.”  Let’s place a number on that likelihood:

LeSean McCoy: New Contract
Year Salary Expected Outcome Expected Value Adjustment
2015 99.9% $16,000,000
2016 $2,550,000 97.4% $2,483,700 $2,500,000
2017 $6,250,000 78.4% $4,900,000
2018 $6,325,000 51.0% $3,225,750
2019 $6,425,000 31.8% $2,043,150
Subtotal $12,652,600 $18,500,000
Total $31,152,600
2015-2017 $25,883,700

Just in terms of the 2015-2017 seasons, McCoy has increased his Expected Contract Value by more than $10 million.  And once again, his Expected Outcomes in the fourth and fifth years of his contract are quite strong at 51.0% and 31.8%.  Rather than tack on outsized salaries at the end of the contract that would likely leave McCoy released and subject to free agency as a 30 or 31 year old RB, the Rosenhaus brothers have designed a contract that has a solid chance of seeing completion.

Cut Tracker:

NFC East   |   AFC East

NFC West   |   AFC West

NFC South   |   AFC South

NFC North   |   AFC North

In total, I ran the model on 479 players.  Of those 479 players, Expected Contract Value determined an Expected Outcome greater than 50% (meaning more likely to be kept under contract than released) for 314 players, and an Expected Outcome less than 50% (meaning more likely to be released than kept under contract) for 165 players.  Of those 314 players with Expected Outcomes  greater than 50%, 11 have been released so far (according to reports, Percy Harvin may join this list) .  Of those 165 players with Expected Outcomes less than 50%, 49 have been released so far.  Of those 165 players, two have received extensions (Marshawn Lynch, Greg Olson), and two have been traded (Mercedes Lewis, Matt Cassell).

Due to the probabilistic nature of the output, this isn’t really the best way to measure the results.  Eventually, we will break it down by probabilistic ranges.  So we will report the “accuracy” for the >90% players, the 80-89% players, the 70-79% players, etc.  The expectation is that the model will be most accurate for >90% players and <10% players, and least accurate for 50-59% players and 40-40% players, as the latter two ranges represent contract seasons for which the input variables point to a near toss-up.  Additionally, we note that the final results cannot be judged until after final roster cuts are made at the conclusion of the preseason, as contract considerations surely contribute to those decisions as well.

Keep in mind, a pay cut (noted below in italics) is treated the same as a release (the player has implicitly acknowledged that he would not receive more on the open market, which is the same thing as being released and then re-signing without actually being released), but a conversion of salary or roster bonus to signing bonus is not treated as a release (the player has received all his money and is now in a better position).  A retirement is also treated as a release.

“Correctly Predicted” Releases:

Pierre Thomas (49.0%), Todd Herremans (48.1%), Trent Cole (47.9%), Anthony Fasano (47.5%), Tyvon Branch (46.7%),* Brodrick Bunkley (46.5%), Justin Blalock (46.3%), Brandon Gibson (44.7%), J.D. Walton (43.7%), Barry Cofield (42.7%), Keith Rivers (42.0%), DeAngelo Williams (41.5%),* Jake Long (38.8%), Zach Miller (38.6%), Brad Jones (38.5%), Patrick Willis (37.0%), Chris Johnson (36.6%), Kendall Langford (35.2%), Marques Colston (34.9%), Joe Mays (32.8%), Brandon Fields (32.6%), Tamba Hali (31.6%), Thomas DeCoud (29.3%), Cary Williams (29.2%), Lamarr Woodley (28.7%), Mike DeVito (28.4%), AJ Hawk (28.3%), Darrelle Revis (27.2%), Mathias Kiwanuka (27.0%), Cortland Finnegan (26.3%), Harry Douglas (24.7%), James Casey (24.3%), Charlie Johnson (24.3%), Steven Jackson (23.5%), Vince Wilfork (22.2%), Chris Canty (22.1%), Donnie Avery (21.7%), Stephen Bowen (21.2%), Robert Geathers (19.7%), Darnell Dockett (18.3%), Andre Johnson (17.7%), Cullen Jenkins (16.6%), Chris Myers (15.3%), Scott Wells (14.1%), Justin Smith (13.1%), Jarrett Johnson (12.5%)*, Shaun Phillips (12.3%), Josh McCown (7.8%), Peyton Manning (5.4%).

*Last week I listed DeAngelo Williams and Tyvon Branch in the “Incorrectly Predicted” category, but Jason since pointed out to me that the remaning years of their deals were scheduled to void.  This changed a number of the variables, with the result being that their Expected Outcomes decreased.  This is not an issue with the ECV model, but rather with my interpretation of each of the contracts.

“Incorrectly Predicted” Non-Releases:

Michael Oher (77.0%), David Wilson (75.3%), Brian Hartline (72.7%), Jacoby Jones (71.9%), Vance Walker (71.8%), Curtis Lofton (62.8%), LaRon Landry (58.0%), Reggie Bush (57.2%), Jon Beason (54.3%), Ricky Jean-Francois (52.3%), Ted Ginn (52.1%).

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

Introduction Part 2:  Inputs & Outputs

Introduction Part 3:  Contract Comparison

Introduction Part 4:  Salary Cap Budgeting

Introduction Part 5:  Frequently Asked Questions

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 overthecap.com, Bryce operated eaglescap.com 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 bryce.l.johnston@gmail.com or via Twitter @NFLCapAnalytics.

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 njb50@georgetown.edu.