Show Me the Money:The Factors that Affect the NBA Free Agent Market

Nik Oza, Camden Hu, Xavier Weisenreder and Nick Barton

Introduction• What factors contribute to a team’s ability to land

a franchise-altering free agent? • Which teams in the NBA could reasonably expect

to do so in the next five years? • How does the probability vary on a case-by-case

and year-to-year basis?

Franchise-Altering Players• Our Definition:• 25.6% of Salary Cap (15 million in 2013-14) for UFAs• Maxed-out RFAs

• Makes sense given max salary constraints with NBA contracts

• Definition helps to reduce hindsight bias; just because a player didn’t pan out doesn’t mean he wasn’t considered a franchise-altering player at the time

Restricted Free Agents• 2005 CBA – present: 27/27 maxed out RFAs signed

with same team (right to match)• Max offer sheets always get matched• Max RFAs are very unlikely targets, and should not

be part of other teams’ plans

Case Studies: RFAs• 2 RFAs changed teams (Kenyon Martin & Lamar

Odom) *1999 CBA• Kenyon Martin sign and trade to Nuggets• Lamar Odom signs with the Heat

Unrestricted Free Agents• 9 of 26 UFA who hit the market changed teams• Another 12 were extended before hitting the

market as a UFA

Case Studies: UFA “Overpays”• Define “overpay” as more than any other offer• Bulls sign Carlos Boozer for 5 years/$90 million• Bulls sign Ben Wallace for 4 years/$60 million• Magic sign Rashard Lewis for 6 years/$118 million

• UFA “overpays” never justify cost

Case Studies: UFA• Amar’e Stoudemire signs with the Knicks to lock

up max contract• Very probable max offers elsewhere

• Heat re-sign Dwyane Wade, sign-and-trade for LeBron James and Chris Bosh• Max offers elsewhere• LeBron: coach/management conflict

• Rockets sign Dwight Howard in free agency• Max offers elsewhere• Coach/management conflict

Statistical Analysis

Logistic Regressions• Limited Sample Size• No Significant Variable

Found

1. K-Means Clustering, 2-5 means

2. Poisson for count of UFA signed by each Cluster

• Team Win% last 10 seasons (significant)

• Total Championships• Avg Temperature in-

season• Rank of Market Size

(significant)• State Income Tax Rate

Strategy 1: Overpay!• If a team lands a free agent due to paying more

than any other team, he is probably not a franchise-altering free agent

• The Winner’s Curse• Examples:• Rashard Lewis• Carlos Boozer

Strategy 2: Other Superstars• James, Wade, and Bosh • Dwight Howard (joining with James Harden)• Might need a coach/management conflict• Requires intelligent cap foresight

Strategy 3: Don’t Count on It

• It takes an extraordinary situation in order to acquire a premier free agent without overpaying or other stars

• 67 “franchise-altering” players since 2003:• 56 signed with same team• 7 “overpays” at the time• 3 joined up with other stars• Amar’e Stoudemire locks in max deal in NYC market

The Other Route: Trading for Franchise-Altering Players• Shaquille O’Neal• Pau Gasol• Vince Carter• Ray Allen*• Kevin Garnett• Carmelo Anthony*

• Deron Williams*• Chris Paul• Joe Johnson*• Jason Kidd• James Harden• Dwight Howard (LAL)

*xRAPM-based WAR (Replacement Level = 4 pts below avg) didn’t justify contract

Projecting the Next 5 Years• Big Market• Winning teams• Cap space• Star(s) under contract (or ability to match via RFA)

Projecting Summer 2014

Players• James*, Wade*, Bosh*• Duncan*• Nowitzki*• Carmelo Anthony

Teams• Lakers• Suns?

Projecting Summer 2015

Players• Rondo• Dragic• Love• Aldridge• M. Gasol• Hibbert

Teams• Knicks*• Mavericks• Rockets• Suns• Blazers

Projecting Summer 2016

Players• Conley• Durant*• Noah?• Howard?

Teams• 76ers• Raptors• Celtics• Pelicans• Jazz?• Cavs?• Hornets?• T’Wolves?• Wizards?

Projecting Summer 2017

Players• Rose• Curry• Chris Paul• Blake Griffin• Westbrook• Ibaka• Jrue

Holiday?

Teams• 76ers• Warriors• Pacers• Raptors• Magic?• 2016 teams

that save cap space for 2017

• Possible franchise-altering player movement!!!

Conclusion

• It is very unlikely that a franchise-altering player is acquired from another team through UFA/RFA

• Slight advantage for:• Big Market Teams•Winning Teams• Teams with Stars Under Contract

• With multiple stars available in UFA, more likely to be franchise-altering player movement• 2017?

Appendix

  Estimate Std. Error Z value Pr(>|z|)

Intercept 2.015e+00 7.751e+00 0.260 0.795

Age -1.742e-02 2.080e-01 -0.084 0.933

Old team Win% -1.192e+00 3.603e+00 -0.331 0.741

PER -1.442e-01 1.978e-01 -0.729 0.466

Last Contract Total Salary

1.947e-08 2.161e-08 0.901 0.368

Rings at Signing 9.211e-01 1.009e+00 0.912 0.362

No. of Old Team AS

-5.981e-02 6.560e-01 -0.091 0.927

Old Team Market Rank

1.167e-02 6.133e-02 0.190 0.849

Old Team Weather

3.587e-02 5.075e-02 0.707 0.480

Old Team State Income Tax Rate

1.956e+00 1.205e+01 0.162 0.871

• Logistic Regression for UFA change teams or no

No Variable shows up as significant

Appendix• K-means clustering on:• Win% last 10 years• Rank of Market Size• Income tax rate• Total championships• Weather

• Poisson Regression for count of UFA signed/resigned/extended by 3 clusters of teams

Estimate

Std. Error

z value Pr(>|z|)

(Intercept)

-2.66065 2.99696 -0.888 0.3747

Win% over last 10 years

7.67806 5.71029 1.345 0.1788

Market rank

-0.06993 0.03247 -2.154 0.0313*

Appendix• Poisson Regression for count of UFA

signed/resigned/extended by 4 clusters of teams

Estimate

Std. Error

z value Pr(>|z|)

(Intercept)

-1.31413 2.26635 -0.58 0.56202

Win% over last 10 years

5.72779 4.57953 1.251 0.21103

Market rank

-0.10258 0.02903 -3.5340.00041**

*

Appendix• Poisson Regression for count of UFA

signed/resigned/extended by 5 clusters of teams

  EstimateStd. Error

Z value

Pr(>|z|)

Intercept -5.53178 2.97049 -1.862 0.06257 .Win% over

Last 10 years13.79608 6.11270 2.257

0.02401 *

Market rank -0.08832 0.02878 -3.0680.00215

**

References• http://www.basketball-reference.com• http://www.shamsports.com• http://www.cbafaq.com/salarycap.htm• http://www.gotbuckets.com• http://stats-for-the-nba.appspot.com