The Basketball Distribution

Little math problem. I’ll fix it soon.
EDIT: I have now switched these to being based on a much more accurate player rating, RAPM via http://stats-for-the-nba.appspot.com


I’ve made Free Agency ratings for each signing and team. These ratings are based on a few things:

First, Production ($). This refers to the estimated value of a player’s Wins Above Replacement, which is based off my limited, but useful SPR rating. The basic math (which is somewhat based on the CBA) is roughly: $1.5 million x WARP + $1 million = Production $.

 Some issues with this are:

1) A player’s WARP will be diminished if they are injured
2) SPR ratings are not great at measuring defense

The latter I don’t have a quick fix for. The former, I made a quick adjustment to. A player’s initial value is multiplied up to 68*** games (i.e. if a player’s production was $1 million and they played 34 games, their injury-adjusted production would be $2 million). If they played more than 68 games, they got full credit for that # of games played.

Here are the best deals so far, followed by each team’s gained/lost value (measured by the “Difference” column in the first table).


*Please note that this does not include any player whose terms are not listed on Basketball Reference’s free agency tracker (I know Manu, for example, isn’t on here)*


PlayerOTmNTm2012 Injury-Adjusted Production ($)Free Agency Signed $ / YearDiff
Paul MillsapUTAATL21533840.13950000012033840.13
Andre IguodalaDENGSW21350126.84120000009350126.84
Jose CalderonDETDAL16254169.9572500009004169.954
Al-Farouq AminuNOPNOP11022611.3337000007322611.329
Devin HarrisATLDAL8802482.29530000005802482.295
Nikola PekovicMINMIN11672940.3360465005626440.333
Chris PaulLACLAC26762960.65214000005362960.654
Matt BarnesLACLAC9069688.5663833333.3335236355.233
David WestINDIND17096725.7120000005096725.699
Tiago SplitterSASSAS13889325.2490000004889325.242
J.R. SmithNYKNYK10850257.1361750004675257.135
Tony AllenMEMMEM9387290.20450000004387290.204
Mike DunleavyMILCHI7280329.76130000004280329.761
Josh SmithATLDET17765109.74135000004265109.742
Mario ChalmersMIAMIA8088888.51140000004088888.511
Kyle KorverATLATL9485217.16360000003485217.163
Pablo PrigioniNYKNYK5281693.11620000003281693.116
Josh McRobertsCHACHA5925759.64927500003175759.649
Dorell WrightPHIPOR6119130.34430000003119130.344
Carlos DelfinoHOUMIL6214626.45132500002964626.451
Darren CollisonDALLAC4815418.72519000002915418.725
Francisco GarciaHOUHOU3729013.57913000002429013.579
Dwight HowardLALHOU24287664.47220000002287664.472
Omri CasspiCLEHOU2915467.17813000001615467.178
Jodie MeeksLALLAL2975483.07815500001425483.078
Chase BudingerMINMIN6581692.3135333333.3331248358.98
Gerald HendersonCHACHA5384280.98942000001184280.989
Greg StiemsmaMINNOP3816887.41627000001116887.416
Dante CunninghamMINMIN3228831.03821800001048831.038
Rashard LewisMIAMIA2397628.531400000997628.5303
C.J. WatsonBRKIND2890695.1552000000890695.1553
Kevin MartinOKCMIN8032773.5887250000782773.5881
Wayne EllingtonCLEDAL3254898.3032500000754898.3028
Jeff TeagueATLMIL8603022.7478000000603022.7467
Chris KamanDALLAL3606845.6563200000406845.6565
Ray AllenMIAMIA3583235.1993200000383235.1986
Zaza PachuliaATLMIL5522598.8585333333.333189265.5244
O.J. MayoDALMIL8158844.7518000000158844.7507
Tyler HansbroughINDIND4238255.5415000088255.5003
Jarrett JackGSWCLE6290126.604625000040126.60363
Patrick MillsSASSAS1076270.9311133950-57679.06913
Earl WatsonUTAPOR1271447.8621400000-128552.1381
Boris DiawSASSAS4422152.9224702500-280347.0777
J.J. HicksonPORDEN4525962.2245000000-474037.776
Jerryd BaylessMEMMEM2207495.7223100000-892504.278
Chris CopelandNYKIND1847691.0343050000-1202308.966
Eric MaynorPORWAS709401.20112000000-1290598.799
Aaron GrayTORTOR1264898.4812690875-1425976.519
Al JeffersonUTACHA11830947.2313500000-1669052.773
Ronnie BrewerOKCMIN3319730.4525000000-1680269.548
Trevor ArizaWASWAS6011169.5717700000-1688830.429
J.J. RedickMILLAC4901608.9016750000-1848391.099
Randy FoyeUTADEN721589.50143000000-2278410.499
Carl LandryGSWSAC3529099.0846500000-2970900.916
Marco BelinelliCHISAS-596789.69313000000-3596789.693
Shawn MarionDALDAL5505117.7979316796-3811678.203
Marvin WilliamsUTAUTA3674119.9517500000-3825880.049
Tyreke EvansSACNOP6861293.13211000000-4138706.868
Emeka OkaforWASWAS9848117.62814487500-4639382.372
Earl ClarkLALCLE-797022.12624500000-5297022.126
Charlie VillanuevaDETDET2911016.1378580000-5668983.863
Richard JeffersonGSWUTA1526167.711046000-9519832.3
Ben GordonCHACHA-4652922.60513200000-17852922.6



TeamValue Change by Not Resigning Value Change By FA Signing/ResigningTotal
GSW$12,450,606.61 $9,350,127$21,800,733.45
LAC$- $11,666,344$11,666,343.51
NYK$1,202,308.97 $7,956,950$9,159,259.22
DAL$(3,481,109.13)$11,749,872$8,268,763.22
CHI$3,596,789.69 $4,280,330$7,877,119.45
MIN$(1,116,887.42)$7,026,134$5,909,246.97
MIA$- $5,469,752$5,469,752.24
IND$- $4,873,367$4,873,367.39
LAL$3,009,357.65 $1,832,329$4,841,686.39
POR$1,764,636.57 $2,990,578$4,755,214.78
ATL$(10,859,880.31)$15,519,057$4,659,176.98
NOP$- $4,300,792$4,300,791.88
MEM$- $3,494,786$3,494,785.93
HOU$(2,964,626.45)$6,332,145$3,367,518.78
MIL$(2,431,938.66)$3,915,759$1,483,820.81
SAC$4,138,706.87 -$2,970,901$1,167,805.95
SAS$- $954,509$954,509.40
TOR$- -$1,425,977$(1,425,976.52)
WAS$- -$7,618,812$(7,618,811.60)
CLE$(2,370,365.48)-$5,256,896$(7,627,261.00)
DET$(9,004,169.95)-$1,403,874$(10,408,044.07)
DEN$(9,350,126.84)-$2,752,448$(12,102,575.11)
CHA$- -$15,161,935$(15,161,934.74)
UTA$(7,957,824.72)-$13,345,712$(21,303,537.07)
***I used the Excel solver and asked it what standard # of games most accurately reflects money earned in the NBA. This was done by running the following math on each player-season:

2012 Raw Production $

(x / Games Played) * 2012 Raw Production $
=
Difference

Solver minimized this difference, and came up with about 68 games. 

Sports League Maps

Sports have always been about community. Whether it’s cheering from the sidelines of a youth soccer match, following a local baseball club, or joining an adult recreational basketball league, people naturally gravitate toward opportunities to play, compete, and connect with others who share their passion. One of the simplest yet most effective tools for bringing these communities together is the sports league map.

Sports league maps make it easy for players, parents, coaches, and fans to discover leagues operating in their area. Instead of relying on scattered internet searches, social media groups, or word-of-mouth recommendations, users can view an interactive map that displays nearby organizations along with essential information such as locations, age groups, sports offered, registration details, and contact information. This centralized approach removes much of the frustration from finding the right league.

Sports League Maps Help Connect Fans with Local Sports Leagues

sec sports league map

For families with children, league maps can be especially valuable. Parents often look for programs that are close to home, fit their schedules, and match their child’s age and skill level. A map allows them to compare multiple options in minutes, helping them identify the most convenient leagues without spending hours researching individual websites. It also exposes families to organizations they may never have discovered otherwise, increasing participation across the community.

Adult athletes benefit just as much. Many people relocate for work or school and want to continue playing the sports they enjoy. Whether someone is searching for a competitive softball league, a casual volleyball group, a tennis ladder, or a weekend soccer club, a well-designed sports league map can quickly point them toward opportunities that match their interests. This helps newcomers become active members of their communities while expanding local leagues with fresh participants.

League maps also provide significant advantages for the organizations themselves. Smaller leagues often operate with limited marketing budgets, making it difficult to reach potential players beyond their immediate networks. Appearing on a comprehensive sports league map increases visibility, helping organizations attract participants who are actively searching for places to play. As more leagues participate, the directory becomes increasingly valuable, creating a positive cycle that benefits both users and organizers.

Fans gain another important benefit from sports league maps. Local sports often receive less media attention than professional teams, even though they provide exciting competition and foster strong community connections. Interactive maps help fans discover nearby games, tournaments, and clubs they can support throughout the year. Attending local events strengthens community spirit, supports volunteer organizations, and gives athletes the encouragement that comes from playing in front of enthusiastic crowds.

Modern sports league maps frequently include advanced features that extend beyond simple locations. Filters allow users to search by sport, age division, skill level, season, or competitive format. Some platforms include links to schedules, registration forms, league standings, social media pages, and official websites, making the map a complete resource rather than just a navigation tool.

As participation in youth, amateur, and recreational sports continues to grow, sports league maps play an increasingly important role in connecting communities. They simplify the search process, increase visibility for local organizations, encourage participation, and make it easier for fans to support the teams and athletes in their own neighborhoods. By bringing information together in one accessible place, sports league maps help strengthen the local sports ecosystem and ensure that more people can enjoy the lifelong benefits of athletic competition and community involvement.