The aim of this work is to evaluate the productivity change of the NBA teams during the last seven seasons (from 2006-07 to 2012-13). Within that period of time, a new collective bargaining agreement (CBA) of the National Basketball Association (NBA) was ratified before season 2011-12, ending a 161-day lockout. The Malmquist Productivity Index (MPI) has been used to measure the total factor productivity, while an input-oriented Network DEA approach is used to compute the distance of each observation to the corresponding frontier. The results reveal that there has been technological progress for the last few seasons, excluding that of the 2011 lockout, and an increasing efficiency change. This means that best practices are improving and that most teams have been reducing their payrolls to catch up with these practices, thus backing up the owners’ proposal to reduce players’ income. Also regression results show that changes in the number of wins are more dependent upon scale efficiency change than upon budget or efficiency changes.
AizembergL., CostaM., GraçaT., MartinsA., AnguloL., & BaptistaJ.C.C. (2011). Measuring the NBA teams cross-efficiency by DEA game.Proceedings of the 3rd IMA International Conference on Mathematics in Sport, June 22–24, 2011, Salford Quays, UK.
2.
BankerR. D. (1984). Estimating most productive scale size using data envelopment analysis.European Journal of Operational Research, 17, 35–44.
3.
BarrosC. P., AssafA., & Sá-EarpF. (2010). Brazilian football league technical efficiency: SimarA. and Wilson approach. Journal of Sports Economics, 11, 641–651.
4.
BarrosC. P., & Garcia-del-BarrioP. (2011). Productivity drivers and market dynamics in the Spanish first division football league.Journal of Productivity Analysis, 35, 5–13.
5.
BenitoB., SolanaJ., & Moreno, M.-R. (2012). Assessing the efficiency of local entities in the provision of public sports facilities.International Journal of Sport Finance, 7, 46–72.
6.
BerriD. J. (2012). Did the players give up money to make the NBA better? Exploring the 2011 collective bargaining agreement in the National Basketball Association. International Journal of Sport Finance, 7, 158–175.
7.
BerriD. J., LeedsM. A., LeedsE. M., & MondelloM. (2009). The role of managers in team performance.International Journal of Sport Finance, 4, 75–93.
8.
CharnesA., CooperW. W., & RhodesE. (1978). Measuring the efficiency of decision making units.European Journal of Operational Research, 2, 429–444.
9.
CoatesD., & HumphreysB. R. (2001). The economic consequences of professional sports strikes and lockouts.Southern Economic Journal, 67, 737–747.
10.
CooperW. W., SeifordL. M., & ToneK. (2006). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software (2nd ed.). New York, NY: Springer.
11.
CooperW. W., RamónN., RuizJ. L., & SirventI. (2011). Avoiding large differences in weights in cross-efficiency evaluations: Application to the ranking of basketball players.Journal of CENTRUM Cathedra, 4, 197–215.
12.
CooperW. W., RuizJ. L., & SirventI. (2009). Selecting non-zero weights to evaluate effectiveness of basketball players with DEA.European Journal of Operational Research, 195, 563–574.
FäreR., GrosskopfS., LindgrenB., & RoosP. (1992). Productivity changes in Swedish pharmacies 1980-1989: A nonparametric malmquist approach.Journal of Productivity Analysis, 3(3), 85–101.
15.
FäreR., GrosskopfS., NorrisM., & ZhangZ. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries.The American Economic Review, 84(1), 66–83.
16.
González-GómezF., & Picazo-TadeoA. J. (2010). Can we be satisfied with our football team? Evidence from Spanish professional football.Journal of Sports Economics, 11, 418–442.
17.
KaoC. (2009). Efficiency decomposition in network data envelopment analysis: A relational model.European Journal of Operational Research, 192, 949–962.
18.
LewisH. F., & SextonT. R. (2004a). Data envelopment analysis with reverse inputs and outputs.Journal of Productivity Analysis, 21, 113–132.
19.
LewisH. F., & SextonT. R. (2004b). Network DEA: Efficiency analysis of organizations with complex internal structure.Computers and Operations Research, 31, 1365–1410.
20.
LewisH. F., LockK. A., & SextonT. R. (2009). Organizational capability, efficiency, and effectiveness in Major League Baseball: 1901-2002.European Journal of Operational Research, 197, 731–740.
21.
LozanoS. (2011). Scale and cost efficiency analysis of networks of processes.Expert Systems with Applications, 38, 6612–6617.
22.
MorenoP., & LozanoS. (in press). A network DEA assessment of team efficiency in the NBA.Annals of Operations Research.
23.
MorseA. L., ShapiroS. L., McevoyC. D., & RascherD. A. (2008). The effects of roster turnover on demand in the National Basketball Association.International Journal of Sport Finance, 3, 8–18.
24.
RamónN., RuizJ. L., & SirventI. (2012). Common sets of weights as summaries of DEA profiles of weights: With an application to the ranking of professional tennis players.Expert Systems with Applications, 39, 4882–4889.
25.
RibeiroA. S., & LimaF. (2012). Portuguese football league efficiency and players’ wages.Applied Economics Letters, 19, 599–602.
26.
RobstJ., VanGilderJ., BerriD. J., & VanceC. (2011). 'Defense wins championships?’ The answer from the gridiron.International Journal of Sport Finance, 6, 72–84.
27.
SextonT. R., & LewisH. F. (2003). Two-stage DEA: An application to Major League Baseball.Journal of Productivity Analysis, 19, 227–249.
28.
TiedemannT., FrancksenT., & Latacz-LohmannU. (2011). Assessing the performance of German Bundesliga football players: A non-parametric metafrontier approach.Central European Journal of Operations Research, 19, 571–587.
29.
WuJ., ZhouZ., & LiangL. (2010). Measuring the Performance of Nations at Beijing Summer Olympics Using Integer-Valued DEA Model.Journal of Sports Economics, 11, 549–566.
