Evaluating NBA player performance using bounded integer data envelopment analysis

References

• Aizemberg L, Roboredo MC, Ramos TG, de Mello JCCS, Meza LA, Alves AM. 2014. Measuring the NBA teams' cross-efficiency by DEA game. Am J Oper Res. 4:101–112.
   Google Scholar
• Andersen P, Petersen NC. 1993. A procedure for ranking efficient units in data envelopment analysis. Manag Sci. 39:1261–1264.
   Web of Science ®Google Scholar
• Anderson TR, Sharp GP. 1997. A new measure of football batters using DEA. Ann Oper Res. 73:141–155.
   Web of Science ®Google Scholar
• Barros CP, Assaf A, Sá-Earp F. 2010. Brazilian football league technical efficiency: a Simar and Wilson approach. J Sports Econ. 11:641–651.
   Web of Science ®Google Scholar
• Chen WC, Johnson AL. 2010. The dynamics of performance space of major league baseball pitchers 1871-2006”, Ann Oper Res. 181:287–302.
   Web of Science ®Google Scholar
• Chen Y. 2005. Measuring super-efficiency in DEA in the presence of infeasibility. Eur J Oper Res. 161:545–551.
   Web of Science ®Google Scholar
• Chen Y, Cook WD, Du J, Hu HH, Zhu J. 2015. Bounded and discrete data and Likert scales in data envelopment analysis: application to regional energy efficiency in China. Ann Oper Res. doi: 10.1007/s10479-015-1827-3.
   Web of Science ®Google Scholar
• Chen Y, Liang L. 2011. Super-efficiency DEA in the presence of infeasibility: one model approach. Eur J Oper Res. 213:359–360.
   Web of Science ®Google Scholar
• Collier T, Johnson AL, Ruggiero J. 2011. Measuring technical efficiency in sports. J Sports Econ. 12:579–598.
   Web of Science ®Google Scholar
• Cook WD, Harrison J, Imanirad R, Rouse P, Zhu J. 2013. Data envelopment analysis with non-homogeneous DMUs. Oper Res. 61:666–676.
   Web of Science ®Google Scholar
• Cook WD, Liang L, Zha Y, Zhu J. 2009. A modified super-efficiency DEA model for infeasibility. J Oper Res Soc. 69:276–281.
   Web of Science ®Google Scholar
• Cook WD, Tone K, Zhu J. 2014. Data envelopment analysis: prior to choosing a model. Omega. 44:1–4.
   Web of Science ®Google Scholar
• Cooper WW, Park KS, Pastor JT. 1999. RAM: a range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. J Prod Anal. 11:5–42.
   Web of Science ®Google Scholar
• Cooper WW, Pastor JT, Borras F, Aparicio J, Pastor D. 2011. BAM: a bounded adjusted measure of efficiency for use with bounded additive models. J Prod Anal. 35:85–94.
   Web of Science ®Google Scholar
• Cooper WW, Ramón N, Ruiz JL, Sirvent I. 2011. Avoiding large differences in weights in cross-efficiency evaluations: application to the ranking of basketball players. J Centrum Cathedra. 4:197–215.
   Google Scholar
• Cooper WW, Ruiz JL, Sirvent I. 2009. Selecting non-zero weights to evaluate effectiveness of basketball players with DEA. Eur J Oper Res. 195:563–574.
   Web of Science ®Google Scholar
• González-Gómez F, Picazo-Tadeo AJ. 2010. Can we be satisfied with our football team? Evidence from Spanish professional football. J Sports Econ. 11:418–442.
   Web of Science ®Google Scholar
• Gutiérrez E, Lozano S. 2014. A DEA approach to performance-based budgeting of formula one constructors. J Sports Econ. 15:180–200.
   Web of Science ®Google Scholar
• Kazemi Matin R, Kuosmanen T. 2009. Theory of integer-valued data envelopment analysis under alternative returns to scale axioms. Omega. 37:988–955.
   Web of Science ®Google Scholar
• Kuosmanen T, Kazemi Matin R. 2009. Theory of integer-valued data envelopment analysis. Eur J Oper Res. 192:658–667.
   Web of Science ®Google Scholar
• Kuosmanen T, Keshvaril A, Kazemi Matin R. 2016. Discrete and integer valued inputs and outputs in data envelopment analysis. In: J Zhu, editor. Data envelopment analysis: a handbook of models and method. New York, NY: Springer. 67–103.
   Google Scholar
• Lee HS, Chu CW, Zhu J. 2011. Super-efficiency DEA in the presence of infeasibility. Eur J Oper Res. 212:141–147.
   Web of Science ®Google Scholar
• Lee BL, Worthington AC. 2013. A note on the ‘Linsanity’ of measuring the relative efficiency of National Basketball Association guards. Appl Econ. 45:4193–4202.
   Web of Science ®Google Scholar
• Lewis HF, Sexton TR, Lock KA. 2007. Player salaries, organizational efficiency, and competitiveness in major league baseball. J Sports Econ. 8:266–294.
   Web of Science ®Google Scholar
• Lim S, Zhu J. 2013. Incorporating performance measures with target levels in data envelopment analysis. Eur J Oper Res. 230:634–642.
   Web of Science ®Google Scholar
• Lozano S, Villa G. 2006. Data envelopment analysis of integer-valued inputs and outputs. Comput Oper Res. 33:3004–3014.
   Web of Science ®Google Scholar
• Moreno P, Lozano S. 2014. A network DEA assessment of team efficiency in the NBA. Ann Oper Res. 214:99–124.
   Web of Science ®Google Scholar
• Ruiz JL, Pastor D, Pastor JT. 2013. Assessing professional tennis players using data envelopment analysis (DEA). J Sports Econ. 14:276–302.
   Web of Science ®Google Scholar
• Seiford LM, Zhu J. 1999. Infeasibility of super-efficiency data envelopment analysis models. INFOR. 37:174–187.
   Web of Science ®Google Scholar
• Sexton TR, Lewis HF. 2003. Two-stage DEA: an application to major league baseball. J Prod Anal. 19:227–249.
   Web of Science ®Google Scholar
• Sinuany-Stern Z, Israeli Y, Bar-Eli M. 2006. Application of the analytic hierarchy process for the evaluation of basketball teams. Int J Sport Manag Marketing. 3:193–207.
   Google Scholar
• Volz B. 2009. Minority status and managerial survival in major league baseball. J Sports Econ. 10:522–542.
   Web of Science ®Google Scholar
• Yang CH, Lin HY, Chen CP. 2014. Measuring the efficiency of NBA teams: additive efficiency decomposition in two-stage DEA. Ann Oper Res. 217:565–589.
   Web of Science ®Google Scholar