Un método probabilístico para las clasificaciones estadísticas de jugadores en baloncesto.

Bibliographic References

• Anderson, D. R.; Burnham, K. P., y Thompson, W. L. (2000). Null hypothesis testing: Problems, prevalence, and an alternative. The Journal of Wildlife Management, 64, 912-923.
• Belloni, A., y Didier, G. (2008). On the Behrens--Fisher problem: A globally convergent algorithm and a finite-sample study of the Wald, LR and LM Tests. Annals of Statistics, 36 (5), 2377-2408.
• Berri, D. J. (1999). Who is most valuable? Measuring the player’s production of wins in the National Basketball Association. Managerial and Decision Economics, 20 (8), 411- 427.
• Berri, D. J. (2008). A simple measure of worker productivity in the National Basketball Association. En Brad Humphreys and Dennis Howard (Eds). The Business of Sport (pp. 1-40); Westport, Conn: Praeger.
• Berri, D. J.; Schmidt, M. B., y Brook, S. L. (2006). The wages of wins: Taking measure of the many myths in modern sport. Palo Alto,CA: Stanford University Press.
• Casas, J. M. (1997). Inferencia estadística. Segunda Edición. Madrid: Editorial Centro de Estudios Ramón Areces, S. A.
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
• Cohen, J. (1990). Things I have learned (so far). American Psychologist 45 (12), 1304– 1312.
• Cooper, W. W.; Ruiz, J. L., y Sirvent, I. (2009). Selecting non-zero weights to evaluate effectiveness of basketball players with DEA. European Journal of Operational Research, 195 (2), 563–574.
• Curran-Everett D. (2000). Multiple comparisons: Philosophies and illustrations. American Journal of Physiology Regulatory, Integrative and Comparative Physiology, 279, R1- R8.
• Felipo, J. (2005). Fórmulas para ganar: La revolución estadística del basket. Barcelona: Zona 131.
• Grissom, R. J., y Kim, J.J. (2005). Effect sizes for research. A broad practical approach Mahwah, NJ: LEA.
• Hayduk, L. A. (1996). LISREL Issues, Debates and Strategies. Baltimore and London: John Hopkins University Press.
• Hesterberg, T.; Moore, D. S., Monaghan, S., Clipson, A., y Epstein, R. (2005). Bootstrap Methods and Permutation Tests, 2nd edition. N.Y.: W. H. Freeman.
• Hollinger, J. (2005). Pro Basketball Forecast. Washington, D.C.: Potomac, Inc.
• Kubatko, J.; Oliver, D., Pelton, K, y Rosenbaum, D. T. (2007). A starting point for analyzing basketball statistics. Journal of Quantitative Analysis in Sports, 3 (3), Article 1.
• Levy, P.S., y Lemeshow, S. (1999). Sampling of populations: Methods and applications (3ª ed). Wiley series in probability and statistics. Survey Methodology Section.
• Lewis, M. M. (2003). Moneyball: The art of winning an unfair game. W.W. Norton & Company Inc.
• Lombardía, M. J.; González-Manteiga, W., y Prada-Sánchez, J. M. (2004). Una revisión de la estimación de la función de distribución y métodos de remuestreo bootstrap en poblaciones finitas. Estadística española, 46 (155), 149-175.
• Lumley, T.; Diehr, P., Emerson, S., y Chen L. (2002). The importance of the normality assumption in large public health data sets. Annual Review Public Health, 23, 151- 169.
• Mayo, D. G., y Cox, D. R. (2006). Frequentists statistics as a theory of inductive inference. En Rojo, J. (ed.), 2nd Lehmann SymposiumOptimally. IMS Lecture Notes-Monographs Series: 1-28.
• McGoldrick, K., y Voeks, L. (2005). We got game! An analysis of win/loss probability and efficiency differences between the NBA and WNBA. Journal of Sports Economics, 6 (1), 5-23.
• Meehl, P. E. (1990). Why summaries of research on psychological theories are often uninterpretable. Psychological Reports, 66 (Monograph Suppl. 1-V66), 195-244.
• Mora, J. L., y Solomon, P. J. (2002). Worrying about normality. Critical Care and Resuscitation, 4 (4), 316-319
• Nickerson, R. S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5, 241-301.
• Olejnik, S., y Algina, J. (2000). Measures of effect size for comparative studies: Applications, interpretations, and limitations. Contemporary Educational Psychology, 25, 241-286.
• Oliver, D. (2004). Basketball on paper. Rules and tools for performance analysis. Washington, D. C.: Brassey’s, INC.
• Page, G. L.; Fellingham, G. W., y Reese, C. S. (2007). Using box-scores to determine a position's contribution to winning basketball games. Journal of Quantitative Analysis in Sports, 3 (4), Article 1.
• Ries, A., y Trout, J. (1999). Las 22 leyes inmutables del marketing. McGraww-Hill.
• Ree, M. J., y Carreta, T. H. (2006). The role of measurement error in familiar statistics. Organizational Research Methods, 9 (1), 99-112.
• Rosenthal, R., Rosnow, R. L., y Rubin, D. B. (2000). Contrasts and effect sizes in behavioral research: A correlational approach. Cambridge University Press.
• Rosnow, R. L., y Rosenthal, R. (1996). Computing contrasts, effect sizes, and counternulls on other people’s published data: General procedures for researchconsumers. Psychological Methods, 1, 331-340.
• Rothman, K. J. (1990). No adjustments are needed for multiple comparisons. Epidemiology, 1 (1), 43-46.
• Rothman, K. J.; Greenland, S., y Lash, T. L. (2008). Modern Epidemiology. Third edition. Philadelphia: Luppincott, Williams & Wilkins.
• Savitz, D. A., y Olshan, A. F. (1998). Decribing data requires o adjustment for multiple comparisons: a reply form Savitz and Olshan. American Journal of Epidemiology, 147, 813-814.
• Sawilowsky, S. S. (2002). Fermat, Schubert, Einstein, and Behrens-Fisher: The probable difference between two means when σ1 ≠ σ2. Journal of Modern Applied Statistical Methods, 1 (2), 461-472.
• Sawilowsky, S. S. (2005). Misconceptions leading to choosing the t Test over the Wilcoxon Mann-Whitney test for shift in location parameter. Journal of Modern Applied Statistical Methods, 4 (2), 598-600.
• Sterman, J. (2002). All models are wrong: reflections on becoming a systems scientist. System Dynamics Review, 18 (4), 501–531.
• Vegas, E. (1997). El problema de Behrens-Fisher en la investigación biomédica. Análisis crítico de un estudio clínico mediante simulación. Qüestiió, 21 (1-2), 293-316.
• West, B. T. (2006). A simple and flexible rating method for predicting success in the NCAA basketball tournament. Journal of Quantitative Analysis in Sports, 2 (3), Article 3.
• Winston, W. L. (2009). Mathletics. New Yersey: Princeton University Press.