Dynamic criteria: a longitudinal analysis of professional basketball players' outcomes

  1. García Izquierdo, Antonio León 1
  2. Ramos Villagrasa, Pedro J.
  3. Navarro Cid, José 2
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  2. 2 Universitat de Barcelona
    info

    Universitat de Barcelona

    Barcelona, España

    ROR https://ror.org/021018s57

Revista:
The Spanish Journal of Psychology

ISSN: 1138-7416

Ano de publicación: 2012

Volume: 15

Número: 3

Páxinas: 1133-1146

Tipo: Artigo

DOI: 10.5209/REV_SJOP.2012.V15.N3.39403 DIALNET GOOGLE SCHOLAR lock_openAcceso aberto editor

Outras publicacións en: The Spanish Journal of Psychology

Obxectivos de Desenvolvemento Sustentable

Resumo

En este artículo describimos las fluctuaciones en el tiempo del rendimiento de jugadores profesionales de baloncesto buscando patrones deterministas y de qué tipo son. Para ello, analizamos los resultados de 94 jugadores profesionales mediante un estudio longitudinal de series temporales de diez años de duración. Analizamos las series temporales utilizando las técnicas que se proponen desde la teoría de sistemas dinámicos no lineales. Mediante estas técnicas podemos descubrir los patrones subyacentes de los resultados sin tener que realizar asunciones previas sobre la linealidad o no linealidad de los datos, ni transformaciones de los mismos para que se ajusten a priori a una distribución. En los resultados encontrados, la mayoría de los jugadores muestran un patrón determinista (88.30%), de los cuales la mayoría son caóticos (81.92%) que obtienen mejores resultados que los lineales. El alto número de patrones caóticos encontrados parece indicar que debemos ser precavidos a la hora de evaluar y tomar decisiones sobre el rendimiento de los jugadores, y que la gestión de equipos debe asumir que la incertidumbre es una parte importante en este contexto.

Información de financiamento

This research is partially supported by Ministerio de Ciencia e Innovación, project reference DER2010-21686-C02-01, for the first author. Financial support for the second author (grant: UNOV-09-BECDOC-S) given by the Universidad de Oviedo and Banco Santander is acknowledged.

Referencias bibliográficas

  • Abbott, A., Button, C., Pepping, G. J., & Collins, D. (2005). Unnatural selection: Talent identification and development in sport. Nonlinear Dynamics in Psychology and Life Sciences, 9, 61-88. (Pubitemid 40306176)
  • Alfermann, D., & Stambulova, N. (2007). Career transitions and career termination. In G. Tenenbaum & R. C. Eklund (Eds.), Handbook of Sport Psychology (pp. 712-733) Hoboken, NJ: Wiley, http://dx.doi.org/10.1002/ 978111827001 1.ch32
  • Austin, J. T., & Villanova, P. (1992). The criterion problem: 1917-1992. Journal of Applied Psychology, 77, 836-874. http://dx.doi. Org/10.1037/0021-9010.77.6.836
  • Barnes, C. M., & Morgeson, F. P. (2007). Typical performance, maximal performance, and performance variability: Expanding our understanding of how organizations value performance. Human Performance, 20, 259-274. http://dx.doi.org/10.1080/08959280701333289
  • Barrett, G. V., Caldwel, M. S., & Alexander, R. A. (1985). The concept of dynamic criteria: A critical reanalysis. Personnel Psychology, 38, 41-56. http://dx.doi. Org/10.1111/j. 1744-6570.1985.tb00540.x
  • Beal, D. J., Weiss, H. M., Barros, E., & MacDermid, S. M. (2005). An episodic process model of affective influences on performance. Journal of Applied Psychology, 90, 1054-1068. http://dx.doi.org/10.1037/0021-9010.90.6.1054 (Pubitemid 43011567)
  • Campbell, J. P., Blake, M., & Oswald, F. (1996). The substantive nature of job performance variability. In K. R. Murphy (Ed.), Individual differences and behavior in organizations (pp. 258-299). San Francisco, CA: Jossey Bass.
  • Ceja, L., & Navarro, J. (2009). Dynamics of flow: A nonlinear perspective. Journal of Happiness Studies, 10, 665-684. http://dx.doi.org/10. 1007/s 10902-008-9113-6
  • Ceja, L., & Navarro, J. (2011). Dynamic patterns of flow in the workplace: Characterizing within-individual variability using a complexity science approach. Journal of Organizational Behavior, 32, 627-651. http://dx.doi.org/10.1002/job.747
  • Chan, D. (2005). Current directions in personnel selection research. Current Directions in Psychological Science, 14, 220-223. http://dx.doi.org/10- 1111/j.0963-7214.2005.00368.x (Pubitemid 41595991)
  • Chatfield, C. (1996). The analysis of time series: An introduction. New York, NY: Chapman & Hall.
  • Cheng, Y. T, & Van de Ven, A. H. (1996). Learning the innovation journey: Order out of chaos. Organization Science, 7, 593-614. http://dx.doi.org/10.1287/orsc.7.6.593 (Pubitemid 126416095)
  • Chow, J. Y., Davids, K., Button, C., Shuttleworth, R., Renshaw, I., & Araiijo, D. (2006). Nonlinear pedagogy: A constrainsled framework for understanding emergence of game play and movement skills. Nonlinear dynamics in Psychology and Life Sciences, 10, 71-103. (Pubitemid 43471729)
  • Cooper, W. W., Ruiz, J. L., & Sirvent, I. (2009). Selecting nonzero weights to evaluate effectiveness of basketball players with DEA. European Journal of Operational Research, 195, 563-574. http://dx.doi.org/10.1016/j.ejor. 2008.02.012
  • Côté, J. (1999). The influence of the family in the development of talent in sport. Sport Psychologist, 13, 395-417.
  • Day, D. V., Sin, H. P., & Chen, T. T. (2004). Assessing the burdens of leadership: Effects of formal leadership roles on individual performance over time. Personnel Psychology, 57, 573-605. http://dx.doi.org/10.1111/j. 1744-6570.2004.00001.x (Pubitemid 39483273)
  • Deadrick, D. L., Bennett, N., & Russell, C. J. (1997). Using hierarchical linear modeling to examine dynamic performance criteria over time. Journal of Management, 23, 745-757. http://dx.doi.org/10.1016/S0149-2063 (97) 90027-1 (Pubitemid 127173460)
  • Deadrick, D. L., & Madigan, R. M. (1990). Dynamic criteria revisited: A longitudinal study of performance stability and predictive validity. Personnel Psychology, 43, 1\1-1AA. http://dx.doi.org/10.1111/j.1744-6570.1990. tb00680.x
  • Dolan, K. T, & Spano, M. L. (2001). Surrogate for nonlinear time series analysis. Physical Review, 64, 1-4. http://dx.doi.org/10.1103/PhysRevE. 64.046128
  • Dunnette, M. D. (1963). A note on the criterion. Journal of Applied Psychology, 47, 251-254. http://dx.doi.org/10.1037/h0040836
  • Efron, B. (1982). The jackknife, the bootstrap and other resampling plans. Philadelphia, PA: Society for Industrial and Applied Mathematics, http://dx.doi.org/10-1137/1.9781611970319
  • Fisher, C. D. (2008). What if we took within-person performance variability seriously? Industrial and Organizational Psychology, 1, 185-189. http://dx.doi. Org/10.1111/j.1754-9434.2008.00036.x
  • Ghiselli, E. E., & Haire, M. (1960). The validation of selection tests in the light of the dynamic character of criteria Personnel Psychology, 13, 225-231. http://dx.doi.org/10.1111/j. 1744-6570.1960.tb01352.x
  • Guastello, S. J. (1995). Chaos, catastrophe, and human affairs: Applications of nonlinear dynamics to work, organizations, and social evolution. Mahwah, NJ: Lawrence Erlbaum.
  • Guastello, S. J., & Guastello, D. (1998). Origins of coordination and team effectiveness: A perspective from game theory and nonlinear dynamics. Journal of Applied Psychology, 83, 423-437. http://dx.doi.org/10.1037//0021- 9010.83.3.423
  • Guastello, S. J. Koopmans, M., & Pincus, D. (2009). Chaos and complexity in psychology: The theory of nonlinear dynamic systems. New York, NY: Cambridge University Press.
  • Guion, R. M. (1998). Some virtues of dissatisfaction in the science and practice of personnel selection. Human Resource Management Review, 8, 351-365. http://dx.doi.org/10.1016/SI 053-4822 (99) 00004-2 (Pubitemid 128347945)
  • Hanges, P. J., Schneider, B., & Niles, K. (1990). Stability of performance: An interactionist perspective. Journal of Applied Psychology, 75, 658-667. http://dx.doi.org/10.1037//0021-9010.75.6.658
  • Hardy, L., & Parfitt, G. (1991). A catastrophe model of anxiety and performance. British Journal of Psychology, 82, 163-178. http://dx.doi. Org/10.1111/j.2044-8295.1991.tb02391.x
  • Hardy, L., Parfitt, G., & Pates, J. (1994). Performance catastrophes in sport: A test of the hysteresis hypothesis. Journal of Sports Sciences, 12, 327-334. http://dx.doi.org/10.1080/02640419 408732178 (Pubitemid 24224566)
  • Hardy, L., Beattie, S., & Woodman, T. (2007) Anxiety induced performance catastrophes: Investigating effort required as an asymmetry factor. British Journal of Psychology, 98, 15-31. http://dx.doi.org/10.1348/ 000712606X103428
  • Hardy, L., Jones, J. G., & Gould, D. (1996). Understanding psychological preparation for sport: Theory and practice of elite performers. Chichester, England: Wiley.
  • Heath, R. A. (2000). Nonlinear dynamics: Techniques and applications in Psychology. Mahwah, NJ: Erlbaum.
  • Hofmann, D. A., Jacobs, R., & Baratta, J. E. (1993). Dynamic criteria and the measurement of change. Journal of Applied Psychology, 78, 194-204. http://dx.doi.org/10.1037//0021-9010.78.2.194
  • Hofmann, D. A., Jacobs, R., & Gerras, S. J. (1992). Mapping individual performance over time. Journal of Applied Psychology, 77, 185-195. http://dx.doi.org/10.1037//0021-9010.77.2.185
  • Hulin, C. L., Henry, R., & Noon, S. L. (1990). Adding a dimension: Time as a factor in the generalizability of predictive relationships. Psychological Bulletin, 107, 328-340. http://dx.doi.org/10.1037//0033-2909.107. 3.328
  • Katz, D., & Kahn, R. L. (1978). The social psychology of organizations, 2nd Ed. New York, NY: John Wiley and Sons.
  • Kreindler, D. M., & Lumsden, C. J. (2007). The effects of irregular sampling and missing data on largest Lyapunov exponents. Nonlinear Dynamics, Psychology, and Life Sciences, 11, 401-412.
  • Kugiumtzis, D. (2002). Surrogate data test on time series. In A. Soofi & L. Cao (Eds.), Modelling and forecasting financial data, techniques of nonlinear dynamics (pp. 267-282). Norwell, MA: Kluwer Academic Publishers.
  • Landis, R. S. (2001). A note on the stability of team performance. Journal of Applied Psychology, 86, 446-450. http://dx.doi.org/10.1037//0021- 9010.86.3.446 (Pubitemid 33403970)
  • Maguire, S., McKelvey, B., Mirabeau, L., & Õztas, N. (2006). Complexity science and organization studies. In S. R. Clegg, C. Hardy, T. B. Lawrence, & W. R. Nord (Eds.), The Sage handbook of organization studies (pp. 165-214). London, England: Sage.
  • Mathews, K. M., White, M. C, & Long, R. G. (1999). Why study the complexity in the social sciences? Human Relations, 52, 439-162. http://dx.doi.org/10.1177/001872679905200402
  • Molenaar, P. C. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement, 2, 201-218. http://dx.doi.org/10.1207/sl5366359mea0204-1
  • Navarro, J., & Arrieta, C. (2010). Chaos in human behavior: The case of work motivation. The Spanish Journal of Psychology, 13, 244-256.
  • Nowak, A., Lewenstein, M., & Vallacher, R. R. (1994). Toward a dynamic social psychology. In R. R. Vallacher & A. Nowak (Eds.), Dynamic systems in Social Psychology (pp. 279-293). San Diego, CA: Academic Press
  • Nowak, A., & Vallacher, R. A. (1998). Dynamic social psychology. New York, NY: Guildford Press.
  • Passos, P., Milho, J., Fonseca, S., Borges, J., Araujo, D., & Davids, K. (2011). Interpersonal distance regulates functional grouping tendencies of agents in team sports. Journal of Motor Behavior, 43, 155-163. http://dx.doi.org/10.1080/00222895.2011.552078
  • Pastor, J., & García-Izquierdo, A. L. (2007). Complejidad y Psicologia social de las organizaciones [Complexity and social psychology of organizations]. Psicothema, 19, 212-217.
  • Pettigrew, A. M. (1990). Longitudinal field research on change: Theory and practice. Organization Science, 1, 267-292. http://dx.doi.org/10.1287/orsc 1.3.267
  • Ployhart, R., & Hakel, M. D. (1998). The substantive nature of performance variability: Predicting interindividual differences in intraindividual performance. Personnel Psychology, 51, 859-901. http://dx.doi.org/10.1111/j.1744-6570.1998.tb00744.x
  • Rambo, W. W., Chomiak, A. M., & Price, J. M. (1983). Consistency of performance under stable conditions of work. Journal of Applied Psychology, 68, 78-87. http://dx.doi.org/10.103 7//0021-9010.68.1.78
  • Ramos-Villagrasa, P. J., & García-Izquierdo, A. L. (2011). Técnicas de analisis de patrones caóticos: Revisión de estudios empíricos en Psicología [Analysis techniques of chaotic patterns: A review of empirical studies in Psychology]. Anales de Psicología, 27, 239-248.
  • Reb, J., & Cropanzano, R. (2007). Evaluating dynamic performance: The influence of salient gestalt characteristics on performance ratings. Journal of Applied Psychology, 92, 490-499. http://dx.doi.org/10.1037/0021-9010.92.2.490
  • Reb, J., & Greguras, G. J. (2008). Dynamic Performance and the Performance-Performance Rating Relation. Industrial and Organizational Psychology, 1, 194-196. http://dx.doi.org/10.1111/J.1754-9434.2008.00038. X
  • Reb, J., & Greguras, G. J. (2010). Understanding performance ratings: Dynamic performance, attributions, and rating purpose. Journal of Applied Psychology, 95, 213-220. http://dx.doi.org/I0.1037/a0017237
  • Sackett, P. R., & Lievens, F. (2008). Personnel selection. Annual Review of Psychology, 59, 419-450. http://dx.doi.org/10.1146/annurev. psych.59.103006.093716
  • Schreiber, T., & Schmitz, A. (1996). Improved surrogate data for nonlinearity test. Physical Review Letters, 77, 635-638. http://dx.doi.org/10. 1103/PhysRevLett.77.635 (Pubitemid 126624517)
  • Schreiber, T., & Schmitz, A. (1997). Influence of Gaussian noise on the correlation exponent. Physical Review, 56, 274-277. http://dx.doi.org/10. 1103/PhysRevE.56.274 (Pubitemid 127602038)
  • Schroeck, F. E. Jr. (1994). New mathematical techniques for pattern recognition. In R. Vallacher & A. Nowak (Eds.), Dynamic systems in social psychology (pp. 71-93). San Diego, CA: Academic Press
  • Stewart, G. L., & Nandkeolyar, A. K. (2007). Exploring how constraints created by other people influence intraindividual variation in objective performance measures. Journal of Applied Psychology, 92, 1149-1158. http://dx.doi.org/10.1037/0021-9010.92.4.1149
  • Sturman, M. C. (2003). Searching for the inverted U-shaped relationship between time and performance: Meta-analysis of the experience/performance, tenure/performance, and age/performance relationships. Journal of Management, 29, 609-640. http://dx.doi.org/10.1016/S0149-2063 (03) 00028-X (Pubitemid 37041639)
  • Sturman, M. C., Cheramie, R. A., & Cashen, L. H. (2005). The impact of job complexity and performance measurement on the temporal consistency, stability, and test-retest reliability of employee job performance ratings. Journal of Applied Psychology, 90, 269-283. http://ox.doi.org/10.1037/0021-9010. 90.2.269 (Pubitemid 40551464)
  • Sturman, M. C, & Trevor, C. O. (2001). The implications of linking the dynamic performance and employee turnover literatures. Journal of Applied Psychology, 86, 684-696. http://dx.doi.org/10.1037//0021-9010.86.4.684 (Pubitemid 33570889)
  • Theiler, J., Lindsay, P. S., & Rubin, D. M. (1994). Detecting nonlinearity in data with long coherence times. In A. S. Weigend, & N. A. Gershenfeld (Eds.), Time series prediction: Forecasting the future and understanding the past (pp. 429-456). Reading, MA: Addison-Wesley.
  • Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., & Farmer, J. D. (1992). Testing for nonlinearity in time series: The method of surrogate data. Physica D: Nonlinear Phenomena, 58, 77-94. http://dx.doi.org/10.1016/0167-2789 (92) 90102-S
  • Thoresen, C. J., Bradley, J. C., Bliese, P. D., & Thoresen, J. D. (2004). The Big Five personality traits and individual job performance growth trajectories in maintenance and transitional job stages. Journal of Applied Psychology, 89, 835-853. http://dx.doi.org/10.1037/0021-9010.89.5.835 (Pubitemid 39443371)
  • Vallacher, R. R., & Nowak, A. (1997). The emergence of dynamical social psychology. Psychological Inquiry, 8, 73-79. http://dx.doi.org/10.1207/ sl5327965pli0802-1
  • Wall, T. D., Cordery, J. L., & Clegg, C. W. (2002). Empowerment, performance, and operational uncertainty. Applied Psychology: An International Review, 51, 146-169. http://dx.doi.org/10.1111/1464-0597.00083
  • Wiggins, S. (1988). Global bifurcations and chaos: Analytical methods. Berlin, Germany: Springer-Verlag.
  • Zickar, M. J., & Slaughter, J. E. (1999). Examining creative performance over time using hierarchical linear modelling: An illustration using film directors. Human Performance, 12, 211-230. http://dx.doi.org/10.1207/ sl5327043hupl203&4-2
  • Zyphur, M. J., Bradley, J. C., Landis, R. S., & Thoresen, C. J. (2007). The effects of cognitive ability and conscientiousness on performance over time: A censored latent growth model. Human Performance, 21, 1-27. http://dx.doi.org/10.1080/08959280701521967