Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-25T13:27:17.491Z Has data issue: false hasContentIssue false
  • English
  • Français

Dynamic Criteria: a Longitudinal Analysis of Professional Basketball Players' Outcomes

Published online by Cambridge University Press:  10 January 2013

Antonio León García-Izquierdo ,
Pedro José Ramos-Villagrasa  and
José Navarro
Antonio León García-Izquierdo*
Affiliation:
Universidad de Oviedo (Spain)
Pedro José Ramos-Villagrasa
Affiliation:
Universidad de Oviedo (Spain)
José Navarro
Affiliation:
Universidad de Barcelona (Spain)
*
Correspondence concerning this article should be addressed to Antonio León García- Izquierdo. Área de Psicología Social, Universidad de Oviedo. Plaza Feijóo, s/n, 33003 – Oviedo (Spain). Phone: +34-985104164. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper describes the fluctuations of temporal criteria dynamics in the context of professional sport. Specifically, we try to verify the underlying deterministic patterns in the outcomes of professional basketball players. We use a longitudinal approach based on the analysis of the outcomes of 94 basketball players over ten years, covering practically players' entire career development. Time series were analyzed with techniques derived from nonlinear dynamical systems theory. These techniques analyze the underlying patterns in outcomes without previous shape assumptions (linear or nonlinear). These techniques are capable of detecting an intermediate situation between randomness and determinism, called chaos. So they are very useful for the study of dynamic criteria in organizations. We have found most players (88.30%) have a deterministic pattern in their outcomes, and most cases are chaotic (81.92%). Players with chaotic patterns have higher outcomes than players with linear patterns. Moreover, players with power forward and center positions achieve better results than other players. The high number of chaotic patterns found suggests caution when appraising individual outcomes, when coaches try to find the appropriate combination of players to design a competitive team, and other personnel decisions. Management efforts must be made to assume this uncertainty.

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.

Keywords

dynamic criterianonlinear dynamical systems theorychaossportbasketballcriterios dinámicosteoría de sistemas dinámicos no linealescaosdeportebaloncesto
Type
Research Article
Information
The Spanish Journal of Psychology , Volume 15 , Issue 3 , November 2012 , pp. 1133 - 1146
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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.Google ScholarPubMed
Alfermann, D., & Stambulova, N. (2007). Career transitions and career termination. In Tenenbaum, G. & Eklund, R. C. (Eds.), Handbook of Sport Psychology (pp. 712–733) Hoboken, NJ: Wiley. http://dx.doi.org/10.1002/9781118270011.ch32CrossRefGoogle Scholar
Austin, J. T., & Villanova, P. (1992). The criterion problem: 1917-1992. Journal of Applied Psychology, 77, 836–874. http://dx.doi.org/10.1037/0021-9010.77.6.836CrossRefGoogle Scholar
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/10.1080/08959280701333289CrossRefGoogle Scholar
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.xCrossRefGoogle Scholar
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/10.1037/0021-9010.90.6.1054CrossRefGoogle ScholarPubMed
Campbell, J. P., Blake, M., & Oswald, F. (1996). The substantive nature of job performance variability. In Murphy, K. R. (Ed.), Individual differences and behavior in organizations (pp. 258–299). San Francisco, CA: Jossey Bass.Google Scholar
Ceja, L., & Navarro, J. (2009). Dynamics of flow: A nonlinear perspective. Journal of Happiness Studies, 10, 665–684. http://dx.doi.org/10.1007/s10902-008-9113-6CrossRefGoogle Scholar
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.747CrossRefGoogle Scholar
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.xCrossRefGoogle Scholar
Chatfield, C. (1996). The analysis of time series: An introduction. New York, NY: Chapman & Hall.Google Scholar
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.7.6.593CrossRefGoogle Scholar
Chow, J. Y., Davids, K., Button, C., Shuttleworth, R., Renshaw, I., & Araújo, 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.Google ScholarPubMed
Cooper, W. W., Ruiz, J. L., & Sirvent, I. (2009). Selecting non-zero 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.2008.02.012CrossRefGoogle Scholar
Côté, J. (1999). The influence of the family in the development of talent in sport. Sport Psychologist, 13, 395–417.CrossRefGoogle Scholar
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.xCrossRefGoogle Scholar
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)90027-1CrossRefGoogle Scholar
Deadrick, D. L., & Madigan, R. M. (1990). Dynamic criteria revisited: A longitudinal study of performance stability and predictive validity. Personnel Psychology, 43, 717–744. http://dx.doi.org/10.1111/j.1744-6570.1990.tb00680.xCrossRefGoogle Scholar
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.64.046128Google ScholarPubMed
Dunnette, M. D. (1963). A note on the criterion. Journal of Applied Psychology, 47, 251–254. http://dx.doi.org/10.1037/h0040836CrossRefGoogle Scholar
Efron, B. (1982). The jackknife, the bootstrap and other resampling plans. Philadelphia, PA: Society for Industrial and Applied Mathematics. http://dx.doi.org/10.1137/1.9781611970319CrossRefGoogle Scholar
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.xCrossRefGoogle Scholar
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.xCrossRefGoogle Scholar
Guastello, S. J. (1995). Chaos, catastrophe, and human affairs: Applications of nonlinear dynamics to work, organizations, and social evolution. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
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//0021-9010.83.3.423CrossRefGoogle Scholar
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.Google Scholar
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/S1053-4822(99)00004-2CrossRefGoogle Scholar
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//0021-9010.75.6.658CrossRefGoogle Scholar
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.xCrossRefGoogle ScholarPubMed
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/10.1080/02640419408732178CrossRefGoogle Scholar
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/10.1348/000712606X103428CrossRefGoogle ScholarPubMed
Hardy, L., Jones, J. G., & Gould, D. (1996). Understanding psychological preparation for sport: Theory and practice of elite performers. Chichester, England: Wiley.Google Scholar
Heath, R. A. (2000). Nonlinear dynamics: Techniques and applications in Psychology. Mahwah, NJ: Erlbaum.Google Scholar
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//0021-9010.78.2.194CrossRefGoogle Scholar
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//0021-9010.77.2.185CrossRefGoogle Scholar
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//0033-2909.107.3.328CrossRefGoogle Scholar
Katz, D., & Kahn, R. L. (1978). The social psychology of organizations, 2nd Ed. New York, NY: John Wiley and Sons.Google Scholar
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.Google ScholarPubMed
Kugiumtzis, D. (2002). Surrogate data test on time series. In Soofi, A. & Cao, L. (Eds.), Modelling and forecasting financial data, techniques of nonlinear dynamics (pp. 267–282). Norwell, MA: Kluwer Academic Publishers.CrossRefGoogle Scholar
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//0021-9010.86.3.446CrossRefGoogle ScholarPubMed
Maguire, S., McKelvey, B., Mirabeau, L., & Öztas, N. (2006). Complexity science and organization studies. In Clegg, S. R., Hardy, C., Lawrence, T.B., & Nord, W. R. (Eds.), The Sage handbook of organization studies (pp. 165–214). London, England: Sage.CrossRefGoogle Scholar
Mathews, K. M., White, M. C., & Long, R. G. (1999). Why study the complexity in the social sciences? Human Relations, 52, 439–462. http://dx.doi.org/10.1177/001872679905200402CrossRefGoogle Scholar
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/s15366359mea0204_1Google Scholar
Navarro, J., & Arrieta, C. (2010). Chaos in human behavior: The case of work motivation. The Spanish Journal of Psychology, 13, 244–256.CrossRefGoogle ScholarPubMed
Nowak, A., Lewenstein, M., & Vallacher, R. R. (1994). Toward a dynamic social psychology. In Vallacher, R. R. & Nowak, A. (Eds.), Dynamic systems in Social Psychology (pp. 279–293). San Diego, CA: Academic PressGoogle Scholar
Nowak, A., & Vallacher, R. A. (1998). Dynamic social psychology. New York, NY: Guildford Press.Google Scholar
Passos, P., Milho, J., Fonseca, S., Borges, J., Araújo, 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/10.1080/00222895.2011.552078CrossRefGoogle ScholarPubMed
Pastor, J., & García-Izquierdo, A. L. (2007). Complejidad y Psicología social de las organizaciones [Complexity and social psychology of organizations]. Psicothema, 19, 212–217.Google Scholar
Pettigrew, A. M. (1990). Longitudinal field research on change: Theory and practice. Organization Science, 1, 267–292. http://dx.doi.org/10.1287/orsc.1.3.267CrossRefGoogle Scholar
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.xCrossRefGoogle Scholar
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/10.1037//0021-9010.68.1.78CrossRefGoogle Scholar
Ramos-Villagrasa, P. J., & García-Izquierdo, A. L. (2011). Técnicas de análisis 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.Google Scholar
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/10.1037/0021-9010.92.2.490CrossRefGoogle ScholarPubMed
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.xCrossRefGoogle Scholar
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/10.1037/a0017237CrossRefGoogle ScholarPubMed
Sackett, P. R., & Lievens, F. (2008). Personnel selection. Annual Review of Psychology, 59, 419–450. http://dx.doi.org/10.1146/annurev.psych.59.103006.093716CrossRefGoogle ScholarPubMed
Schreiber, T., & Schmitz, A. (1996). Improved surrogate data for nonlinearity test. Physical Review Letters, 77, 635–638. http://dx.doi.org/10.1103/PhysRevLett.77.635CrossRefGoogle Scholar
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.56.274Google Scholar
Schroeck, F. E. Jr. (1994). New mathematical techniques for pattern recognition. In Vallacher, R. & Nowak, A. (Eds.), Dynamic systems in social psychology (pp. 71–93). San Diego, CA: Academic PressGoogle Scholar
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/10.1037/0021-9010.92.4.1149CrossRefGoogle ScholarPubMed
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-xCrossRefGoogle Scholar
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://dx.doi.org/10.1037/0021-9010.90.2.269CrossRefGoogle ScholarPubMed
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//0021-9010.86.4.684CrossRefGoogle ScholarPubMed
Theiler, J., Lindsay, P. S., & Rubin, D. M. (1994). Detecting nonlinearity in data with long coherence times. In Weigend, A. S., & Gershenfeld, N. A. (Eds.), Time series prediction: Forecasting the future and understanding the past (pp. 429–456). Reading, MA: Addison-Wesley.Google Scholar
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-SCrossRefGoogle Scholar
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/10.1037/0021-9010.89.5.835CrossRefGoogle ScholarPubMed
Vallacher, R. R., & Nowak, A. (1997). The emergence of dynamical social psychology. Psychological Inquiry, 8, 73–79. http://dx.doi.org/10.1207/s15327965pli0802_1CrossRefGoogle Scholar
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/10.1111/1464-0597.00083CrossRefGoogle Scholar
Wiggins, S. (1988). Global bifurcations and chaos: Analytical methods. Berlin, Germany: Springer-Verlag.CrossRefGoogle Scholar
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/s15327043hup1203&4_2CrossRefGoogle Scholar
Zyphur, M. J., Bradley, J.C., 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/10.1080/08959280701521967CrossRefGoogle Scholar