Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-09T19:55:19.319Z Has data issue: false hasContentIssue false
  • English
  • Français

Towards a Decision Quality Model for Shot Selection in Basketball: an Exploratory Study

Published online by Cambridge University Press:  20 September 2016

Ernesto Suárez-Cadenas ,
Javier Courel-Ibáñez ,
David Cárdenas  and
José C. Perales
Ernesto Suárez-Cadenas*
Affiliation:
University of Granada (Spain)
Javier Courel-Ibáñez
Affiliation:
University of Granada (Spain)
David Cárdenas
Affiliation:
University of Granada (Spain)
José C. Perales
Affiliation:
University of Granada (Spain)
*
*Correspondence concerning this article should be addressed to Ernesto Suárez-Cadenas. University of Granada - Sport Sciences. Carretera de Alfacar. s/n. 18071. Granada (Spain). E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

We take the first steps towards a shot selection quality model in basketball that incorporates decisional cues that might be predictive, not only of proximal results (e.g., scoring), but also of distal results (e.g., winning/losing the match). 2976 jump-shots from 50 Euroleague matches were sampled, following systematic observation guidelines. The decisional cues under scrutiny were shooting opposition, distance and lateral angle, disposition to offensive rebound and disposition to defensive balance at the moment of shooting. A first set of regressions between decisional cues and proximal results showed higher opposition and distance to decrease the probability of scoring (OR = .81; p < .001 and OR = .89; p = .013); a better disposition towards rebound to increase the chances of catching rebound (OR = 1.57; p < .001); and better defensive balance disposition to decrease the probability of a fast break (OR = 1.27; p < .036). A second set of regressions between proximal and distal results showed shooting and offensive rebound effectiveness to predict total points scored (β = .62; p < .001 and β = .32; p < .001) and game result (winning/losing the game; OR = 1.12; p < .001 and OR = 1.05; p = .021). Finally, an analysis of the impact of decisional cues on distal results showed a positive relationship between likelihood of winning and average team’s disposition to offensive rebound (OR = 1.18; p = .018). These results cast light on the actual weights (validities) of the different cues involved in predicting outcomes of shooting decisions. This evidence could help coaches provide objective feedback about players’ shooting performance beyond hit percentages.

Keywords

basketball shootingcue validitiesdecision qualityperformance indicator
Type
Research Article
Information
The Spanish Journal of Psychology , Volume 19 , 2016 , E55
Copyright
Copyright © Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2016 

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.)

Footnotes

Any aspect of the work covered in this manuscript has been conducted with the ethical approval of all relevant bodies.

References

Altman, D. G. (1991). Practical statistics for medical research. London, UK: Chapman & Hall.Google Scholar
Álvarez, A., Ortega, E., Gómez, M. Á., & Salado, J. (2009). Study of the defensive performance indicators in peak performance basketball. Revista de Psicología del Deporte, 18, 379–384.Google Scholar
Araújo, D., Davids, K., & Hristovski, R. (2006). The ecological dynamics of decision making in sport. Psychology of Sport and Exercise, 7, 653–676. http://dx.doi.org/10.1016/j.psychsport.2006.07.002 Google Scholar
Bar-Eli, M., Plessner, H., & Raab, M. (2011). Judgement, decision-making and success in sport. Oxford, UK: Wiley-Blackwell.CrossRefGoogle Scholar
Brunswik, E. (1955). Representative design and probabilistic theory in a functional psychology. Psychological Review, 62, 193–217. http://dx.doi.org/10.1037/h0047470 Google Scholar
Csataljay, G., O’Donoghue, P., Hughes, M., & Dancs, H. (2009). Performance indicators that distinguish winning and losing teams in basketball. International Journal of Performance Analysis in Sport, 9(1), 60–66.Google Scholar
Csapo, P., Avugos, S., Raab, M., & Bar-Eli, M. (2015). The effect of perceived streakiness on the shot-taking behavior of basketball players. European Journal of Sport Science, 15, 647–657. http://dx.doi.org/10.1080/17461391.2014.982205 Google Scholar
Drust, B. (2010). Performance analysis research: Meeting the challenge. Journal of Sports Sciences, 28, 921–922. http://dx.doi.org/10.1080/02640411003740769 CrossRefGoogle ScholarPubMed
Dyer, N. G., Hanges, P., & Hall, R. J. (2005). Applying multilevel confirmatory factor analysis techniques to the study of leadership. The Leadership Quarterly, 16(1), 149–167. http://dx.doi.org/10.1016/j.leaqua.2004.09.009 Google Scholar
Farrow, D., & Raab, M. (2008). A recipe for expert decision making. In Farrow, D., Baker, J., & MacMahon, C. (Eds.), Developing sport expertise: Researchers and coaches put theory into practice (pp. 137–158). London, UK: Routledge.Google Scholar
Gabín, B., Camerino, O., Anguera, M. T., & Castañer, M. (2012). Lince: Multiplatform sport analysis software. Procedia Computer Science Technology, 46, 4692–4694.Google Scholar
García-González, L., Araújo, D., Carvalho, J., & Iglesias, D. (2011). An overview of theories and research methods on decision making in tennis. Revista de Psicologia del Deporte, 20, 645–666.Google Scholar
García, J., Ibáñez, S. J., Gómez, M. A., & Sampaio, J. (2014). Basketball Game-related statistics discriminating ACB league teams according to game location, game outcome and final score differences. International Journal of Performance Analysis in Sport, 14, 443–452.Google Scholar
Garefis, A., Tsitskaris, G., Mexas, K., & Kyriakou, D. (2007). Comparison of the effectiveness of fast breaks in two high level basketball championships. International Journal of Performance Analysis in Sport, 7, 9–17.Google Scholar
Goldman, M., & Rao, J. M. (2011, March). Allocative and dynamic efficiency in NBA decision making. In Proceedings of the 5 a MIT Sloan Sports Analytics Conference (pp. 4–5), Boston, USA.Google Scholar
Hastie, R., & Dawes, R. M. (2010). Rational choice in an uncertain world: The psychology of judgment and decision making (pp. 47–49). California, CA: SAGE.Google Scholar
Hogarth, R. (2008). On the learning of intuition. In Plessner, H., Betsch, C., & Betsch, T. (Eds.), Intuition in judgment and decision making (pp. 91–105). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Jiménez-Sánchez, A. C., Buñuel, P. S. L., Ibáñez, S. J., & Lorenzo, A. (2012). The perception female basketball players who play internationally have about their decision making. Revista Internacional de Medicina y Ciencias de la Actividad Física y Del Deporte, 12, 589–610.Google Scholar
Krause, J. V., Meyer, D., & Meyer, J. (2008). Basketball skills and drills (3rd Ed.). Champaign, IL: Human Kinetics.Google Scholar
Kubatko, J., Oliver, D., Pelton, K., & Rosenbaum, D. T. (2007). A starting point for analyzing basketball statistics. Journal of Quantitative Analysis in Sports, 3. http://dx.doi.org/10.2202/1559-0410.1070 Google Scholar
Lorenzo, A., Gómez, M. Á., Ortega, E., Ibáñez, S. J., & Sampaio, J. (2010). Game related statistics which discriminate between winning and losing under-16 male basketball games. Journal of Sports Science & Medicine, 9, 664.Google ScholarPubMed
Llorca-Miralles, J., Sánchez-Delgado, G., Piñar, M. I., Cárdenas, D., & Perales, J. C. (2013). Basketball training influences shot selection assessment: A multi-attribute decision-making approach. Revista de Psicologia del Deporte, 22(1), 223–226.Google Scholar
McGarry, T., O’Donoghue, P., & Sampaio, J. (2013). Routledge handbook of sports performance analysis. New York, NY: Routledge.Google Scholar
Neiman, T., & Loewenstein, Y. (2011). Reinforcement learning in professional basketball players. Nature Communications, 2. http://dx.doi.org/10.1038/ncomms1580 Google Scholar
Oliver, D. (2004). Basketball on paper: Rules and tools for performance analysis. Dulles, VA: Brassey’s, Inc.Google Scholar
Perales, J. C., Cardenas, D., Pinar, M. I., Sanchez-Delgado, G., & Courel, J. (2011). Differential effect of incidental and intentional instruction in learning about decision-making conditions when shooting in basketball. Revista de Psicologia del Deporte, 20, 729–745.Google Scholar
Ribas, R. L., Navarro, R. M., Tavares, F., & Gómez, M. A. (2011). Analysis of number of players involved in rebound situations in Euroleague Basketball Games. Open Sports Sciences Journal, 4, 10–13. Retrieved from http://benthamopen.com/contents/pdf/TOSSJ/TOSSJ-4-10.pdf Google Scholar
Raab, M. (2002). T-ECHO: Model of decision making to explain behavior in experiments and simulations under time pressure. Psychology of Sport and Exercise, 3, 151–171. http://dx.doi.org/10.1016/S1469-0292(01)00014-0 Google Scholar
Skinner, B. (2012). The problem of shot selection in basketball. PLoS ONE 7(1), e30776. http://dx.doi.org/10.1371/journal.pone.0030776 Google Scholar
Sampaio, J., Drinkwater, E. J., & Leite, N. M. (2010). Effects of season period, team quality, and playing time on basketball players’ game-related statistics. European Journal of Sport Science, 10, 141–149. http://dx.doi.org/10.1080/17461390903311935 Google Scholar
Suárez-Cadenas, E., Cárdenas, D., Sánchez-Delgado, G., & Perales, J. C. (2015). The hidden cost of coaching: Intentional training of shot adequacy discrimination in basketball hampers utilization of informative incidental cues. Perceptual and Motor Skills, 120(1), 139–158.Google Scholar
Tsamourtzis, E., & Athanasiou, N. (2004). Registration of rebound possession zones in basketball. International Journal of Performance Analysis in Sport, 4, 34–39.Google Scholar
Wooden, J., & Nater, S. (2006). John Wooden’s UCLA offense. Champaign, IL: Human Kinetics.Google Scholar