Comparaciones estadísticas y elaboración de rankings de rendimiento de jugadores de baloncesto (Statistical comparisons and creation of rankings of performance of basketball players)

Autores/as

DOI:

https://doi.org/10.47197/retos.v55.103749

Palabras clave:

rendimiento, baloncesto, estadística avanzada, error de estimación, rankings

Resumen

Esta investigación ha propuesto un método para realizar rankings de jugadores de baloncesto en base a métricas estadísticas normalizadas provenientes del box-score. Por medio de la consideración del error de cada estimación, tomando como población finita el número de partidos de la temporada regular, este método es mucho más robusto y riguroso que las simples comparaciones de valores promedio puntuales. A través del análisis de cada partido de la temporada regular de la NBA 2020/21 se registraron los 72 partidos de cada uno de los 30 equipos de la competición, obteniendo 22989 registros diferentes, los cuales a su vez estaban ligados a decenas de indicadores de rendimiento en el partido. Los resultados muestran que, efectivamente, el método basado en estadística inferencial y elaboración de rankings usando métricas normalizadas provee ventajas para realizar una comparación mucho más rigurosa del rendimiento de jugadores.

Palabras clave: rendimiento, baloncesto, estadística avanzada, error de estimación, rankings

Abstract. This research has proposed a method to perform rankings of basketball players based on normalized statistical metrics from the box-score. By considering the error of each estimate, taking the number of regular season games as a finite population, this method is much more robust and rigorous than simple comparisons of specific average values. Through the analysis of each match of the 2020/21 NBA regular season, the 72 matches of each of the 30 teams in the competition were recorded, obtaining 22989 different records, which in turn were linked to dozens of performance indicators in the match. The results show that, indeed, the method based on inferential statistics and development of rankings using normalized metrics provides advantages for making a much more rigorous comparison of player performance.

Keywords: performance, basketball, advanced statistics, estimation error, rankings

Citas

Abbas, N. M. (2019, agosto 13). NBA data analytics: Changing the game. Descargado desde: https://towardsdatascience.com/nba-data-analytics-changing-the-game-a9ad59d1f116

Arkes, J. & Martínez, J. A. (2011). Finally, Evidence for a Momentum Effect in the NBA. Journal of Quantitative Analysis in Sports, 7(3), 1–14. https://doi.org/10.2202/1559-0410.1304

Berri, D. J., & Bradbury, J. C. (2010). Working in the land of metricians. Journal of Sports Economics, 11(1), 29-47. https://doi.org/10.1177/15270025093548

Berri, D. J., & Schmidt, M. B. (2010). Stumbling on wins: Two economists expose the pitfalls on the road to victory in professional sports. FT Press

Berri, D. J. (2012). Measuring performance in the National Basketball Association. InStephen Shmanske, S. and Kahane, L. (Eds): The Handbook of Sports Economics. Oxford University

Casals, M. & Martínez, J. A. (2013). Modelling player performance in basketball through mixed models. International Journal of Performance Analysis in Sports, 13 (1), 64-82. https://doi.org/10.1080/24748668.2013.11868632

Deeks, M. (2022, diciembre 31). Have Shooting Specialists Become Overvalued In The NBA?. Descargado desde: https://www.forbes.com/sites/markdeeks/2022/12/31/have-shooting-specialists-become-overvalued-in-the-nba/?sh=3a638f1f2e65

Deshpande, S. & Jensen, S. (2016). Estimating an NBA player’s impact on his team’s chances of winning. Journal of Quantitative Analysis in Sports, 12(2), 51-72. https://doi.org/10.1515/jqas-2015-0027

Dowsett, B. (2023, octubre 20). ‘An exciting next few years’: will Hawk-Eye spark an NBA data revolution? Descargado desde: https://www.theguardian.com/sport/2023/oct/20/nba-hawkeye-data-analytics-insights

Duque Ramos, V. H., Reina Román, M., Mancha Triguero, D., Ibáñez Godoy, S. J., & Saenz Lopez, P. (2021). Relación de la carga de entrenamiento con las emociones y el rendimiento en baloncesto formativo (Relation of training load with emotions and performance in formative basketball). Retos, 40, 164–173. https://doi.org/10.47197/retos.v1i40.82441

Ertug, G. &. Castellucci, F. (2013). Getting what you need: How reputation and status affect team performance, hiring, and salaries in the NBA. Academy of Management Journal, 56 (2), 407–431. https://doi.org/10.5465/amj.2010.1084

Grenha, P., Moura, J., Guimarães, E., Fonseca, P., Sousa, F., & Janeira, M. (2022). Efectos de un programa de autoentrenamiento sobre el rendimiento y cinemática de tiro en jóvenes jugadores de baloncesto: un caso de estudio (Effects of a self-training program on shooting performance and kinematics in young basketball players: a case stu. Retos, 43, 256–263. https://doi.org/10.47197/retos.v43i0.87380

Hollinger J. (2005). Pro Basketball Forecast, 2005–06. Washington, DC: Potomac

Katris C. (2023). Investigation of FIBA World Cup 2019 Evidence Using Advanced Statistical Analysis and Quantitative Tools. Engineering Proceedings, 39 (1), 85. https://doi.org/10.3390/engproc2023039085

Kubatko, J., Oliver, D., Pelton, K. & Rosenbaum, D. (2007). A Starting Point for Analyzing Basketball Statistics. Journal of Quantitative Analysis in Sports, 3(3). https://doi.org/10.2202/1559-0410.1070

Kula, F. & Koçer, R. G. (2020). Why is it difficult to understand statistical inference? Reflections on the opposing directions of construction and application of inference framework, Teaching Mathematics and its Applications: An International Journal of the IMA, 39(4), 248-265. https://doi.org/10.1093/teamat/hrz014

Lamas, L., Santana, F., Heiner, M., Ugrinowitsch, C. & Fellingham, G. (2015) Modeling the Offensive-Defensive Interaction and Resulting Outcomes in Basketball. PLoS ONE 10 (12): e0144435. https://doi.org/10.1371/journal.pone.0144435

Lewis, M. M. (2003). Moneyball: The art of winning an unfair game. W.W. Norton & Company Inc.

Martínez, J. A. (2012). Factors determining production (FDP) in basketball. Economic & Business Letters, 1(1), 21-29. https://doi.org/10.17811/ebl.1.1.2012.21-29

Martínez, J. A. (2019). A more robust estimation and extension of factors determining production (FDP) of basketball players. International Journal of Physical Education, Sports and Health, 6(3), 81-85.

Martínez, J. A. & Martínez, L. (2010). Un método probabilístico para las clasificaciones estadísticas de jugadores en baloncesto. Revista Internacional de Ciencias del Deporte. 18(6), 3-36.

Papadaki, I. & Tsagris, M. (2022). Are NBA Players’ Salaries in Accordance with Their Performance on Court?. In: Terzioğlu, M.K. (eds) Advances in Econometrics, Operational Research, Data Science and Actuarial Studies. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-85254-2_25

Pradhan, S. (2018). Ranking regular seasons in the NBA’s Modern Era using grey relational analysis. Journal of Sports Analytics, 4(1), 31-63. https://doi.org/10.3233/JSA-160165

Pradhan, S. & Chachad, R. (2021) Re-ranking regular seasons in the National Basketball Association’s modern era : A replication and extension of Pradhan (2018). Journal of Statistics and Management Systems, 24 (7), 1503-1522. https://doi.org/10.1080/09720510.2020.1848040

Sigler, K. J. & Sackley, W. H. (2000), NBA players: are they paid for performance? Managerial Finance, 26 (7), 46-51. https://doi.org/10.1108/03074350010766783

Tener, Z. & Franks, A. (2021). Modeling Player and Team Performance in Basketball. Annual Review of Statistics and Its Application, 8(1), 1-23. https://doi.org/10.1146/annurev-statistics-040720-015536

Wilson, F. (2018, noviembre 14 ). Why don’t we understand statistics? Fixed mindsets may be to blame Descargado desde: https://www.frontiersin.org/news/2018/11/14/mathematics-statistics-education/

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Publicado

2024-03-27

Cómo citar

Martínez García, J. A. (2024). Comparaciones estadísticas y elaboración de rankings de rendimiento de jugadores de baloncesto (Statistical comparisons and creation of rankings of performance of basketball players). Retos, 55, 170–176. https://doi.org/10.47197/retos.v55.103749

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Sección

Artículos de carácter científico: trabajos de investigaciones básicas y/o aplicadas