Regular point scoring by professional basketball players


Abstract


In this paper we study the performance of basketball players paying special attention to stability and regularity with the focus on points scored. To this end we model regularity using the median absolute deviation for variables explaining its variation and we employed the Cochran variance outlier test to identify the players with largest variance in his performance. Also we analyze the ordinal patterns of player’s performance considering short term evaluations (3 games per week). Our research provides an advancement on a simple but important question in basketball metrics: how to measure regularity in points scored and which factors may influence it.


Keywords: performance; regularity;linear mixed model; ordinal patterns

References


Alamar, B. (2013). Sports analytics: A guide for coaches, managers, and other decision makers. Columbia University Press.

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, 411-427

Berri, D. J. (2008). A simple measure of worker productivity in the National Basketball Association. in The Business of Sport, eds.

Berri, D. J., Brook, S. L., & Schmidt, M. B. (2007). Does One Simply Need to Score to Score? International Journal of Sport Finance 2 (4), 190-205

Brad Humphreys and Dennis Howard, editors, 3 volumes, Westport, Conn.

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

Esteller-Moré, A., & Eres-García, M. (2002). A note on consistent players’ valuation. Journal of Sports Economics, 3 (4), 354-360.

Franks, A., D'Amour, A., Cervone, D. & Bornn, L. (2016). Meta-Analytics: Tools for Understanding the Statistical Properties of Sports Metrics. Working paper: Cornell University

Hausman, J. A. (1978). Specification test in econometrics. Econometrica, 46, 1251-1271.

Kubatko, J., Oliver, D., Pelton, K, & Rosenbaum, D. T. (2007). A starting point for analyzing basketball statistics. Journal of Quantitative Analysis in Sports 3 (3), Article 1

Martínez, J. A. (2012). Factors determining production (FDP) in basketball. Economic & Business Letters, 1 (1), 21-29.

Mossop, B. (2012). Basketball isn't a sport; it's a statistical network. Retrieved from: https://www.wired.com/2012/12/basketball-network-analysis/

Pelton, K. (2016). Basic concepts (not math) at the heart of sports analytics. Retrieved from: http://www.espn.com/nba/story/_/id/17678246/basic-concepts-not-math-heart-sports-analytics

Petersen, I. (2013). Fantasy Football Analytics. Retrieved from: http://fantasyfootballanalytics.net/2013/03/isaac-petersen.html

Piette, J., Sathyanarayan, A. & Kai, Z. (2010). Scoring and shooting abilities of NBA players. Journal of Quantitative Analysis in Sports 6 (1), Article 1

Salmerón-Gómez, R., & Gómez-Haro, S. (2016). Ampliando horizontes sobre medición del rendimiento y regularidad en el baloncesto profesional. RICYDE. Revista internacional de ciencias del deporte. 45(12), 234- 249. http://dx.doi.org/10.5232/ricyde2016.04502

Williams, D. (2016). How Does an Athlete’s Approach to the Indoor Season Impact Their Ability to Perform when it Matters Most?. Retrieved from: http://www.thestatszone.com/articles/how-does-an-athletes-approach-to-the-indoor-season-impact-their-ability-to-perform-when-it-matters-most


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