On the credibility of basketball scoring efficiency


Abstract


Our aim deals with appraising the scoring efficiency of a player in terms ofpoints scored per hundred possessions. A Bayesian approach to theproblem, should reflect not only individual scoring skills, but also takinginto account the collective performance. In this wide context, credibilitytheory becomes an adequate mechanism deciding whether scoringefficiency calculation to be more or less plausible. We model the scoringper possession process by means of the conjugated family Multinomial-Dirichletin order to obtain a net scoring efficiency credibility formula.

Keywords: Credibility factor, Multinomial-Dirichlet, scoring efficiency

References


Bailey, A. (1945). A generalized theory of credibility. Proceedings of the Casualty Actuarial Society, 32: 13–20.

Basketball Reference (2017: Accessed 03-03). Available online at: http://www.basketball-reference.com.

Bruce, S. (2016). A scalable framework for NBA player and team comparisons using player tracking data. Journal of Sports Analytics, 2(2): 107–119.

Bühlmann, H. (1967). Experience rating and credibility. Astin Bulletin, 4: 199–207.

Chen, X., Chen, Y. and Wilbur, K.C. (2013). There’s no ‘I’ in ‘team’: Estimating NBA players’ offensive production. SSRN Electronic Journal. Available online at: http://dx.doi.org/10.2139/ssrn.1861192.

ESPN.com Insider (2017: Accessed 03-03). Available online at: http://insider.espn.com/nba/hollinger/statistics.

Goldsberry, K., and Weiss, E. (2013). The Dwight effect: A new ensemble of interior defense analytics for the NBA. MIT Sloan Sports Analytics Conference.

Heilmann, W.R. (1989). Decision theoretic foundations of credibility theory. Insurance: Mathematics and Economics, 8: 77–95.

Hollinger, J. (2002). Pro basketball prospectus: 2002 Edition (Pro basketball forecast). Potomac Books.

Kubatko, J., Oliver, D., Pelton, K. and Rosenbaum, D. (2007). A starting point for analyzing basketball statistics. Journal of Quantitative Analysis in Sports, 33(3): 1-22.

Lamas, L., Santana, F., Heiner, M., Ugrinowitsch, C. and Fellingham, G. (2015). Modeling the offensive-defensive interaction and resulting outcomes in basketball. PloS one, 10: e0144435.

Lee, T.C., Judge, G.G. and Zellner, A. (1968). Maximum likelihood and Bayesian estimation of transition probabilities. Journal of the American Statistical Association, 63: 1162-1179.

Mertz, J., Hoover, D.L., Burke, J.M., Bellar, D., Jones, L.M., Leitzelar, B. and Judge, L.W. (2016). Ranking the greatest NBA players: A sport metrics analysis. International Journal of Performance Analysis in Sport, 16(3): 737-759.

NBA.com Stats (2017: Accessed 03-03). Available online at: http://stats.nba.com.

Oliver, D. (2004). Basketball on paper: Rules and tools for performance analysis. Brassey's Inc., First edition.

Parker, R.J. (2010). Modeling basketball's points per possession with application to predicting the outcome of college basketball games. Unpublished working paper. Available online at: http://www.basketballgeek.com/downloads/ryan_bach_essay.draft.pdf.

Pomeroy, K. (2004). National eficiency. Available online at: http://kenpom.com/blog/national-efficiency.

Skinner, B. (2010). The price of anarchy in basketball. Journal of Quantitative Analysis in Sports, 6(1): 3-6.

Zimmermann, A. (2016). Basketball predictions in the NCAAB and NBA: Similarities and differences. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(5): 350-364.


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