All-NBA Teams’ Selection Based on Unsupervised Learning

João Vítor Rocha da Silva and Paulo Canas Rodrigues
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João Vítor Rocha da Silva: Department of Statistics, Federal University of Bahia, Salvador CEP: 40.170-110, Brazil
Paulo Canas Rodrigues: Department of Statistics, Federal University of Bahia, Salvador CEP: 40.170-110, Brazil

Stats, 2022, vol. 5, issue 1, 1-18

Abstract: All-NBA Teams’ selections have great implications for the players’ and teams’ futures. Since contract extensions are highly related to awards, which can be seen as indexes that measure a players’ production in a year, team selection is of mutual interest for athletes and franchises. In this paper, we are interested in studying the current selection format. In particular, this study aims to: (i) identify the factors that are taken into consideration by voters when choosing the three All-NBA Teams; and (ii) suggest a new selection format to evaluate players’ performances. Average game-related statistics of all active NBA players in regular seasons from 2013-14 to 2018-19, were analyzed using LASSO (Logistic) Regression and Principal Component Analysis (PCA). It was possible: (i) to determine an All-NBA player profile; (ii) to determine that this profile can cause a misrepresentation of players’ modern and versatile gameplay styles; and (iii) to suggest a new way to evaluate and select players, through PCA. As the results of this paper a model is presented that may help not only the NBA to better evaluate players, but any basketball league; it also may be a source to researchers that aim to investigate player performance, development, and their impact over many seasons.

Keywords: sports analysis; principal component analysis; LASSO regression (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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