University of Worcester Worcester Research and Publications

Key Game Indicators in NBA Players’ Performance Profiles

Dehesa, R., Vaquera, Alejandro, Gonçalves, B. ORCID:, Mateus, N. ORCID:, Gomez-Ruano, M.A. and Sampaio, J. (2019) Key Game Indicators in NBA Players’ Performance Profiles. Kinesiology, 51 (1). pp. 92-101. ISSN 1848-638X

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The aim of the present study was to identify and describe players’ performances in NBA games using individual and team-based game variables. The sample was composed by 535 balanced games (score differences below or equal to eight points) from the regular season (n=502) and the playoffs (n=33). A total of 472 players were analysed. The individual-based variables were: minutes on court, effective field-goal percentage, free-throws/field-goals ratio, offensive rebound percentage, turnover percentage and playing position. The team-based variables were: team points minus opponent’s points (on and off court), NET score (player’s on values minus his/her off values), maximum negative and positive point difference, team’s winning percentage, game pace, defensive and offensive ratings. A two-step cluster analysis was performed to identify the player’s profiles during regular season and playoff games. The results identified five performance profiles during regular season games and four performance profiles during playoff games. The profiles identified were mainly characterized by the game quarter and the negative NET indicator (players’ performance on court minus their performance off court) in regular season games and the positive NET indicator during playoff games and second and third game-quarters. Coaching staffs can fine-tune these profiles to develop more team-specific models and, conversely, use the results to monitor and rebuild team formation under the constrained dynamics of the game and competition stages.

Item Type: Article
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This is an open access article under the CC BY-NC 4.0 license.

Uncontrolled Discrete Keywords: physical therapy, sports therapy and rehabilitation, collective behaviour, decision-making, game statistics, machine learning, cluster analysis, elite basketball
Subjects: Q Science > QP Physiology
Divisions: College of Business, Psychology and Sport > School of Sport and Exercise Science
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Copyright Info: Open Access Journal
SWORD Depositor: Prof. Pub Router
Depositing User: Alejandro Vaquera
Date Deposited: 28 Apr 2019 12:50
Last Modified: 17 Jun 2020 17:28

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