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KEY GAME INDICATORS IN NBA PLAYERS’ PERFORMANCE PROFILES

机译:NBA球员表现档案中的关键比赛指标

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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.
机译:本研究的目的是使用个人和基于团队的游戏变量来识别和描述球员在NBA游戏中的表现。该样本由常规赛(n = 502)和季后赛(n = 33)的535场平衡比赛(得分差异低于或等于8分)组成。共分析了472位玩家。基于个人的变量包括:出场时间,有效命中率,罚球/命中率,进攻篮板率,失误率和比赛位置。基于团队的变量包括:团队得分减去对手得分(场内外),净得分(球员的进入值减去他/她的脱离值),最大负分和正分差,团队的获胜百分比,比赛节奏,防守和进攻评级。在常规赛和季后赛中,进行了两步聚类分析,以识别玩家的个人资料。结果确定了常规赛期间的五个表现概况和季后赛期间的四个表现概况。所确定的配置文件的主要特征是常规赛中的比赛季度和净NET指标(球员在场上的表现减去场外表现),在季后赛以及第二和第三季度中表现为正NET指标。教练人员可以微调这些配置文件,以开发更多针对特定团队的模型,反之,在比赛和比赛阶段的动力有限的情况下,使用结果来监视和重建团队的组成。

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